Deconvoluting the Electrophysiological Signatures of Myocardial Ischemia using a Validated Machine Learning Framework

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While these three conditions alter cardiomyocyte electrophysiology, it is difficult to discern how much each one individually contributes to the resulting changes in action potential (AP). In this study, we test whether machine learning can deconvolute these distinct ischemic patterns within a single AP. Methods We developed a multi-target regression model trained on data generated by the Luo-Rudy (1991) computational model of a ventricular cardiomyocyte, simulating a wide range of ischemic conditions. The model was designed to predict two continuous variables: extracellular potassium concentration ([K+]o) and intracellular pH (pHi). Results The model achieved high accuracy on a held-out test set, with mean squared errors (MSE) below 0.25 for [K+]o and below 0.01 for pHi. To further generalize this model, we applied this trained model to a structurally distinct model, the Ten Tusscher (2006) framework. We were able to accurately predict [K+]o and pHi from APs, demonstrating that the learned principles are robust. A feature importance analysis revealed that resting membrane potential (RMP) was the strongest predictor for [K+]o, while action potential duration (APD) is most important for predicting pHi, underscoring these distinct cardiomyocyte electrophysiological patterns Conclusions Our approach can distinguish distinct ischemic drivers and has potential for in silico drug screening and mechanistic analysis. 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F1000Research 2026, 14 :1341 ( https://doi.org/10.12688/f1000research.171338.2 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Revised Deconvoluting the Electrophysiological Signatures of Myocardial Ischemia using a Validated Machine Learning Framework [version 2; peer review: 2 approved] Ahmad Mahmood https://orcid.org/0000-0001-9107-3704 1 , Kiel Jacqueline https://orcid.org/0009-0006-0435-7324 2 , Joanne Lac https://orcid.org/0009-0004-3533-434X 3 Ahmad Mahmood https://orcid.org/0000-0001-9107-3704 1 , Kiel Jacqueline https://orcid.org/0009-0006-0435-7324 2 , Joanne Lac https://orcid.org/0009-0004-3533-434X 3 PUBLISHED 31 Jan 2026 Author details Author details 1 Royal Free London NHS Foundation Trust, London, England, UK 2 Boston University, Boston, Massachusetts, USA 3 University College London, London, England, UK Ahmad Mahmood Roles: Conceptualization, Investigation, Methodology, Software, Supervision, Validation, Writing – Original Draft Preparation Kiel Jacqueline Roles: Data Curation, Formal Analysis, Software, Writing – Review & Editing Joanne Lac Roles: Funding Acquisition, Project Administration OPEN PEER REVIEW DETAILS REVIEWER STATUS Abstract Background Myocardial ischemia is a dynamic, complex process characterized by hyperkalemia, acidosis, and ATP depletion. While these three conditions alter cardiomyocyte electrophysiology, it is difficult to discern how much each one individually contributes to the resulting changes in action potential (AP). In this study, we test whether machine learning can deconvolute these distinct ischemic patterns within a single AP. Methods We developed a multi-target regression model trained on data generated by the Luo-Rudy (1991) computational model of a ventricular cardiomyocyte, simulating a wide range of ischemic conditions. The model was designed to predict two continuous variables: extracellular potassium concentration ([K + ]o) and intracellular pH (pHi). Results The model achieved high accuracy on a held-out test set, with mean squared errors (MSE) below 0.25 for [K + ]o and below 0.01 for pHi. To further generalize this model, we applied this trained model to a structurally distinct model, the Ten Tusscher (2006) framework. We were able to accurately predict [K + ]o and pHi from APs, demonstrating that the learned principles are robust. A feature importance analysis revealed that resting membrane potential (RMP) was the strongest predictor for [K + ]o, while action potential duration (APD) is most important for predicting pHi, underscoring these distinct cardiomyocyte electrophysiological patterns Conclusions Our approach can distinguish distinct ischemic drivers and has potential for in silico drug screening and mechanistic analysis. READ ALL READ LESS Keywords Myocardial ischemia; Machine learning; Electrophysiology; Cardiomyocyte; Action potential; Computational modeling Corresponding Author(s) Joanne Lac ( [email protected] ) Close Corresponding author: Joanne Lac Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2026 Mahmood A et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Mahmood A, Jacqueline K and Lac J. Deconvoluting the Electrophysiological Signatures of Myocardial Ischemia using a Validated Machine Learning Framework [version 2; peer review: 2 approved] . F1000Research 2026, 14 :1341 ( https://doi.org/10.12688/f1000research.171338.2 ) First published: 01 Dec 2025, 14 :1341 ( https://doi.org/10.12688/f1000research.171338.1 ) Latest published: 31 Jan 2026, 14 :1341 ( https://doi.org/10.12688/f1000research.171338.2 ) Revised Amendments from Version 1 In this revised version, we have expanded the literature discussion to better contextualize our study within recent advancements in machine learning applied to cardiac risk prediction. Specifically, we now reference and contrast our approach with two recent studies: one using ECG waveform features to predict obstructive coronary artery disease (Yilmaz et al., 2023) and another focusing on myocardial injury prediction in elderly surgical patients (Cicek et al., 2024). Additionally, we have added a schematic diagram (Figure 1) summarizing the full end-to-end pipeline, from in-silico simulation and feature extraction to machine learning and application, to support accessibility for readers less familiar with computational electrophysiology. Minor clarifications were also made regarding Random Forest hyperparameter tuning and model evaluation procedures. In this revised version, we have expanded the literature discussion to better contextualize our study within recent advancements in machine learning applied to cardiac risk prediction. Specifically, we now reference and contrast our approach with two recent studies: one using ECG waveform features to predict obstructive coronary artery disease (Yilmaz et al., 2023) and another focusing on myocardial injury prediction in elderly surgical patients (Cicek et al., 2024). Additionally, we have added a schematic diagram (Figure 1) summarizing the full end-to-end pipeline, from in-silico simulation and feature extraction to machine learning and application, to support accessibility for readers less familiar with computational electrophysiology. Minor clarifications were also made regarding Random Forest hyperparameter tuning and model evaluation procedures. See the authors' detailed response to the review by Mert İlker Hayıroğlu See the authors' detailed response to the review by Cenitta D READ REVIEWER RESPONSES Author summary During a myocardial infarction, or heart attack, cardiac myocytes experience an oxygen deficiency, increase in potassium outside the cell, and an increase in intracellular acid. These conditions contribute to an altered AP, however, it is difficult to discern the individual contributions of each condition. In this study, we explored whether a computer could learn to identify distinct signatures of damage. By simulating ischemia in a virtual heart cell, we generated thousands of APs using trusted computer models. With a trained machine learning system with high accuracy, we then analyzed the shape of these APs to predict the levels of extracellular potassium and intracellular acidity. To ensure the generalizability of our findings to further models, we tested our method on an entirely different, modern cardiac cell model. Our system maintained high accuracy despite its lack of familiarity with this model, supporting the robustness of our approach. Finally, we showed that the system could be used to evaluate how well a simulated drug protects heart cells, suggesting a new direction for testing therapies virtually. Introduction Myocardial ischemia, a defining feature of heart attacks, initiates a series of interconnected pathological processes at the cellular level. As blood flow becomes restricted, oxygen supply rapidly diminishes, inhibiting aerobic metabolism and depleting ATP. This energy shortage leads to a cascade of disruptions in the cardiomyocyte’s internal environment, most notably, a rise in extracellular potassium concentration ([K + ]o) due to Na + /K + pump failure, and a drop in intracellular pH (pHi) caused by lactate buildup through anaerobic glycolysis. 1 Together, these stressors, hyperkalemia, acidosis, and ATP depletion, profoundly alter the electrophysiological properties of cardiomyocytes. Hyperkalemia causes depolarization of the RMP, while acidosis interferes with the function of key ion channels, including the fast sodium current (INa) and the L-type calcium current (ICaL). At the same time, ATP depletion activates the ATP-sensitive potassium current (IK (ATP)), which significantly shortens the APD. 2 The resulting ischemic AP reflects a complex interplay of these pathophysiological changes. Although the general shape of ischemic APs is well documented, characterized by depolarized RMP, slower upstroke, and shortened duration, it remains extremely difficult to determine which specific pathological factor is primarily responsible for these changes in a given case. For instance, two cardiomyocytes may display similarly short APDs due to different underlying combinations of hyperkalemia and acidosis. The ability to isolate and quantify the individual effects of these contributors could greatly improve our understanding of ischemic tissue states and support the development of more precisely targeted therapies. Recent years have seen an increasing interest in using machine learning to identify cardiac pathology from electrophysiological signals. Clinical models have employed ECG-derived features, such as P wave dispersion and QRS duration, to detect obstructive coronary artery disease and stratify ischemic risk in high-risk cohorts. For example, Yilmaz et al. (2023) 3 used ECG features from treadmill stress tests to predict obstructive coronary artery disease with high accuracy. Similarly, Cicek et al. (2024) 4 developed a risk prediction model for perioperative myocardial injury in elderly surgical patients, integrating clinical and signal-derived data. These models underscore the clinical relevance and feasibility of ML-driven signal analysis in cardiology. However, these do not address direct mechanistic insight into the ionic or cellular substrates of the observed abnormalities. In contrast, our in silico modeling approach offers interpretable links between waveform changes and specific pathophysiological drivers, serving as a bridge between computational biology and clinical diagnostics. In this study, we explore the potential of a machine learning approach, specifically a multi-target regression framework, to infer the distinct influences of hyperkalemia and acidosis from AP waveform features. We propose a three-phase strategy to investigate this hypothesis. First, we train Random Forest regressors on a synthetic dataset generated using the Luo-Rudy (1991) 5 computational model. Next, we test the generalizability of the trained models using an independent dataset created with the structurally distinct Ten Tusscher (2006) 6 model. Finally, we apply the validated framework to a simulated drug intervention in a severely ischemic cell to evaluate its potential use in quantifying therapeutic effects in silico. Materials and methods Computational models of the ventricular cardiomyocyte This study utilized two widely accepted computational models of human ventricular cardiomyocytes. 1. Luo-Rudy (1991) Model (LRd): The main training dataset was generated using the LRd model, 5 a robust and extensively validated framework. Its reliability and stability across a broad range of physiological and pathological conditions made it ideal for producing a large-scale dataset for machine learning. 2. Ten Tusscher et al. (2006) Model (TT06): For validation purposes, we used the epicardial configuration of the TT06 model, 6 which offers a more recent and detailed representation of ventricular electrophysiology. It includes refined descriptions of calcium handling and a wider range of ionic currents. The structural differences between TT06 and LRd made it an effective choice to test the generalizability of our machine learning framework. Simulating ischemic conditions To simulate ischemic conditions, we simultaneously varied three key physiological parameters in both models: • Hyperkalemia: Extracellular potassium concentration ([K + ]o) was adjusted from a normal value of 5.4 mM to a maximum of 12.5 mM to represent severe ischemia. • Acidosis: Intracellular pH (pHi) was decreased from a baseline of 7.4 to as low as 6.5. The impact of acidosis was modeled as a reduction in the maximum conductance of the fast sodium current (INa) and the slow inward/L-type calcium current (Isi/ICaL), consistent with experimental findings. 7 • ATP Depletion: Reduced ATP availability was mimicked by activating the ATP-sensitive potassium current (IK (ATP)). The conductance of this current (GK (ATP)) was increased from 0 (representing normal conditions) up to 0.3 mS/μF to simulate progressively severe ischemia. In Silico data generation Using the LRd model, we generated a training dataset composed of 150 AP traces. These simulations were categorized into three groups: ‘Healthy,’ ‘Moderate Ischemia,’ and ‘Severe Ischemia.’ Parameter values were sampled from ranges defined in the accompanying Python script. Each simulation produced a set of electrophysiological features for model training. Separately, we generated a validation dataset of 50 APs using the TT06 model. These were sampled from the ‘Moderate’ and ‘Severe Ischemia’ parameter ranges to evaluate the machine learning framework’s ability to generalize across models. Electrophysiological feature extraction For every simulated AP, six key biomarkers were computed: 1. Resting Membrane Potential (RMP): The membrane potential just before stimulation. 2. Peak Potential (Peak V): The maximum voltage reached during the AP. 3. Action Potential Amplitude (APA): The difference between Peak V and RMP. 4. Maximum Upstroke Velocity (dV/dtmax): The steepest slope of the AP upstroke. 5. Action Potential Duration at 90% Repolarization (APD90). 6. Action Potential Duration at 50% Repolarization (APD50). Machine learning framework All custom code used to generate simulations, extract electrophysiological features, and train the regression models is openly available on Zenodo. 8 To support readers who may be less familiar with computational electrophysiology, we include a schematic ( Figure 1 ) summarizing the full pipeline, from biophysical simulations and feature extraction to machine learning and application. Figure 1. Overview of the in-silico modeling and machine learning pipeline. The workflow consists of four key stages: (1) simulation of cardiac action potentials under varying ischemic conditions using the Luo-Rudy and Ten Tusscher models, (2) extraction of electrophysiological features such as resting membrane potential, AP duration, amplitude, and upstroke velocity, (3) multi-target Random Forest regression with hyperparameter tuning to predict intracellular pH and extracellular potassium levels, and (4) model validation and application to a simulated drug scenario. Random Forest hyperparameters, including the number of estimators, maximum depth, and minimum samples per split, were tuned using a grid search with five-fold cross-validation on the training dataset. Performance was evaluated using mean absolute error across the two target variables (pHi and [K+]o), and the hyperparameter configuration yielding the lowest average error was selected for final model training. Our analysis centered around a multi-target regression framework. The six extracted features served as input variables for two parallel Random Forest Regressor models implemented using scikit-learn in Python. • Model 1 (K + Regressor): Trained to predict the continuous value of extracellular potassium ([K + ]o). • Model 2 (pH Regressor): Trained to predict the continuous value of intracellular pH (pHi). Each regressor consisted of an ensemble of 150 decision trees. Both models were trained exclusively on the dataset derived from the Luo-Rudy model. Cross-model validation protocol The central evaluation of our framework involved cross-model validation. The regressors trained on the LRd dataset were used to estimate [K + ]o and pHi values from the APs in the TT06 validation dataset. Model performance was assessed by calculating the Mean Squared Error (MSE) between predicted and actual values, and by visually inspecting prediction plots. Simulated pharmacological rescue To demonstrate a practical use case, we conducted a virtual pharmacological intervention. We selected one severely ischemic AP from the TT06 validation set. The trained models were first used to assess its baseline ischemic state by predicting [K + ]o and pHi. Next, a simulated drug was applied by setting the conductance of IK (ATP) to zero, mimicking a complete channel block. A new AP was then generated for the “treated” cell, and the updated features were input into the same regressors to quantify any change in the predicted pathological parameters. Code availability The complete Python script used to generate all data, perform the analysis, and create the figures is openly available at Zenodo: https://doi.org/10.5281/zenodo.17216134 . The development repository is also accessible on GitHub: https://github.com/mahmood789/DIF . Results Ischemic severity alters AP morphology in a dose-dependent manner Simulations using the LRd model revealed clear dose-dependent changes in AP waveforms as ischemia progressed. APs showed increasingly depolarized RMPs, reduced amplitude and upstroke velocity, and shorter APDs, all consistent with known electrophysiological effects of ischemia. 1 , 2 Machine learning models deconvolute ischemic factors Both Random Forest regressors performed well on the LRd test set, accurately predicting [K + ]o and pHi. When applied to the independent TT06 dataset, the models retained strong predictive accuracy. MSE values were low, and predicted values tracked closely with actual parameters, demonstrating that the models had learned generalizable physiological relationships rather than model-specific patterns. Cross-model validation protocol to confirm generalizability The central evaluation of our framework involved cross-model validation. The regressors trained on the LRd dataset were used to estimate [K + ]o and pHi values from the APs in the TT06 validation dataset. Model performance was assessed by calculating the Mean Squared Error (MSE) between predicted and actual values, and by visually inspecting prediction plots. Feature importance reveals distinct electrophysiological signatures Analyzing feature importance revealed that [K + ]o predictions depended heavily on RMP, expected, given the Nernst relationship between potassium concentration and membrane potential. By contrast, pHi predictions drew primarily on APD90 and APA, reflecting acidosis’ broader effects on multiple ion channels. 1 , 7 Application: Quantifying the efficacy of a simulated pharmacological intervention To demonstrate the framework’s practical utility, we selected a severely ischemic “patient” cell from the Ten Tusscher validation set. The model diagnosed this cell with elevated predicted [K + ]o and reduced pHi. We then simulated targeted drug intervention by blocking IK (ATP), which visibly improved the AP waveform, restoring a more hyperpolarized resting potential and prolonging its duration. When this treated AP was re-analyzed, the framework reflected a clear improvement in predicted ionic values, highlighting a partial rescue of the ischemic state. These results illustrate the model’s potential for in silico screening and quantitative evaluation of anti-ischemic therapies. Discussion This study introduces a machine learning-based method for inferring specific ischemic stressors, hyperkalemia and acidosis, from single AP waveforms. Our results show that although these conditions co-occur and influence similar AP features, their electrophysiological “signatures” can be analyzed and separated by trained models. That said, the relatively modest dataset size (150 training APs and 50 validation APs) raises questions about robustness. Although the regressors performed well, expanding the training set or applying data augmentation strategies would strengthen confidence in model generalizability and reduce the risk of overfitting. Crucially, the regressors trained on the Luo-Rudy model maintained high predictive accuracy when applied to the Ten Tusscher model. We selected Random Forest regressors for their interpretability and robustness with limited data. Nonetheless, alternative machine learning methods, such as gradient boosting or neural networks, could capture nonlinearities differently. Future work may compare performance across these approaches to identify optimal strategies for ischemic feature deconvolution. This cross-model validation provides strong evidence that the learned relationships reflect underlying principles of cardiac electrophysiology. Feature importance analysis reinforced physiological expectations: RMP reflected extracellular potassium levels, 1 while APD and amplitude were indicative of pH-related effects. 7 The model’s successful use in simulating and quantifying a drug’s electrophysiological impact also points to its utility as a screening tool in computational pharmacology. While clinical machine learning approaches using ECG data have shown significant promise in diagnostic and risk stratification tasks, they often provide limited insight into the underlying physiological mechanisms driving the observed signals. For example, Yilmaz et al. (2023) 3 used morphological features from P, QRS, and T waves during treadmill exercise testing to accurately predict obstructive coronary artery disease. Similarly, Cicek et al. (2024) 4 developed a risk prediction model for perioperative myocardial injury in elderly patients undergoing non-elective surgery, combining signal-derived and clinical variables. These studies demonstrate the growing clinical relevance of data-driven approaches to cardiac risk assessment. However, such models typically focus on statistical associations between features and outcomes, without directly modeling the underlying ionic or metabolic processes. In contrast, our in silico modeling framework explicitly simulates the biophysical effects of ischemic stressors, allowing for a transparent mapping between waveform changes and specific physiological drivers ([K + ]o and pHi). By learning from synthetic action potentials tied to known physiological conditions, our approach offers a novel, interpretable bridge between computational modeling and clinical diagnostics. This distinction situates our work not as a replacement for clinical machine learning tools, but as a complementary platform for hypothesis generation, virtual screening, and mechanistic insight. Limitations of this study include the use of simplified cell models and the exclusion of other ischemic contributors like mechanical stretch, sympathetic stimulation, or reactive oxygen species. While our framework demonstrates strong predictive accuracy in silico, it has not yet been tested against experimental data. A clear next step is validation against electrophysiological recordings in ischemic cardiomyocytes, either through patch-clamp experiments or extracellular field potential measurements in animal ischemia models. Such experimental validation would not only strengthen biological plausibility but also provide critical insight into the translational robustness of the approach. Additionally, although ATP-sensitive currents were incorporated through IK (ATP), the framework did not directly predict ATP depletion as an output. Extending the model to infer ATP levels would provide a more complete representation of ischemic pathophysiology, particularly in the context of metabolic stress. Future work should also explore tissue-level simulations and intercellular coupling to bridge the gap between single-cell models and translational applications. Nonetheless, the present findings offer a strong foundation for data-driven approaches to studying and treating myocardial ischemia. In particular, future applications could move beyond single-cell models to incorporate tissue-level simulations, extracellular field potentials, or even clinical ECG recordings. Such extensions would help bridge the gap between mechanistic insight and translational utility in human patients. Ethics and consent Ethical approval and consent were not required for this study. Data availability All data underlying the results are available from the Zenodo repository: https://doi.org/10.5281/zenodo.17216134 . 8 The dataset includes simulated action potential traces, extracted electrophysiological features, and Python scripts used for model training and analysis. - Values behind reported means and figures are provided in the dataset. - No participant data or personal identifiers were used. - Data are shared under a Creative Commons Zero v1.0 Universal Software availability • Source code available from: https://github.com/mahmood789/DIF • Archived source code available from: https://doi.org/10.5281/zenodo.17216134 8 • License: CC0-1.0 license Extended data Extended data files, including the full list of simulation parameters, feature extraction scripts, and validation datasets, are available on Zenodo: https://doi.org/10.5281/zenodo.17216134 . 8 These materials are shared under the Creative Commons Zero v1.0 Universal. References 1. Carmeliet E: Cardiac ionic currents and acute ischemia: from channels to arrhythmias. Physiol. Rev. 1999; 79 (3): 917–1017. PubMed Abstract | Publisher Full Text 2. Wilde AA, Janse MJ: The KATP channel in ischaemia and arrhythmogenesis. Ann. Med. 2000; 32 (8): 557–567. 3. Yilmaz A, Hayıroğlu MI, Salturk S, et al. : Machine Learning Approach on High Risk Treadmill Exercise Test to Predict Obstructive Coronary Artery Disease by using P, QRS, and T waves’ Features. Curr. Probl. Cardiol. 2023; 48 (2): 101482. Publisher Full Text 4. Cicek V, Babaoglu M, Saylik F, et al. : A New Risk Prediction Model for the Assessment of Myocardial Injury in Elderly Patients Undergoing Non-Elective Surgery. J. Cardiovasc. Dev. Dis. 2024; 12 (1): 6. Publisher Full Text 5. Luo CH, Rudy Y: A model of the ventricular cardiac action potential. Depolarization, repolarization, and their interaction. Circ. Res. 1991; 68 (6): 1501–1526. PubMed Abstract | Publisher Full Text 6. Ten Tusscher KH, Noble D, Noble PJ, et al. : A model for human ventricular tissue. Am. J. Phys. Heart Circ. Phys. 2006; 291 (3): H1088–H1100. PubMed Abstract | Publisher Full Text 7. Orchard CH, Cingolani E: Acidosis and cardiac contractility. J. Mol. Cell. Cardiol. 2021; 153 : 63–74. 8. Ahmad M, Kiel J: Deconvoluting the Electrophysiological Signatures of Myocardial Ischemia using a Validated Machine Learning Framework (Version 1.0) [Computer software]. Zenodo. 2025. Publisher Full Text Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 01 Dec 2025 ADD YOUR COMMENT Comment Author details Author details 1 Royal Free London NHS Foundation Trust, London, England, UK 2 Boston University, Boston, Massachusetts, USA 3 University College London, London, England, UK Ahmad Mahmood Roles: Conceptualization, Investigation, Methodology, Software, Supervision, Validation, Writing – Original Draft Preparation Kiel Jacqueline Roles: Data Curation, Formal Analysis, Software, Writing – Review & Editing Joanne Lac Roles: Funding Acquisition, Project Administration Competing interests No competing interests were disclosed. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (2) version 2 Revised Published: 31 Jan 2026, 14:1341 https://doi.org/10.12688/f1000research.171338.2 version 1 Published: 01 Dec 2025, 14:1341 https://doi.org/10.12688/f1000research.171338.1 Copyright © 2026 Mahmood A et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Mahmood A, Jacqueline K and Lac J. Deconvoluting the Electrophysiological Signatures of Myocardial Ischemia using a Validated Machine Learning Framework [version 2; peer review: 2 approved] . F1000Research 2026, 14 :1341 ( https://doi.org/10.12688/f1000research.171338.2 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 2 VERSION 2 PUBLISHED 31 Jan 2026 Revised Views 0 Cite How to cite this report: D C. Reviewer Report For: Deconvoluting the Electrophysiological Signatures of Myocardial Ischemia using a Validated Machine Learning Framework [version 2; peer review: 2 approved] . F1000Research 2026, 14 :1341 ( https://doi.org/10.5256/f1000research.195736.r454211 ) The direct URL for this report is: https://f1000research.com/articles/14-1341/v2#referee-response-454211 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 10 Feb 2026 Cenitta D , Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India Approved VIEWS 0 https://doi.org/10.5256/f1000research.195736.r454211 The revised version shows clear ... Continue reading READ ALL The revised version shows clear improvement and meets the requirements. Competing Interests: No competing interests were disclosed. Reviewer Expertise: Machine learning, Heart disease prediction I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT D C. Reviewer Report For: Deconvoluting the Electrophysiological Signatures of Myocardial Ischemia using a Validated Machine Learning Framework [version 2; peer review: 2 approved] . F1000Research 2026, 14 :1341 ( https://doi.org/10.5256/f1000research.195736.r454211 ) The direct URL for this report is: https://f1000research.com/articles/14-1341/v2#referee-response-454211 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Hayıroğlu Mİ. Reviewer Report For: Deconvoluting the Electrophysiological Signatures of Myocardial Ischemia using a Validated Machine Learning Framework [version 2; peer review: 2 approved] . F1000Research 2026, 14 :1341 ( https://doi.org/10.5256/f1000research.195736.r454212 ) The direct URL for this report is: https://f1000research.com/articles/14-1341/v2#referee-response-454212 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 02 Feb 2026 Mert İlker Hayıroğlu , Dr. Siyami Ersek Thoracic and Cardiovascular Surgery Training and Research Hospital, Istanbul, Turkey Approved VIEWS 0 https://doi.org/10.5256/f1000research.195736.r454212 The manuscript is acceptable ... Continue reading READ ALL The manuscript is acceptable in its final status Competing Interests: No competing interests were disclosed. Reviewer Expertise: cardiology I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Hayıroğlu Mİ. Reviewer Report For: Deconvoluting the Electrophysiological Signatures of Myocardial Ischemia using a Validated Machine Learning Framework [version 2; peer review: 2 approved] . F1000Research 2026, 14 :1341 ( https://doi.org/10.5256/f1000research.195736.r454212 ) The direct URL for this report is: https://f1000research.com/articles/14-1341/v2#referee-response-454212 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Version 1 VERSION 1 PUBLISHED 01 Dec 2025 Views 0 Cite How to cite this report: D C. Reviewer Report For: Deconvoluting the Electrophysiological Signatures of Myocardial Ischemia using a Validated Machine Learning Framework [version 2; peer review: 2 approved] . F1000Research 2026, 14 :1341 ( https://doi.org/10.5256/f1000research.188937.r438218 ) The direct URL for this report is: https://f1000research.com/articles/14-1341/v1#referee-response-438218 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 10 Jan 2026 Cenitta D , Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.188937.r438218 This study presents a machine learning–based framework to deconvolute key electrophysiological contributors to myocardial ischemia—specifically extracellular hyperkalemia and intracellular acidosis—from single cardiomyocyte action potential (AP) waveforms. Using simulated data generated from the Luo–Rudy (1991) ventricular cell model, the authors trained ... Continue reading READ ALL This study presents a machine learning–based framework to deconvolute key electrophysiological contributors to myocardial ischemia—specifically extracellular hyperkalemia and intracellular acidosis—from single cardiomyocyte action potential (AP) waveforms. Using simulated data generated from the Luo–Rudy (1991) ventricular cell model, the authors trained multi-target Random Forest regression models to predict extracellular potassium concentration ([K⁺]o) and intracellular pH (pHi). The robustness and generalizability of the framework were evaluated via cross-model validation using the structurally distinct Ten Tusscher (2006) model. Feature-importance analysis highlighted physiologically meaningful relationships, and a simulated pharmacological intervention was used to demonstrate potential translational utility. Overall, the work is technically sound, well structured, and reproducible. Major Comments 1. Clarity of Presentation and Use of Current Literature (Partly) The manuscript is clearly written, logically organized, and presents results in a transparent and interpretable manner. The methodological workflow—from simulation to feature extraction, regression modeling, and validation—is easy to follow. However, the literature review and discussion would benefit from broader contextualization within recent machine learning applications in cardiology. While the foundational electrophysiology and computational modeling references are appropriate, the manuscript does not sufficiently engage with recent AI-driven studies on myocardial injury prediction, ischemia assessment, or ECG-based machine learning diagnostics. Incorporating recent clinical and signal-based ML studies would strengthen the manuscript’s relevance and better position the contribution within the current research landscape. Required revision: Expand the Introduction or Discussion to include recent machine learning studies related to myocardial ischemia, cardiac risk prediction, or electrophysiological signal analysis, particularly those using ECG or waveform-based features. Explicitly contrast the present mechanistic, in-silico approach with clinically oriented ML models to clarify novelty and scope. 2. Study Design and Technical Soundness (Yes) The study design is appropriate and well justified. The use of two distinct and well-established ventricular cardiomyocyte models for training and cross-validation is a notable strength. The choice of Random Forest regression is suitable given the dataset size, the nonlinear relationships involved, and the emphasis on interpretability. The cross-model validation strategy convincingly demonstrates that the learned relationships reflect underlying electrophysiological principles rather than model-specific artifacts. 3. Methods and Reproducibility (Yes) The manuscript provides sufficient methodological detail to enable replication. Model parameters, simulation ranges, extracted electrophysiological features, and evaluation metrics are clearly described. Importantly, the authors have made all source code, simulated datasets, and extended data publicly available, which fully supports transparency and reproducibility. 4. Statistical Analysis (Yes) The statistical analysis is appropriate for the study objectives. The use of mean squared error for continuous regression targets is suitable, and interpretation of results is consistent with the reported metrics. Feature-importance analysis is used appropriately to support physiological interpretability rather than overstated causal claims. 5. Data Availability and Reproducibility (Yes) All underlying data and code are openly accessible via Zenodo and GitHub, including raw simulation outputs and values behind figures. This fully satisfies reproducibility requirements. 6. Validity of Conclusions (Yes) The conclusions are well supported by the results. Claims are appropriately scoped to in-silico modeling, and limitations—such as dataset size, simplified cellular models, and lack of experimental validation—are openly acknowledged. The discussion avoids overgeneralization to clinical settings, which is commendable. Minor Suggestions (Optional Improvements) Clarify whether hyperparameter tuning was performed for the Random Forest models and, if so, how it was conducted. Consider adding a brief schematic summarizing the end-to-end pipeline for readers less familiar with computational electrophysiology. Overall Recommendation This is a technically strong and reproducible study that makes a meaningful methodological contribution to computational cardiology and electrophysiological modeling. The primary issue to be addressed is the limited engagement with recent machine learning literature in myocardial ischemia and cardiac diagnostics. Addressing this point would significantly enhance the manuscript’s contextual depth and appeal to a broader readership. Once this revision is made, the article will meet the standards of scientific soundness and relevance expected for indexing. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Machine learning, Heart disease prediction I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT D C. Reviewer Report For: Deconvoluting the Electrophysiological Signatures of Myocardial Ischemia using a Validated Machine Learning Framework [version 2; peer review: 2 approved] . F1000Research 2026, 14 :1341 ( https://doi.org/10.5256/f1000research.188937.r438218 ) The direct URL for this report is: https://f1000research.com/articles/14-1341/v1#referee-response-438218 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 31 Jan 2026 Jacqueline Kiel , Boston University, Boston, USA 31 Jan 2026 Author Response We thank the reviewer for their thoughtful and constructive feedback. In response to the suggestion to broaden the discussion of current machine learning literature in cardiac diagnostics, we have revised ... Continue reading We thank the reviewer for their thoughtful and constructive feedback. In response to the suggestion to broaden the discussion of current machine learning literature in cardiac diagnostics, we have revised the manuscript to incorporate and contextualize the two cited studies: Yilmaz et al. (2023) on ECG-based prediction of obstructive coronary artery disease using features from treadmill stress tests, and Cicek et al. (2024) on a clinical risk model for perioperative myocardial injury in elderly patients. These works are now discussed in the Introduction and Discussion sections to emphasize the expanding clinical relevance of data-driven cardiac diagnostics. In the revised Discussion, we also clarify how our in-silico modeling framework complements such signal-based models by offering a physiologically grounded, interpretable approach that links specific waveform features to biophysical drivers (e.g., [K⁺]ₒ and pHi ). We believe this enhancement significantly strengthens the manuscript's connection to the broader landscape of AI in cardiology and addresses the reviewer's concern regarding contextual depth. Additionally, to support readers less familiar with computational electrophysiology, we have included a schematic (Figure 1) summarizing the full modeling pipeline; from simulation and feature extraction through to machine learning and application. We believe this visual overview improves the manuscript’s accessibility and responds directly to the reviewer’s helpful recommendation. Thank you again for your valuable input, which has helped improve the clarity, accessibility, and clinical relevance of our work. We thank the reviewer for their thoughtful and constructive feedback. In response to the suggestion to broaden the discussion of current machine learning literature in cardiac diagnostics, we have revised the manuscript to incorporate and contextualize the two cited studies: Yilmaz et al. (2023) on ECG-based prediction of obstructive coronary artery disease using features from treadmill stress tests, and Cicek et al. (2024) on a clinical risk model for perioperative myocardial injury in elderly patients. These works are now discussed in the Introduction and Discussion sections to emphasize the expanding clinical relevance of data-driven cardiac diagnostics. In the revised Discussion, we also clarify how our in-silico modeling framework complements such signal-based models by offering a physiologically grounded, interpretable approach that links specific waveform features to biophysical drivers (e.g., [K⁺]ₒ and pHi ). We believe this enhancement significantly strengthens the manuscript's connection to the broader landscape of AI in cardiology and addresses the reviewer's concern regarding contextual depth. Additionally, to support readers less familiar with computational electrophysiology, we have included a schematic (Figure 1) summarizing the full modeling pipeline; from simulation and feature extraction through to machine learning and application. We believe this visual overview improves the manuscript’s accessibility and responds directly to the reviewer’s helpful recommendation. Thank you again for your valuable input, which has helped improve the clarity, accessibility, and clinical relevance of our work. Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 31 Jan 2026 Jacqueline Kiel , Boston University, Boston, USA 31 Jan 2026 Author Response We thank the reviewer for their thoughtful and constructive feedback. In response to the suggestion to broaden the discussion of current machine learning literature in cardiac diagnostics, we have revised ... Continue reading We thank the reviewer for their thoughtful and constructive feedback. In response to the suggestion to broaden the discussion of current machine learning literature in cardiac diagnostics, we have revised the manuscript to incorporate and contextualize the two cited studies: Yilmaz et al. (2023) on ECG-based prediction of obstructive coronary artery disease using features from treadmill stress tests, and Cicek et al. (2024) on a clinical risk model for perioperative myocardial injury in elderly patients. These works are now discussed in the Introduction and Discussion sections to emphasize the expanding clinical relevance of data-driven cardiac diagnostics. In the revised Discussion, we also clarify how our in-silico modeling framework complements such signal-based models by offering a physiologically grounded, interpretable approach that links specific waveform features to biophysical drivers (e.g., [K⁺]ₒ and pHi ). We believe this enhancement significantly strengthens the manuscript's connection to the broader landscape of AI in cardiology and addresses the reviewer's concern regarding contextual depth. Additionally, to support readers less familiar with computational electrophysiology, we have included a schematic (Figure 1) summarizing the full modeling pipeline; from simulation and feature extraction through to machine learning and application. We believe this visual overview improves the manuscript’s accessibility and responds directly to the reviewer’s helpful recommendation. Thank you again for your valuable input, which has helped improve the clarity, accessibility, and clinical relevance of our work. We thank the reviewer for their thoughtful and constructive feedback. In response to the suggestion to broaden the discussion of current machine learning literature in cardiac diagnostics, we have revised the manuscript to incorporate and contextualize the two cited studies: Yilmaz et al. (2023) on ECG-based prediction of obstructive coronary artery disease using features from treadmill stress tests, and Cicek et al. (2024) on a clinical risk model for perioperative myocardial injury in elderly patients. These works are now discussed in the Introduction and Discussion sections to emphasize the expanding clinical relevance of data-driven cardiac diagnostics. In the revised Discussion, we also clarify how our in-silico modeling framework complements such signal-based models by offering a physiologically grounded, interpretable approach that links specific waveform features to biophysical drivers (e.g., [K⁺]ₒ and pHi ). We believe this enhancement significantly strengthens the manuscript's connection to the broader landscape of AI in cardiology and addresses the reviewer's concern regarding contextual depth. Additionally, to support readers less familiar with computational electrophysiology, we have included a schematic (Figure 1) summarizing the full modeling pipeline; from simulation and feature extraction through to machine learning and application. We believe this visual overview improves the manuscript’s accessibility and responds directly to the reviewer’s helpful recommendation. Thank you again for your valuable input, which has helped improve the clarity, accessibility, and clinical relevance of our work. Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Hayıroğlu Mİ. Reviewer Report For: Deconvoluting the Electrophysiological Signatures of Myocardial Ischemia using a Validated Machine Learning Framework [version 2; peer review: 2 approved] . F1000Research 2026, 14 :1341 ( https://doi.org/10.5256/f1000research.188937.r438213 ) The direct URL for this report is: https://f1000research.com/articles/14-1341/v1#referee-response-438213 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 30 Dec 2025 Mert İlker Hayıroğlu , Dr. Siyami Ersek Thoracic and Cardiovascular Surgery Training and Research Hospital, Istanbul, Turkey Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.188937.r438213 The manuscript presents a novel and technically sound approach to distinguishing the specific contributions of hyperkalemia and acidosis to action potential morphology using a multi-target regression model, with a particularly impressive cross-model validation strategy. However, the discussion regarding the broader ... Continue reading READ ALL The manuscript presents a novel and technically sound approach to distinguishing the specific contributions of hyperkalemia and acidosis to action potential morphology using a multi-target regression model, with a particularly impressive cross-model validation strategy. However, the discussion regarding the broader clinical applicability and the current landscape of machine learning in predicting myocardial injury and coronary disease could be significantly enriched. To better contextualize the study within recent advancements in AI-driven cardiac risk assessment and signal analysis, the authors are encouraged to reference and discuss recent relevant works such as 'A New Risk Prediction Model for the Assessment of Myocardial Injury in Elderly Patients Undergoing Non-Elective Surgery' and 'Machine Learning Approach on High Risk Treadmill Exercise Test to Predict Obstructive Coronary Artery Disease by using P, QRS, and T waves' Features'. Incorporating these studies would highlight the expanding role of machine learning in both electrophysiological modeling and clinical diagnostics, thereby strengthening the manuscript's relevance to a wider audience Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes References 1. Yilmaz A, Hayıroğlu M, Salturk S, Pay L, et al.: Machine Learning Approach on High Risk Treadmill Exercise Test to Predict Obstructive Coronary Artery Disease by using P, QRS, and T waves’ Features. Current Problems in Cardiology . 2023; 48 (2). Publisher Full Text 2. Cicek V, Babaoglu M, Saylik F, Yavuz S, et al.: A New Risk Prediction Model for the Assessment of Myocardial Injury in Elderly Patients Undergoing Non-Elective Surgery. Journal of Cardiovascular Development and Disease . 2024; 12 (1). Publisher Full Text Competing Interests: No competing interests were disclosed. Reviewer Expertise: cardiology I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Hayıroğlu Mİ. Reviewer Report For: Deconvoluting the Electrophysiological Signatures of Myocardial Ischemia using a Validated Machine Learning Framework [version 2; peer review: 2 approved] . F1000Research 2026, 14 :1341 ( https://doi.org/10.5256/f1000research.188937.r438213 ) The direct URL for this report is: https://f1000research.com/articles/14-1341/v1#referee-response-438213 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 31 Jan 2026 Jacqueline Kiel , Boston University, Boston, USA 31 Jan 2026 Author Response We sincerely thank the reviewer for their positive evaluation of the technical contribution of our study and for highlighting the importance of broader clinical contextualization. In response to the reviewer’s ... Continue reading We sincerely thank the reviewer for their positive evaluation of the technical contribution of our study and for highlighting the importance of broader clinical contextualization. In response to the reviewer’s recommendation, we have revised both the Introduction and Discussion to incorporate recent developments in machine learning applied to cardiac diagnostics. Specifically, we now reference and discuss two suggested studies: Yilmaz et al. (2023) Cicek et al. (2024) These additions help to position our mechanistic modeling framework within the broader landscape of AI-driven cardiac risk assessment. We emphasize how our in silico approach complements clinical ML models by providing interpretable, physiology-based insights into ischemic waveform changes; specifically the contributions of [K⁺]ₒ and pHi, which are not easily accessible through observational data alone. Additionally, we have included a schematic diagram (Figure 1) summarizing our end-to-end pipeline, from biophysical simulation through feature extraction and multi-target regression, to validation and application. We hope this will aid readers less familiar with computational electrophysiology in understanding the structure and goals of the study. We are grateful for the reviewer’s thoughtful input, which helped us significantly strengthen the clarity, clinical relevance, and accessibility of the manuscript. We sincerely thank the reviewer for their positive evaluation of the technical contribution of our study and for highlighting the importance of broader clinical contextualization. In response to the reviewer’s recommendation, we have revised both the Introduction and Discussion to incorporate recent developments in machine learning applied to cardiac diagnostics. Specifically, we now reference and discuss two suggested studies: Yilmaz et al. (2023) Cicek et al. (2024) These additions help to position our mechanistic modeling framework within the broader landscape of AI-driven cardiac risk assessment. We emphasize how our in silico approach complements clinical ML models by providing interpretable, physiology-based insights into ischemic waveform changes; specifically the contributions of [K⁺]ₒ and pHi, which are not easily accessible through observational data alone. Additionally, we have included a schematic diagram (Figure 1) summarizing our end-to-end pipeline, from biophysical simulation through feature extraction and multi-target regression, to validation and application. We hope this will aid readers less familiar with computational electrophysiology in understanding the structure and goals of the study. We are grateful for the reviewer’s thoughtful input, which helped us significantly strengthen the clarity, clinical relevance, and accessibility of the manuscript. Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 31 Jan 2026 Jacqueline Kiel , Boston University, Boston, USA 31 Jan 2026 Author Response We sincerely thank the reviewer for their positive evaluation of the technical contribution of our study and for highlighting the importance of broader clinical contextualization. In response to the reviewer’s ... Continue reading We sincerely thank the reviewer for their positive evaluation of the technical contribution of our study and for highlighting the importance of broader clinical contextualization. In response to the reviewer’s recommendation, we have revised both the Introduction and Discussion to incorporate recent developments in machine learning applied to cardiac diagnostics. Specifically, we now reference and discuss two suggested studies: Yilmaz et al. (2023) Cicek et al. (2024) These additions help to position our mechanistic modeling framework within the broader landscape of AI-driven cardiac risk assessment. We emphasize how our in silico approach complements clinical ML models by providing interpretable, physiology-based insights into ischemic waveform changes; specifically the contributions of [K⁺]ₒ and pHi, which are not easily accessible through observational data alone. Additionally, we have included a schematic diagram (Figure 1) summarizing our end-to-end pipeline, from biophysical simulation through feature extraction and multi-target regression, to validation and application. We hope this will aid readers less familiar with computational electrophysiology in understanding the structure and goals of the study. We are grateful for the reviewer’s thoughtful input, which helped us significantly strengthen the clarity, clinical relevance, and accessibility of the manuscript. We sincerely thank the reviewer for their positive evaluation of the technical contribution of our study and for highlighting the importance of broader clinical contextualization. In response to the reviewer’s recommendation, we have revised both the Introduction and Discussion to incorporate recent developments in machine learning applied to cardiac diagnostics. Specifically, we now reference and discuss two suggested studies: Yilmaz et al. (2023) Cicek et al. (2024) These additions help to position our mechanistic modeling framework within the broader landscape of AI-driven cardiac risk assessment. We emphasize how our in silico approach complements clinical ML models by providing interpretable, physiology-based insights into ischemic waveform changes; specifically the contributions of [K⁺]ₒ and pHi, which are not easily accessible through observational data alone. Additionally, we have included a schematic diagram (Figure 1) summarizing our end-to-end pipeline, from biophysical simulation through feature extraction and multi-target regression, to validation and application. We hope this will aid readers less familiar with computational electrophysiology in understanding the structure and goals of the study. We are grateful for the reviewer’s thoughtful input, which helped us significantly strengthen the clarity, clinical relevance, and accessibility of the manuscript. Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 01 Dec 2025 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 Version 2 (revision) 31 Jan 26 read read Version 1 01 Dec 25 read read Mert İlker Hayıroğlu , Dr. Siyami Ersek Thoracic and Cardiovascular Surgery Training and Research Hospital, Istanbul, Turkey Cenitta D , Manipal Academy of Higher Education, Manipal, India Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 D C. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 10 Feb 2026 | for Version 2 Cenitta D , Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India 0 Views copyright © 2026 D C. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The revised version shows clear improvement and meets the requirements. Competing Interests No competing interests were disclosed. Reviewer Expertise Machine learning, Heart disease prediction I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) D C. Peer Review Report For: Deconvoluting the Electrophysiological Signatures of Myocardial Ischemia using a Validated Machine Learning Framework [version 2; peer review: 2 approved] . F1000Research 2026, 14 :1341 ( https://doi.org/10.5256/f1000research.195736.r454211) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-1341/v2#referee-response-454211 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Hayıroğlu M. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 02 Feb 2026 | for Version 2 Mert İlker Hayıroğlu , Dr. Siyami Ersek Thoracic and Cardiovascular Surgery Training and Research Hospital, Istanbul, Turkey 0 Views copyright © 2026 Hayıroğlu M. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The manuscript is acceptable in its final status Competing Interests No competing interests were disclosed. Reviewer Expertise cardiology I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Hayıroğlu Mİ. Peer Review Report For: Deconvoluting the Electrophysiological Signatures of Myocardial Ischemia using a Validated Machine Learning Framework [version 2; peer review: 2 approved] . F1000Research 2026, 14 :1341 ( https://doi.org/10.5256/f1000research.195736.r454212) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-1341/v2#referee-response-454212 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 D C. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 10 Jan 2026 | for Version 1 Cenitta D , Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India 0 Views copyright © 2026 D C. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions This study presents a machine learning–based framework to deconvolute key electrophysiological contributors to myocardial ischemia—specifically extracellular hyperkalemia and intracellular acidosis—from single cardiomyocyte action potential (AP) waveforms. Using simulated data generated from the Luo–Rudy (1991) ventricular cell model, the authors trained multi-target Random Forest regression models to predict extracellular potassium concentration ([K⁺]o) and intracellular pH (pHi). The robustness and generalizability of the framework were evaluated via cross-model validation using the structurally distinct Ten Tusscher (2006) model. Feature-importance analysis highlighted physiologically meaningful relationships, and a simulated pharmacological intervention was used to demonstrate potential translational utility. Overall, the work is technically sound, well structured, and reproducible. Major Comments 1. Clarity of Presentation and Use of Current Literature (Partly) The manuscript is clearly written, logically organized, and presents results in a transparent and interpretable manner. The methodological workflow—from simulation to feature extraction, regression modeling, and validation—is easy to follow. However, the literature review and discussion would benefit from broader contextualization within recent machine learning applications in cardiology. While the foundational electrophysiology and computational modeling references are appropriate, the manuscript does not sufficiently engage with recent AI-driven studies on myocardial injury prediction, ischemia assessment, or ECG-based machine learning diagnostics. Incorporating recent clinical and signal-based ML studies would strengthen the manuscript’s relevance and better position the contribution within the current research landscape. Required revision: Expand the Introduction or Discussion to include recent machine learning studies related to myocardial ischemia, cardiac risk prediction, or electrophysiological signal analysis, particularly those using ECG or waveform-based features. Explicitly contrast the present mechanistic, in-silico approach with clinically oriented ML models to clarify novelty and scope. 2. Study Design and Technical Soundness (Yes) The study design is appropriate and well justified. The use of two distinct and well-established ventricular cardiomyocyte models for training and cross-validation is a notable strength. The choice of Random Forest regression is suitable given the dataset size, the nonlinear relationships involved, and the emphasis on interpretability. The cross-model validation strategy convincingly demonstrates that the learned relationships reflect underlying electrophysiological principles rather than model-specific artifacts. 3. Methods and Reproducibility (Yes) The manuscript provides sufficient methodological detail to enable replication. Model parameters, simulation ranges, extracted electrophysiological features, and evaluation metrics are clearly described. Importantly, the authors have made all source code, simulated datasets, and extended data publicly available, which fully supports transparency and reproducibility. 4. Statistical Analysis (Yes) The statistical analysis is appropriate for the study objectives. The use of mean squared error for continuous regression targets is suitable, and interpretation of results is consistent with the reported metrics. Feature-importance analysis is used appropriately to support physiological interpretability rather than overstated causal claims. 5. Data Availability and Reproducibility (Yes) All underlying data and code are openly accessible via Zenodo and GitHub, including raw simulation outputs and values behind figures. This fully satisfies reproducibility requirements. 6. Validity of Conclusions (Yes) The conclusions are well supported by the results. Claims are appropriately scoped to in-silico modeling, and limitations—such as dataset size, simplified cellular models, and lack of experimental validation—are openly acknowledged. The discussion avoids overgeneralization to clinical settings, which is commendable. Minor Suggestions (Optional Improvements) Clarify whether hyperparameter tuning was performed for the Random Forest models and, if so, how it was conducted. Consider adding a brief schematic summarizing the end-to-end pipeline for readers less familiar with computational electrophysiology. Overall Recommendation This is a technically strong and reproducible study that makes a meaningful methodological contribution to computational cardiology and electrophysiological modeling. The primary issue to be addressed is the limited engagement with recent machine learning literature in myocardial ischemia and cardiac diagnostics. Addressing this point would significantly enhance the manuscript’s contextual depth and appeal to a broader readership. Once this revision is made, the article will meet the standards of scientific soundness and relevance expected for indexing. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Machine learning, Heart disease prediction I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 31 Jan 2026 Jacqueline Kiel, Boston University, Boston, USA We thank the reviewer for their thoughtful and constructive feedback. In response to the suggestion to broaden the discussion of current machine learning literature in cardiac diagnostics, we have revised the manuscript to incorporate and contextualize the two cited studies: Yilmaz et al. (2023) on ECG-based prediction of obstructive coronary artery disease using features from treadmill stress tests, and Cicek et al. (2024) on a clinical risk model for perioperative myocardial injury in elderly patients. These works are now discussed in the Introduction and Discussion sections to emphasize the expanding clinical relevance of data-driven cardiac diagnostics. In the revised Discussion, we also clarify how our in-silico modeling framework complements such signal-based models by offering a physiologically grounded, interpretable approach that links specific waveform features to biophysical drivers (e.g., [K⁺]ₒ and pHi ). We believe this enhancement significantly strengthens the manuscript's connection to the broader landscape of AI in cardiology and addresses the reviewer's concern regarding contextual depth. Additionally, to support readers less familiar with computational electrophysiology, we have included a schematic (Figure 1) summarizing the full modeling pipeline; from simulation and feature extraction through to machine learning and application. We believe this visual overview improves the manuscript’s accessibility and responds directly to the reviewer’s helpful recommendation. Thank you again for your valuable input, which has helped improve the clarity, accessibility, and clinical relevance of our work. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern D C. Peer Review Report For: Deconvoluting the Electrophysiological Signatures of Myocardial Ischemia using a Validated Machine Learning Framework [version 2; peer review: 2 approved] . F1000Research 2026, 14 :1341 ( https://doi.org/10.5256/f1000research.188937.r438218) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-1341/v1#referee-response-438218 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Hayıroğlu M. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 30 Dec 2025 | for Version 1 Mert İlker Hayıroğlu , Dr. Siyami Ersek Thoracic and Cardiovascular Surgery Training and Research Hospital, Istanbul, Turkey 0 Views copyright © 2026 Hayıroğlu M. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The manuscript presents a novel and technically sound approach to distinguishing the specific contributions of hyperkalemia and acidosis to action potential morphology using a multi-target regression model, with a particularly impressive cross-model validation strategy. However, the discussion regarding the broader clinical applicability and the current landscape of machine learning in predicting myocardial injury and coronary disease could be significantly enriched. To better contextualize the study within recent advancements in AI-driven cardiac risk assessment and signal analysis, the authors are encouraged to reference and discuss recent relevant works such as 'A New Risk Prediction Model for the Assessment of Myocardial Injury in Elderly Patients Undergoing Non-Elective Surgery' and 'Machine Learning Approach on High Risk Treadmill Exercise Test to Predict Obstructive Coronary Artery Disease by using P, QRS, and T waves' Features'. Incorporating these studies would highlight the expanding role of machine learning in both electrophysiological modeling and clinical diagnostics, thereby strengthening the manuscript's relevance to a wider audience Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes References 1. Yilmaz A, Hayıroğlu M, Salturk S, Pay L, et al.: Machine Learning Approach on High Risk Treadmill Exercise Test to Predict Obstructive Coronary Artery Disease by using P, QRS, and T waves’ Features. Current Problems in Cardiology . 2023; 48 (2). Publisher Full Text 2. Cicek V, Babaoglu M, Saylik F, Yavuz S, et al.: A New Risk Prediction Model for the Assessment of Myocardial Injury in Elderly Patients Undergoing Non-Elective Surgery. Journal of Cardiovascular Development and Disease . 2024; 12 (1). Publisher Full Text Competing Interests No competing interests were disclosed. Reviewer Expertise cardiology I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 31 Jan 2026 Jacqueline Kiel, Boston University, Boston, USA We sincerely thank the reviewer for their positive evaluation of the technical contribution of our study and for highlighting the importance of broader clinical contextualization. In response to the reviewer’s recommendation, we have revised both the Introduction and Discussion to incorporate recent developments in machine learning applied to cardiac diagnostics. Specifically, we now reference and discuss two suggested studies: Yilmaz et al. (2023) Cicek et al. (2024) These additions help to position our mechanistic modeling framework within the broader landscape of AI-driven cardiac risk assessment. We emphasize how our in silico approach complements clinical ML models by providing interpretable, physiology-based insights into ischemic waveform changes; specifically the contributions of [K⁺]ₒ and pHi, which are not easily accessible through observational data alone. Additionally, we have included a schematic diagram (Figure 1) summarizing our end-to-end pipeline, from biophysical simulation through feature extraction and multi-target regression, to validation and application. We hope this will aid readers less familiar with computational electrophysiology in understanding the structure and goals of the study. We are grateful for the reviewer’s thoughtful input, which helped us significantly strengthen the clarity, clinical relevance, and accessibility of the manuscript. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Hayıroğlu Mİ. Peer Review Report For: Deconvoluting the Electrophysiological Signatures of Myocardial Ischemia using a Validated Machine Learning Framework [version 2; peer review: 2 approved] . F1000Research 2026, 14 :1341 ( https://doi.org/10.5256/f1000research.188937.r438213) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-1341/v1#referee-response-438213 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. 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