Electrocardiogram Signal Classification Based on Deep Learning Techniques

One of the most often used diagnostic tools in medicine and healthcare is the electrocardiogram (ECG). When it comes to healthcare prediction problems requiring ECG data, deep learning techniques seem promising. This paper aims to apply deep learning techniques to classify MIT-BIH arrhythmias on publicly available datasets. A new electrocardiogram classification for employing a spectrogram of signals algorithm is proposed. The proposed model depends on convolutional neural networks to automatically learn the characteristics of features and has used convolutional neural networks to detect normal and abnormal ECG heartbeats, with an average detection accuracy of 99.22%.