Recognition of life-threatening arrhythmias by ECG scalograms

This work is devoted to the automatic classification of six classes of life-threatening arrhythmias using short ECG fragments of 2s-length. This task is extremely important for the detection of life-threatening arrhythmias with continuous monitoring. Especially dangerous are ventricular fibrillation and high-frequency heartbeat ventricular tachycardia. Timely detection of these dangerous disorders in the clinic allows doctors to effectively use electrical defibrillation, which saves the patient's life. A feature of our approach is the use of a unique technique for converting ECG signals into images (scalograms) using a continuous wavelet transform. For arrhythmia classification, the AlexNet neural network with a well-known deep learning architecture, which is commonly used in image classification tasks, is used. The experiments used data from the PhysioNet database, as well as synthesized ECG data obtained using the SMOTE method. The experimental results show that the proposed approach allows achieving an average accuracy of 98.7% for all classes, which exceeds the maximum accuracy estimates of 93.18% previously obtained by other researchers.