Rhythm interpretation using deep learning neural networks

Abstract

Out-of-hospital cardiac arrest (OHCA) is a leading cause of death in the industrialized world, with an estimated annual incidence that varies by 52.5 (in Asia), 86.4 (in Europe), 98.1 (in North America), and 111.9 (in Australia) per 100,000 person-years. Lethal ventricular arrhythmias are the most frequent causes of OHCA. A defibrillation shock is an effective treatment, and early defibrillation is one of the key factors in survival from OHCA. However, chest compressions, ventilations, and drug play a key role in the treatment of cardiac arrest. Under resuscitation, automated external defibrillator (AED) require peri-shock pauses to analyze for a shockable rhythm. This peri-shock are associated with a decrease in survival to hospital discharge.

The objective of this thesis is to determine if different artificial neural network (ANN) structures can be used as a classifier, to determine the underlying heart rhythm under chest compressions to remove peri-shock pauses during cardiac arrest. The analysis is conducted from data obtained from 394 OHCA patients, where two datasets were used. Both containing 3-second segments with clinical rhythm annotations resulting in 2446 and 422415 segments.

Results in this thesis suggest that there is no clear best method in the different neural network methods for ECG data. However, FNN demonstrates the most promising results with an accuracy of 52.30%. This result emphasizes the problem regarding classification of ECG data with compression artifacts.