Signal preprocessing for multimodal classification of 12-channel electrocardiogram signals
Abstract
Signal preprocessing for multimodal classification of 12-channel electrocardiogram signals
Incoming article date: 01.08.2024Automatic classification of electrocardiogram signals will allow providing timely medical care to patients when providing first aid. Neural network models of electrocardiogram signal classification, including the stage of preliminary signal processing, allow increasing the accuracy of classifying electrocardiograms into a particular category of arrhythmia. The paper presents a computational method for preliminary processing of electrocardiogram signals, including noise reduction using discrete wavelet transform and extraction of morphological features using frequency analysis methods. The results of modeling the classification of 12-channel electrocardiogram signals using the stage of their preliminary processing showed an increase in classification accuracy by 23.2% compared to classification without preliminary signal processing.
Keywords: electrocardiogram signal classification, long-term short-term memory neural network, metadata, signal preprocessing wavelet transform, spectral analysis, PhysioNet Computing in Cardiology Challenge 2021