Potential of Rule-Based Methods and Deep Learning Architectures for ECG Diagnostics

Giovanni Bortolan,Iana Simova,Ivaylo Christov

doi:10.3390/diagnostics11091678

Abstract

The main objective of this study is to propose relatively simple techniques for the automatic diagnosis of electrocardiogram (ECG) signals based on a classical rule-based method and a convolutional deep learning architecture. The validation task was performed in the framework of the PhysioNet/Computing in Cardiology Challenge 2020, where seven databases consisting of 66,361 recordings with 12-lead ECGs were considered for training, validation and test sets. A total of 24 different diagnostic classes are considered in the entire training set. The rule-based method uses morphological and time-frequency ECG descriptors that are defined for each diagnostic label. These rules are extracted from the knowledge base of a cardiologist or from a textbook, with no direct learning procedure in the first phase, whereas a refinement was tested in the second phase. The deep learning method considers both raw ECG and median beat signals. These data are processed via continuous wavelet transform analysis, obtaining a time-frequency domain representation, with the generation of specific images (ECG scalograms). These images are then used for the training of a convolutional neural network based on GoogLeNet topology for ECG diagnostic classification. Cross-validation evaluation was performed for testing purposes. A total of 217 teams submitted 1395 algorithms during the Challenge. The diagnostic accuracy of our algorithm produced a challenge validation score of 0.325 (CPU time = 35 min) for the rule-based method, and a 0.426 (CPU time = 1664 min) for the deep learning method, which resulted in our team attaining 12th place in the competition.

Highlights

The automatic detection and classification of cardiac abnormalities from 12-leadECG signals has been an area of research interest for a long time [1]
The use of continuous wavelet transforms (CWTs) for ECG signal processing is present in several studies; for example, in [8] the CWT was considered for multiscale parameter estimation for delineation of the fiducial points of P-QRS-T waves
Recent examples of diagnostic 12-lead ECG classification have been reported. They come from the use of a deep neural network for the classification of six diagnostic classes [3], whereas the study in [9] considered the analysis of 12-lead ECG signals based on deep learning for the classification of four types of arrhythmias

Summary

Introduction

The automatic detection and classification of cardiac abnormalities from 12-leadECG signals has been an area of research interest for a long time [1]. There has been much focus on research employing the use of deep learning with medical images [4], time series classification [5], and object detection [6]. Recent examples of diagnostic 12-lead ECG classification have been reported. They come from the use of a deep neural network for the classification of six diagnostic classes [3], whereas the study in [9] considered the analysis of 12-lead ECG signals based on deep learning for the classification of four types of arrhythmias. A deep learning neural network model was tested in a database of 6788 12-lead ECG records for the identification of nine diagnostic classes [10]

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Conclusion