Practical Lessons on 12-Lead ECG Classification: Meta-Analysis of Methods From PhysioNet/Computing in Cardiology Challenge 2020

Abstract

Cardiovascular diseases (CVDs) are one of the most fatal disease groups worldwide. Electrocardiogram (ECG) is a widely used tool for automatically detecting cardiac abnormalities, thereby helping to control and manage CVDs. To encourage more multidisciplinary researches, PhysioNet/Computing in Cardiology Challenge 2020 (Challenge 2020) provided a public platform involving multi-center databases and automatic evaluations for ECG classification tasks. As a result, 41 teams successfully submitted their solutions and were qualified for rankings. Although Challenge 2020 was a success, there has been no in-depth methodological meta-analysis of these solutions, making it difficult for researchers to benefit from the solutions and results. In this study, we aim to systematically review the 41 solutions in terms of data processing, feature engineering, model architecture, and training strategy. For each perspective, we visualize and statistically analyze the effectiveness of the common techniques, and discuss the methodological advantages and disadvantages. Finally, we summarize five practical lessons based on the aforementioned analysis: (1) Data augmentation should be employed and adapted to specific scenarios; (2) Combining different features can improve performance; (3) A hybrid design of different types of deep neural networks (DNNs) is better than using a single type; (4) The use of end-to-end architectures should depend on the task being solved; (5) Multiple models are better than one. We expect that our meta-analysis will help accelerate the research related to ECG classification based on machine-learning models.

A robust multiple heartbeats classification with weight-based loss based on convolutional neural network and bidirectional long short-term memory

“…Over the years, many ML/AI-based algorithms with different datasets to focus on the ECG arrhythmia classification for automatic detection have been developed. A general overview of ECG arrhythmia classification using machine learning and deep learning methods is presented in ( Luz et al, 2016 ; Kooman et al, 2020 ; Xie et al, 2020 ; Hong et al, 2021 ; NehaSardana et al, 2021 ; Merdjanovska and Rashkovska, 2022 ). There are many different databases available for arrhythmia research, such as PTB-XL ( Wagner et al, 2020 ; Prabhakararao and Dandapat, 2021 ; Smigiel et al, 2021 ; Karthik et al, 2022 ; Palczynski et al, 2022 ), and MIT-BIH ( Acharya et al, 2017 ; Goldberger et al, 2000 ; Sayantan et al, 2018 ; Nurmaini et al, 2020 ; Yildirim et al, 2018 ; Huang et al, 2019 ; Wang et al, 2019 ).…”

“…Wang et al ( Wang et al, 2020 ) attempted the use of four-channel of ECG as vector representation of learning input in their models, achieving the F 1 score of 92.38%. Smigiel et al ( Smigiel et al, 2021 ) carried out three neural network architectures on PTB-XL Database ( Kooman et al, 2020 ; Wagner et al, 2020 ; Hong et al, 2021 ; Merdjanovska and Rashkovska, 2022 ), and the proposed convolutional network with entropy features achieved the highest accuracy in every classification task, scoring 89.2%, 76.5%, and 69.8% accuracy for 2, 5, and 20 classes, respectively. Huang et al ( Huang et al, 2019 ) proposed a 2-D CNN to classify ECG arrhythmia.…”

Estimating Critical Value from Electrocardiogram using a Deep Ordinal Convolutional Neural Network

“…Recently, deep neural networks (or deep learning methods) have achieved state-of-the-art performances in many areas such as speech recognition, computer vision, and natural language processing [8]. They also show great potentials on cardiovascular management [9,10], disease detection [11,[11][12][13][14][15][16][17][18][19][20][21], and biometric human identification [22,23], and many other ECG analysis tasks [24][25][26][27][28]. However, there are no deep learning models designed for ECG critical value estimation so far.…”

Estimating critical values from electrocardiogram using a deep ordinal convolutional neural network

“…Recently, deep neural networks (or deep learning methods) have achieved state-of-the-art performances in many areas such as speech recognition, computer vision, and natural language processing [ 8 ]. They also show great potentials on cardiovascular management [ 9 , 10 ], disease detection [ 11 – 22 ], and biometric human identification [ 23 , 24 ], and many other ECG analysis tasks [ 25 – 29 ]. However, there are no deep learning models designed for ECG critical value estimation so far.…”