What is the clinical evidence from this study?

Study design: Other. Population: Cardiovascular diseases (Arrhythmia, Myocardial Infarction, Ventricular Hypertrophy, Atrial Hypertrophy) (n=18370). Intervention: Multi-Task Group Bi-LSTM (MTGBi-LSTM) framework vs. Other deep learning models (Baseline Bi-LSTM, CNN, RNN). Primary outcome: F1 score for ECG classification.

November 12, 2019Open Access

A Multi-Task Group Bi-LSTM Networks Application on Electrocardiogram Classification

QLQiujie LvZhengzhou University HCHsin‐Yi ChenInstitute of Biomedical Sciences, Academia Sinica WZWei-Bin ZhongSun Yat-sen University

Key Result

The Multi-Task Group Bi-LSTM framework achieved an accuracy of 88.86% and an F1 score of 92.39% in classifying multiple cardiovascular diseases from 12-lead ECG signals.

Structured PICO

Does the MTGBi-LSTM framework improve the classification accuracy of multi-lead ECGs for cardiovascular diseases?

Population

Over 170,000 clinical 12-lead ECG recordings from the Chinese Cardiovascular Disease Database (CCDD)

Intervention

Multi-Task Group Bi-LSTM (MTGBi-LSTM) framework with attention mechanism and dynamic weighted loss

Comparator

Baseline Bi-LSTM, Group Bi-LSTM, AGBi-LSTM, and A&LGBi-LSTM models

Outcome

Classification accuracy, precision, recall, and F1 score for multiple cardiovascular diseasessurrogate

The MTGBi-LSTM framework provides a highly accurate and effective deep learning tool for the computer-aided diagnosis of cardiovascular diseases using multi-lead ECGs.

Limitations

ECG data of myocardial infarction and atrial hypertrophy are too little and the data distribution is extremely unbalanced.
In many cases, the location of the disease is ambiguous and the abnormal rhythm is very similar.
Lack of information about subjects and limited signal duration can make it difficult to draw reasonable conclusions from ECG.

Abstract

BACKGROUND: Cardiovascular diseases (CVD) are the leading cause of death globally. Electrocardiogram (ECG) analysis can provide thoroughly assessment for different CVDs efficiently. We propose a multi-task group bidirectional long short-term memory (MTGBi-LSTM) framework to intelligent recognize multiple CVDs based on multi-lead ECG signals. METHODS: This model employs a Group Bi-LSTM (GBi-LSTM) and Residual Group Convolutional Neural Network (Res-GCNN) to learn the dual feature representation of ECG space and time series. GBi-LSTM is divided into Global Bi-LSTM and Intra-Group Bi-LSTM, which can learn the features of each ECG lead and the relationship between leads. Then, through attention mechanism, the different lead information of ECG is integrated to make the model to possess the powerful feature discriminability. Through multi-task learning, the model can fully mine the association information between diseases and obtain more accurate diagnostic results. In addition, we propose a dynamic weighted loss function to better quantify the loss to overcome the imbalance between classes. RESULTS: Based on more than 170,000 clinical 12-lead ECG analysis, the MTGBi-LSTM method achieved accuracy, precision, recall and F1 of 88.86%, 90.67%, 94.19% and 92.39%, respectively. The experimental results show that the proposed MTGBi-LSTM method can reliably realize ECG analysis and provide an effective tool for computer-aided diagnosis of CVD.

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Cite This Study

Lv et al. (Tue,) conducted a other in Cardiovascular diseases (Arrhythmia, Myocardial Infarction, Ventricular Hypertrophy, Atrial Hypertrophy) (n=18,370). Multi-Task Group Bi-LSTM (MTGBi-LSTM) framework vs. Other deep learning models (Baseline Bi-LSTM, CNN, RNN) was evaluated on F1 score for ECG classification. The Multi-Task Group Bi-LSTM framework achieved an accuracy of 88.86% and an F1 score of 92.39% in classifying multiple cardiovascular diseases from 12-lead ECG signals.

synapsesocial.com/papers/6a160c29354578fa93b5654a https://doi.org/https://doi.org/10.1109/jtehm.2019.2952610

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