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Published on: April 26, 2024
Deep Learning Algorithm Classifies Heartbeat Events Based on Electrocardiogram Signals
Yongbo Liang1,2, Shimin Yin1, Qunfeng Tang2,3
1School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin, China.
Insights
A new deep learning model combining CNN and BiLSTM significantly reduces training time for heartbeat classification from ECG signals, achieving high accuracy. This efficient method aids in cardiovascular disease screening.
Area of Science:
- Cardiology
- Biomedical Engineering
- Artificial Intelligence
Background:
- Cardiovascular diseases (CVDs) are a leading global health threat, necessitating advanced diagnostic tools.
- Electrocardiogram (ECG) is a critical non-invasive method for CVD screening and diagnosis.
- Existing methods for ECG analysis face challenges in accuracy and efficiency.
Purpose of the Study:
- To develop and evaluate a novel deep learning algorithm for heartbeat event classification using ECG signals.
- To compare the proposed deep learning approach with an evolutionary neural system method.
- To assess the model's performance on single- and multiple-lead ECG datasets.
Main Methods:
- A deep learning model combining Convolutional Neural Network (CNN) with Bidirectional Long Short-Term Memory (BiLSTM) was proposed.
- The CNN-BiLSTM model (Method II) was compared against an evolutionary neural system approach (Method I).
- Experiments were conducted using single-lead (Database I) and 12-lead (Database II) ECG data from multiple challenge datasets.
Main Results:
- The CNN-BiLSTM model (Method II) achieved significantly faster training times (1 hour) compared to Method I (28.3 hours).
- Method II demonstrated high accuracy, reaching 80%, 82.6%, and 85% on the China Physiological Signal Challenge 2018, PhysioNet Challenge 2017, and MIT-BIH Arrhythmia datasets, respectively.
- While Method I showed slightly better performance, Method II offered a more practical and efficient solution for heartbeat classification.
Conclusions:
- The novel CNN-BiLSTM deep learning approach provides an efficient and accurate method for heartbeat event classification from ECG signals.
- This approach holds significant potential for improving cardiovascular disease screening and diagnosis.
- The reduced training time makes the CNN-BiLSTM model a viable and scalable solution for clinical applications.
Abstract:
Cardiovascular diseases (CVDs) have become the number 1 threat to human health. Their numerous complications mean that many countries remain unable to prevent the rapid growth of such diseases, although significant health resources have been invested toward their prevention and management. Electrocardiogram (ECG) is the most important non-invasive physiological signal for CVD screening and diagnosis. For exploring the heartbeat event classification model using single- or multiple-lead ECG signals, we proposed a novel deep learning algorithm and conducted a systemic comparison based on the different methods and databases. This new approach aims to improve accuracy and reduce training time by combining the convolutional neural network (CNN) with the bidirectional long short-term memory (BiLSTM). To our knowledge, this approach has not been investigated to date. In this study, Database I with single-lead ECG and Database II with 12-lead ECG were used to explore a practical and viable heartbeat event classification model. An evolutionary neural system approach (Method I) and a deep learning approach (Method II) that combines CNN with BiLSTM network were compared and evaluated in processing heartbeat event classification. Overall, Method I achieved slightly better performance than Method II. However, Method I took, on average, 28.3 h to train the model, whereas Method II needed only 1 h. Method II achieved an accuracy of 80, 82.6, and 85% compared with the China Physiological Signal Challenge 2018, PhysioNet Challenge 2017, and Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia datasets, respectively. These results are impressive compared with the performance of state-of-the-art algorithms used for the same purpose.
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