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Updated: Aug 16, 2025
Semi-automated Optical Heartbeat Analysis of Small Hearts
Published on: September 16, 2009
A robust multiple heartbeats classification with weight-based loss based on convolutional neural network and
Mengting Yang1,2,3, Weichao Liu1, Henggui Zhang1,4
1Key Laboratory of Medical Electrophysiology, Ministry of Education and Medical Electrophysiological Key Laboratory of Sichuan Province, (Collaborative Innovation Center for Prevention of Cardiovascular Diseases), Institute of Cardiovascular Research, Southwest Medical University, Luzhou, China.
Insights
This study introduces a lightweight deep learning model for accurate electrocardiogram (ECG) heartbeats classification, addressing data imbalance and enabling use in portable devices. The novel CNN-BiLSTM approach achieves high accuracy for diagnosing cardiac arrhythmias.
Area of Science:
- Biomedical Engineering
- Artificial Intelligence in Medicine
- Cardiology
Background:
- Electrocardiogram (ECG) analysis is crucial for diagnosing cardiac arrhythmias but is labor-intensive and prone to subjective errors.
- Deep learning shows promise for ECG analysis, yet struggles with imbalanced datasets and computationally intensive preprocessing.
- A need exists for efficient, lightweight algorithms for real-time ECG analysis on portable devices.
Purpose of the Study:
- To develop a robust and efficient deep learning method for classifying heartbeats suitable for wearable ECG monitors.
- To create an automated system that minimizes reliance on manual feature extraction and noise reduction.
Main Methods:
- A novel, lightweight deep learning architecture combining Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) was proposed.
- A weight-based loss function was implemented to mitigate classification bias from imbalanced ECG datasets.
- The k-fold cross-validation method was employed to enhance model reliability and avoid validation set division bias.
Main Results:
- The algorithm achieved high performance on the MIT-BIH Arrhythmia Database.
- Key metrics include 99.33% accuracy, 93.67% sensitivity, 99.18% specificity, 89.85% positive prediction, and 91.65% F1-score.
- The model processed raw ECG signals without extensive preprocessing, demonstrating efficiency.
Conclusions:
- The developed CNN-BiLSTM model offers an efficient and accurate solution for automated heartbeats classification.
- The weight-based loss function effectively addresses class imbalance in ECG data.
- This lightweight approach is suitable for deployment on portable ECG sensors, advancing remote cardiac monitoring.
Abstract:
Background: Analysis of electrocardiogram (ECG) provides a straightforward and non-invasive approach for cardiologists to diagnose and classify the nature and severity of variant cardiac diseases including cardiac arrhythmia. However, the interpretation and analysis of ECG are highly working-load demanding, and the subjective may lead to false diagnoses and heartbeats classification. In recent years, many deep learning works showed an excellent role in accurate heartbeats classification. However, the imbalance of heartbeat classes is universal in most of the available ECG databases since abnormal heartbeats are always relatively rare in real life scenarios. In addition, many existing approaches achieved prominent results by removing noise and extracting features in data preprocessing, which relies heavily on powerful computers. It is a pressing need to develop efficient and automatic light weighted algorithms for accurate heartbeats classification that can be used in portable ECG sensors. Objective: This study aims at developing a robust and efficient deep learning method, which can be embedded into wearable or portable ECG monitors for classifying heartbeats. Methods: We proposed a novel and light weighted deep learning architecture with weight-based loss based on a convolutional neural network (CNN) and bidirectional long short-term memory (Bi-LSTM) that can automatically identify five types of ECG heartbeats according to the AAMI EC57 standard. It was also true that the raw ECG signals were simply segmented without noise removal and other feature extraction processing. Moreover, to tackle the challenge of classification bias due to imbalanced ECG datasets for different types of arrhythmias, we introduced a weight-based loss function to reduce the influence of over-weighted categories in the ECG dataset. For avoiding the influence of the division of validation dataset, k-fold method was adopted to improve the reliability of the model. Results: The proposed algorithm is trained and tested on MIT-BIH Arrhythmia Database, and achieves an average of 99.33% accuracy, 93.67% sensitivity, 99.18% specificity, 89.85% positive prediction, and 91.65% F1 score.
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