Key result
A four-heartbeat 1-D convolutional neural network achieves ~95% arrhythmia classification accuracy while balancing computational efficiency.
Why the study?
Cardiac arrhythmias are difficult to diagnose in real-world scenarios, and the optimal number of heartbeats for precise machine-learning classification remains understudied.
A 1-D convolutional neural network using four heartbeats as input optimizes the balance between arrhythmia detection accuracy and computational efficiency for wearable ECG devices.
May support efficient wearable ECG arrhythmia screening; leaves open prospective clinical validation before adoption.
BACKGROUNDS AND OBJECTIVES: Cardiac arrhythmias, characterized by irregular heartbeats, are difficult to diagnose in real-world scenarios. Machine learning has advanced arrhythmia detection; however, the optimal number of heartbeats for precise classification remains understudied. This study addresses this using machine learning while assessing the performance of arrhythmia detection across inter-patient and intra-patient conditions. Furthermore, the performance-resource trade-offs are evaluated for practical deployment in mobile health (mHealth) applications. METHODS: Beat-wise segmentation and resampling techniques were utilized for preprocessing electrocardiography (ECG) signals to ensure consistent input lengths. A 1-D convolutional neural network was used to classify the eight multi-labeled arrhythmias. The dataset comprised real-world ECG recordings from the HiCardi wireless device alongside data from the MIT-BIH Arrhythmia database. Model performance was assessed through fivefold cross-validation under both inter-patient and intra-patient conditions. RESULTS: The proposed model demonstrated peak accuracy at four beats under inter-patient conditions, with minimal improvements beyond this point. This configuration achieved a balance between performance (94.82% accuracy) and resource consumption (training time: 72.27 s per epoch; prediction time: 155 μs per segment). Real-world simulations validated the feasibility of real-time arrhythmia detection for approximately 5000 patients. CONCLUSION: Utilizing four heartbeats as the input size for arrhythmia classification results in a trade-off between accuracy and computational efficiency. This discovery has significant implications for real-time wearable ECG devices, where both performance and resource constraints are crucial considerations. This insight is expected to serve as a valuable reference for enhancing the design and implementation of arrhythmia detection systems for scalable and efficient mHealth applications.
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Lee et al. (2025) studied Cardiac arrhythmias. 1-D convolutional neural network with 4-beat input was evaluated on Arrhythmia classification accuracy. A 1-D convolutional neural network using four heartbeats as input achieved 94.82% accuracy for arrhythmia classification, balancing performance and computational efficiency.
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