Bridging the Gap in ECG Classification: Integrating Self-supervised Learning with Human-in-the-Loop Amid Medical Equipment Hardware Constraints

Citation:

Guilherme Silva, Pedro Silva, Gladston Moreira, and Eduardo Luz. 2024. “Bridging the Gap in ECG Classification: Integrating Self-supervised Learning with Human-in-the-Loop Amid Medical Equipment Hardware Constraints.” In Applied Reconfigurable Computing. Architectures, Tools, and Applications, edited by Iouliia Skliarova, Piedad Brox Jiménez, Mário Véstias, and Pedro C. Diniz, Pp. 63–74. Cham: Springer Nature Switzerland.

Abstract:

Arrhythmia, a cardiac condition, is frequently diagnosed by classifying heartbeats using electrocardiograms (ECG). This classification is a crucial step in medical diagnosis and can be significantly improved by employing computational methods to analyze the ECG data. Despite the extensive literature on this subject, the high inter-patient variability and noise in ECG signals pose challenges in the development of computational methods. Deep learning methods represent state-of-the-art solutions to diverse problems in computer vision, signal processing, and pattern recognition, mainly due to advancements enabled by self-supervised learning. In this work, we propose a self-supervised approach for ECG beat classification and a specific pretext task for ECG, termed ECGPuzzle. This approach allows for fine-tuning a deep learning model to an individual, improving the model's generalization. Given the low computational power of medical equipment and the need for on-site training, hardware acceleration is indispensable. Thus, we investigate the feasibility of this proposal on three distinct computational systems and discuss potential manners to train a model on an embedded system.

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