Key result
Reseek-Arrhythmia deep learning model achieves ~99% accuracy for detecting and classifying heart arrhythmias.
Why the study?
Developing efficient automatic heartbeat monitoring for arrhythmia detection faces challenges such as data quality, heart rate segmentation, data imbalance, patient variations, and distinguishing supraventricular irregular heartbeats.
The Reseek-Arrhythmia deep learning model demonstrates high accuracy in detecting and classifying arrhythmias from standard ECG datasets.
Strong benchmark accuracy supports AI arrhythmia classifier refinement; prospective clinical validation needed before practice adoption.
Arrhythmia is a cardiac condition characterized by an irregular heart rhythm that hinders the proper circulation of blood, posing a severe risk to individuals’ lives. Globally, arrhythmias are recognized as a significant health concern, accounting for nearly 12 percent of all deaths. As a result, there has been a growing focus on utilizing artificial intelligence for the detection and classification of abnormal heartbeats. In recent years, self-operated heartbeat detection research has gained popularity due to its cost-effectiveness and potential for expediting therapy for individuals at risk of arrhythmias. However, building an efficient automatic heartbeat monitoring approach for arrhythmia identification and classification comes with several significant challenges. These challenges include addressing issues related to data quality, determining the range for heart rate segmentation, managing data imbalance difficulties, handling intra- and inter-patient variations, distinguishing supraventricular irregular heartbeats from regular heartbeats, and ensuring model interpretability. In this study, we propose the Reseek-Arrhythmia model, which leverages deep learning techniques to automatically detect and classify heart arrhythmia diseases. The model combines different convolutional blocks and identity blocks, along with essential components such as convolution layers, batch normalization layers, and activation layers. To train and evaluate the model, we utilized the MIT-BIH and PTB datasets. Remarkably, the proposed model achieves outstanding performance with an accuracy of 99.35% and 93.50% and an acceptable loss of 0.688 and 0.2564, respectively.
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Haq et al. (2023) studied Arrhythmia. Reseek-Arrhythmia model was evaluated on Model accuracy and loss. The Reseek-Arrhythmia deep learning model achieved an accuracy of 99.35% and 93.50% on the MIT-BIH and PTB datasets, respectively, for detecting and classifying heart arrhythmias.
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