Key result
KecNet classifies arrhythmias with ~99% accuracy on the MIT-BIH database.
Why the study?
Deep neural networks for ECG arrhythmia classification require substantial resources, creating a need for deep learning algorithms suitable for resource-constrained mobile devices.
Does KecNet improve arrhythmia classification accuracy and lower computational cost compared to standard deep neural networks?
Does KecNet improve arrhythmia classification accuracy and lower computational cost compared to standard deep neural networks?
KecNet offers a highly accurate and computationally lightweight deep learning solution for robust, real-time arrhythmia classification on mobile and wearable devices.
KecNet supports exploration of lightweight interpretable models for wearable arrhythmia detection; leaves open prospective clinical validation before any practice change.
Acquiring electrocardiographic (ECG) signals and performing arrhythmia classification in mobile device scenarios have the advantages of short response time, almost no network bandwidth consumption, and human resource savings. In recent years, deep neural networks have become a popular method to efficiently and accurately simulate nonlinear patterns of ECG data in a data-driven manner but require more resources. Therefore, it is crucial to design deep learning (DL) algorithms that are more suitable for resource-constrained mobile devices. In this paper, KecNet, a lightweight neural network construction scheme based on domain knowledge, is proposed to model ECG data by effectively leveraging signal analysis and medical knowledge. To evaluate the performance of KecNet, we use the Association for the Advancement of Medical Instrumentation (AAMI) protocol and the MIT-BIH arrhythmia database to classify five arrhythmia categories. The result shows that the ACC, SEN, and PRE achieve 99.31%, 99.45%, and 98.78%, respectively. In addition, it also possesses high robustness to noisy environments, low memory usage, and physical interpretability advantages. Benefiting from these advantages, KecNet can be applied in practice, especially wearable and lightweight mobile devices for arrhythmia classification.
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Lu et al. (2021) studied Arrhythmia (n=48). KecNet vs. Standard CNNs (GoogleNet, MobileNet, SqueezeNet) was evaluated on Classification accuracy (ACC) for 5 arrhythmia categories. KecNet achieved an arrhythmia classification accuracy of 99.31%, sensitivity of 99.45%, and precision of 98.78% on the MIT-BIH database.
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