Key result
The proposed O-WCNN model achieved 99.48% and 99.46% accuracy in classifying ventricular ectopic beats and supraventricular ectopic beats, respectively.
Why the study?
Regular monitoring and accurate diagnosis of arrhythmia are critically important to reduce mortality from cardiovascular diseases, prompting the need for more efficient and accurate neural network models for classification.
The O-WCNN deep learning model effectively integrates spatial and spectral features to achieve >99% accuracy in classifying ECG arrhythmias.
Requires prospective validation before clinical use; leaves open real-world performance and outcome impact.
The regular monitoring and accurate diagnosis of arrhythmia are critically important, leading to a reduction in mortality rate due to cardiovascular diseases (CVD) such as heart stroke or cardiac arrest. This paper proposes a novel convolutional neural network (CNN) model for arrhythmia classification. The proposed model offers the following improvements compared with traditional CNN models. Firstly, the multi-channel model can concatenate spectral and spatial feature maps. Secondly, the structural unit is composed of a depthwise separable convolution layer followed by activation and batch normalization layers. The structural unit offers effective utilization of network parameters. Also, the optimization of hyperparameters is done using Hyperopt library, based on Sequential Model-Based Global Optimization algorithm (SMBO). These improvements make the network more efficient and accurate for arrhythmia classification. The proposed model is evaluated using tenfold cross-validation following both subject-oriented inter-patient and class-oriented intra-patient evaluation protocols. Our model achieved 99.48% and 99.46% accuracy in VEB (ventricular ectopic beat) and SVEB (supraventricular ectopic beat) class classification, respectively. The model is compared with state-of-the-art models and has shown significant performance improvement.
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Jangra et al. (2021) studied Arrhythmia (n=100,589). O-WCNN model vs. State-of-the-art CNN models was evaluated on Classification accuracy for ventricular ectopic beats (VEB) and supraventricular ectopic beats (SVEB). The proposed O-WCNN model achieved 99.48% and 99.46% accuracy in classifying ventricular ectopic beats and supraventricular ectopic beats, respectively.
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