O-WCNN: an optimized integration of spatial and spectral feature map for arrhythmia classification

• Published in: Complex & intelligent systems Vol. 9; no. 3; pp. 2685 - 2698
• Main Authors: Jangra, Manisha, Dhull, Sanjeev Kumar, Singh, Krishna Kant, Singh, Akansha, Cheng, Xiaochun
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.06.2023; Springer Nature B.V; Springer
• Subjects: Algorithms; Arrhythmia; Artificial neural networks; Cardiac arrhythmia; Classification; CNN; Complexity; Computational Intelligence; Data Structures and Information Theory; Deep learning; Depthwise separable convolution; ECG; Engineering; Feature maps; Global optimization; Original; Original Article; Wavelet transform; Deep learning; ECG; CNN; Wavelet transform; Depthwise separable convolution; Arrhythmia
• ISSN: 2199-4536; 2198-6053; 2198-6053
• DOI: 10.1007/s40747-021-00371-4

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.