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

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 classificati...

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Veröffentlicht in:Complex & intelligent systems Jg. 9; H. 3; S. 2685 - 2698
Hauptverfasser: Jangra, Manisha, Dhull, Sanjeev Kumar, Singh, Krishna Kant, Singh, Akansha, Cheng, Xiaochun
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cham Springer International Publishing 01.06.2023
Springer Nature B.V
Springer
Schlagworte:
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
Online-Zugang:Volltext
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Zusammenfassung: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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ISSN:2199-4536
2198-6053
2198-6053
DOI:10.1007/s40747-021-00371-4