ECG Arrhythmia Measurement and Classification for Portable Monitoring
Globally, cardiovascular disease kills more than 500000 people every year, thus becoming the primary reason for death. Nevertheless, cardiovascular health monitoring is essential for accurate analysis and therapy of heart disease. In this work, a novel deep learning-based StrIppeD NAS-Network (SID-N...
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| Vydáno v: | Measurement science review Ročník 24; číslo 4; s. 118 - 128 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Bratislava
Sciendo
01.08.2024
De Gruyter Brill Sp. z o.o., Paradigm Publishing Services |
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| ISSN: | 1335-8871, 1335-8871 |
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| Abstract | Globally, cardiovascular disease kills more than 500000 people every year, thus becoming the primary reason for death. Nevertheless, cardiovascular health monitoring is essential for accurate analysis and therapy of heart disease. In this work, a novel deep learning-based StrIppeD NAS-Network (SID-NASNet) for arrhythmia categorization into octa-classes with electrocardiogram (ECG) signals is presented. First, the ECG signals are recorded in real time using 12-lead electrodes. Then, the Discrete Wavelet Transform (DWT) is used to denoise the signals to reduce repetition and increase resilience. The noise-free ECG signals are fed into a K-means clustering algorithm to group ECG signal segments into a set number of clusters to identify patterns that may indicate heart abnormalities. Subsequently, the deep learning-based NASNet with Stripped convolutional layers is used to detect ECG irregularities of arrhythmia. Each sample point is examined for its local fractal dimension before extracting the heartbeat waveforms within a predetermined window length. A bio-inspired Dingo Optimization (DO) algorithm is used in the SID-NASNet to normalize the parameters to improve the efficiency of the network with low network complexity. The efficiency of the proposed SID-NASNet is assessed with specificity, accuracy, precision, F1 score and recall based on the MIT-BIH arrhythmia dataset. From the test results, the proposed SID-NASNet achieves an accuracy of 98.22% for effective categorization of ECG signals. The proposed SID-NASNet improves the overall accuracy of 1.24%, 3.76%, 1.87%, and 0.22% better than ECG-NET, Deep Learning (DL)-based GAN, 1D-CNN, and GAN-Long-Short Term Memory (LSTM), respectively. |
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| AbstractList | Globally, cardiovascular disease kills more than 500000 people every year, thus becoming the primary reason for death. Nevertheless, cardiovascular health monitoring is essential for accurate analysis and therapy of heart disease. In this work, a novel deep learning-based StrIppeD NAS-Network (SID-NASNet) for arrhythmia categorization into octa-classes with electrocardiogram (ECG) signals is presented. First, the ECG signals are recorded in real time using 12-lead electrodes. Then, the Discrete Wavelet Transform (DWT) is used to denoise the signals to reduce repetition and increase resilience. The noise-free ECG signals are fed into a K-means clustering algorithm to group ECG signal segments into a set number of clusters to identify patterns that may indicate heart abnormalities. Subsequently, the deep learning-based NASNet with Stripped convolutional layers is used to detect ECG irregularities of arrhythmia. Each sample point is examined for its local fractal dimension before extracting the heartbeat waveforms within a predetermined window length. A bio-inspired Dingo Optimization (DO) algorithm is used in the SID-NASNet to normalize the parameters to improve the efficiency of the network with low network complexity. The efficiency of the proposed SID-NASNet is assessed with specificity, accuracy, precision, F1 score and recall based on the MIT-BIH arrhythmia dataset. From the test results, the proposed SID-NASNet achieves an accuracy of 98.22% for effective categorization of ECG signals. The proposed SID-NASNet improves the overall accuracy of 1.24%, 3.76%, 1.87%, and 0.22% better than ECG-NET, Deep Learning (DL)-based GAN, 1D-CNN, and GAN-Long-Short Term Memory (LSTM), respectively. |
| Author | P Ajitha Gladis, K. Muthukumaran, N Ahilan, A Jenifer, L |
| Author_xml | – sequence: 1 givenname: K. surname: P Ajitha Gladis fullname: P Ajitha Gladis, K. email: kpajithagladis@gmail.com organization: Department of Information Technology, CSI Institute of Technology, Thovalai, Tamilnadu- 629302, India – sequence: 2 givenname: A surname: Ahilan fullname: Ahilan, A email: listentoahil@gmail.com organization: Department of Electronics and Communication Engineering, PSN College of Engineering and Technology, Tirunelveli, Tamil Nadu, India – sequence: 3 givenname: N surname: Muthukumaran fullname: Muthukumaran, N email: kumaranece@gmail.com organization: Centre for Computational Imaging and Machine Vision, Department of Electronics and Communication Engineering, Sri Eshwar College of Engineering, Coimbatore – 641202, Tamil Nadu, India – sequence: 4 givenname: L surname: Jenifer fullname: Jenifer, L email: Jenifer54312@outlook.com organization: Department of Electrical and Electronics Engineering, Sathyabama Institute of Science and Technology, Chennai, 600119, India |
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| SubjectTerms | Abnormalities Accuracy Algorithms Arrhythmia arrhythmia classification Cardiac arrhythmia Classification Cluster analysis Clustering Deep learning Dingo optimization algorithm Discrete Wavelet Transform ECG signal Electrocardiography Fractal geometry Heart diseases Machine learning Parameter identification stripped convolution Telemedicine Vector quantization Waveforms Wavelet transforms |
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| Title | ECG Arrhythmia Measurement and Classification for Portable Monitoring |
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