Synopsis
ECG signals give important diagnostic information in cardiology. To take advantage of it, the ECGs are to be properly processed for effective analysis and interpretation. New methods for processing and analysis of ECG signals are proposed in this book. In general, the ECG signals are quasi-periodic of low amplitude (several mV). They are often affected by noise signals which include powerline interference, baseline wander, EMG artifacts, etc,. Hence, the data may be corrupted with these noise signals and it is essential to be filtered for the real-time heart monitoring systems. A new method called Gaussian Mean Variant (GMV) filtering is proposed for ECG filtering. Statistical measures like root mean square error (RMSE), root mean square deviation (RMSD) and root mean square variation (RMSV) established the efficacy of the proposed method compared to adaptive filters and other conventional methods. The successive step of ECG signal analysis is formation of a feature space. To this end, a method based on Integrated Peak Analyzer (IPA) is proposed for extraction of features. An efficient method named Multimodal Decision Learning (MDL) algorithm is proposed for classification of ECGs.
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About the Author
Rajender Naik Guguloth received doctoral degree in June 2017. He is presently working as an Assistant Professor of Electrical & Electronics Engineering in KITS Warangal, India.Ashoka Reddy Komalla received doctoral degree in 2008 and he is presently working as Professor of Electronics and Communications in KITS Warangal, Telangana State, India.
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Signal Processing methods for ECG Analysis
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -ECG signals give important diagnostic information in cardiology. To take advantage of it, the ECGs are to be properly processed for effective analysis and interpretation. New methods for processing and analysis of ECG signals are proposed in this book. In general, the ECG signals are quasi-periodic of low amplitude (several mV). They are often affected by noise signals which include powerline interference, baseline wander, EMG artifacts, etc, Hence, the data may be corrupted with these noise signals and it is essential to be filtered for the real-time heart monitoring systems. A new method called Gaussian Mean Variant (GMV) filtering is proposed for ECG filtering. Statistical measures like root mean square error (RMSE), root mean square deviation (RMSD) and root mean square variation (RMSV) established the efficacy of the proposed method compared to adaptive filters and other conventional methods. The successive step of ECG signal analysis is formation of a feature space. To this end, a method based on Integrated Peak Analyzer (IPA) is proposed for extraction of features. An efficient method named Multimodal Decision Learning (MDL) algorithm is proposed for classification of ECGs. 176 pp. Englisch. Seller Inventory # 9783330003842
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Guguloth Rajender NaikRajender Naik Guguloth received doctoral degree in June 2017. He is presently working as an Assistant Professor of Electrical & Electronics Engineering in KITS Warangal, India.Ashoka Reddy Komalla received docto. Seller Inventory # 175752435
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Signal Processing methods for ECG Analysis
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -ECG signals give important diagnostic information in cardiology. To take advantage of it, the ECGs are to be properly processed for effective analysis and interpretation. New methods for processing and analysis of ECG signals are proposed in this book. In general, the ECG signals are quasi-periodic of low amplitude (several mV). They are often affected by noise signals which include powerline interference, baseline wander, EMG artifacts, etc, Hence, the data may be corrupted with these noise signals and it is essential to be filtered for the real-time heart monitoring systems. A new method called Gaussian Mean Variant (GMV) filtering is proposed for ECG filtering. Statistical measures like root mean square error (RMSE), root mean square deviation (RMSD) and root mean square variation (RMSV) established the efficacy of the proposed method compared to adaptive filters and other conventional methods. The successive step of ECG signal analysis is formation of a feature space. To this end, a method based on Integrated Peak Analyzer (IPA) is proposed for extraction of features. An efficient method named Multimodal Decision Learning (MDL) algorithm is proposed for classification of ECGs.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 176 pp. Englisch. Seller Inventory # 9783330003842
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Signal Processing methods for ECG Analysis
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - ECG signals give important diagnostic information in cardiology. To take advantage of it, the ECGs are to be properly processed for effective analysis and interpretation. New methods for processing and analysis of ECG signals are proposed in this book. In general, the ECG signals are quasi-periodic of low amplitude (several mV). They are often affected by noise signals which include powerline interference, baseline wander, EMG artifacts, etc, Hence, the data may be corrupted with these noise signals and it is essential to be filtered for the real-time heart monitoring systems. A new method called Gaussian Mean Variant (GMV) filtering is proposed for ECG filtering. Statistical measures like root mean square error (RMSE), root mean square deviation (RMSD) and root mean square variation (RMSV) established the efficacy of the proposed method compared to adaptive filters and other conventional methods. The successive step of ECG signal analysis is formation of a feature space. To this end, a method based on Integrated Peak Analyzer (IPA) is proposed for extraction of features. An efficient method named Multimodal Decision Learning (MDL) algorithm is proposed for classification of ECGs. Seller Inventory # 9783330003842