Classification of ECG signals using multi-cumulants based evolutionary hybrid classifier

Abstract Every human being has a different electro-cardio-graphy (ECG) waveform that provides information about the well being of a human heart. Therefore, ECG waveform can be used as an effective identification measure in biometrics and many such applications of human identification. To achieve fas...

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Autores principales:	Sahil Dalal, Virendra P. Vishwakarma
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e0b662f041614d5b8901e3b32e54f30e
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spelling	oai:doaj.org-article:e0b662f041614d5b8901e3b32e54f30e2021-12-02T16:26:23ZClassification of ECG signals using multi-cumulants based evolutionary hybrid classifier10.1038/s41598-021-94363-62045-2322https://doaj.org/article/e0b662f041614d5b8901e3b32e54f30e2021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-94363-6https://doaj.org/toc/2045-2322Abstract Every human being has a different electro-cardio-graphy (ECG) waveform that provides information about the well being of a human heart. Therefore, ECG waveform can be used as an effective identification measure in biometrics and many such applications of human identification. To achieve fast and accurate identification of human beings using ECG signals, a novel robust approach has been introduced here. The databases of ECG utilized during the experimentation are MLII, UCI repository arrhythmia and PTBDB databases. All these databases are imbalanced; hence, resampling techniques are helpful in making the databases balanced. Noise removal is performed with discrete wavelet transform (DWT) and features are obtained with multi-cumulants. This approach is mainly based on features extracted from the ECG data in terms of multi-cumulants. The multi-cumulants feature based ECG data is classified using kernel extreme learning machine (KELM). The parameters of multi-cumulants and KELM are optimized using genetic algorithm (GA). Excellent classification rate is achieved with 100% accuracy on MLII and UCI repository arrhythmia databases, and 99.57% on PTBDB database. Comparison with existing state-of-art approaches has also been performed to prove the efficacy of the proposed approach. Here, the process of classification in the proposed approach is named as evolutionary hybrid classifier.Sahil DalalVirendra P. VishwakarmaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-25 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sahil Dalal Virendra P. Vishwakarma Classification of ECG signals using multi-cumulants based evolutionary hybrid classifier
description	Abstract Every human being has a different electro-cardio-graphy (ECG) waveform that provides information about the well being of a human heart. Therefore, ECG waveform can be used as an effective identification measure in biometrics and many such applications of human identification. To achieve fast and accurate identification of human beings using ECG signals, a novel robust approach has been introduced here. The databases of ECG utilized during the experimentation are MLII, UCI repository arrhythmia and PTBDB databases. All these databases are imbalanced; hence, resampling techniques are helpful in making the databases balanced. Noise removal is performed with discrete wavelet transform (DWT) and features are obtained with multi-cumulants. This approach is mainly based on features extracted from the ECG data in terms of multi-cumulants. The multi-cumulants feature based ECG data is classified using kernel extreme learning machine (KELM). The parameters of multi-cumulants and KELM are optimized using genetic algorithm (GA). Excellent classification rate is achieved with 100% accuracy on MLII and UCI repository arrhythmia databases, and 99.57% on PTBDB database. Comparison with existing state-of-art approaches has also been performed to prove the efficacy of the proposed approach. Here, the process of classification in the proposed approach is named as evolutionary hybrid classifier.
format	article
author	Sahil Dalal Virendra P. Vishwakarma
author_facet	Sahil Dalal Virendra P. Vishwakarma
author_sort	Sahil Dalal
title	Classification of ECG signals using multi-cumulants based evolutionary hybrid classifier
title_short	Classification of ECG signals using multi-cumulants based evolutionary hybrid classifier
title_full	Classification of ECG signals using multi-cumulants based evolutionary hybrid classifier
title_fullStr	Classification of ECG signals using multi-cumulants based evolutionary hybrid classifier
title_full_unstemmed	Classification of ECG signals using multi-cumulants based evolutionary hybrid classifier
title_sort	classification of ecg signals using multi-cumulants based evolutionary hybrid classifier
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/e0b662f041614d5b8901e3b32e54f30e
work_keys_str_mv	AT sahildalal classificationofecgsignalsusingmulticumulantsbasedevolutionaryhybridclassifier AT virendrapvishwakarma classificationofecgsignalsusingmulticumulantsbasedevolutionaryhybridclassifier
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Classification of ECG signals using multi-cumulants based evolutionary hybrid classifier

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