EEG-ECG Signals Classification for Arrhythmia Detection using Decision Trees

Ghazanfar Latif; Faisal Yousif Al Anezi; Mohammad Zikria; Jaafar Alghazo

doi:10.1109/ICISC47916.2020.9171084

EEG-ECG Signals Classification for Arrhythmia Detection using Decision Trees

Ghazanfar Latif, Faisal Yousif Al Anezi, Mohammad Zikria, Jaafar Alghazo

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

20 Scopus citations

Abstract

The study analysis the electrocardiogram (ECG) and electroencephalogram (EEG) signals classification problem by using decision trees. The analysis and classification of heartbeats and brain traces associated with different types of Arrhythmia and Seizure is an active research topic in recent years. In this paper, we discuss different classification techniques for the analysis and classification of ECG and EEG signals. The training and testing is performed on the MIT-BIH arrhythmia and ECG database. For classification, the random forest tree and naïve Bayes algorithms are used. The results provide an improved performance over the current standards used for classification. The accuracy of the designed system is 97.45% which outperforms the various instance learning and supervised machine learning methods.

Original language	English (US)
Title of host publication	Proceedings of the 4th International Conference on Inventive Systems and Control, ICISC 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	192-196
Number of pages	5
ISBN (Electronic)	9781728128139
DOIs	https://doi.org/10.1109/ICISC47916.2020.9171084
State	Published - Jan 2020
Externally published	Yes
Event	4th International Conference on Inventive Systems and Control, ICISC 2020 - Coimbatore, India Duration: Jan 8 2020 → Jan 10 2020

Publication series

Name	Proceedings of the 4th International Conference on Inventive Systems and Control, ICISC 2020

Conference

Conference	4th International Conference on Inventive Systems and Control, ICISC 2020
Country/Territory	India
City	Coimbatore
Period	1/8/20 → 1/10/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Keywords

Decision Trees
ECG Classification
EEG Classification
MIT BIH Arrhythmia Datasets

Publisher link

10.1109/ICISC47916.2020.9171084

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Cite this

Latif, G., Al Anezi, F. Y., Zikria, M., & Alghazo, J. (2020). EEG-ECG Signals Classification for Arrhythmia Detection using Decision Trees. In Proceedings of the 4th International Conference on Inventive Systems and Control, ICISC 2020 (pp. 192-196). Article 9171084 (Proceedings of the 4th International Conference on Inventive Systems and Control, ICISC 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICISC47916.2020.9171084

EEG-ECG Signals Classification for Arrhythmia Detection using Decision Trees. / Latif, Ghazanfar; Al Anezi, Faisal Yousif; Zikria, Mohammad et al.
Proceedings of the 4th International Conference on Inventive Systems and Control, ICISC 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 192-196 9171084 (Proceedings of the 4th International Conference on Inventive Systems and Control, ICISC 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Latif, G, Al Anezi, FY, Zikria, M & Alghazo, J 2020, EEG-ECG Signals Classification for Arrhythmia Detection using Decision Trees. in Proceedings of the 4th International Conference on Inventive Systems and Control, ICISC 2020., 9171084, Proceedings of the 4th International Conference on Inventive Systems and Control, ICISC 2020, Institute of Electrical and Electronics Engineers Inc., pp. 192-196, 4th International Conference on Inventive Systems and Control, ICISC 2020, Coimbatore, India, 1/8/20. https://doi.org/10.1109/ICISC47916.2020.9171084

Latif G, Al Anezi FY, Zikria M, Alghazo J. EEG-ECG Signals Classification for Arrhythmia Detection using Decision Trees. In Proceedings of the 4th International Conference on Inventive Systems and Control, ICISC 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 192-196. 9171084. (Proceedings of the 4th International Conference on Inventive Systems and Control, ICISC 2020). doi: 10.1109/ICISC47916.2020.9171084

Latif, Ghazanfar ; Al Anezi, Faisal Yousif ; Zikria, Mohammad et al. / EEG-ECG Signals Classification for Arrhythmia Detection using Decision Trees. Proceedings of the 4th International Conference on Inventive Systems and Control, ICISC 2020. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 192-196 (Proceedings of the 4th International Conference on Inventive Systems and Control, ICISC 2020).

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abstract = "The study analysis the electrocardiogram (ECG) and electroencephalogram (EEG) signals classification problem by using decision trees. The analysis and classification of heartbeats and brain traces associated with different types of Arrhythmia and Seizure is an active research topic in recent years. In this paper, we discuss different classification techniques for the analysis and classification of ECG and EEG signals. The training and testing is performed on the MIT-BIH arrhythmia and ECG database. For classification, the random forest tree and na{\"i}ve Bayes algorithms are used. The results provide an improved performance over the current standards used for classification. The accuracy of the designed system is 97.45% which outperforms the various instance learning and supervised machine learning methods.",

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PY - 2020/1

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N2 - The study analysis the electrocardiogram (ECG) and electroencephalogram (EEG) signals classification problem by using decision trees. The analysis and classification of heartbeats and brain traces associated with different types of Arrhythmia and Seizure is an active research topic in recent years. In this paper, we discuss different classification techniques for the analysis and classification of ECG and EEG signals. The training and testing is performed on the MIT-BIH arrhythmia and ECG database. For classification, the random forest tree and naïve Bayes algorithms are used. The results provide an improved performance over the current standards used for classification. The accuracy of the designed system is 97.45% which outperforms the various instance learning and supervised machine learning methods.

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EEG-ECG Signals Classification for Arrhythmia Detection using Decision Trees

Abstract

Publication series

Conference

Bibliographical note

Keywords

Publisher link

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