Discrimination of Cardiac Abnormalities Based on Multifractal Analysis in Reservoir Computing Framework
This study proposes a multiclass classification technique based on multifractal spectra for different types of cardiac arrhythmias which are associated with irregularity and/or complex dynamics of the heart. Indeed, the degree of complexity of such dynamics is diverse for different states of cardiac...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Open Journal of Instrumentation and Measurement |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10317876/ |
| _version_ | 1797197423256272896 |
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| author | Basab Bijoy Purkayastha Shovan Barma |
| author_facet | Basab Bijoy Purkayastha Shovan Barma |
| author_sort | Basab Bijoy Purkayastha |
| collection | DOAJ |
| description | This study proposes a multiclass classification technique based on multifractal spectra for different types of cardiac arrhythmias which are associated with irregularity and/or complex dynamics of the heart. Indeed, the degree of complexity of such dynamics is diverse for different states of cardiac condition. Certainly, such physiological responses of the heart dynamics can be discriminated by analyzing electrocardiogram (ECG) signals through different channels. Earlier, ECG-based works for discriminating cardiac arrhythmias consider the heart as a black box system and the analysis is mostly surrounded with time domain statistical averages or spectral analysis. The works ignore one of the key parameters, i.e., the presence of time-localized irregularities which are strongly associated with different kinds of arrhythmias and contribute to subtle variations in the amplitude and shape of the signal dynamical system while analyzing the signal. Therefore, in this work, we proposed a new method based on multifractal analysis to classify different kinds of cardiac conditions. Here, we followed the dynamical systems approach and computed the multifractal spectrum of the embedded phase space structure of the ECG signal. We performed the classification task by an echo state network to reduce the computational burden. For validation, three well-known datasets (Shaoxing Peoples’ Hospital dataset, PTB diagnostic ECG database v1.0.0, and 2017 PhysioNet/CinC Challenge dataset) have been considered. The results and analysis show that the proposed method can achieve a maximum accuracy of up to 96%, which is significantly high. Further, an optimum number of channels/leads has also been evaluated in multichannel ECG analysis. The result and analysis reveal that the effectiveness of the model in classifying various categories of cardiac disorders from ECG. |
| first_indexed | 2024-04-24T06:43:44Z |
| format | Article |
| id | doaj.art-dca1bd63aa534ffbaf86d34f0262a935 |
| institution | Directory Open Access Journal |
| issn | 2768-7236 |
| language | English |
| last_indexed | 2024-04-24T06:43:44Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Instrumentation and Measurement |
| spelling | doaj.art-dca1bd63aa534ffbaf86d34f0262a9352024-04-22T20:23:25ZengIEEEIEEE Open Journal of Instrumentation and Measurement2768-72362023-01-01211110.1109/OJIM.2023.333234410317876Discrimination of Cardiac Abnormalities Based on Multifractal Analysis in Reservoir Computing FrameworkBasab Bijoy Purkayastha0https://orcid.org/0000-0002-7611-6101Shovan Barma1https://orcid.org/0000-0001-8822-7362Electronics and Communication Engineering Department, Indian Institute of Information Technology Guwahati, Guwahati, IndiaElectronics and Communication Engineering Department, Indian Institute of Information Technology Guwahati, Guwahati, IndiaThis study proposes a multiclass classification technique based on multifractal spectra for different types of cardiac arrhythmias which are associated with irregularity and/or complex dynamics of the heart. Indeed, the degree of complexity of such dynamics is diverse for different states of cardiac condition. Certainly, such physiological responses of the heart dynamics can be discriminated by analyzing electrocardiogram (ECG) signals through different channels. Earlier, ECG-based works for discriminating cardiac arrhythmias consider the heart as a black box system and the analysis is mostly surrounded with time domain statistical averages or spectral analysis. The works ignore one of the key parameters, i.e., the presence of time-localized irregularities which are strongly associated with different kinds of arrhythmias and contribute to subtle variations in the amplitude and shape of the signal dynamical system while analyzing the signal. Therefore, in this work, we proposed a new method based on multifractal analysis to classify different kinds of cardiac conditions. Here, we followed the dynamical systems approach and computed the multifractal spectrum of the embedded phase space structure of the ECG signal. We performed the classification task by an echo state network to reduce the computational burden. For validation, three well-known datasets (Shaoxing Peoples’ Hospital dataset, PTB diagnostic ECG database v1.0.0, and 2017 PhysioNet/CinC Challenge dataset) have been considered. The results and analysis show that the proposed method can achieve a maximum accuracy of up to 96%, which is significantly high. Further, an optimum number of channels/leads has also been evaluated in multichannel ECG analysis. The result and analysis reveal that the effectiveness of the model in classifying various categories of cardiac disorders from ECG.https://ieeexplore.ieee.org/document/10317876/Arrhythmia classificationecho state network (ESN)multifractal analysismultivariate multifractal singularity spectrumnonlinear dynamics |
| spellingShingle | Basab Bijoy Purkayastha Shovan Barma Discrimination of Cardiac Abnormalities Based on Multifractal Analysis in Reservoir Computing Framework IEEE Open Journal of Instrumentation and Measurement Arrhythmia classification echo state network (ESN) multifractal analysis multivariate multifractal singularity spectrum nonlinear dynamics |
| title | Discrimination of Cardiac Abnormalities Based on Multifractal Analysis in Reservoir Computing Framework |
| title_full | Discrimination of Cardiac Abnormalities Based on Multifractal Analysis in Reservoir Computing Framework |
| title_fullStr | Discrimination of Cardiac Abnormalities Based on Multifractal Analysis in Reservoir Computing Framework |
| title_full_unstemmed | Discrimination of Cardiac Abnormalities Based on Multifractal Analysis in Reservoir Computing Framework |
| title_short | Discrimination of Cardiac Abnormalities Based on Multifractal Analysis in Reservoir Computing Framework |
| title_sort | discrimination of cardiac abnormalities based on multifractal analysis in reservoir computing framework |
| topic | Arrhythmia classification echo state network (ESN) multifractal analysis multivariate multifractal singularity spectrum nonlinear dynamics |
| url | https://ieeexplore.ieee.org/document/10317876/ |
| work_keys_str_mv | AT basabbijoypurkayastha discriminationofcardiacabnormalitiesbasedonmultifractalanalysisinreservoircomputingframework AT shovanbarma discriminationofcardiacabnormalitiesbasedonmultifractalanalysisinreservoircomputingframework |