Entropy-Based Pattern Learning Based on Singular Spectrum Analysis Components for Assessment of Physiological Signals
Measures of predictability in physiological signals based on entropy metrics have been widely used in the application domain of medical assessment and clinical diagnosis. In this paper, we propose a new entropy-based pattern learning by a combination of singular spectrum analysis (SSA) and entropy m...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/4625218 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849467924228603904 |
|---|---|
| author | Yun Lu Mingjiang Wang Wanqing Wu Qiquan Zhang Yufei Han Tasleem Kausar Shixiong Chen Ming Liu Bo Wang |
| author_facet | Yun Lu Mingjiang Wang Wanqing Wu Qiquan Zhang Yufei Han Tasleem Kausar Shixiong Chen Ming Liu Bo Wang |
| author_sort | Yun Lu |
| collection | DOAJ |
| description | Measures of predictability in physiological signals based on entropy metrics have been widely used in the application domain of medical assessment and clinical diagnosis. In this paper, we propose a new entropy-based pattern learning by a combination of singular spectrum analysis (SSA) and entropy measures for assessment of physiological signals. Physiological signals are first represented as a series of SSA components, and then well-established entropy measures are extracted from the resulting SSA components that can help to facilitate the features extraction from physiological signals. The entropy measures of notable SSA components are used to form input features and fed into pattern classifier. To demonstrate its validity, applicability, and versatility, the proposed entropy-based pattern learning is used to perform medical assessments with three kinds of classical physiological signals, that is, electroencephalogram (EEG), electromyogram (EMG), and RR-interval signals. Experiments demonstrate that in all cases, the proposed entropy-based pattern learning can effectively capture specific biosignal patterns of physiological signals and achieve excellent identification performances for the assessments of EEG, EMG, and RR-interval signals. Besides, through the comparison of the identification performances for entropy-based pattern learning based on the physiological signals themselves and the SSA components, it is concluded that the discriminating power of entropy-based pattern learning based on the SSA components is much stronger than that based on the physiological signals themselves. Since it can be easily extended to any other physiological signal analysis, the proposed entropy-based pattern learning may use as an efficient approach to reveal biosignal patterns for medical assessment of physiological signals. |
| format | Article |
| id | doaj-art-e122ce1b293d4657ba77d99868133d03 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-e122ce1b293d4657ba77d99868133d032025-08-20T03:25:59ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/46252184625218Entropy-Based Pattern Learning Based on Singular Spectrum Analysis Components for Assessment of Physiological SignalsYun Lu0Mingjiang Wang1Wanqing Wu2Qiquan Zhang3Yufei Han4Tasleem Kausar5Shixiong Chen6Ming Liu7Bo Wang8School of Electronic and Information Engineering, Harbin Institute of Technology, Shenzhen 518055, ChinaSchool of Electronic and Information Engineering, Harbin Institute of Technology, Shenzhen 518055, ChinaCAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, ChinaSchool of Electronic and Information Engineering, Harbin Institute of Technology, Shenzhen 518055, ChinaSchool of Electronic and Information Engineering, Harbin Institute of Technology, Shenzhen 518055, ChinaSchool of Electronic and Information Engineering, Harbin Institute of Technology, Shenzhen 518055, ChinaCAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, ChinaSino-German School, Shenzhen Institute of Information Technology, Shenzhen 518172, ChinaThe Key Laboratory of Integrated Microsystems, Peking University Shenzhen Graduate School, Shenzhen 518055, ChinaMeasures of predictability in physiological signals based on entropy metrics have been widely used in the application domain of medical assessment and clinical diagnosis. In this paper, we propose a new entropy-based pattern learning by a combination of singular spectrum analysis (SSA) and entropy measures for assessment of physiological signals. Physiological signals are first represented as a series of SSA components, and then well-established entropy measures are extracted from the resulting SSA components that can help to facilitate the features extraction from physiological signals. The entropy measures of notable SSA components are used to form input features and fed into pattern classifier. To demonstrate its validity, applicability, and versatility, the proposed entropy-based pattern learning is used to perform medical assessments with three kinds of classical physiological signals, that is, electroencephalogram (EEG), electromyogram (EMG), and RR-interval signals. Experiments demonstrate that in all cases, the proposed entropy-based pattern learning can effectively capture specific biosignal patterns of physiological signals and achieve excellent identification performances for the assessments of EEG, EMG, and RR-interval signals. Besides, through the comparison of the identification performances for entropy-based pattern learning based on the physiological signals themselves and the SSA components, it is concluded that the discriminating power of entropy-based pattern learning based on the SSA components is much stronger than that based on the physiological signals themselves. Since it can be easily extended to any other physiological signal analysis, the proposed entropy-based pattern learning may use as an efficient approach to reveal biosignal patterns for medical assessment of physiological signals.http://dx.doi.org/10.1155/2020/4625218 |
| spellingShingle | Yun Lu Mingjiang Wang Wanqing Wu Qiquan Zhang Yufei Han Tasleem Kausar Shixiong Chen Ming Liu Bo Wang Entropy-Based Pattern Learning Based on Singular Spectrum Analysis Components for Assessment of Physiological Signals Complexity |
| title | Entropy-Based Pattern Learning Based on Singular Spectrum Analysis Components for Assessment of Physiological Signals |
| title_full | Entropy-Based Pattern Learning Based on Singular Spectrum Analysis Components for Assessment of Physiological Signals |
| title_fullStr | Entropy-Based Pattern Learning Based on Singular Spectrum Analysis Components for Assessment of Physiological Signals |
| title_full_unstemmed | Entropy-Based Pattern Learning Based on Singular Spectrum Analysis Components for Assessment of Physiological Signals |
| title_short | Entropy-Based Pattern Learning Based on Singular Spectrum Analysis Components for Assessment of Physiological Signals |
| title_sort | entropy based pattern learning based on singular spectrum analysis components for assessment of physiological signals |
| url | http://dx.doi.org/10.1155/2020/4625218 |
| work_keys_str_mv | AT yunlu entropybasedpatternlearningbasedonsingularspectrumanalysiscomponentsforassessmentofphysiologicalsignals AT mingjiangwang entropybasedpatternlearningbasedonsingularspectrumanalysiscomponentsforassessmentofphysiologicalsignals AT wanqingwu entropybasedpatternlearningbasedonsingularspectrumanalysiscomponentsforassessmentofphysiologicalsignals AT qiquanzhang entropybasedpatternlearningbasedonsingularspectrumanalysiscomponentsforassessmentofphysiologicalsignals AT yufeihan entropybasedpatternlearningbasedonsingularspectrumanalysiscomponentsforassessmentofphysiologicalsignals AT tasleemkausar entropybasedpatternlearningbasedonsingularspectrumanalysiscomponentsforassessmentofphysiologicalsignals AT shixiongchen entropybasedpatternlearningbasedonsingularspectrumanalysiscomponentsforassessmentofphysiologicalsignals AT mingliu entropybasedpatternlearningbasedonsingularspectrumanalysiscomponentsforassessmentofphysiologicalsignals AT bowang entropybasedpatternlearningbasedonsingularspectrumanalysiscomponentsforassessmentofphysiologicalsignals |