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Entropy-Based Pattern Learning Based on Singular Spectrum Analysis Components for Assessment of Physiological Signals
المؤلفون المشاركون
Chen, Shixiong
Lu, Yun
Wang, Mingjiang
Wu, Wanqing
Zhang, Qiquan
Han, Yufei
Kausar, Tasleem
Liu, Ming
Wang, Bo
المصدر
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-17، 17ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-01-20
دولة النشر
مصر
عدد الصفحات
17
التخصصات الرئيسية
الملخص EN
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.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Lu, Yun& Wang, Mingjiang& Wu, Wanqing& Zhang, Qiquan& Han, Yufei& Kausar, Tasleem…[et al.]. 2020. Entropy-Based Pattern Learning Based on Singular Spectrum Analysis Components for Assessment of Physiological Signals. Complexity،Vol. 2020, no. 2020, pp.1-17.
https://search.emarefa.net/detail/BIM-1142011
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Lu, Yun…[et al.]. Entropy-Based Pattern Learning Based on Singular Spectrum Analysis Components for Assessment of Physiological Signals. Complexity No. 2020 (2020), pp.1-17.
https://search.emarefa.net/detail/BIM-1142011
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Lu, Yun& Wang, Mingjiang& Wu, Wanqing& Zhang, Qiquan& Han, Yufei& Kausar, Tasleem…[et al.]. Entropy-Based Pattern Learning Based on Singular Spectrum Analysis Components for Assessment of Physiological Signals. Complexity. 2020. Vol. 2020, no. 2020, pp.1-17.
https://search.emarefa.net/detail/BIM-1142011
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1142011
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر
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