Abstract
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.
Highlights
Physiological signal is an invaluable data source, which can be utilized for examining the functioning of the human body [1, 2]
We develop a novel feature engineering approach based on singular spectrum analysis (SSA) to decompose physiological signals for entropy-based pattern learning, which is di erent from the available approaches for entropy-based pattern learning to deal with physiological signals. e novelty of the proposed entropy-based pattern learning is that the combination of SSA and entropy measures can help to facilitate the feature extraction of entropy measures from physiological signals
Physiological signals are first represented as a series of SSA components and extract entropy measures from the resulting SSA components. e resulting SSA components are data adaptive, which are closely related to the temporal structure of physiological signals themselves [46]. e main benefit for the proposed entropy-based pattern learning is that the SSA components can help to facilitate the feature extraction of entropy measures from physiological signals
Summary
Physiological signal is an invaluable data source, which can be utilized for examining the functioning of the human body [1, 2]. With the help of data mining and machine learning techniques, physiological signals are already widely used to assess cognitive states [3], monitor psychological functions [4], diagnose human diseases [5,6,7], and so on. Driven by the strong demand on the practical applications, increasing attention has been paid to using machine learning methods for analysis and assessment of physiological signals in recent years [8]. Narula et al demonstrated an application case which used three machine learning algorithms (support vector machines (SVM), random forests and arti cial neural networks) and echocardiographic data to automatically discriminate the hypertrophic cardiomyopathy from the physiological hypertrophy in athletes [8]. Pattern learning for the analysis and forecasting of physiological signals can be viewed as a promising avenue for healthcare applications based on physiological signals [10, 11]
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