Entropy-Based Pattern Learning Based on Singular Spectrum Analysis Components for Assessment of Physiological Signals

Complexity - Tập 2020 - Trang 1-17 - 2020
Yun Lu1, Mingjiang Wang1, Wanqing Wu2, Qiquan Zhang1, Yufei Han1, Tasleem Kausar1, Shixiong Chen2, Ming Liu3, Bo Wang4
1School of Electronic and Information Engineering, Harbin Institute of Technology, Shenzhen 518055, China
2CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China
3Sino-German School, Shenzhen Institute of Information Technology, Shenzhen 518172, China
4The Key Laboratory of Integrated Microsystems, Peking University Shenzhen Graduate School, Shenzhen 518055, China

Tóm tắt

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.

Từ khóa


Tài liệu tham khảo

10.1016/j.cmpb.2018.04.005

10.1016/j.schres.2018.04.043

10.1007/s11517-016-1454-4

10.1016/j.bbe.2018.05.005

10.3390/e17085241

10.1016/j.pnpbp.2017.03.004

10.1155/2018/8915079

10.1016/j.jacc.2016.08.062

10.1146/annurev-clinpsy-032816-045037

10.1136/heartjnl-2017-311198

10.1109/msp.2015.2481559

10.1103/PhysRevLett.89.068102

10.3389/fphys.2016.00136

10.3390/e19060257

10.1186/s12991-017-0157-z

10.1155/2017/1768264

10.1186/1687-6180-2011-125

10.1007/s00406-014-0525-5

10.3390/e21040338

10.1109/TBME.2018.2810942

10.1016/j.bspc.2011.07.007

10.1109/ACCESS.2018.2833746

10.3390/e20050386

10.3389/fnins.2018.00162

10.1007/s10044-016-0567-6

10.1016/j.compbiomed.2017.06.006

10.1016/j.patrec.2017.03.017

10.3390/e19030099

10.1016/j.bspc.2017.01.001

10.1016/j.knosys.2016.11.024

10.1007/s00034-015-0068-7

10.1109/TAMD.2015.2431497

10.3390/e17085218

10.1007/s00521-014-1786-7

10.1007/s11063-016-9530-1

10.3390/e17020669

10.1016/j.neucom.2013.11.009

10.1103/physreve.64.061907

10.1142/S1793536914500113

2002

10.1016/j.medengphy.2010.11.004

10.1016/j.jhydrol.2018.03.047

10.1016/j.ijforecast.2008.09.007

10.1073/pnas.88.6.2297

10.1152/ajpheart.2000.278.6.H2039

10.1109/TNSRE.2007.897025

10.3390/e19100568

10.22237/jmasm/1257035100

10.1145/1961189.1961199

10.1109/TAMD.2015.2431497

10.3390/e19060251

10.3390/s18030697

2016

10.1109/TBME.2017.2664105

10.1103/PhysRevLett.88.174102

10.1016/j.compbiomed.2016.11.002

Thông tin
Thông tin xuất bản

Complexity

Tập 2020

1-17

Thông tin tác giả