Describing the nonstationarity level of neurological signals based on quantifications of time-frequency representation.
Most neurological signals including electroencephalogram (EEG), evoked potential (EP) and local field potential (LFP) have been known to be time varying and nonstationary, especially in some pathological conditions. Currently, the most widely used quantitative tool for such nonstationary signals is time-frequency representation (TFR) which demonstrates the temporal evolution of different frequency components. However, TFR does not directly provide a quantitative measure of nonstationarity level, e.g., how far the process deviates from stationarity. In this study, we introduced three different quantifications of TFR (qTFR) to characterize the nonstationarity level of the involving signals: (1) degree of stationarity (DS); (2) Shannon entropy (SE) of the marginal spectrum; and (3) Kullback-Leibler distance (KLD) between a TFR and a uniform distribution. These descriptors provide quantitative analysis of stationarity of a signal such that the stationarity of different signals could be compared. In this study, we obtained the TFRs of the EEG signals before and after the hypoxic-ischemic (HI) brain injury and examined the stationarity of the EEG. DS, SE, and KLD can indicate the nonstationarity change of EEG at each frequency following the HI injury, especially in the upper delta-and lower theta-band (e.g., [2 Hz, 8 Hz]) as well as in the beta2 band (e.g., [22 Hz-26 Hz]). Moreover, it is shown that the stationarity of the EEG changes differently in different frequencies following the HI injury.
Tong
Shanbao
S
Biomedical Engineering Department, Shanghai Jiao Tong University, Jiao Yi Lou Building, Room 411, 1954 Huashan Road, Shanghai, 200240, China. shanbao.tong@gmail.com
Li
Zhengjun
Z
Zhu
Yisheng
Y
Thakor
Nitish V
NV
D000465:Algorithms; D001921:Brain / Q000503:physiopathology*; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D002534:Hypoxia, Brain / Q000175:diagnosis*; D008959:Models, Neurological; D015233:Models, Statistical; D012815:Signal Processing, Computer-Assisted; D013269:Stochastic Processes