We applied Lempel-Ziv complexity (LZC) combined with brain electrical activity mapping (BEAM) to study the change of alertness under sleep deprivation in our research. Ten subjects were involved in 36 hours sleep deprivation (SD), during which spontaneous electroencephalogram (EEG) experiments and auditory evoked EEG experiments-Oddball were recorded once every 6 hours. Spontaneous and evoked EEG data were calculated and BEAMs were structured. Results showed that during the 36 hours of SD, alertness could be divided into three stages, i.e. the first 12 hours as the high stage, the middle 12 hours as the rapid decline stage and the last 12 hours as the low stage. During the period SD, LZC of Spontaneous EEG decreased over the whole brain to some extent, but remained consistent with the subjective scales. By BEAMs of event related potential, LZC on frontal cortex decreased, but kept consistent with the behavioral responses. Therefore, LZC can be effective to reflect the change of brain alertness. At the same time LZC could be used as a practical index to monitor real-time alertness because of its simple computation and fast calculation.
This study uses mind-control game training to intervene in patients with mild cognitive impairment to improve their cognitive function. In this study, electroencephalogram (EEG) data of 40 participants were collected before and after two training sessions. The continuous complexity of EEG signals was analyzed to assess the status of cognitive function and explore the effect of mind-control game training on the improvement of cognitive function. The results showed that after two training sessions, the continuous complexity of EEG signal of the subject increased (0.012 44 ± 0.000 29, P < 0.05) and amplitude of curve fluctuation decreased gradually, indicating that with increase of training times, the continuous complexity increased significantly, the cognitive function of brain improved significantly and state was stable. The results of this paper may show that mind-control game training can improve the status of the brain cognitive function, which may provide support and help for the future intervention of cognitive dysfunction.
In the present study carried out in our laboratory, we recorded local field potential (LFP) signals in primary visual cortex (V1 area) of rats during the anesthesia process in the electrophysiological experiments of invasive microelectrode array implant, and obtained time evolutions of complexity measure Lempel-ziv complexity (LZC) by nonlinear dynamic analysis method. Combined with judgment criterion of tail flick latency to thermal stimulus and heart rate, the visual stimulation experiments are carried out to verify the reliability of anesthetized states by complexity analysis. The experimental results demonstrated that the time varying complexity measures LZC of LFP signals of different channels were similar to each other in the anesthesia process. In the same anesthesia state, the difference of complexity measure LZC between neuronal responses before and after visual stimulation was not significant. However, the complexity LZC in different anesthesia depths had statistical significances. Furthermore, complexity threshold value represented the depth of anesthesia was determined using optimization method. The reliability and accuracy of monitoring the depth of anesthesia using complexity measure LZC of LFP were all high. It provided an effective method of realtime monitoring depth of anesthesia for craniotomy patients in clinical operation.
When people are walking, they will produce gait signals and different people will produce different gait signals. The research of the gait signal complexity is really of great significance for medicine. By calculating people's gait signal complexity, we can assess a person's health status and thus timely detect and diagnose diseases. In this study, the Jensen-Shannon divergence (JSD), the method of complexity analysis, was used to calculate the complexity of gait signal in the healthy elderly, healthy young people and patients with Parkinson's disease. Then we detected the experimental data by variance detection. The results showed that the difference among the complexity of the three gait signals was great. Through this research, we have got gait signal complexity range of patients with Parkinson's disease, the healthy elderly and healthy young people, respectively, which would provide an important basis for clinical diagnosis.
Atrial fibrillation (AF) is a common arrhythmia disease. Detection of atrial fibrillation based on electrocardiogram (ECG) is of great significance for clinical diagnosis. Due to the non-linearity and complexity of ECG signals, the procedure to manually diagnose the ECG signals takes a lot of time and is prone to errors. In order to overcome the above problems, a feature extraction method based on RR interval is proposed in this paper. The discrete degree of RR interval is described with the robust coefficient of variation (RCV), the distribution shape of RR interval is described with the skewness parameter (SKP), and the complexity of RR interval is described with the Lempel-Ziv complexity (LZC). Finally, the feature vectors of RCV, SKP, and LZC are input into the support vector machine (SVM) classifier model to achieve automatic classification and detection of atrial fibrillation. To verify the validity and practicability of the proposed method, the MIT-BIH atrial fibrillation database was used to verify the data. The final classification results show that the sensitivity is 95.81%, the specificity is 96.48%, the accuracy is 96.09%, and the specificity of 95.16% is achieved in the MIT-BIH normal sinus rhythm database. The experimental results show that the proposed method is an effective classification method for atrial fibrillation.
Sub-threshold depression refers to a psychological sub-health state that fails to meet the diagnostic criteria for depression. Appropriate intervention can improve the state and reduce the risks of disease development. In this paper, we focus on music neurofeedback stimulation improving emotional state of sub-threshold depression college students.Twenty-four college students with sub-threshold depression participated in the experiment, 16 of whom were members of the experimental group. Decompression music based on spectrum classification was applied to 16 experimental group participants for 10 min/d music neural feedback stimulation with a period of 14 days, and no stimulation was applied to 8 control group participants. Three feature parameters of electroencephalogram (EEG) relative power, sample entropy and complexity were extracted for analysis. The results showed that the relative power of α、β and θ rhythm increased, while δ rhythm decreased after the stimulation of musical nerofeedback in the experimental group. The sample entropy and complexity were significantly increased after the stimulation, and the differences of these parameters pre and post stimulation were statistically significant (P < 0.05), while the differences of all feature parameters in the control group were not statistically significant. In the experimental group, the scores of self-rating depression scale(SDS) decreased after the stimulation of musical nerofeedback, indicating that the depression was improved. The result of this study showed that music neurofeedback stimulation can improve sub-threshold depression and may provides an effective new way for college students to self-regulation of emotion.
The linear analysis for heart rate variability (HRV), including time domain method, frequency domain method and timefrequency analysis, has reached a lot of consensus. The nonlinear analysis has also been widely applied in biomedical and clinical researches. However, for nonlinear HRV analysis, especially for shortterm nonlinear HRV analysis, controversy still exists, and a unified standard and conclusion has not been formed. This paper reviews and discusses three shortterm nonlinear HRV analysis methods (fractal dimension, entropy and complexity) and their principles, progresses and problems in clinical application in detail, in order to provide a reference for accurate application in clinical medicine.
All the collected original electroencephalograph (EEG) signals were the subjects to low-frequency and spike noise. According to this fact, we in this study performed denoising based on the combination of wavelet transform and independent component analysis (ICA). Then we used three characteristic parameters, complexity, approximate entropy and wavelet entropy values, to calculate the preprocessed EEG data. We then made a distinguishing judge on the EEG state by the state change rate of the characteristic parameters. Through the anesthesia and non-anesthesia EEG data processing results showed that each of the three state change rates could reach about 50.5%, 21.6%, 19.5%, respectively, in which the performance of wavelet entropy was the highest. All of them could be used as a foundation in the quantified research of depth of anesthesia based on EEG analysis.
Somatosensory vibration can stimulate somatosensory area of human body, and this stimulation is tranferred to somatosensory nerves, and influences the somatic cortex, which is on post-central gyrus and paracentral lobule posterior of cerebral cortex, so that it alters the functional status of brain. The aim of the present study was to investigate the neural mechanism of brain state induced by somatosensory vibration. Twelve subjects were involved in the 20 Hz vibration stimulation test. Linear and nonlinear methods, such as relative change of relative power (RRP), Lempel-Ziv complexity (LZC) and brain network based on cross mutual information (CMI), were applied to discuss the change of brain under somatosensory vibration stimulation. The experimental results showed the frequency following response (FFR) by RRP of spontaneous electroencephalogram (EEG) in 20 Hz vibration, and no obvious change by LZC. The information transmission among various cortical areas enhanced under 20 Hz vibration stimulation. Therefore, 20 Hz somatosensory vibration may be able to adjust the functional status of brain.
To distinguish the randomness and chaos characteristics of physiological signals and to keep its performance independent of the signal length and parameters are the key judgement of performance of a complexity algorithm. We proposed an encoding Lempel-Ziv (LZ) complexity algorithm to try to explicitly discern between the randomness and chaos characteristics of signals. Our study also compared the effects of length of time series, the sensitivity to dynamical properties change of time series and quantifying the complexity between gauss noise and 1/f pink noise ELZ with those from classic LZ (CLZ), multi-state LZ (MLZ), sample entropy (SampEn) and permutation entropy (PE). The experimental results showed ELZ could not only distinguish the randomness and chaos characteristics of time series on all time length (i.e. 100, 500, 5 000), but also reflected exactly that the complexity of gauss noise was lower than that of pink noise, and responded change of dynamic characteristics of time series in time. The congestive heart failure (CHF) RR Interval database and the normal sinus rhythm (NSR) RR Interval database created by Massachusetts Institute of Technology (MIT) and Boston Beth Israel Hospital(BIH)were used as real data in our study. The results revealed that the ELZ could show the complexity of congestive heart failure which was lower than that of normal sinus rhythm during all lengths of time series (P<0.01), and the ELZ algorithm had better generalization ability and was independent of length of time series.
