Atrial fibrillation (AF) is one of the most common arrhythmias, which does great harm to patients. Effective methods were urgently required to prevent the recurrence of AF. Four methods were used to analyze RR sequence in this paper, and differences between Pre-AF (preceding an episode of AF) and Normal period (far away from episodes of AF) were analyzed to find discriminative criterion. These methods are: power spectral analysis, approximate entropy (ApEn) and sample entropy (SpEn) analysis, recurrence analysis and time series symbolization. The RR sequence data used in this research were downloaded from the Paroxysmal Atrial Fibrillation Prediction Database. Supporting vector machine (SVM) classification was used to evaluate the methods by calculating sensitivity, specificity and accuracy rate. The results showed that the comprehensive utilization of recurrence analysis parameters reached the highest accuracy rate (95%); power spectrum analysis took second place (90%); while the results of entropy analyses and time sequence symbolization were not satisfactory, whose accuracy were both only 70%. In conclusion, the recurrence analysis and power spectrum could be adopted to evaluate the atrial chaotic state effectively, thus having certain reference value for prediction of AF recurrence.
The electroencephalogram (EEG) has proved to be a valuable tool in the study of comprehensive conditions whose effects are manifest in the electrical brain activity, and epilepsy is one of such conditions. In the study, multi-scale permutation entropy (MPE) was proposed to describe dynamical characteristics of EEG recordings from epilepsy and healthy subjects, then all the characteristic parameters were forwarded into a support vector machine (SVM) for classification. The classification accuracies of the MPE with SVM were evaluated by a series of experiments. It is indicated that the dynamical characteristics of EEG data with MPE could identify the differences among healthy, inter-ictal and ictal states, and there was a reduction of MPE of EEG from the healthy and inter-ictal state to the ictal state. Experimental results demonstrated that average classification accuracy was 100% by using the MPE as a feature to characterize the healthy and seizure, while 99.58% accuracy was obtained to distinguish the seizure-free and seizure EEG. In addition, the single-scale permutation entropy (PE) at scales 1-5 was put into the SVM for classification at the same time for comparative analysis. The simulation results demonstrated that the proposed method could be a very powerful algorithm for seizure prediction and could have much better performance than the methods based on single scale PE.
Vigilance is defined as the ability to maintain attention for prolonged periods of time. In order to explore the variation of brain vigilance in work process, we designed addition and subtraction experiment with numbers of three digits to induce the vigilance to change, combined it with psychomotor vigilance task (PVT) to measure this process of electroencephalogram (EEG), extracted and analyzed permutation entropy (PE) of 11 cases of subjects' EEG and made a brief comparison with nonlinear parameter sample entropy (SE). The experimental results showed that:PE could well reflect the dynamic changes of EEG when vigilance decreases, and has advantages of fast arithmetic speed, high noise immunity, and low requirements for EEG length. This can be used as a measure of the brain vigilance indicators.
ObjectiveTo compare and analyze the electroencephalographic (EEG) characteristics of infants with infantile epileptic spasms syndrome (IESS) and healthy infants during sleep using power spectral density (PSD) analysis. MethodsInfants aged 5 to 9 months with IESS were included, along with an equal number of age-matched healthy controls. EEG signals during sleep were recorded using the Nihon Kohden EEG-1200C system. The energy distribution in the theta (θ), alpha (α), sigma (σ), and beta (β) frequency bands, as well as the morphology and values of PSD within the 4 ~ 30 Hz range, were analyzed. Additionally, spectral entropy (SpEn) was calculated to evaluate signal complexity. Results A total of 10 IESS patients and 10 healthy infants were included. There were no significant differences in gender or age between the two groups (P=0.64, P=0.88). In both groups, PSD values showed a linear decreasing trend with increasing frequency. However, the IESS group showed notable differences in PSD morphology, amplitude, and energy distribution compared to controls. These included the absence of a σ-band peak, greater PSD dispersion across electrodes, significant alterations in energy distribution across θ, α, σ, and β bands, and significantly higher PSD values in the 4 ~ 30 Hz range (P<0.000 1). SpEn analysis revealed significantly elevated spectral entropy across the sigma band in the IESS group, indicating a lack of dominant frequencies, increased complexity, reduced rhythmicity, and enhanced disorder. In contrast, healthy controls exhibited elevated SpEn in the alpha band, reflecting the physiological reduction or disappearance of dominant alpha rhythms during sleep. Conclusion Infants with IESS demonstrate distinct EEG characteristics in both PSD and SpEn analyses compared to healthy infants. These quantitative spectral features reflect the underlying abnormalities of EEG in IESS and provide objective insights that complement conventional visual assessment, offering a novel perspective for early diagnosis and therapeutic monitoring.
This paper explores a methodology used to discriminate the electroencephalograph (EEG) signals of patients with vegetative state (VS) and those with minimally conscious state (MCS). The model was derived from the EEG data of 33 patients in a calling name stimulation paradigm. The preprocessing algorithm was applied to remove the noises in the EEG data. Two types of features including sample entropy and multiscale entropy were chosen. Multiple kernel support vector machine was investigated to perform the training and classification. The experimental results showed that the alpha rhythm features of EEG signals in severe disorders of consciousness were significant. We achieved the average classification accuracy of 88.24%. It was concluded that the proposed method for the EEG signal classification for VS and MCS patients was effective. The approach in this study may eventually lead to a reliable tool for identifying severe disorder states of consciousness quantitatively. It would also provide the auxiliary basis of clinical assessment for the consciousness disorder degree.
Heart sound signal is a kind of physiological signal with nonlinear and nonstationary features. In order to improve the accuracy and efficiency of the phonocardiogram (PCG) classification, a new method was proposed by means of support vector machine (SVM) in which the complete ensemble empirical modal decomposition with adaptive noise (CEEMDAN) permutation entropy was as the eigenvector of heart sound signal. Firstly, the PCG was decomposed by CEEMDAN into a number of intrinsic mode functions (IMFs) from high to low frequency. Secondly, the IMFs were sifted according to the correlation coefficient, energy factor and signal-to-noise ratio. Then the instantaneous frequency was extracted by Hilbert transform, and its permutation entropy was constituted into eigenvector. Finally, the accuracy of the method was verified by using a hundred PCG samples selected from the 2016 PhysioNet/CinC Challenge. The results showed that the accuracy rate of the proposed method could reach up to 87%. In comparison with the traditional EMD and EEMD permutation entropy methods, the accuracy rate was increased by 18%–24%, which demonstrates the efficiency of the proposed method.
As an emerging non-invasive brain stimulation technique, transcranial direct current stimulation (tDCS) has received increasing attention in the field of stroke disease rehabilitation. However, its efficacy needs to be further studied. The tDCS has three stimulation modes: bipolar-stimulation mode, anode-stimulation mode and cathode-stimulation mode. Nineteen stroke patients were included in this research (10 with left-hemisphere lesion and 9 with right). Resting electroencephalogram (EEG) signals were collected from subjects before and after bipolar-stimulation, anodal-stimulation, cathodal-stimulation, and pseudo-stimulation, with pseudo-stimulation serving as the control group. The changes of multi-scale intrinsic fuzzy entropy (MIFE) of EEG signals before and after stimulation were compared. The results revealed that MIFE was significantly greater in the frontal and central regions after bipolar-stimulation (P < 0.05), in the left central region after anodal-stimulation (P < 0.05), and in the frontal and right central regions after cathodal-stimulation (P < 0.05) in patients with left-hemisphere lesions. MIFE was significantly greater in the frontal, central and parieto-occipital joint regions after bipolar-stimulation (P < 0.05), in the left frontal and right central regions after anodal- stimulation (P < 0.05), and in the central and right occipital regions after cathodal-stimulation (P < 0.05) in patients with right-hemisphere lesions. However, the difference before and after pseudo-stimulation was not statistically significant (P > 0.05). The results of this paper showed that the bipolar stimulation pattern affected the largest range of brain areas, and it might provide a reference for the clinical study of rehabilitation after stroke.
The automatic classification of epileptic electroencephalogram (EEG) is significant in the diagnosis and therapy of epilepsy. A classification algorithm for epileptic EEG based on wavelet multiscale analysis and extreme learning machine (ELM) is proposed in this paper. Firstly, wavelet multiscale analysis is applied to the original EEG to extract its sub-bands. Then, two nonlinear methods, i.e. Hurst exponent (Hurst) and sample entropy (SamEn) are used to the feature extraction of EEG and its sub-bands. Finally, ELM algorithm is employed in epileptic EEG classification with the nonlinear features. The proposed method in this paper achieved 99.5% classification accuracy for the discrimination between epileptic ictal and interictal EEG. The result implies that this method has good prospects in the diagnosis and therapy of epilepsy.
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.
Wavelet entropy is a quantitative index to describe the complexity of signals. Continuous wavelet transform method was employed to analyze the spontaneous electroencephalogram (EEG) signals of mild, moderate and severe Alzheimer's disease (AD) patients and normal elderly control people in this study. Wavelet power spectrums of EEG signals were calculated based on wavelet coefficients. Wavelet entropies of mild, moderate and severe AD patients were compared with those of normal controls. The correlation analysis between wavelet entropy and MMSE score was carried out. There existed significant difference on wavelet entropy among mild, moderate, severe AD patients and normal controls (P<0.01). Group comparisons showed that wavelet entropy for mild, moderate, severe AD patients was significantly lower than that for normal controls, which was related to the narrow distribution of their wavelet power spectrums. The statistical difference was significant (P<0.05). Further studies showed that the wavelet entropy of EEG and the MMSE score were significantly correlated (r=0.601-0.799, P<0.01). Wavelet entropy is a quantitative indicator describing the complexity of EEG signals. Wavelet entropy is likely to be an electrophysiological index for AD diagnosis and severity assessment.