ObjectiveTo investigate the feasibility of electroencephalography (EEG) power spectrum analysis monitoring noninvasive intracranial pressure (ICP). MethodsBetween September 2008 and May 2009, the EEG signals were recorded in 62 patients (70 cases/times) with central nervous system (CNS). By using self-designed software, EEG power spectrum analysis was conducted and pressure index (PI) was calculated automatically. ICP was measured by lumbar puncture (LP). ResultsThe mean ICP was (239.74±116.25) mm H2O (70-500 mm H2O, 1 mm H2O=0.009 8 kPa), and 52.9% of patients had increased ICP. The mean PI was 0.29±0.20 (0.02-0.85). The Spearman rank test showed that there was a significant negative correlation between PI and ICP (rs=-0.849, P<0.01). The data from the patients with diffuse lesions of CNS and focal lesions were analyzed separately; the results showed there were significant negative correlations between PI and ICP in both groups (rs=-0.815, -0.912; P<0.01). ConclusionThe PI obtained from EEG analysis is correlated with ICP. Analysis of specific parameters from EEG power spectrum might reflect the ICP. Further research should be carried out.
Heart rate variability (HRV) is an important point to judge a person’s state in modern medicine. This paper is aimed to research a person’s fatigue level connected with vagal nerve based on the HRV using the improved Welch method. The process of this method is that it firstly uses a time window function on the signal to be processed, then sets the length of time according to the requirement, and finally makes frequency domain analysis. Compared with classical periodogram method, the variance and consistency of the present method have been improved. We can set time span freely using this method (at present, the time of international standard to measure HRV is 5 minutes). This paper analyses the HRV’s characteristics of fatigue crowd based on the database provided by PhysioNet. We therefore draw the conclusion that the accuracy of Welch analyzing HRV combining with appropriate window function has been improved enormously, and when the person changes to fatigue, the vagal activity is diminished and sympathetic activity is raised.
Seizures during sleep increase the probability of complication and sudden death. Effective prediction of seizures in sleep allows doctors and patients to take timely treatments to reduce the aforementioned probability. Most of the existing methods make use of electroencephalogram (EEG) to predict seizures, which are not specific developed for the sleep. However, EEG during sleep has its characteristics compared with EEG during other states. Therefore, in order to improve the sensitivity and reduce the false alarm rate, this paper utilized the characteristics of EEG to predict seizures during sleep. We firstly constructed the feature vector including the absolute power spectrum, the relative power spectrum and the power spectrum ratio in different frequencies. Secondly, the separation criterion and branch-and-bound method were applied to select features. Finally, support vector machine classifier were trained, which is then employed for online prediction. Compared with the existing method that do not consider the characteristics of sleeping EEG (sensitivity 91.67%, false alarm rate 9.19%), the proposed method was superior in terms of sensitivity (100%) and false alarm rate (2.11%). This method can improve the existing epilepsy prediction methods and has important clinical value.
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.
Transcranial direct current stimulation (tDCS) is a non-invasive low-current brain stimulation technique, which is mainly based on the different polarity of electrode stimulation to make the activation threshold of neurons different, thereby regulating the excitability of the cerebral cortex. In this paper, healthy subjects were randomly divided into three groups: anodal stimulation group, cathodal stimulation group and sham stimulation group, with 5 subjects in each group. Then, the performance data of the three groups of subjects were recorded before and after stimulation to test their mental rotation ability, and resting state and task state electroencephalogram (EEG) data were collected. Finally, through comparative analysis of the behavioral data and EEG data of the three groups of subjects, the effect of electrical stimulation of different polarities on the three-dimensional mental rotation ability was explored. The results of the study found that the correct response time/accuracy rate and the accuracy rate performance of the anodal stimulation group were higher than those of the cathodal stimulation and sham stimulation groups, and there was a significant difference (P < 0.05). The alpha wave power analysis found that the mental rotation mainly activates the frontal lobe, central area, parietal lobe and occipital lobe. In the anodal stimulation group, the alpha wave power changed significantly in the frontal lobe and occipital lobe (P < 0.05). The results of this paper show that anodal stimulation group can improve the mental rotation ability of the subjects to a certain extent. The results of this paper can provide important theoretical support for further research on the mechanism of tDCS on mental rotation ability.