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Background: Heart rate variability (HRV) analysis is an important tool to assess the cardiac autonomic regulation in health and disease. Time-domain and frequency-domain analyses are linear methods that are traditionally used for HRV analysis. Application of non-linear methods in HRV analysis has been shown to provide additional information and has been found to be useful in predicting complications and mortality in cardiovascular disease conditions. HRV analysis during deep breathing is commonly used to assess the integrity and extent of the parasympathetic control of the heart. Aim and Objectives: This study aims to analyze the HRV during deep breathing at 0.1 Hz frequency, 6 breaths/minute using non-linear methods and to see whether they correlate with the time-domain measures of HRV. Materials and Methods: Twelve healthy volunteers performed deep slow breathing at 0.1 Hz frequency for 5 min following recorded prompts. In the time domain, mean heart rate (MHR), SDNN, RMSSD, and pNN50 during baseline and deep breathing were measured. In the non-linear domain, approximate entropy (AppEn), sample entropy (SampEn), and detrended fluctuation analysis DFA (?1) were calculated. The quantitative measures of the Poincare plot, namely, SD1, SD2, and SD2/SD1, which are known to provide linear information, were also estimated. Wilcoxon’s signed-rank test was used to compare the baseline parameters with those of deep breathing. Spearman’s correlation was used to assess the correlation between the parameters obtained from the different methods. Results: There was no significant change in the MHR, RMSSD, pNN50, and SD1 during 0.1 Hz deep breathing while SDNN, SD2, SD2/SD1, and DFA?1 showed a significant increase. Furthermore, 0.1 Hz breathing decreased the AppEn and SampEn measures of HRV. There was a strong correlation among SDNN, RMSSD, pNN50, SD1, SD2, SD2/SD1, and DFA?1, but there was no correlation between any of the above measures and the non-linear measures AppEn and SampEn. Conclusion: While the non-linear measure DFA?1 correlates well with time domain measures of HRV and the quantitative measures of the Poincare plot during 0.1 Hz breathing, AppEn and SampEn do not show such correlation. Instead, they decrease significantly when breathing is voluntarily controlled at 6 breaths/min.
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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 ( < 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.
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In this paper, a feature extraction algorithm of weighted multiple multiscale entropy is proposed to solve the problem of information loss which is caused in the multiscale process of traditional multiscale entropy. Algorithm constructs the multiple data sequences from large to small on each scale. Then, considering the different contribution degrees of multiple data sequences to the entropy of the scale, the proportion of each sequence in the scale sequence is calculated by combining the correlation between the data sequences, so as to reconstruct the sample entropy of each scale. Compared with the traditional multiscale entropy the feature extraction algorithm based on weighted multiple multiscale entropy not only overcomes the problem of information loss, but also fully considers the correlation of sequences and the contribution to total entropy. It reduces the fluctuation between scales, and digs out the details of electroencephalography (EEG). Based on this algorithm, the EEG characteristics of autism spectrum disorder (ASD) children are analyzed, and the classification accuracy of the algorithm is increased by 23.0%, 10.4% and 6.4% as compared with the EEG extraction algorithm of sample entropy, traditional multiscale entropy and multiple multiscale entropy based on the delay value method, respectively. Based on this algorithm, the 19 channel EEG signals of ASD children and healthy children were analyzed. The results showed that the entropy of healthy children was slightly higher than that of the ASD children except the FP2 channel, and the numerical differences of F3, F7, F8, C3 and P3 channels were statistically significant ( <0.05). By classifying the weighted multiple multiscale entropy of each brain region, we found that the accuracy of the anterior temporal lobe (F7, F8) was the highest. It indicated that the anterior temporal lobe can be used as a sensitive brain area for assessing the brain function of ASD children.
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OBJECTIVE: To attempt to establish an objective quantitative indicator to characterize the trigger point activity, so as to evaluate the effect of dry needling on myofascial trigger point activity. METHODS: Twenty-four male Sprague-Dawley rats were randomly divided into blank control group, dry needling (needling) group, stretching exercise (stretching) group and needling plus stretching group (n＝6 per group). The chronic myofascial pain (trigger point) model was established by freedom vertical fall of a wooden striking device onto the mid-point of gastrocnemius belly of the left hind-limb to induce contusion, followed by forcing the rat to make a continuous downgrade running exercise at a speed of 16 m/min for 90 min on the next day which was conducted once a week for 8 weeks. Electromyography (EMG) of the regional myofascial injured point was monitored and recorded using an EMG recorder via electrodes. It was considered success of the model if spontaneous electrical activities appeared in the injured site. After a 4 weeks' recovery, rats of the needling group were treated by filiform needle stimulation (lifting-thrusting-rotating) of the central part of the injured gastrocnemius belly (about 10 mm deep) for 6 min, and those of the stretching group treated by holding the rat's limb to make the hip and knee joints to an angle of about 180°, and the ankle-joint about 90° for 1 min every time, 3 times altogether (with an interval of 1 min between every 2 times). The activity of the trigger point was estimated by the sample entropy of the EMG signal sequence in reference to Richman's and Moorman's methods to estimate the curative effect of both needling and exercise. RESULTS: After the modeling cycle, the mean sample entropies of EMG signals was significantly decreased in the model groups (needling group [0.034±0.010], stretching group [0.045±0.023], needling plus stretching group [0.047±0.034]) relevant to the blank control group (0.985±0.196, P0.05), suggesting a better efficacy of dry needling in easing trigger point activity. CONCLUSION: Dry needling is able to relieve myofascial trigger point activity in rats, which is better than that of simple passive stretching therapy.
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The diaphragm is the main respiratory muscle in the body. The onset detection of the surface diaphragmatic electromyography (sEMGdi) can be used in the respiratory rehabilitation training of the hemiparetic stroke patients, but the existence of electrocardiography (ECG) increases the difficulty of onset detection. Therefore, a method based on sample entropy (SampEn) and individualized threshold, referred to as SampEn method, was proposed to detect onset of muscle activity in this paper, which involved the extraction of SampEn features, the optimization of the SampEn parameters and , the selection of individualized threshold and the establishment of the judgment conditions. In this paper, three methods were used to compare onset detection accuracy with the SampEn method, which contained root mean square (RMS) with wavelet transform (WT), Teager-Kaiser energy operator (TKE) with wavelet transform and TKE without wavelet transform, respectively. sEMGdi signals of 12 healthy subjects in 2 different breathing ways were collected for signal synthesis and methods detection. The cumulative sum of the absolute value of error was used as an judgement value to evaluate the accuracy of the four methods. The results show that SampEn method can achieve higher and more stable detection precision than the other three methods, which is an onset detection method that can adapt to individual differences and achieve high detection accuracy without ECG denoising, providing a basis for sEMGdi based respiratory rehabilitation training and real time interaction.
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Objective To compare the EEG complexity between rats under awaking and differ-ent depth of anesthesia via analyzing sample entropy and fractal dimension.Methods Sixteen SD rats were intraperitoneally injected with urethane twice,first with 500 mg/kg and second with 800 mg/kg one hour later.The scalp EEG was collected in stage of awaking (W),light anesthesia (LA)and heavy anesthesia (HA).The sample entropy (SampEn)and fractal dimension (FD)were computed by MATLAB.The characteristic values were denoised by linear dynamic system method during the whole process.Results The value of SampEn and FD gradually dropped from awaking to heavy anes-thesia.The SampEn and FD in W was significantly higher than the value in LA or in HA (P <0.05). The SampEn and FD in HA was significantly lower than that in LA (P < 0.05 ).Conclusion The SampEn and FD of EEG could be used to monitor the depth of anesthesia.
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Objective To evaluate clinical efficacy and electroencephalogram(EEG)changes quantitatively after the ketogenic diet (KD) by single sample entropy (SampEn) in the treatment of infantile spasms (IS),and to learn the quantitative relationship between the clinical efficacy and EEG.Methods Patients diagnosed as IS were enrolled and started KD in Shenzhen Children's Hospital from April 2010 to December 2013.The SampEn of EEG data in these patients before and after treatment with KD were analyzed.Patients were classified as seizure-free group and non-seizure-free group according to the therapeutic responsiveness to KD.The SampEn findings from two groups were compared to explore the effect of KD on EEG and its related factors.Results Among 35 patients,more than 2 months of treatment,10 cases were seizure free,25 cases still had seizures.SampEn was 0.377 ± 0.246 before treatment,and 0.725 ± 0.405 after treatment in all patients,there was significant difference (Z =-4.351,P =0.000).SampEn was 0.342 ± 0.277 before treatment,and 0.929 ± 0.379 after treatment in seizure free group,there was significant difference between 2 groups(Z =-3.371,P =0.001).While SampEn was 0.391 ± 0.237 before treatment,and 0.643 ± 0.393 after treatment in non-seizure free group,there was a significant difference between 2 groups(Z =-3.371,P =0.001).The mental and motor development was improved after KD with improvement rate were 56％ (14/25 cases) and 70％ (7/10 cases),respectively,but there was no statistical difference(P =0.704).Conclusions No matter seizures are controlled or not,KD can increase the complexity of electrical activity in the brain,which was more obvious in the seizure-free group.Intellectual and movement development can be improved in patients with KD.
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Objective To study the feature extraction methods for the event related potential (ERP) evoked by mental arithmetical tasks through the sample entropy, in order to enhance the features of electroencephalograph (EEG) signals for brain computer interface (BCI).Methods Three types of mental arithmetic tasks including a simple counting, a random number and a stroke of Chinese character counting were proposed and 16 channel EEG signals were recorded from eight healthy subjects.The sample entropy method was then applied in characteristic signal complexity analysis.The characteristic and difference of signal complexity of ERP evoked by three types of mental arithmetical tasks were explored.Results The entropy value for EEG signal evoked by non-target stimulus was higher than that by the target stimulus with the significant difference (P＜0.01).The entropy of the mental arithmetic based on the Chinese characters counting task was significandy higher than that of the other two tasks (P＜0.05).EEG signals evoked by target/non-target were fundamentally signals under the state of attention or non-attention.Conclusions For the Chinese characters counting task, more complex information have been processed by the brain and the non-linear connection between nerve cells are much more complicated and a higher entropy value is achieved.In summary, the mental arithmetic task can effectively activate the relevant brain regions and the sample entropy can distinguish signals evoked by target or non-target stimuli.
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Objective To explore the nonlinear complexity characteristics of electroencephalogram (EEG) in ischemic stroke patients with different course. Methods Sample entropy of all bands of EEG signals in 20 ischemic stroke patients and 10 healthy controls was extracted and analyzed using statistical analysis methods. Results The full-band EEG in sample entropy of stroke patients was significantly lower than that of healthy controls in most locations. Theα-band sample entropy of different course had significant differences in the frontal, temporal and occipital lobe (P<0.05), and the parameters had significant negative linear correlation with the post-stroke time in some locations. Conclusions There is an abnormal neural electrical activity in post-stroke patients. It is feasible to detect the aberrant EEG complexity using sample entropy, which is worth of further research.