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Hummgenic changes in large scale temporal correlation of EEG in BP
Abstract
In this paper we present results of detrended fluctuation analysis (DFA) on raw EEG data obtained from subjects performing Bhramari Pranayama (BP).BP is characterized by the production of low frequency humming sound like that of bumble bee by sustaining pronunciation of nasal /m/ sound while keeping ears occluded and oral cavity closed at lips. BP is effective in healing many neuronal disorders. We hypothesize that the humming sound is playing such healing role by changing brain wave patterns. The results of DFA analysis show that BP changes scaling exponents for the raw EEG data in the frontal and temporal region of the brain. Decrement in scaling exponent lowers decay of temporal correlations in data while increment in scaling exponent provides rapid decay. The estimated exponent were found to lie between 0.5 and 1.6 which also show that EEG signals are generated through fractal process and contain long range temporal correlates. It is hypothesized that the interaction of self humming and brain wave signals might be actor behind bringing good effects by BP.
... This acoustic vibration could have significant impact in producing the desired effect of Bhr.P. Since, for brain we don't have any stretching exercises like other parts of the body, vibration of head is a good alternative for that and vibration by one's own voice might not be harmful for the brain tissues. 30,31 Hence it is evident that it influences multiple systems in the body and there is definitely a scope to have desirable effects on respiratory system, autonomic nervous system, stress, anxiety level, over all emotional status of the practitioner etc. At a deeper level, the studies could even focus on the effect of Bhr.P on stress markers (Cortisol, alpha amylase, MDA etc) too. ...
Background
Pranayama, a branch of yoga practice is extremely beneficial to mankind in maintaining sound physical and mental health and this article aims to attain an insight on the studies conducted on the effectiveness of Bhramari Pranayama (Bhr.P) on health. The studies done until May 2016 were found using Medline, Embase, Google scholar and manual search. Studies conducted on the health effectiveness of Bhr.P specifically were included on the basis of prisma guidelines. The data were defined by their objectives, methodology, study setting, findings, interventions done and implications suggested in the study. Methodological Quality Rating Scale (MQRS) and Newcastle-Ottawa Scale (NOS) were used in reviewing and reporting results of the included studies. 6 studies satisfied the inclusion criteria; 2 studies were done on the cold pressor test, one on heart rate and BP, one on EEG changes, one each on the inhibitory response and tinnitus condition. In the included studies, the Bhr.P practices have shown para-sympathetic dominance. There are some encouraging effects of Bhr.P on various physiological systems. Methodological quality of the included studies was evaluated to be very low and none of them were RCTs. Yet the available studies are heterogeneous, dealing in different grounds and this heterogeneity serves as a resource for the limited scope of studies on Bhr.P. Therefore, further large-scale, properly designed, randomized trials of Bhr.P on various systems have to be done to justify these effects efficiently.
... Artificial neural network (ANN) is a kind of data processing algorithm established through simulating the brain neural network features, having strong learning ability and adaptability [12]. There are many types of ANNs, such as BPNN, Elman neutral network, learning vector quantization (LVQ) neutral network, and the wavelet neutral network (WNN) [13][14][15][16]. The BPNN algorithm, however, is one of the relatively mature algorithms, and belongs to the multilayer forward neural network. ...
In this paper, in order to solve the existing problems of the low recognition rate and poor real-time performance in limb motor imagery, the integrated back-propagation neural network (IBPNN) was applied to the pattern recognition research of motor imagery EEG signals (imagining left-hand movement, imagining right-hand movement and imagining no movement). According to the motor imagery EEG data categories to be recognized, the IBPNN was designed to consist of 3 single three-layer back-propagation neural networks (BPNN), and every single neural network was dedicated to recognizing one kind of motor imagery. It simplified the complicated classification problems into three mutually independent two-class classifications by the IBPNN. The parallel computing characteristic of IBPNN not only improved the generation ability for network, but also shortened the operation time. The experimental results showed that, while comparing the single BPNN and Elman neural network, IBPNN was more competent in recognizing limb motor imagery EEG signals. Also among these three networks, IBPNN had the least number of iterations, the shortest operation time and the best consistency of actual output and expected output, and had lifted the success recognition rate above 97 percent while other single network is around 93 percent.
Background
In chronic subjective tinnitus (CST) studies, the effect of bee-humming respiratory training (BHRT), a type of pranayama breathing technique, on autonomic cardiovascular/respiratory physiological functions (systolic pressure, rates of breathing and pulse, and diastolic pressure), tinnitus annoyance (TA), tinnitus loudness (TL), and quality of life measures as tinnitus handicap inventory (THI) did not receive significant attention.
Objective
This study aimed to investigate the efficacy of BHRT on TL, THI, TA, and cardiovascular/respiratory autonomic functions in bilateral CST elderly (aged ≥65 years old).
The design, setting, participants, and intervention
The current study employs a prospective, single-blind design; it is a randomized-controlled for-tinnitus behavioral intervention. Forty-six CST elderly subjects were randomly assigned to one of two groups: the BHRT group (23 patients) or the control group (23 patients). The 4-week BHRT intervention was applied 30 min daily.
Outcome measures
TL, THI, TA, and cardiovascular/respiratory autonomic functions were evaluated.
Results
Significant BHRT-induced reductions of all measures were detected in the BHRT group, whereas no significant changes were observed in the controlled elderly group.
Conclusion
The complementary choice for BHRT is considered an effective method to improve TL, TA, THI, and cardiovascular/respiratory autonomic functions in CST elderly.
Objectives:
Bee-Humming Breathing (BHB) exercise is a simple yogic practice recommended for its favorable effect on cardiac physiology, including blood pressure (BP) and autonomic nervous system. However, strong evidence supporting its effectiveness is lacking. The present study was designed to evaluate the immediate effect of BHB exercise on blood pressure parameters and heart rate variability (HRV) in patients with essential hypertension.
Study methods:
We conducted a randomized control trial including 70 patients with essential hypertension, randomly allocated to perform either BHB exercise (n=35) or placebo slow breathing exercise (n = 35) for 5-minutes duration. Blood pressure and HRV were measured before, during, and after the practice.
Results:
There was no significant decrease in systolic [effect size (95% CI): 2.22 (-13.20, 17.64); p 0.77], diastolic [4.54 (-17.40, 26.48); p 0.68] and mean blood pressures [1.37 (-8.78, 11.52); p 0.78] after BHB exercise in comparison to the control group in our study. The HRV analysis showed a significant increase in the HF power [6.8 (1.47, 12.12); p 0.01], and decrease in the LF power [-26.47 (-34.25, -18.68); p < 0.01] during the recovery phase of the 5-minute BHB exercise in comparison to the control group.
Conclusions:
This is the first randomized controlled trial to show that though a single short session of BHB exercise in hypertensive patients does not significantly reduce BP, it significantly augments the parasympathetic tone as indicated by a significant improvement in HRV parameters.
Clinical trial registration number:
CTRI/2018/08/015215.
We discuss a method of analyzing spontaneous human EEG time series, which emphasizes scale-independent behavior. We use detrended fluctuation analysis to quantify the temporal fluctuations as a function of window width, and show how power-law scaling behavior is frequently manifest over two distinct temporal ranges. These ranges encompass time scales associated with meaningful aspects of cortical physiology. This paper shows a simple way of quantifying the existence of such scaling behavior, and determining the characteristic time scale which separates the two regions. By making a qualitative connection with the discrete Fourier transform, we show how the violation of scaling between the two regions is associated with the normal human alpha rhythm, but that the existence scale-independent behavior on either side of the alpha rhythm enables a succinct description of the complex dynamics not accessible in the Fourier power spectrum.
This study examines the role of coping in the onset of post-traumatic stress disorder (PTSD) in a nonpatient population following exposure to a natural disaster. In contrast to other studies, the use of all coping strategies was found to be associated with the presence of PTSD rather than the absence of symptoms. These data suggest that coping (in this sense) represents a psychological process used to contain the distress caused by symptoms as well as to manage environmental adversity.
Long-range power-law correlations have been reported recently for DNA sequences containing noncoding regions. We address the question of whether such correlations may be a trivial consequence of the known mosaic structure ("patchiness") of DNA. We analyze two classes of controls consisting of patchy nucleotide sequences generated by different algorithms--one without and one with long-range power-law correlations. Although both types of sequences are highly heterogenous, they are quantitatively distinguishable by an alternative fluctuation analysis method that differentiates local patchiness from long-range correlations. Application of this analysis to selected DNA sequences demonstrates that patchiness is not sufficient to account for long-range correlation properties.
The healthy heartbeat is traditionally thought to be regulated according to the classical principle of homeostasis whereby physiologic systems operate to reduce variability and achieve an equilibrium-like state [Physiol. Rev. 9, 399-431 (1929)]. However, recent studies [Phys. Rev. Lett. 70, 1343-1346 (1993); Fractals in Biology and Medicine (Birkhauser-Verlag, Basel, 1994), pp. 55-65] reveal that under normal conditions, beat-to-beat fluctuations in heart rate display the kind of long-range correlations typically exhibited by dynamical systems far from equilibrium [Phys. Rev. Lett. 59, 381-384 (1987)]. In contrast, heart rate time series from patients with severe congestive heart failure show a breakdown of this long-range correlation behavior. We describe a new method--detrended fluctuation analysis (DFA)--for quantifying this correlation property in non-stationary physiological time series. Application of this technique shows evidence for a crossover phenomenon associated with a change in short and long-range scaling exponents. This method may be of use in distinguishing healthy from pathologic data sets based on differences in these scaling properties.
According to classical concepts of physiologic control, healthy systems are self-regulated to reduce variability and maintain physiologic constancy. Contrary to the predictions of homeostasis, however, the output of a wide variety of systems, such as the normal human heartbeat, fluctuates in a complex manner, even under resting conditions. Scaling techniques adapted from statistical physics reveal the presence of long-range, power-law correlations, as part of multifractal cascades operating over a wide range of time scales. These scaling properties suggest that the nonlinear regulatory systems are operating far from equilibrium, and that maintaining constancy is not the goal of physiologic control. In contrast, for subjects at high risk of sudden death (including those with heart failure), fractal organization, along with certain nonlinear interactions, breaks down. Application of fractal analysis may provide new approaches to assessing cardiac risk and forecasting sudden cardiac death, as well as to monitoring the aging process. Similar approaches show promise in assessing other regulatory systems, such as human gait control in health and disease. Elucidating the fractal and nonlinear mechanisms involved in physiologic control and complex signaling networks is emerging as a major challenge in the postgenomic era.
Spontaneous cortical activity--ongoing activity in the absence of intentional sensory input--has been studied extensively, using methods ranging from EEG (electroencephalography), through voltage sensitive dye imaging, down to recordings from single neurons. Ongoing cortical activity has been shown to play a critical role in development, and must also be essential for processing sensory perception, because it modulates stimulus-evoked activity, and is correlated with behaviour. Yet its role in the processing of external information and its relationship to internal representations of sensory attributes remains unknown. Using voltage sensitive dye imaging, we previously established a close link between ongoing activity in the visual cortex of anaesthetized cats and the spontaneous firing of a single neuron. Here we report that such activity encompasses a set of dynamically switching cortical states, many of which correspond closely to orientation maps. When such an orientation state emerged spontaneously, it spanned several hypercolumns and was often followed by a state corresponding to a proximal orientation. We suggest that dynamically switching cortical states could represent the brain's internal context, and therefore reflect or influence memory, perception and behaviour.
The spontaneous or background discharge patterns of in vivo single neuron is mostly considered as neuronal noise, which is assumed to be devoid of any correlation between successive inter-spike-intervals (ISI). Such random fluctuations are modeled only statistically by stochastic point process, lacking any temporal correlation. In this study, we have investigated the nature of spontaneous irregular fluctuations of single neurons from human hippocampus-amygdala complex by three different methods: (i) detrended fluctuation analysis (DFA), (ii) multiscale entropy (MSE), (iii) rate estimate convergence. Both the DFA and MSE analysis showed the presence of long-range power-law correlation over time in the ISI sequences. Moreover, we observed that the individual spike trains presented non-random structure on longer time-scales and showed slow convergence of rate estimates with increasing counting time. This power-law correlation and the slow convergence of statistical moments were eliminated by randomly shuffling the ISIs even though the distributions of ISIs were preserved. Thus the power-law relationship arose from long-term correlations among ISIs that were destroyed by shuffling the data. Further, we found that neurons which showed long-range correlations also showed statistically significant correlated firing as measured by correlation coefficient or mutual information function. The presence of long-range correlations indicates the history-effect or memory in the firing pattern by the associative formation of a neuronal assembly.
A method of EEG analysis is described which provides new insights into EEG pathology in cerebral ischaemia. The method is based on a variant of detrended fluctuation analysis (DFA), which reduces short (10 s) segments of spontaneous EEG time series to two dimensionless scaling exponents. The spatial variability of each exponent is expressed in terms of its statistical moments across EEG channels. Linear discriminant analysis combines the moments into concise indices, which distinguish normal and stroke groups remarkably well. On average over the scalp, stroke patients have larger fluctuations on the longest time scales. This is consistent with the notion of EEG slowing, but extends that notion to a wider range of time scales. The higher moments show that stroke patients have markedly reduced variability over the scalp. This contradicts the notion of a purely focal EEG scalp topography and argues instead for a highly distributed effect. In these indices, subacute patients appear further from normal than acute patients.
Neuroimaging has revealed robust large-scale patterns of high neuronal activity in the human brain in the classical eyes-closed wakeful rest condition, pointing to the presence of a baseline of sustained endogenous processing in the absence of stimulus-driven neuronal activity. This baseline state has been shown to differ in major depressive disorder. More recently, several studies have documented that despite having a complex temporal structure, baseline oscillatory activity is characterized by persistent autocorrelations for tens of seconds that are highly replicable within and across subjects. The functional significance of these long-range temporal correlations has remained unknown.
We recorded neuromagnetic activity in patients with a major depressive disorder and in healthy control subjects during eyes-closed wakeful rest and quantified the long-range temporal correlations in the amplitude fluctuations of different frequency bands. We found that temporal correlations in the theta-frequency band (3-7 Hz) were almost absent in the 5-100 s time range in the patients but prominent in the control subjects. The magnitude of temporal correlations over the left temporocentral region predicted the severity of depression in the patients.
These data indicate that long-range temporal correlations in theta oscillations are a salient characteristic of the healthy human brain and may have diagnostic potential in psychiatric disorders. We propose a link between the abnormal temporal structure of theta oscillations in the depressive patients and the systems-level impairments of limbic-cortical networks that have been identified in recent anatomical and functional studies of patients with major depressive disorder.
Sleep has been regarded as a testing situation for the autonomic nervous system, because its activity is modulated by sleep stages. Sleep-related breathing disorders also influence the autonomic nervous system and can cause heart rate changes known as cyclical variation. We investigated the effect of sleep stages and sleep apnea on autonomic activity by analyzing heart rate variability (HRV). Since spectral analysis is suited for the identification of cyclical variations and detrended fluctuation analysis can analyze the scaling behavior and detect long-range correlations, we compared the results of both complementary techniques in 14 healthy subjects, 33 patients with moderate, and 31 patients with severe sleep apnea. The spectral parameters VLF, LF, HF, and LF/HF confirmed increasing parasympathetic activity from wakefulness and REM over light sleep to deep sleep, which is reduced in patients with sleep apnea. Discriminance analysis was used on a person and sleep stage basis to determine the best method for the separation of sleep stages and sleep apnea severity. Using spectral parameters 69.7% of the apnea severity assignments and 54.6% of the sleep stage assignments were correct, while using scaling analysis these numbers increased to 74.4% and 85.0%, respectively. We conclude that changes in HRV are better quantified by scaling analysis than by spectral analysis.
The human electroencephalogram (EEG) that records the brain electrical activities shows a high degree of fluctuations both spatially on the scalp and temporally over various time scales. Since the human brain dynamics is that of a highly nonlinear system, we shall examine the nature of the fluctuations in the EEG time series in the framework of nonlinear analysis. By using detrended fluctuation analysis we find scaling behaviors that provide very useful and hitherto unrealized information about the characteristics of the brain function. What is a particle physicist doing with EEG? What I am going to tell you is something not out of reach of most of us gathered here, since we are all familiar with the study of correlations and fluctuations. The brain may be far more complicated than the physics of particle production, but the observables are not more complicated, only dierent. After a little fluctuation analysis, the EEG signals can be reduced to a set of numbers in just the same way that an event of multiparticle production is described by a set of numbers specifying the momenta of the detected particles. From that point onward the technique of treating those numbers in the extraction of useful information is almost the same. It is quite rewarding to come to the realization that an interdisciplinary area can be developed to bridge the gulf separating particle physics and neuroscience, and I am here to share that excitement with you. Human neuroscience at the global scale is at this stage mainly an induc- tive science based primarily on the phenomenology of noninvasive probing of the human brain. There is essentially no in-principle deduction except at the cellular and molecular levels. EEG is just one among several meth- ods of getting information about the brain activities. It is cheaper and more convenient than, for example, MEG (magnetoencephalogram) and MRI (magnetic resonance imaging). Recent development of the EEG de-
According to classical concepts of physiologic control, healthy systems are self-regulated to reduce variability and maintain physiologic constancy. Contrary to the predictions of homeostasis, however, the output of a wide variety of systems, such as the normal human heartbeat, fluctuates in a complex manner, even under resting conditions. Scaling techniques adapted from statistical physics reveal the presence of long-range, power-law correlations, as part of multifractal cascades operating over a wide range of time scales. These scaling properties suggest that the nonlinear regulatory systems are operating far from equilibrium, and that maintaining constancy is not the goal of physiologic control. In contrast, for subjects at high risk of sudden death (including those with heart failure), fractal organization, along with certain nonlinear interactions, breaks down. Application of fractal analysis may provide new approaches to assessing cardiac risk and forecasting sudden cardiac death, as well as to monitoring the aging process. Similar approaches show promise in assessing other regulatory systems, such as human gait control in health and disease. Elucidating the fractal and nonlinear mechanisms involved in physiologic control and complex signaling networks is emerging as a major challenge in the postgenomic era.
Vibrations of human skull, as produced by loud vocalisation, exert a massaging effect on the brain and facilitate elution of metabolic products from the brain into the cerebrospinal fluid (CSF). In addition, these vibrations, through their effect on arachnoid villi, speed up the flow of CSF from the subarachnoid space into the blood within the superior sagittal sinus and lacunae lateralis. In this way, the speed of renewal of CSF is increased, which again contributes to a faster cleaning process of the brain. The most important feature of human evolution is enlargement of the brain. This by itself would not be enough. The Neandertals had a brain 15% larger than we have, yet they did not survive in competition with modern humans. Their brains were more polluted, because their massive skulls did not vibrate and therefore the brains were not sufficiently cleaned. In the evolution of modern humans the thinning of cranial bones was important. In addition, the chin remained jutting out of the face as in no other hominids, in order to maintain the distance from the chin to the hyoid bone equal to the distance from the latter to the styloid process. This situation facilitates transmission of laryngeal vibrations onto the skull base via the mandible.
Non-linear EEG analysis provides a possibility for studying the dynamical changes in cortical networks related to mental activity. In this study the correlation dimension D2 was used to study local changes in complexity, and the mutual dimension Dm was used to assess changes in the dynamical coupling between different brain areas. EEGs were recorded in 25 healthy subjects under three conditions: (1) eyes closed, (2) eyes open, and (3) mental arithmetic with eyes closed (serial subtraction of 7s from 1000). In the eyes-closed condition, D2 was lower at parieto-occipital sites. D2 increased during the eye-open and arithmetic conditions. Contrary to the D2, the Dm showed no regional differences in the eyes closed condition. A clear increase in Dm was seen during eyes open and arithmetic. We conclude that both the correlation dimension and the mutual dimension are very sensitive to EEG changes during simple visual information processing and during mental arithmetic. However, these measures seem to be relatively non-specific, and correlate only weakly with performance on the arithmetic task.
Fractal dimensions has been proposed as a useful measure for the characterisation of electrophysiological time series. But one of the problems of this approach, is the difficulty to record time series long enough of determine the 'real' fractal dimension. Nevertheless it is possible to calculate fractal dimensions for very short data-segments. Using time series of different length it is possible to show, that there is a monotoneous relation between fractal dimension and the number of data-points. This relation could be further interpreted with the help of an extrapolation scheme. In addition this effect is also seen with surrogate data, generated from that signal. We conclude that it is feasible to use fractal dimension as a tool to characterise the complexity for short electroencephalographic (EEG) time series, but it is not possible to decide whether the brain is a chaotic system or not.
We evaluate the suitability of an enhanced detrended fluctuation analysis for studying fetal heart rate series involving imperfect quality of information. Our results indicate that to explore persistent long-range correlations, or fractality, the collection requirements of the data can be relaxed by allowing the possibility of using averaged fetal heart rate series. In addition, it also appears feasible to employ, without producing major alterations in the long-range scaling behaviour, fragmented fetal heart rate series involving up to 50% of random missing values, or up to 50 min of consecutive missing samples in recordings of approximately equal to 8 h length. These are crucial advantages to overcome the often variable quality of fetal data. Consequently, these findings may open the possibility of obtaining information concerning the development of neural processes from fetal heart rate series, despite their non-stationary and fragmented nature.
Quantification of the fractal scaling properties of human sleep EEG dynamics was sought and each normal sleep stage was compared with that of sleep apnea. The fractal scaling exponents that quantify power-law correlations were computed using detrended fluctuation analysis. Six healthy subjects, aged 30-35 years, participated and six recordings of the apnea were acquired from MIT/BIH polysomnography database. The data were 8-h baseline recordings (23:00-07:00 h). The EEG signals from the C4-A1 derivation were acquired with a resolution of 250 Hz. The sleep stages were visually scored for 30 s epochs, according to the criteria of Rechtschaffen and Kales. The mean scaling exponents increased from the awake stage to stages 1, 2 and 3-4, but decreased during rapid eye movement (REM) sleep. The scaling exponents of the apnea were lower than those of the healthy subject for all the stages. The scaling exponents could be attributed to the fractal nature of EEG, which would be more appropriate for describing the complexity of EEG due to its assumption of non-stationarity.
The consequences of survivors of natural disasters in relation to post-traumatic stress disorder are discussed in detail with brief mention about the treatment schedule.
Fractal analysis was applied to study the trends of EEG signals in the hypnotic condition. The subjects were 19 psychiatric outpatients. Hypnotizability was measured with the Hypnotic Induction Profile (HIP). Fifty-four sets of EEG data were analyzed by detrended fluctuation analysis (DFA), a well-established fractal analysis technique. The scaling exponents, which are the results of fractal analysis, are reduced toward white noise during the hypnotic condition, which differentiates the hypnotic condition from the waking condition. Further, the decrease in the scaling exponents during hypnosis was solely associated with the eye-roll sign within specific cortical areas (F3, C4, and O1/2) closely related to eye movements and attention. In conclusion, the present study has found that the application of the fractal analysis technique can demonstrate the electrophysiological correlations with hypnotic influence on cerebral activity.
Electroencephalogram (EEG) - the recorded representation of electrical activity of the brain contain useful information about the state of the brain. Recent studies indicate that nonlinear methods can extract valuable information from neuronal dynamics. We compare the dynamical properties of EEG signals of healthy subjects with epileptic subjects using nonlinear time series analysis techniques. Chaotic invariants like correlation dimension (D2) , largest Lyapunov exponent (lambda1), Hurst exponent (H) and Kolmogorov entropy (K) are used to characterize the signal. Our study showed clear differences in dynamical properties of brain electrical activity of the normal and epileptic subjects with a confidence level of more than 90%. Furthermore to support this claim fractal dimension (FD) analysis is performed. The results indicate reduction in value of FD for epileptic EEG indicating reduction in system complexity.
Based on detrended fluctuation analysis (DFA), we explore the characteristics of multichannel electroencephalogram (EEG), which is recorded from many subjects performing different mental tasks. The results show that mental EEG exhibits long-range power-law correlations by calculating its scaling exponents (alpha), which can reflect the kinds of mental tasks. The scaling exponent of letter-composing is different from that of multiplication especially at positions C3 and C4, and at positions O1 and O2 the scaling exponent of rotation is also different distinctively from that of multiplication. Detrended fluctuation analysis exhibits its robustness against noises in our works. We could benefit more from the results of this paper in designing mental tasks and selecting brain areas in brain-computer interface systems.
The aim of the present paper is to study the fluctuations of the sleep EEG over various time scales during a specific pathological condition: major depressive episode. Focus is made on scaling behaviour, which is the signature of the absence of characteristic time scale, and the presence of long-range correlations associated to physiological constancy preservation, variability reduction and mostly adaptability.
Whole night sleep electroencephalogram signals were recorded in 24 men: 10 untreated patients with a major depressive episode (41.70+/-8.11 years) and 14 healthy subjects (42.43+/-5.67 years). Scaling in these time series was investigated with detrended fluctuation analysis (time range: 0.16-2.00s). Scaling exponents (alpha) were determined in stage 2, slow wave sleep (stages 3 and 4) and during REM sleep. Forty-five epochs of 20s were chosen randomly in each of these stages.
The median values of alpha were lower in patients during stage 2 and SWS.
Major depressive episodes are characterized by a modification in the correlation structure of the sleep EEG time series. The finding which shows decreasing rate of the temporal correlations being different within the two groups in stage 2 and SWS provides an electrophysiologic argument that the underlying neuronal dynamics are modified during acute depression.
The observed modifications in scaling behaviour in acutely depressed patients could be an explanation of the sleep fragmentation and instability found during major depressive episode.
The depth of anesthesia estimation has been of great interest in recent decades. In this paper, we present a new methodology to quantify the levels of consciousness. Our algorithm takes advantage of the fractal and self-similarity properties of the EEG signal. We have tried to find the effect of anesthetic agents by using the detrended fluctuation analysis (DFA) as a self similarity estimator of a fractal process. The implementation results confirm that the DFA on the raw EEG data can clearly discriminate between aware to moderate anesthesia levels, but the moderate to deep anesthesia cannot be discriminated. We have extended the idea by considering that the self-similarity property of fractal signal has a better resolution on the wavelet domain. By applying the DFA on different scales of wavelet coefficients and quantifying the relative drift between the lines generated by DFA, the depth of anesthesia can be discriminated precisely.
The science of pranayama
- S Sivanand
S. Sivanand, " The science of pranayama, " Divine life society, India, 1997.
Does humming sound play healing role in Bhramari Pranayama
- R Prasad
- F Matsuno
EEG Changes After Bhramari Pranayama
- R K Prasad
- F Matsuno
- H Bakardjian
- F Vialatteand
- A Cichocki
Nonlinear unstable systems
- R C Baker
- B Charlie
Detecting mental EEG properties using detrended fluctuation analysis
- Z Liang
- Y Ning
- An
- H Li
- Feng