Using entropy of snoring, respiratory effort and electrocardiography signals during sleep for OSA detection and severity classification.

STUDY OBJECTIVES: Obstructive sleep apnea (OSA) is a very prevalent disease and its diagnosis is based on polysomnography (PSG). We investigated whether snoring-sound-, very low frequency electrocardiogram (ECG-VLF)- and thoraco-abdominal effort- PSG signal entropy values could be used as surrogate markers for detection of OSA and OSA severity classification. METHODS: The raw data of the snoring-, ECG- and abdominal and thoracic excursion signal recordings of two consecutive full-night PSGs of 86 consecutive patients (22 female, 53.74 ± 12.4 years) were analyzed retrospectively. Four epochs (30 s each, manually scored according to the American Academy of Sleep Medicine standard) of each sleep stage (N1, N2, N3, REM, awake) were used as the ground truth. Sampling entropy (SampEn) of all the above signals was calculated and group comparisons between the OSA severity groups were performed. In total, (86x4x5 = )1720 epochs/group/night were included in the training set as an input for a support vector machine (SVM) algorithm to classify the OSA severity classes. Analyses were performed for first- and second-night PSG recordings separately. RESULTS: Twenty-seven patients had mild (RDI = ≥ 5/h but <15/h), 21 patients moderate (RDI ≥15/h but <30/h) and 23 patients severe OSA (RDI ≥30/h). Fifteen patients had an RDI <5/h and were therefore considered non-OSA. Using SE on the above three PSG signal data and using a SVM pipeline, it was possible to distinguish between the four OSA severity classes. The best metric was snoring signal-SE. The area-under-the-curve (AUC) calculations showed reproducible significant results for both nights of PSG. The second night data were even more significant, with non-OSA (R) vs. light OSA (L) 0.61, R vs. moderate (M) 0.68, R vs. heavy OSA (H) 0.84, L vs. M 0.63, M vs. H 0.65 and L vs. H 0.82. The results were not confounded by age or gender. CONCLUSIONS: SampEn of either snoring-, very low ECG-frequencies- or thoraco-abdominal effort signals alone may be used as a surrogate marker to diagnose OSA and even predict OSA severity. More specifically, in this exploratory study snoring signal SampEn showed the greatest predictive accuracy for OSA among the three signals. Second night data showed even more accurate results for all three parameters than first-night recordings. Therefore, technologies using only parts of the PSG signal, e.g. sound-recording devices, may be used for OSA screening and OSA severity group classification.

Local field potential activity dynamics in response to deep brain stimulation of the subthalamic nucleus in Parkinson's disease.

Local field potentials (LFPs) may afford insight into the mechanisms of action of deep brain stimulation (DBS) and potential feedback signals for adaptive DBS. In Parkinson's disease (PD) DBS of the subthalamic nucleus (STN) suppresses spontaneous activity in the beta band and drives evoked resonant neural activity (ERNA). Here, we investigate how STN LFP activities change over time following the onset and offset of DBS. To this end we recorded LFPs from the STN in 14 PD patients during long (mean: 181.2 s) and short (14.2 s) blocks of continuous stimulation at 130 Hz. LFP activities were evaluated in the temporal and spectral domains. During long stimulation blocks, the frequency and amplitude of the ERNA decreased before reaching a steady state after ~70 s. Maximal ERNA amplitudes diminished over repeated stimulation blocks. Upon DBS cessation, the ERNA was revealed as an under-damped oscillation, and was more marked and lasted longer after short duration stimulation blocks. In contrast, activity in the beta band suppressed within 0.5 s of continuous DBS onset and drifted less over time. Spontaneous activity was also suppressed in the low gamma band, suggesting that the effects of high frequency stimulation on spontaneous oscillations may not be selective for pathological beta activity. High frequency oscillations were present in only six STN recordings before stimulation onset and their frequency was depressed by stimulation. The different dynamics of the ERNA and beta activity with stimulation imply different DBS mechanisms and may impact how these activities may be used in adaptive feedback.

Discrimination of physiological tremor from pathological tremor using accelerometer and surface EMG signals.

BACKGROUND AND OBJECTIVE: Although careful clinical examination and medical history are the most important steps towards a diagnostic separation between different tremors, the electro-physiological analysis of the tremor using accelerometry and electromyography (EMG) of the affected limbs are promising tools. METHODS: A soft-decision wavelet-based decomposition technique is applied with 8 decomposition stages to estimate the power spectral density of accelerometer and surface EMG signals (sEMG) sampled at 800 Hz. A discrimination factor between physiological tremor (PH) and pathological tremor, namely, essential tremor (ET) and the tremor caused by Parkinson's disease (PD), is obtained by summing the power entropy in band 6 (B6: 7.8125-9.375 Hz) and band 11 (B11: 15.625-17.1875 Hz). RESULTS: A discrimination accuracy of 93.87% is obtained between the PH group and the ET & PD group using a voting between three results obtained from the accelerometer signal and two sEMG signals. CONCLUSION: Biomedical signal processing techniques based on high resolution wavelet spectral analysis of accelerometer and sEMG signals are implemented to efficiently perform classification between physiological tremor and pathological tremor.

[Pathophysiology of tremor]. / Pathophysiologie des Tremors.

Tremor is clinically defined as a rhythmic, oscillating movement of parts of the body, which functionally leads to impairment of the coordination and execution of targeted movements. It can be a symptom of a primary disease, such as resting tremor in Parkinson's disease or occur as an independent disease, such as essential or orthostatic tremor. For the development of tremor, cerebral components as well as mechanisms at the spinal and muscular level play an important role. This review presents the results of new imaging and electrophysiological studies that have led to important advances in our understanding of the pathophysiology of tremor. We discuss pathophysiological models for the development of resting tremor in Parkinson's disease, essential and orthostatic tremor. We describe recent developments starting from the classical generator model, with an onset of pathological oscillations in distinct cerebral regions, to a network perspective in which tremor arises and spreads through existing anatomical or newly emerged pathological brain networks. In particular translational approaches are presented and discussed. These could serve in the future as a basis for the development of new therapeutic strategies.

Establishing and validating a new source analysis method using phase.

Electroencephalogram (EEG) measures the brain oscillatory activity non-invasively. The localization of deep brain generators of the electric fields is essential for understanding neuronal function in healthy humans and for damasking specific regions that cause abnormal activity in patients with neurological disorders. The aim of this study was to test whether the phase estimation from scalp data can be reliably used to identify the number of dipoles in source analyses. The steps performed included: i) modeling different phasic oscillatory signals using auto-regressive processes at a particular frequency, ii) simulation of two different noises, namely white and colored noise, having different signal-to-noise ratios, iii) simulation of dipoles at different areas in the brain and iv) estimation of the number of dipoles by calculating the phase differences of the simulated signals. Moreover we applied this method of source analysis on real data from temporal lobe epilepsy (TLE) patients. The analytical framework was successful in identifying the sources and their orientations in the simulated data and identified the epileptogenic area in the studied patients which was confirmed by pathological studies after TLE surgery.

Effect of high-frequency subthalamic neurostimulation on gait and freezing of gait in Parkinson's disease: a systematic review and meta-analysis.

The aim of this meta-analysis was to summarize the short- and long-term effects of bilateral deep brain stimulation of the subthalamic nucleus (STN-DBS) on gait and freezing of gait (FOG) in Parkinson's disease and to detect predictors of post-stimulation outcome. A comprehensive review of the literature was conducted up to October 2015 using Medline Ovid databases for studies analyzing the effect of bilateral STN-DBS on FOG and/or gait. Sixteen studies with available data for the gait item (no. 29) of the Unified Parkinson's Disease Rating Scale (UPDRS) and six studies with the FOG item (no. 14) were included. Data were summarized for the following follow-up periods: 6-15, 24-48 and >48 months. For the medication (Med)-Off/stimulation(Stim)-On condition compared with baseline Med-Off, STN-DBS significantly improved gait on average from 2.43 to 0.96, 2.53 to 1.31 and 2.56 to 1.40 points at 6-15, 24-48 and >48 months, respectively (P < 0.05). Pre-operative levodopa responsiveness of UPDRS-III and Med-Off severity of gait were the predictors of this beneficial effect. STN-DBS significantly improved FOG for the Med-Off/Stim-On condition compared with baseline on average from 2.26 to 0.82, 2.43 to 1.13 and 2.48 to 1.38 points at 6-15, 24-48 and >48 months, respectively (P < 0.05). There was no significant effect in the Med-On/Stim-On condition. This meta-analysis showed a robust improvement of gait and FOG by STN-DBS for more than 4 years in the Med-Off/Stim-On condition. No beneficial effect was found for the On state of medication. Pre-operative levodopa responsiveness of global motor performance (UPDRS-III) is the strongest predictor of the effect of deep brain stimulation on gait.

Comparison of imaging modalities and source-localization algorithms in locating the induced activity during deep brain stimulation of the STN.

One of the most commonly used therapy to treat patients with Parkinson's disease (PD) is deep brain stimulation (DBS) of the subthalamic nucleus (STN). Identifying the most optimal target area for the placement of the DBS electrodes have become one of the intensive research area. In this study, the first aim is to investigate the capabilities of different source-analysis techniques in detecting deep sources located at the sub-cortical level and validating it using the a-priori information about the location of the source, that is, the STN. Secondly, we aim at an investigation of whether EEG or MEG is best suited in mapping the DBS-induced brain activity. To do this, simultaneous EEG and MEG measurement were used to record the DBS-induced electromagnetic potentials and fields. The boundary-element method (BEM) have been used to solve the forward problem. The position of the DBS electrodes was then estimated using the dipole (moving, rotating, and fixed MUSIC), and current-density-reconstruction (CDR) (minimum-norm and sLORETA) approaches. The source-localization results from the dipole approaches demonstrated that the fixed MUSIC algorithm best localizes deep focal sources, whereas the moving dipole detects not only the region of interest but also neighboring regions that are affected by stimulating the STN. The results from the CDR approaches validated the capability of sLORETA in detecting the STN compared to minimum-norm. Moreover, the source-localization results using the EEG modality outperformed that of the MEG by locating the DBS-induced activity in the STN.

Detection of steering direction using EEG recordings based on sample entropy and time-frequency analysis.

Monitoring driver's intentions beforehand is an ambitious aim, which will bring a huge impact on the society by preventing traffic accidents. Hence, in this preliminary study we recorded high resolution electroencephalography (EEG) from 5 subjects while driving a car under real conditions along with an accelerometer which detects the onset of steering. Two sensor-level analyses, sample entropy and time-frequency analysis, have been implemented to observe the dynamics before the onset of steering. Thus, in order to classify the steering direction we applied a machine learning algorithm consisting of: dimensionality reduction and classification using principal-component-analysis (PCA) and support-vector-machine (SVM), respectively. The results showed an increase of the sample entropy and the estimated power values in the theta and alpha frequency bands, 100 ms before the onset of steering. The detection of steering direction depicted that sample entropy gives a higher classification accuracy (73.5% ±6.8) as compared to that of using the estimated power for theta and alpha frequency bands (62.6% ±5.6).

Functional connectivity analysis using whole brain and regional network metrics in MS patients.

In the present study we investigated brain network connectivity differences between patients with relapsing-remitting multiple sclerosis (RRMS) and healthy controls (HC) as derived from functional resonance magnetic imaging (fMRI) using graph theory. Resting state fMRI data of 18 RRMS patients (12 female, mean age ± SD: 42 ± 12.06 years) and 25 HC (8 female, 29.2 ± 5.38 years) were analyzed. In order to obtain information of differences in entire brain network, we focused on both, local and global network connectivity parameters. And the regional connectivity differences were assessed using regional network parameters. RRMS patients presented a significant increase of modularity in comparison to HC, pointing towards a network structure with densely interconnected nodes within one module, while the number of connections with other modules outside decreases. This higher decomposable network favours cost-efficient local information processing and promotes long-range disconnection. In addition, at the regional anatomical level, the network parameters clustering coefficient and local efficiency were increased in the insula, the superior parietal gyrus and the temporal pole. Our study indicates that modularity as derived from fMRI can be seen as a characteristic connectivity feature that is increased in MS patients compared to HC. Furthermore, specific anatomical regions linked to perception, motor function and cognition were mainly involved in the enhanced local information processing.

Network effects and pathways in Deep brain stimulation in Parkinson's disease.

Deep brain stimulation of subthalamic nucleus (STN-DBS) became a standard therapeutic option in Parkinson's disease (PD), even though the underlying modulated network of STN-DBS is still poorly described. Probabilistic tractography and connectivity analysis as derived from diffusion tensor imaging (DTI) were performed together with modelling of implanted electrode positions and linked postoperative clinical outcome. Fifteen patients with idiopathic PD without dementia were selected for DBS treatment. After pre-processing, probabilistic tractography was run from cortical and subcortical seeds of the hypothesized network to targets represented by the positions of the active DBS contacts. The performed analysis showed that the projections of the stimulation site to supplementary motor area (SMA) and primary motor cortex (M1) are mainly involved in the network effects of STN-DBS. An involvement of the "hyperdirected pathway" and a clear delimitation of the cortico-spinal tract were demonstrated. This study shows the effects of STN-DBS in PD distinctly rely on the network connections of the stimulated region to M1 and SMA, motor and premotor regions.

Testing the effects of pre-processing on voxel based morphometry analysis.

Voxel based morphometry (VBM) is an automated analysis technique which allows voxel-wise comparison of mainly grey-matter volumes between two magnetic resonance images (MRI). Two main analysis processes in VBM are possible. One is cross-sectional data analysis, where one group is compared with another to depict see the regions in the brain, which show changes in their grey-matter volume. Second is longitudinal data analysis, where MRIs, taken at different time points, are compared to see the regions in the brain that show changes in their grey matter volume for one time point with respect to another time point. Both types of analyses require pre-processing steps before performing the statistical analysis. In this study, we examined grey matter differences for patients with blepharospasmus (BFS) before and after treatment, at two different time points. The main evidence base therapy for this condition is the "botulinum toxin" injection in the respective muscles. The main aim of this study was to look at the effects of different pre-processing steps, namely, normalization and smoothing on the results of the longitudinal data analysis. A second aim was to analyze structural grey-matter differences before and after the treatment. Our results showed that the DARTEL normalization and the lower width for smoothing as preprocessing steps delivered pathophysiological plausible results. The longitudinal analysis revealed significant temporal differences after the injection of the botulinum toxin injection mainly in patients with BFS.

Testing different ICA algorithms and connectivity analyses on MS patients.

Multiple sclerosis (MS) is a progressive neurological disorder that affects the central nervous system. Functional magnetic resonance imaging (fMRI) has been employed to track the course and disease progression in patients with MS. The two main aims of this study were to apply in a data-driven approach the independent component analysis (ICA) in the spatial domain to depict the active sources and to look at the effective connectivity between the identified spatial sources. Several ICA algorithms have been proposed for fMRI data analysis. In this study, we aimed to test two well characterized algorithms, namely, the fast ICA and the complex infomax algorithms, followed by two effective connectivity algorithms, namely, Granger causality (GC) and generalized partial directed coherence (GPDC), to illustrate the connections between the spatial sources in patients with MS. The results obtained from the ICA analyses showed the involvement of the default mode network sources. The connectivity analyses depicted significant changes between the two applied algorithms. The significance of this study was to demonstrate the robustness of the analyzed algorithms in patients with MS and to validate them before applying them on larger datasets of patients with MS.

Differentiating tremor patients using spiral analyses.

Essential tremor follows an autosomal dominant type of inheritance in the majority of patients, yet its genetic basis has not been identified. The age of onset in this tremor is bimodal, one in young age and another when they are old. The old onset is referred to as senile tremor in this study. The precise pathology is still not completely understood for both these tremors. We wanted to develop an easy diagnostic tool to differentiate these two tremors clinically. In this study, the spirals were asked to be drawn by 30 patients, 15 from each group. The spirals were recorded digitally from each hand, with and without the spiral template, using a Wacom intuos version 4 tablets. The aim of the study was to look at the easy diagnostic measures from these spirals to distinguish the two cohorts of patients. The first measure was to use the well-known clinical scores like the number of complete circles without the template, width, height, axis, and degree of severity. The second measure was to estimate the peak frequency and the peak amplitude for the position, velocity, and acceleration data, in the frequency domain. The well-known clinical scores, most of them, did not show any significant difference between the two patient cohorts except the degree of severity which showed significant difference. The peak frequency and the peak amplitude in most of the data were not significantly different between the two cohorts of patients, only the peak amplitude from the acceleration data showed significant difference. Thus, we could use these two parameters to differentiate between the two tremors patient groups, which would be an easy clinical diagnostic tool without the need for any complicated analyses.

Comparison of EEG and MEG in source localization of induced human gamma-band oscillations during visual stimulus.

High frequency gamma oscillations are indications of information processing in cortical neuronal networks. Recently, non-invasive detection of these oscillations have become one of the main research areas in magnetoencephalography (MEG) and electroencephalography (EEG) studies. The aim of this study, which is a continuation of our previous MEG study, is to compare the capability of the two modalities (EEG and MEG) in localizing the source of the induced gamma activity due to a visual stimulus, using a spatial filtering technique known as dynamic imaging of coherent sources (DICS). To do this, the brain activity was recorded using simultaneous MEG and EEG measurement and the data were analyzed with respect to time, frequency, and location of the strongest response. The spherical head modeling technique, such as, the three-shell concentric spheres and an overlapping sphere (local sphere) have been used as a forward model to calculate the external electromagnetic potentials and fields recorded by the EEG and MEG, respectively. Our results from the time-frequency analysis, at the sensor level, revealed that the parieto-occipital electrodes and sensors from both modalities showed a clear and sustained gamma-band activity throughout the post-stimulus duration and that both modalities showed similar strongest gamma-band peaks. It was difficult to interpret the spatial pattern of the gamma-band oscillatory response on the scalp, at the sensor level, for both modalities. However, the source analysis result revealed that MEG3 sensor type, which measure the derivative along the longitude, showed the source more focally and close to the visual cortex (cuneus) as compared to that of the EEG.

Voxel seed coherent source analysis on transient global amnesia patients.

Transient global amnesia (TGA) is a rare neurological disorder with a sudden, temporary episode of memory loss which usually occurs in old age. The episodic loss of memory becomes normal after a stipulated time of approximately 24 hours. The precise pathology is not yet completely understood. Moreover, there is no proper neuroimaging method to assess this condition. In this study, the EEG was measured at two time points one with the occurrence of the episode (acute) and the second time point after the patient returns to the normal memory condition (follow-up). The aim of the study was to look at the pathological network involved during the acute phase and the follow up phase in these patients for the five frequency bands, namely, delta, theta, alpha, beta, and gamma. The method used for the source analyses was a beamforming approach called dynamic imaging of coherent sources in the frequency domain. The seed voxel was the lesion area taken from the anatomical MRI of each patient. The cortical and subcortical network comprised of the caudate and cerebellum in case of the delta band frequency. Two temporal sources in case of the theta band. Temporal, medial frontal, parietal, putamen, and thalamus sources were found in case of the alpha band. Prefrontal, parietal, and thalamus sources were found in case of the beta band. Temporal and thalamus in case of the gamma band frequency. All these sources were involved in the acute phase. Moreover, in the follow-up phase the motor area, in all frequency bands except gamma band, was additionally active followed by parietal and occipital regions in alpha and gamma frequencies. The differences involved in the network of sources between the two phases gives us better understanding of this neurological disorder.

Jejunal diverticulosis - rare cause of gastrointestinal bleed.

Jejunal diverticulosis was first described by Somerling in 1794 and by Sir Astley Cooper in 1807. Jejunal diverticula are rare. Hemorrhage from jejunal diverticula usually presents as gastrointestinal bleeding. Here, we present a case of severe gastrointestinal bleeding presenting as malena due to jejunal diverticulosis.

Effect of the Raga Ananda Bhairavi in Post Operative Pain Relief Management.

Music is considered as an universal language and has influences the human existence at various levels.In recent years music therapy has evolved as a challenge of research with a clinical approach involving science and art. Music therapy has been used for various therapeutic reasons like Alzheimer's disease,Hypertension and mental disorders to name a few. We conducted a study to establish the effect of the classical ragam Anandhabhairavi on post operative pain relief. A randomized controlled study involving 60 patients who were to undergo surgery was conducted at PSG Institute of Medical Sciences and Research,Coimbatore.30 patients selected at random and were exposed to the ragam Anandhabhairavi which was played in their room pre operatively (from the day they got admitted for surgery) and 3 days post operatively. The control group did not listen to the music during their stay in the hospital. An observation chart was attached in which the requirement of analgesics by the patient was recorded. On completion of the study and on analysis,the ragam Anandhabhairavi had a significant effect in post operative pain management which was evidenced by the reduction in analgesic requirement by 50 % in those who listened to the ragam.A significant p value of <0.001 was obtained.

Impact of head modeling and sensor types in localizing human gamma-band oscillations.

An effective mechanism in neuronal communication is oscillatory neuronal synchronization. The neuronal gamma-band (30-100 Hz) synchronization is associated with attention which is induced by a certain visual stimuli. Numerous studies have shown that the gamma-band activity is observed in the visual cortex. However, impact of different head modeling techniques and sensor types to localize gamma-band activity have not yet been reported. To do this, the brain activity was recorded using 306 magnetoencephalography (MEG) sensors, consisting of 102 magnetometers and 102 pairs of planar gradiometers (one measuring the derivative of the magnetic field along the latitude and the other along the longitude), and the data were analyzed with respect to time, frequency, and location of the strongest response. The spherical head models with a single-shell and overlapping spheres (local sphere) have been used as a forward model for calculating the external magnetic fields generated from the gamma-band activity. For each sensor type, the subject-specific frequency range of the gamma-band activity was obtained from the spectral analysis. The identified frequency range of interest with the highest gamma-band activity is then localized using a spatial-filtering technique known as dynamic imaging of coherent sources (DICS). The source analysis for all the subjects revealed that the gradiometer sensors which measure the derivative along the longitude, showed sources close to the visual cortex (cuneus) as compared to the other gradiometer sensors which measure the derivative along the latitude. However, using the magnetometer sensors, it was not possible to localize the sources in the region of interest. When comparing the two head models, the local-sphere model helps in localizing the source more focally as compared to the single-shell head model.

Validating the effect of muscle artifact suppression in localizing focal epilepsy.

Source localization of an epileptic seizure is becoming an important diagnostic tool in pre-surgical evaluation of epileptic patients. However, for localizing the epileptogenic zone precisely, the epileptic activity needs to be isolated from other activities that are not related to the epileptic source. In this study, we aim at an investigation of the effect of muscle artifact suppression by using a low-pass filter (LPF), independent component analysis (ICA), and a combination of ICA-LPF prior to source localization in focal epilepsy. These techniques were applied on the EEG data obtained from a left-temporal lobe epileptic patient by artificially contaminating the isolated spike interval, present in the four left-temporal electrodes, with a muscle artifact. The results show that the muscle artifact was fully suppressed. Applying the dipole and current-density reconstruction (CDR) source-analysis algorithms on the filtered data, we were able to identify the location of the epileptogenic zone similar to that of the original undistorted data.