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Characterization of fibromyalgia using sleep EEG signals with nonlinear dynamical features

Paul JK, Iype T, R D, Hagiwara Y, Koh JW, Acharya UR

Comput Biol Med, 2019 08;111:103331.
PMID: 31284155 DOI: 10.1016/j.compbiomed.2019.103331

Fibromyalgia is an intense musculoskeletal pain causing sleep, fatigue, and mood problems. Sleep studies have suggested that 70%-80% of fibromyalgia patients complain of non-restorative sleep. The abnormalities in sleep have been implicated as both a cause and effect of the disease. In this paper, the electroencephalogram (EEG) signals of sleep stages 2 and 3 are used to classify the normal and fibromyalgia classes automatically. We have used various nonlinear parameters, namely sample entropy (SampEn), fractal dimension (FD), higher order spectra (HOS), largest Lyapunov exponent (LLE), Kolmogorov complexity (KC), Hurst exponent (HE), energy, and power in various frequency bands from the EEG signals. Then these features are subjected to Student's t-test to select the clinically significant features, and are classified using the support vector machine (SVM) classifier. Our proposed method can classify normal and fibromyalgia subjects using the stage 2 sleep EEG signals with an accuracy of 96.15%, sensitivity and specificity of 96.88% and 95.65%, respectively. Performance of the developed system can be improved further by adding more subjects in each class, and can be employed for clinical use.

Matched MeSH terms: Electroencephalography/classification*
Bayesian Algorithm Based Localization of EEG Recorded Electromagnetic Brain Activity

Jatoi MA, Kamel N, Musavi SHA, López JD

Curr Med Imaging Rev, 2019;15(2):184-193.
PMID: 31975664 DOI: 10.2174/1573405613666170629112918

BACKGROUND: Electrical signals are generated inside human brain due to any mental or physical task. This causes activation of several sources inside brain which are localized using various optimization algorithms.
METHODS: Such activity is recorded through various neuroimaging techniques like fMRI, EEG, MEG etc. EEG signals based localization is termed as EEG source localization. The source localization problem is defined by two complementary problems; the forward problem and the inverse problem. The forward problem involves the modeling how the electromagnetic sources cause measurement in sensor space, while the inverse problem refers to the estimation of the sources (causes) from observed data (consequences). Usually, this inverse problem is ill-posed. In other words, there are many solutions to the inverse problem that explains the same data. This ill-posed problem can be finessed by using prior information within a Bayesian framework. This research work discusses source reconstruction for EEG data using a Bayesian framework. In particular, MSP, LORETA and MNE are compared.
RESULTS: The results are compared in terms of variational free energy approximation to model evidence and in terms of variance accounted for in the sensor space. The results are taken for real time EEG data and synthetically generated EEG data at an SNR level of 10dB.
CONCLUSION: In brief, it was seen that MSP has the highest evidence and lowest localization error when compared to classical models. Furthermore, the plausibility and consistency of the source reconstruction speaks to the ability of MSP technique to localize active brain sources.

Matched MeSH terms: Electroencephalography/methods*
Towards multilevel mental stress assessment using SVM with ECOC: an EEG approach

Al-Shargie F, Tang TB, Badruddin N, Kiguchi M

Med Biol Eng Comput, 2018 Jan;56(1):125-136.
PMID: 29043535 DOI: 10.1007/s11517-017-1733-8

Mental stress has been identified as one of the major contributing factors that leads to various diseases such as heart attack, depression, and stroke. To avoid this, stress quantification is important for clinical intervention and disease prevention. This study aims to investigate the feasibility of exploiting electroencephalography (EEG) signals to discriminate between different stress levels. We propose a new assessment protocol whereby the stress level is represented by the complexity of mental arithmetic (MA) task for example, at three levels of difficulty, and the stressors are time pressure and negative feedback. Using 18-male subjects, the experimental results showed that there were significant differences in EEG response between the control and stress conditions at different levels of MA task with p values

Matched MeSH terms: Electroencephalography*
Fulltext The usefulness of early ultrasonography, electroencephalography and clinical parameters in predicting adverse outcomes in asphyxiated term infants

Ong LC, Kanaheswari Y, Chandran V, Rohana J, Yong SC, Boo NY

Singapore Med J, 2009 Jul;50(7):705-9.
PMID: 19644627

The early identification of asphyxiated infants at high risk of adverse outcomes and the early selection of those who might benefit from neuroprotective therapies are required. A prospective observational study was conducted to determine if there were any early clinical, neuroimaging or neurophysiological parameters that might predict the outcome in term newborns with asphyxia.

Matched MeSH terms: Electroencephalography/methods*
Fulltext Detection of Moderate Traumatic Brain Injury from Resting-State Eye-Closed Electroencephalography

Lai CQ, Ibrahim H, Abd Hamid AI, Abdullah MZ, Azman A, Abdullah JM

Comput Intell Neurosci, 2020;2020:8923906.
PMID: 32256555 DOI: 10.1155/2020/8923906

Traumatic brain injury (TBI) is one of the injuries that can bring serious consequences if medical attention has been delayed. Commonly, analysis of computed tomography (CT) or magnetic resonance imaging (MRI) is required to determine the severity of a moderate TBI patient. However, due to the rising number of TBI patients these days, employing the CT scan or MRI scan to every potential patient is not only expensive, but also time consuming. Therefore, in this paper, we investigate the possibility of using electroencephalography (EEG) with computational intelligence as an alternative approach to detect the severity of moderate TBI patients. EEG procedure is much cheaper than CT or MRI. Although EEG does not have high spatial resolutions as compared with CT and MRI, it has high temporal resolutions. The analysis and prediction of moderate TBI from EEG using conventional computational intelligence approaches are tedious as they normally involve complex preprocessing, feature extraction, or feature selection of the signal. Thus, we propose an approach that uses convolutional neural network (CNN) to automatically classify healthy subjects and moderate TBI patients. The input to this computational intelligence system is the resting-state eye-closed EEG, without undergoing preprocessing and feature selection. The EEG dataset used includes 15 healthy volunteers and 15 moderate TBI patients, which is acquired at the Hospital Universiti Sains Malaysia, Kelantan, Malaysia. The performance of the proposed method has been compared with four other existing methods. With the average classification accuracy of 72.46%, the proposed method outperforms the other four methods. This result indicates that the proposed method has the potential to be used as a preliminary screening for moderate TBI, for selection of the patients for further diagnosis and treatment planning.

Matched MeSH terms: Electroencephalography*
Fulltext Electroencephalographic changes associated with antinociceptive actions of lidocaine, ketamine, meloxicam, and morphine administration in minimally anaesthetized dogs

Kaka U, Hui Cheng C, Meng GY, Fakurazi S, Kaka A, Behan AA, et al.

Biomed Res Int, 2015;2015:305367.
PMID: 25695060 DOI: 10.1155/2015/305367

Effects of ketamine and lidocaine on electroencephalographic (EEG) changes were evaluated in minimally anaesthetized dogs, subjected to electric stimulus. Six dogs were subjected to six treatments in a crossover design with a washout period of one week. Dogs were subjected to intravenous boluses of lidocaine 2 mg/kg, ketamine 3 mg/kg, meloxicam 0.2 mg/kg, morphine 0.2 mg/kg and loading doses of lidocaine 2 mg/kg followed by continuous rate infusion (CRI) of 50 and 100 mcg/kg/min, and ketamine 3 mg/kg followed by CRI of 10 and 50 mcg/kg/min. Electroencephalogram was recorded during electrical stimulation prior to any drug treatment (before treatment) and during electrical stimulation following treatment with the drugs (after treatment) under anaesthesia. Anaesthesia was induced with propofol and maintained with halothane at a stable concentration between 0.85 and 0.95%. Pretreatment median frequency was evidently increased (P < 0.05) for all treatment groups. Lidocaine, ketamine, and morphine depressed the median frequency resulting from the posttreatment stimulation. The depression of median frequency suggested evident antinociceptive effects of these treatments in dogs. It is therefore concluded that lidocaine and ketamine can be used in the analgesic protocol for the postoperative pain management in dogs.

Matched MeSH terms: Electroencephalography/methods
Fulltext Correlation-based common spatial pattern (CCSP): A novel extension of CSP for classification of motor imagery signal

Darvish Ghanbar K, Yousefi Rezaii T, Farzamnia A, Saad I

PLoS One, 2021;16(3):e0248511.
PMID: 33788862 DOI: 10.1371/journal.pone.0248511

Common spatial pattern (CSP) is shown to be an effective pre-processing algorithm in order to discriminate different classes of motor-based EEG signals by obtaining suitable spatial filters. The performance of these filters can be improved by regularized CSP, in which available prior information is added in terms of regularization terms into the objective function of conventional CSP. Variety of prior information can be used in this way. In this paper, we used time correlation between different classes of EEG signal as the prior information, which is clarified similarity between different classes of signal for regularizing CSP. Furthermore, the proposed objective function can be easily extended to more than two-class problems. We used three different standard datasets to evaluate the performance of the proposed method. Correlation-based CSP (CCSP) outperformed original CSP as well as the existing regularized CSP, Principle Component Cnalysis (PCA) and Fisher Discriminate Analysis (FDA) in both two-class and multi-class scenarios. The simulation results showed that the proposed method outperformed conventional CSP by 6.9% in 2-class and 2.23% in multi-class problem in term of mean classification accuracy.

Matched MeSH terms: Electroencephalography/methods*
Acute effects of methadone on EEG power spectrum and event-related potentials among heroin dependents

Motlagh F, Ibrahim F, Rashid R, Shafiabady N, Seghatoleslam T, Habil H

Psychopharmacology (Berl), 2018 Nov;235(11):3273-3288.
PMID: 30310960 DOI: 10.1007/s00213-018-5035-0

Methadone as the most prevalent opioid substitution medication has been shown to influence the neurophysiological functions among heroin addicts. However, there is no firm conclusion on acute neuroelectrophysiological changes among methadone-treated subjects as well as the effectiveness of methadone in restoring brain electrical abnormalities among heroin addicts. This study aims to investigate the acute and short-term effects of methadone administration on the brain's electrophysiological properties before and after daily methadone intake over 10 weeks of treatment among heroin addicts. EEG spectral analysis and single-trial event-related potential (ERP) measurements were used to investigate possible alterations in the brain's electrical activities, as well as the cognitive attributes associated with MMN and P3. The results confirmed abnormal brain activities predominantly in the beta band and diminished information processing ability including lower amplitude and prolonged latency of cognitive responses among heroin addicts compared to healthy controls. In addition, the alteration of EEG activities in the frontal and central regions was found to be associated with the withdrawal symptoms of drug users. Certain brain regions were found to be influenced significantly by methadone intake; acute effects of methadone induction appeared to be associative to its dosage. The findings suggest that methadone administration affects cognitive performance and activates the cortical neuronal networks, resulting in cognitive responses enhancement which may be influential in reorganizing cognitive dysfunctions among heroin addicts. This study also supports the notion that the brain's oscillation powers and ERPs can be utilized as neurophysiological indices for assessing the addiction treatment traits.

Matched MeSH terms: Electroencephalography/drug effects*; Electroencephalography/methods
Fulltext Effect of Muslim prayer (Salat) on α electroencephalography and its relationship with autonomic nervous system activity

Doufesh H, Ibrahim F, Ismail NA, Wan Ahmad WA

J Altern Complement Med, 2014 Jul;20(7):558-62.
PMID: 24827587 DOI: 10.1089/acm.2013.0426

OBJECTIVES: This study investigated the effect of Muslim prayer (salat) on the α relative power (RPα) of electroencephalography (EEG) and autonomic nervous activity and the relationship between them by using spectral analysis of EEG and heart rate variability (HRV).
METHODS: Thirty healthy Muslim men participated in the study. Their electrocardiograms and EEGs were continuously recorded before, during, and after salat practice with a computer-based data acquisition system (MP150, BIOPAC Systems Inc., Camino Goleta, California). Power spectral analysis was conducted to extract the RPα and HRV components.
RESULTS: During salat, a significant increase (p

Matched MeSH terms: Electroencephalography
Fulltext Randomised prior feedback modulates neural signals of outcome monitoring

Mushtaq F, Wilkie RM, Mon-Williams MA, Schaefer A

Neuroimage, 2016 Jan 15;125:868-879.
PMID: 26497268 DOI: 10.1016/j.neuroimage.2015.10.046

Substantial evidence indicates that decision outcomes are typically evaluated relative to expectations learned from relatively long sequences of previous outcomes. This mechanism is thought to play a key role in general learning and adaptation processes but relatively little is known about the determinants of outcome evaluation when the capacity to learn from series of prior events is difficult or impossible. To investigate this issue, we examined how the feedback-related negativity (FRN) is modulated by information briefly presented before outcome evaluation. The FRN is a brain potential time-locked to the delivery of decision feedback and it is widely thought to be sensitive to prior expectations. We conducted a multi-trial gambling task in which outcomes at each trial were fully randomised to minimise the capacity to learn from long sequences of prior outcomes. Event-related potentials for outcomes (Win/Loss) in the current trial (Outcomet) were separated according to the type of outcomes that occurred in the preceding two trials (Outcomet-1 and Outcomet-2). We found that FRN voltage was more positive during the processing of win feedback when it was preceded by wins at Outcomet-1 compared to win feedback preceded by losses at Outcomet-1. However, no influence of preceding outcomes was found on FRN activity relative to the processing of loss feedback. We also found no effects of Outcomet-2 on FRN amplitude relative to current feedback. Additional analyses indicated that this effect was largest for trials in which participants selected a decision different to the gamble chosen in the previous trial. These findings are inconsistent with models that solely relate the FRN to prediction error computation. Instead, our results suggest that if stable predictions about future events are weak or non-existent, then outcome processing can be determined by affective systems. More specifically, our results indicate that the FRN is likely to reflect the activity of positive affective systems in these contexts. Importantly, our findings indicate that a multifactorial explanation of the nature of the FRN is necessary and such an account must incorporate affective and motivational factors in outcome processing.

Matched MeSH terms: Electroencephalography
[Observation of a photosensitive syndrome in Macaca nemestrina (author's transl)]

Balzamo E

Rev Electroencephalogr Neurophysiol Clin, 1981 Nov;11(2):289-93.
PMID: 7323374

Out of a group of 12 M. nemestrina (originating from Malaysia), 9 adults had shown clinical signs induced by ILS at 25 c/sec. Six of them (3 males, 3 females) were very photosensitive; however, only 2 presented eyelid and/or head jerks after the end of ILS (level 4), but never a generalized seizure. Tactile periorbital stimuli favoured myoclonus. In all but the two of level 4, the intensity of clinical signs varied from one day to the next. In all implanted adult macaques, spontaneous paroxysmal EEG activities were seen during slow sleep in mostly anterior areas, but also during waking and REM sleep in some of them; however, their occurrence depended upon the individual and were not in all cases related to their level of photosensitivity. During ILS, paroxysmal discharges (spikes and waves and/or polyspikes and waves), isolated or in bursts at 3-4/sec were bilateral and symmetrical. They started in fronto-rolandic regions, then became generalized. This observation constitutes a new fact since the discovery, in 1966, of the photomyoclonic syndrome of Papio papio, Macaca nemestrina being another species of subhuman primates with a marked predisposition to photosensitive epilepsy.

Matched MeSH terms: Electroencephalography
Fulltext Subacute sclerosing panencephalitis--a case report

Ting WC, Tan CT, Gong NC

Med J Malaysia, 1980 Jun;34(4):418-22.
PMID: 7219275

Two Malaysian boys of Chinese origin who satisfy the necessary criteria of subacute sclerosing panencephalitis are reported. A brief description of the symptomatology, epidemiology, laboratory finding, pathology, pathogenesis and treatment of the illness was also given.

Matched MeSH terms: Electroencephalography
Clinical and electroencephalographic features of complex partial seizures in infants

Pratap RC, Gururaj AK

Acta Neurol. Scand., 1989 Feb;79(2):123-7.
PMID: 2496576

The clinical and electroencephalographic (EEG) features were evaluated in a consecutive series of 50 infants with complex partial seizures. The age of onset of seizures showed a peak at age of 2 months. Significant development delay was seen in 60% of the infants. In 92% an underlying aetiological factor could be identified. Birth asphyxia was the commonest aetiological factor (30%). The seizure patterns were most frequently described as behavioural arrest, upward deviation of eyes, tonic posturing of the limbs, apnoea and cyanosis. Interictal EEG showed bilateral temporal lobe foci in 22%, unilateral foci in 78% and multiple foci in 46% of the cases. The response of the seizures to anticonvulsant drugs is discussed.

Matched MeSH terms: Electroencephalography
Fulltext Wearable IoT Smart-Log Patch: An Edge Computing-Based Bayesian Deep Learning Network System for Multi Access Physical Monitoring System

Manogaran G, Shakeel PM, Fouad H, Nam Y, Baskar S, Chilamkurti N, et al.

Sensors (Basel), 2019 Jul 09;19(13).
PMID: 31324070 DOI: 10.3390/s19133030

According to the survey on various health centres, smart log-based multi access physical monitoring system determines the health conditions of humans and their associated problems present in their lifestyle. At present, deficiency in significant nutrients leads to deterioration of organs, which creates various health problems, particularly for infants, children, and adults. Due to the importance of a multi access physical monitoring system, children and adolescents' physical activities should be continuously monitored for eliminating difficulties in their life using a smart environment system. Nowadays, in real-time necessity on multi access physical monitoring systems, information requirements and the effective diagnosis of health condition is the challenging task in practice. In this research, wearable smart-log patch with Internet of Things (IoT) sensors has been designed and developed with multimedia technology. Further, the data computation in that smart-log patch has been analysed using edge computing on Bayesian deep learning network (EC-BDLN), which helps to infer and identify various physical data collected from the humans in an accurate manner to monitor their physical activities. Then, the efficiency of this wearable IoT system with multimedia technology is evaluated using experimental results and discussed in terms of accuracy, efficiency, mean residual error, delay, and less energy consumption. This state-of-the-art smart-log patch is considered as one of evolutionary research in health checking of multi access physical monitoring systems with multimedia technology.

Matched MeSH terms: Electroencephalography
Fulltext Association Between Residual Inhibition and Neural Activity in Patients with Tinnitus: Protocol for a Controlled Within- and Between-Subject Comparison Study

Hu S, Anschuetz L, Huth ME, Sznitman R, Blaser D, Kompis M, et al.

JMIR Res Protoc, 2019 Jan 09;8(1):e12270.
PMID: 30626571 DOI: 10.2196/12270

BACKGROUND: Electroencephalography (EEG) studies indicate possible associations between tinnitus and changes in the neural activity. However, inconsistent results require further investigation to better understand such heterogeneity and inform the interpretation of previous findings.
OBJECTIVE: This study aims to investigate the feasibility of EEG measurements as an objective indicator for the identification of tinnitus-associated neural activities.
METHODS: To reduce heterogeneity, participants served as their own control using residual inhibition (RI) to modulate the tinnitus perception in a within-subject EEG study design with a tinnitus group. In addition, comparison with a nontinnitus control group allowed for a between-subjects comparison. We will apply RI stimulation to generate tinnitus and nontinnitus conditions in the same subject. Furthermore, high-frequency audiometry (up to 13 kHz) and tinnitometry will be performed.
RESULTS: This work was funded by the Infrastructure Grant of the University of Bern, Bern, Switzerland and Bernafon AG, Bern, Switzerland. Enrollment for the study described in this protocol commenced in February 2018. Data analysis is currently under way and the first results are expected to be submitted for publication in 2019.
CONCLUSIONS: This study design helps in comparing the neural activity between conditions in the same individual, thereby addressing a notable limitation of previous EEG tinnitus studies. In addition, the high-frequency assessment will help to analyze and classify tinnitus symptoms beyond the conventional clinical standard.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/12270.

Matched MeSH terms: Electroencephalography
Fulltext Preliminary Study of Memory Enhancement of USIM Students with Internet Addiction Disorder by the Effect of Quran Therapy in Terms of EEG Power Spectra

Saleh, N. N., Tamam, S.

Physics and Technology in Medicine, 2020;1(1):45-51.
MyJurnal

The advancement of internet nowadays have drives the university students place great reliance on the internet for almost all matter. However, if the internet is heavily used, it may have a detrimental effect which could contribute to an Internet Addiction Disorders (IAD) among university students. IAD could create problem in developing relationships like normal people and appear to have low quality of memory. Thus, this is definitely not good to students. Knowing that by listening to Quran verse has a potential to improve the memory, hence this study is carried out. The study utilized a brain-computer interface Emotiv EPOC+, multichannel electroencephalography (EEG) detection during dual N-back tasks (spatial and verbal) where subjects were given with load level from 2-back to 3-back. Five (5) out of two hundred (200) participants of USIM’s students (20-23 years old) were recruited to continue with the brain scan experiment after getting a high score which indicated their severe level of addiction in a screening addiction test, Young’s Internet Addiction Test. Subjects were administered with a pre‐ and post‐tests to analyze and evaluate the effects of implementing Quran listening in their dailies for two months. The behavioral assessment exhibited accuracy increment between pre- and post- tests by 13.4% from 9.4%. While the EEG power value showed there were significant differences (p=0.002) in brainwaves between the pre- and the post- tests for 2 back; as the dominant brainwave found at the frontal and prefrontal cortex for delta and theta bands. The research revealed that the theta power band presented as the most dominant brain wave associated with N-back task for the enhancement of working memory which influence by the listening to Surah in Al-Quran.

Matched MeSH terms: Electroencephalography
Fulltext Bimodal Data Fusion of Simultaneous Measurements of EEG and fNIRS during Lower Limb Movements

Al-Quraishi MS, Elamvazuthi I, Tang TB, Al-Qurishi M, Adil SH, Ebrahim M

Brain Sci, 2021 May 27;11(6).
PMID: 34071982 DOI: 10.3390/brainsci11060713

Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) have temporal and spatial characteristics that may complement each other and, therefore, pose an intriguing approach for brain-computer interaction (BCI). In this work, the relationship between the hemodynamic response and brain oscillation activity was investigated using the concurrent recording of fNIRS and EEG during ankle joint movements. Twenty subjects participated in this experiment. The EEG was recorded using 20 electrodes and hemodynamic responses were recorded using 32 optodes positioned over the motor cortex areas. The event-related desynchronization (ERD) feature was extracted from the EEG signal in the alpha band (8-11) Hz, and the concentration change of the oxy-hemoglobin (oxyHb) was evaluated from the hemodynamics response. During the motor execution of the ankle joint movements, a decrease in the alpha (8-11) Hz amplitude (desynchronization) was found to be correlated with an increase of the oxyHb (r = -0.64061, p < 0.00001) observed on the Cz electrode and the average of the fNIRS channels (ch28, ch25, ch32, ch35) close to the foot area representation. Then, the correlated channels in both modalities were used for ankle joint movement classification. The result demonstrates that the integrated modality based on the correlated channels provides a substantial enhancement in ankle joint classification accuracy of 93.01 ± 5.60% (p < 0.01) compared with single modality. These results highlight the potential of the bimodal fNIR-EEG approach for the development of future BCI for lower limb rehabilitation.

Matched MeSH terms: Electroencephalography
Fulltext The metastable brain associated with autistic-like traits of typically developing individuals

Sase T, Kitajo K

PLoS Comput Biol, 2021 04;17(4):e1008929.
PMID: 33861737 DOI: 10.1371/journal.pcbi.1008929

Metastability in the brain is thought to be a mechanism involved in the dynamic organization of cognitive and behavioral functions across multiple spatiotemporal scales. However, it is not clear how such organization is realized in underlying neural oscillations in a high-dimensional state space. It was shown that macroscopic oscillations often form phase-phase coupling (PPC) and phase-amplitude coupling (PAC), which result in synchronization and amplitude modulation, respectively, even without external stimuli. These oscillations can also make spontaneous transitions across synchronous states at rest. Using resting-state electroencephalographic signals and the autism-spectrum quotient scores acquired from healthy humans, we show experimental evidence that the PAC combined with PPC allows amplitude modulation to be transient, and that the metastable dynamics with this transient modulation is associated with autistic-like traits. In individuals with a longer attention span, such dynamics tended to show fewer transitions between states by forming delta-alpha PAC. We identified these states as two-dimensional metastable states that could share consistent patterns across individuals. Our findings suggest that the human brain dynamically organizes inter-individual differences in a hierarchy of macroscopic oscillations with multiple timescales by utilizing metastability.

Matched MeSH terms: Electroencephalography
Effects of Muslims praying (Salat) on EEG gamma activity

Doufesh H, Ibrahim F, Safari M

Complement Ther Clin Pract, 2016 Aug;24:6-10.
PMID: 27502795 DOI: 10.1016/j.ctcp.2016.04.004

This study investigates the difference of mean gamma EEG power between actual and mimic Salat practices in twenty healthy Muslim subjects. In the actual Salat practice, the participants were asked to recite and performing the physical steps in all four stages of Salat; whereas in the mimic Salat practice, they were instructed to perform only the physical steps without recitation. The gamma power during actual Salat was statistically higher than during mimic Salat in the frontal and parietal regions in all stages. In the actual Salat practice, the left hemisphere exhibited significantly higher mean gamma power in all cerebral regions and all stages, except the central-parietal region in the sitting position, and the frontal area in the bowing position. Increased gamma power during Salat, possibly related to an increase in cognitive and attentional processing, supports the concept of Salat as a focus attention meditation.

Matched MeSH terms: Electroencephalography
Fulltext An Englishman with Progressive Deterioration in Cognitive Function

Hamidon, B.B., Sapiah, S.

International Medical Journal Malaysia, 2006;5(1):1-6.
MyJurnal

A 72-year old Englishman was admitted with rapid deterioration in cognitive function, gait disturbance, and cerebellar signs and lapsed into a coma within one week of admission to the hospital. He had long-standing hypertension and hypercholesterolaemia, for which he was on regular medication. He had suffered recurrent episodes of stroke between September 1997 and May 2001. Three months prior to presentation, he became forgetful and generally mentally slow, affecting his daily activities. He was also noted to have fluctuations in his conscious level, associated with myoclonic jerks of the limbs. The brain MRI revealed hyperintense lesions on T2- weighted images in the periventricular region, left corona radiata, centrum semiovale, pons, midbrain and right thalamus. The electroencephalograph revealed periodic sharp wave complexes, strongly suggestive of Creutzfeldt-Jakob disease. However, we were not able to get a tissue diagnosis or send the cerebrospinal fluid for protein 14-3-3.

Matched MeSH terms: Electroencephalography

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