Electroencephalogram (EEG) signals derived from polysomnography recordings play an important role in assessing the physiological and behavioral changes during onset of sleep. This paper suggests a spike rhythmicity based feature for discriminating the wake and sleep state. The polysomnography recordings are segmented into 1 second EEG patterns to ensure stationarity of the signal and four windowing scheme overlaps (0%, 50%, 60% and 75%) of EEG pattern are introduced to study the influence of the pre-processing procedure. The application of spike rhythmicity feature helps to estimate the number of spikes from the given pattern with a threshold of 25%.Then non parametric statistical analysis using Wilcoxon signed rank test is introduced to evaluate the impact of statistical measures such as mean, standard deviation, p-value and box-plot analysis under various conditions .The statistical test shows significant difference between wake and sleep with p<0.005 for the applied feature, thus demonstrating the efficiency of simple thresholding in distinguishing sleep and wake stage .
Electroencephalogram (EEG) based sleep stage analysis considered to be the gold standard method for assessment of sleep architecture. Of importance, transition between the first two stages, wake-sleep stage 1 found to be reliable quantitative tool for drowsiness and fatigue detection. The selection of appropriate feature pattern for EEGs is a quite challenging task due to its non-linear and non-stationary nature of the EEG signals. This research work attempts to provide a comparative study of time influence of time domain feature, relative spike amplitude (RSA) with entropy feature, fuzzy entropy(FE) for recognizing the transition between wake and sleep stage 1. EEGs extracted from offline polysomnography database is used and the extracted RSA and FE wake-sleep stage 1 derived EEG features are further classified using a feedback recurrent Elman neural network (REN) classifier. EEGs are segmented into 1s pattern. Simulation of the REN classifier revealed that the FE feature with REN yields a CA of 99.6% compared to that of with RSA feature.
Electroencephalogram (EEG) signals resulting from recordings of polysomnography play a significant role in determining the changes in physiology and behavior during sleep. This study aims at demarcating the sleep patterns of yogic and non-yogic subjects. Frequency domain features based on power spectral density methods were explored in this study. The EEG recordings were segmented into 1s and 0.5s. EEG patterns with four windowing scheme overlaps (0%, 50%, 60% and 75%) to ensure stationarity of the signal in order to investigate the effect of the pre-processing stage. In order to recognize the yoga and non-yoga group through N3 sleep stage, non-linear KNN classifier was introduced and performance was evaluated in terms of sensitivity and specificity. The experimental results show that modified covariance PSD estimate is the best method in classifying the sleep stage N3 of yogic and non-yogic subjects with 95% confidence interval, sensitivity, specificity and accuracy of 97.3%, 98% and 97%, respectively.
Detection of Sleep onset is one of complex processes in the area of sleep medicine. The transition from wake state to sleep is termed as sleep onset and is identified using distinct markers like behavioural features, physiological features and changes in EEG. Extraction of appropriate features from EEG recordings helps in automated recognition and classification of wake-sleep transition. This research study proposes the introduction of Hurst exponent (HE) to indicate the transition between wake and sleep derived from EEG recordings. Being the non-linear chaotic parameter, Hurst exponent quantifies correlation among the time series data and this property has been exploited for sleep EEGs. Two typical channels O1 and O2 were used for the study and Hurst exponents were estimated for the EEG segments followed by classification using two linear classifiers, LDA and KNN. The statistical analysis confirms that the mean value of HE is lower for sleep than wake. The preliminary study reveals a classification accuracy of 99.96% with HE features with KNN classifier. The procedure needs to be tested with larger datasets.
Electroencephalogram (EEG) signals derived from polysomnography recordings play an important role in assessing the physiological and behavioral changes during onset of sleep. This paper suggests a spike rhythmicity based feature for discriminating the wake and sleep state. The polysomnography recordings are segmented into 1 second EEG patterns to ensure stationarity of the signal and four windowing scheme overlaps (0%, 50%, 60% and 75%)of EEG pattern are introduced to study the influence of the pre-processing procedure. The application of spike rhythmicity feature helps to estimate the number of spikes from the given pattern with a threshold of 25%.Then non parametric statistical analysis using Wilcoxon signed rank test is introduced to evaluate the impact of statistical measures such as mean, standard deviation, p-value and box-plot analysis under various conditions .The statistical test shows significant difference between wake and sleep with p<0.005 for the applied feature, thus demonstrating the efficiency of simple thresholding in distinguishing sleep and wake stage .
Modern games consists of digital gaming consoles that involves interaction with a user and has an interface to generate visual feedback through 2D/3D monitor. These games have several psychological side effects like loss of spatial awareness, back pains, insomnia, addiction, aggression, stress, and hypertension. Virtual reality (VR) Gaming is one of the most emerging and novel technologies in the field of entertainment. Evaluation of this new technology has become important in order to analyze the effects of its predecessors (2D and 3D gaming). The main focus of this paper is on detection of stress levels in individuals due to VR gaming and classify them depending on their sympathetic and parasympathetic dominance. This is done through acquisition of electrocardiogram (ECG) and photo plethysmograph signals (PPG) signals and extracting their time domain and frequency domain features before, during and after gaming (Fatma Uysal and Mahmut Tokmakçi, 2018. Evaluation of stress parameters based on heart rate variability measurement. Department of Biomedical Engineering, Erciyes University, Kayseri, Turkey. fatmauysal@erciyes.edu.tr, tokmakci@erciyes.edu.tr., da Silva1, A.G.C.B., Arauj, D.N., et al, 2018. Increase in perceived stress is correlated to lower heart rate variability in healthy young subjects. Departamento de Fisioterapia, Universidade Federal do Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil. s/n., 81531–980, Curitiba, Parana, Brazil. E-mail: fernandoaldias@gmail.com.). The physiological signal variation is analyzed by performing Heart Rate Variability (HRV) analysis over ECG signals which is one of the fast emerging methods in non-invasive research and clinical tools for assessing autonomic nervous system function (Juan Sztajzel, 2004. Heart rate variability: Aa non-invasive electrocardiographic method to measure the autonomic nervous system. Cardiology Center and Medical Policlinics, University Hospital, Geneva, Switzerland, SWISS MED WKLY 2004;134:514–522. www.smw.ch). Pulse-transmissiontime-variability (PTTV), which is extracted, has high coherence with heart rate variability and is also used as an objective measure of stress. In this paper we obtain the response of an individual during VR gaming and correlate them with the HRV/PTT parameters. The game chosen for the data acquisition was ‘VR city view rope crossing-360 android VR,’ during which data recording is done. It was found that there was a quantitative increase in physiological stress when individuals were exposed to virtual high heights in comparison with time relative to unaltered viewing. Mean Heart rate showed a significant increase during gaming for both boys and girls which indicates that the body is under the influence of a sympathetic activity like a physical exercise.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.