The electroencephalography (EEG) sensor has become a prominent sensor in the study of brain activity. Its applications extend from research studies to medical applications. This review paper explores various types of EEG sensors and their applications. This paper is for an audience that comprises engineers, scientists and clinicians who are interested in learning more about the EEG sensors, the various types, their applications and which EEG sensor would suit a specific task. The paper also lists the details of each of the sensors currently available in the market, their technical specs, battery life, and where they have been used and what their limitations are.

Electroencephalography (EEG) technology applications and available devices

Lack of sleep causes many sleep disorders such as nocturnal frontal lobe epilepsy, narcolepsy, bruxism, sleep apnea, insomnia, periodic limb movement disorder, and rapid eye movement behavioral disorder. Out of all, bruxism is a common behavior, which is found in 8-31% of the population. Bruxism is a sleep disorder in which individuals involuntarily grinds and clenches the teeth. The main aim of this work is to detect sleep bruxism by analyzing the electroencephalogram (EEG) spectrum analysis of the change in the domain of different stages of sleep. The present research was performed in different stages such as collection of the data, preprocessing of the EEG signal, analysis of the C4-P4 and C4-A1 channels, comparison between healthy humans and bruxism patients, and classification using decision tree method. In this study, the channels C4-P4 and C4-A1 of the EEG signal were combined for the detection of bruxism by using Welch technique, which mainly focused on two sleep stages such as S1 and rapid eye movement. The total number of EEG channels of healthy humans and bruxism patients analyzed in this work were 15 and 18, respectively. The results showed that the individual accuracy of the C4-P4 and C4-A1 channels was 81.70% and 74.11%, respectively. The combined accuracy of both C4-P4 and C4-A1 channels was 81.25%. The specificity of combined result was higher than individual. In addition, the value of theta activity during detection is consistent throughout the period, and the accuracy of S1 stage is better than rapid eye movement stage. We proposed that the theta activity of S1 could be taken for the detection of bruxism. The proposed approach in the detection of the bruxism is negligible in noise as it is in mathematical form and has taken very less time as compared with the traditional systems. The present research work would provide a fast and effective detection system of the sleep bruxism with high accuracy for medical big data applications.

/pdf/sleep-bruxism-detection-using-decision-tree-method-by-the-1kumsk2jyx.pdf

Sleep Bruxism Detection Using Decision Tree Method by the Combination of C4-P4 and C4-A1 Channels of Scalp EEG

Bruxism is a sleep disorder in which the patient clinches and gnashes their teeth. Bruxism detection using traditional methods is time-consuming, cumbersome, and expensive. Therefore, an automatic tool to detect this disorder will alleviate the doctor workload and give valuable help to patients. In this paper, we targeted this goal and designed an automatic method to detect bruxism from the physiological signals using a novel hybrid classifier. We began with data collection. Then, we performed the analysis of the physiological signals and the estimation of the power spectral density. After that, we designed the novel hybrid classifier to enable the detection of bruxism based on these data. The classification of the subjects into “healthy” or “bruxism” from the electroencephalogram channel (C4-A1) obtained a maximum specificity of 92% and an accuracy of 94%. Besides, the classification of the sleep stages such as the wake (w) stage and rapid eye movement (REM) stage from the electrocardiogram channel (ECG1-ECG2) obtained a maximum specificity of 86% and an accuracy of 95%. The combined bruxism classification and the sleep stages classification from the electroencephalogram channel (C4-P4) obtained a maximum specificity of 90% and an accuracy of 97%. The results show that more accurate bruxism detection is achieved by exploiting the electroencephalogram signal (C4-P4). The present work can be applied for home monitoring systems for bruxism detection.

https://www.mdpi.com/2076-3417/10/21/7410/pdf

A Novel Hybrid Machine Learning Classification for the Detection of Bruxism Patients Using Physiological Signals

Lack of adequate sleep is a major source of many harmful diseases related to heart, brain, psychological changes, high blood pressure, diabetes, weight gain etc. The 40 to 50 % of the world's population is suffering from poor or inadequate sleep. Insomnia is a sleep disorder in which individual complaint of difficulties in starting/continuing sleep at least four weeks regularly. It is estimated that 70% of the heart diseases are generated during insomnia sleep disorder. The main objective of this study to determine the all work conducted on insomnia detection and to make a database. We used two procedures including network visualization techniques on two databases including PubMed and Web of Science to complete this study. We found 169 and 36 previous publications of insomnia detection in the PubMed and the Web of Science databases, respectively. We analyzed 10 datasets, 2 databases, 21 genes, and 23 publications with 30105 subjects of insomnia detection. This work has revealed the future way and gap so far directed on insomnia detection and has also tried to provide objectives for the future work to be proficient in a scientific and significant manner.

Progress in Detection of Insomnia Sleep Disorder: A Comprehensive Review.

High Performance Architecture and Grid Computing

Sleep disorder, is a medicinal disorder of the snooze patterns of a person or living thing. Some sleep disorders are serious adequate to obstruct with normal substantial, mental and emotional performance. In this research article, attribute and waveform of EEG Signals of human being are analyzed. The aim of this research is to draw the result in the form of signal spectrum analysis of the changes in the domain of different stages of sleep. The observations obtained on the EEG patterns can be analyzed for its utility in the diagnosis of sleep disorders like anxiety and depression. These disorders are reflected as the changes in the electrical activities and chemical activities in the brain that can be observed by capturing the brain signals and the images. In this paper Short Time frequency analysis of Power Spectrum Density (STFAPSD) approach applied on Electroencephalogram (EEG) Signals for Diagnosis of Rapid Eye Movement Behavior Disorder (RBD). Obtained results by this method are found and suitable for the differentiation normal people and RBD patient

/pdf/detection-of-rapid-eye-movement-behaviour-disorder-using-1426h3i4pl.pdf

Detection of rapid eye movement behaviour disorder using short timefrequency analysis of PSD approach applied on EEG signal (ROC-LOC).

This paper is designed to introduce the reader to the fundamental information for future or next Generation Technology .Forth Generation system implement in few countries & 4G systems is still a predominantly research and development and make an overview of next generation system i.e.5G communication. In this Paper presents an overview of the fifth generation mobile networks with emphasis on current and future trends in the areas of wireless networking, multimedia technology, network architecture, and network services. Related research in the development of future mobile systems has been highlighted. Beginning with prognoses of 5G Communication, architecture model of wireless communication & comparison with all the generation technology.

Vision of 5G Communication

Narcolepsy is a sleep disorder in which the subject's brain is chronically unable to regulate sleep-wake cycles. This study aims at identifying narcoleptics from normal individuals. Our approach involves implementation of Short Time Frequency analysis of PSD approach applied on EEG signals, post filtering ROC-LOC channels. In this research article, a comprehensive analysis of EEG signals for S0, S2 and S3 stages of sleep has been performed. The analysis and calculation is performed in all stages of sleep and a comparative database, with Power Spectral Density comparisons between healthy and affected individuals, been prepared. Hence segregation of narcoleptic occurrence events based on delta and alpha segments, of EEG signals, has been successfully performed herein.

Diagnosis of narcolepsy sleep disorder for different stages of sleep using Short Time Frequency analysis of PSD approach applied on EEG signal

This sleep disorder is reflected as the changes in the electrical activities and chemical activities in the brain that can be observed by capturing the brain signals and the images. In this research, Short Time-frequency analysis of Power Spectrum Density (STFAPSD) approach applied on Electroencephalogram (EEG) Signals for prediction of RBD sleep disorder. Collection of Electroencephalogram (EEG) of normal subjects & different type of sleep disordered subjects & application of signal processing on EEG data for development the algorithm for detection of sleep disorder and implementation in MATLAB.

Prediction of REM (Rapid Eye Movement) Sleep Behaviour Disorder using EEG Signal applied EMG1 and EMG2 Channel

This paper contains the study of nocturnal frontal lobe epilepsy (NFLE). This paper comprises the symptoms, causes, diagnosis, treatment etc. NFLE is a disease that normally occurs during sleeps in nights and arouses from frontal lobe and some people may suffer from mild seizure. The aim of the paper is to study the frequency analysis of PSD which is observed when the EEG signals are applied. The analysis is performed during all stages of sleep. Clipped signal of duration 1 minute has been used which consist of EEG signal of respective channel for different sleep stages. These clipped signals are passed to different windows, then the average and normalized power is measured and compared to the NFLE patient. Results are based on the delta, theta, alpha and beta segments of EEG signals.

Diagnosis of Nocturnal Frontal Lobe Epilepsy (NFLE) Sleep Disorder Using Short Time Frequency Analysis of PSD Approach Applied on EEG Signal

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