scholarly journals Automatic Sleep Scoring Stages using Real-Time EMG Signals

2021 ◽  
Vol 4 (1) ◽  
pp. 56-59
Author(s):  
Hemu Farooq ◽  
Anuj Jain ◽  
V. K. Sharma
Keyword(s):  
Sleep Disorders ◽  
Real Time ◽  
Scoring System ◽  
Sleep Stage ◽  
Clinical Information ◽  
Well Being ◽  
Physical And Mental Health ◽  
Sleep Scoring ◽  
Sleep Stage Classification ◽  
Reliable Classification

Sleep is completely regarded as obligatory component for an individual’s prosperity and is an extremely important element for the overall mental and physical well-being of an individual. It is a condition in which physical and mental health of an individual are in condition of halt. The conception of sleep is considered extremely peculiar and is a topic of discussion and it has attracted the researchers all over the world. Proper analysis of sleep scoring system and its different stages gives clinical information when diagnosing on patients having sleep disorders. Since, manual sleep stage classification is a hectic process as it takes sufficient time for sleep experts to perform data analysis. Besides, mistakes and irregularities in between classification of same data can be recurrent. Therefore, there is a great use of automatic scoring system to support reliable classification. The proposed work provides an insight to use the automatic scheme which is based on real time EMG signals. EMG is an electro neurological diagnostic tool which evaluates and records the electrical activity generated by muscle cells. The sleep scoring analysis can be applied by recording Electroencephalogram (EEG), Electromyogram (EMG), and Electrooculogram (EOG) based on epoch which is defined as a period of 30 second length segments, and this method of sleep scoring system is also called polysomnography test or PSG test. The standard database of EMG signals was collected from different hospitals in sleep laboratory which gives the different stages of sleep. These are Waking, Non-REM1 (stage-1), Non-REM2 (stage-2), Non-REM3 (stage-3), REM. The main motive of the proposed work is the synchronization of EEG, EMG, EOG in order to understand different stages of sleep when they are simultaneously recorded. The procedure can be useful in clinics, particularly for scientists in studying the wakefulness and sleep stage correlation and thus helps in diagnosing some sleep disorders.

scholarly journals An end-to-end framework for real-time automatic sleep stage classification

SLEEP ◽  
2018 ◽  
Vol 41 (5) ◽  
Author(s):  
Amiya Patanaik ◽  
Ju Lynn Ong ◽  
Joshua J Gooley ◽  
Sonia Ancoli-Israel ◽  
Michael W L Chee
Keyword(s):  
Real Time ◽  
Sleep Stage ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
End To End

scholarly journals Shallow Classifier with Sampling for Sleep Stage Classification of Autism Patients

2018 ◽  
Vol 7 (4.44) ◽  
pp. 194
Author(s):  
Intan Nurma Yulita ◽  
Mohamad Ivan Fanany ◽  
Aniati Murni Arymurthy
Keyword(s):  
Sleep Disorders ◽  
Nearest Neighbor ◽  
Sleep Stage ◽  
The Other ◽  
Sleep Stages ◽  
K Nearest Neighbor ◽  
Class Imbalance Problem ◽  
Other Hand ◽  
Stage Classification ◽  
Sleep Stage Classification

Autism is a brain development disorder that affects the patient's ability to communicate and interact with others. Most people with autism get sleep disorders. But they have some difficulty to communicate, so this problem is getting worse. The alternative that can be done is to detect sleep disorders through polysomnography. One of the test purposes is to classify the sleep stages. The doctors need a long time to process it. This paper presents an automatic sleep stage classification. The classification was based on the shallow classifiers, namely naive Bayes, k-nearest neighbor (KNN), multi-layer perceptron (MLP), and C4.5 (a type of decision tree). On the other hand, this dataset has a class imbalance problem. As a solution, this study carried out the mechanism of resampling. The results show that the use of d as a measure of the uniformity of data distribution greatly influenced the classification performance. The higher d, the more uniform the distribution of data (0 <= d <= 1). The performance with d = 1 was higher than d = 0. On the other hand, KNN was the best classifier. The highest accuracy and F-measure were 83.07 and 82.80 respectively. 

scholarly journals Sleep Stage Classification for Prediction of Human Sleep Disorders by Using Machine Learning Approach

2020 ◽  
Vol 5 (7) ◽  
pp. 1268-1272
Author(s):  
Mayuri A. Rakhonde ◽  
Dr. Kishor P. Wagh ◽  
Prof. Ravi V. Mante
Keyword(s):  
Machine Learning ◽  
Sleep Disorders ◽  
Learning Algorithm ◽  
Sleep Stage ◽  
Stochastic Gradient Descent ◽  
Sleep Stages ◽  
Power Spectral ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
Welch Method

Sleep is a fundamental need of human body. In order to maintain health, sufficient sleep is must. Efficiency of sleep is based on sleep stages. Sleep stage classification is required to identify sleep disorders. Sleep stage classification identifies different stages of sleep. In this paper, we used Stochastic Gradient Descent(SGD) a machine learning algorithm for sleep stage classification. In feature extraction, Power Spectral Density(Welch method) is used. We acheived 89% overall accuracy using this model.

scholarly journals 0441 An Automatic Sleep Scoring System Based on Ensemble Convolutional Neural Network and Spectrogram of Sleep Physiological Signal

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A169-A169
Author(s):  
C Kuo ◽  
G Chen
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Scoring System ◽  
Sleep Stage ◽  
Sleep Stages ◽  
Neural Network Ensemble ◽  
Sleep Scoring ◽  
Physiological Signal ◽  
Time Required ◽  
Sleep Stage Scoring

Abstract Introduction Manual sleep stage scoring is time consuming and subjective. Therefore, several studies focused on developing automated sleep scoring algorithms. The previously reported the automatic sleep scoring have been develop usually using small dataset, which less than 100 subjects. In this study, an automatic sleep scoring system based on ensemble convolutional neural network (ensemble-CNN) and spectrogram of sleep physiological signal was proposed and evaluated using a large dataset with sleep disorder. Methods The spectrograms were computed from each 30-s EEG and EOG of 994 subjects from PhysioNet 2018 challenge dataset, using the continuous wavelet transform, which were fed into an ensemble-CNN classification for training. The ensemble-CNN contained five pretrained models, ResNet-101, Inception-v4, DenseNet-201, Xception, and NASNet models, because these models’ architectures are different which can learn different features from the spectrograms to obtain high accuracy. The probabilities of five models were averaged to decide the sleep stage for each spectrogram. After classifying sleep stage, a smoothing process was used for sleep continuity. Moreover, the total 80% data from PhysioNet dataset were randomly assigned to the training set, and the remaining data were assigned to the testing set. Results To validate the robustness of the proposed system, the validation procedure was repeated five times. The performance measures were averaged over the five runs. The overall agreement and kappa coefficient of the proposed method are 82% and 0.73, respectively. The sensitivity of the sleep stages of Wake, N1, N2, N3, and REM are 90.0%, 48.6%, 84.9%, 84.2%, and 81.9%, respectively. Conclusion The performance of the proposed method was achieved expert level, and it was noted that the ensemble-CNN is a promising solution for automatic sleep stage scoring. This method can assist clinical staff in reducing the time required for sleep stage scoring in the future. Support This work was supported by the Ministry of Science and Technology, Taiwan. (MOST 106-2218-E-035-013-MY2, 108-2221-E-035-064, and 108-2634-F-006-012).

A Simple Composite Dynamic Digital Tool to Communicate Complex Physical and Mental Health needs and Measure Outcomes: The Cornwall Health Radar

2017 ◽  
Vol 41 (S1) ◽  
pp. S93-S94
Author(s):  
R. Shankar ◽  
C. Quick ◽  
J. Dawson ◽  
P. Annal
Keyword(s):  
Cognitive Complexity ◽  
Clinical Information ◽  
Well Being ◽  
Health Needs ◽  
Physical And Mental Health ◽  
Digital Tool ◽  
Care Plans ◽  
Common Framework ◽  
Evidenced Based ◽  
Multiple Health

IntroductionClinician-patient communication is a major factor in influencing outcomes of healthcare. Complexity increases if an individual has multiple health needs requiring support of different clinicians or agencies.AimTo develop and evidence a simple dynamic computerised tool to capture and communicate outcomes of intervention or alteration in clinical need in patients with multiple chronic health needs.MethodA MS Excel algorithm was designed for swift capture of clinical information discussed in an appointment using pre-designed set of evidenced based domains. An instant personalized single screen visual is produced to facilitate information sharing and decision-making. The display is responsive to compare changes across time. A prototype was conceptually tested in an epilepsy clinic for people with Intellectual disability (ID) due to the unique challenges posed in this population.ResultsEvidence across 300 patients with ID and epilepsy showed the tool works by enhancing reflective communication, compliance and therapeutic relationship. Medication and appointment compliance was 95% and patient satisfaction over 90%.ConclusionTo discuss all influencing health factors in a consultation is a communication challenge esp. if the patient has multiple health needs. A picture equals 1000 words and helps address the cognitive complexity of verbal information. The radar offers an evidenced based common framework to host care plans of different health conditions. It provides individualised easy view person centred care plans to allow patients to gain insight on how the different conditions impact on their overall well being and be active participants. The tool will be practically demonstrated.Disclosure of interestThe authors have not supplied their declaration of competing interest.

NONLINEAR VOLTERRA COEFFICIENTS FOR FEATURE EXTRACTION IN SLEEP STAGE CLASSIFICATION

2017 ◽  
Vol 29 (01) ◽  
pp. 1750007 ◽  
Author(s):  
Malihe Hassani ◽  
Mohammad-Reza Karami
Keyword(s):  
Sleep Stage ◽  
Sleep Stages ◽  
Eeg Signals ◽  
Classification Rate ◽  
Sleep Scoring ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
Males And Females ◽  
Neural Classifier ◽  
Better Than

This paper presents a new method for sleep scoring based on nonlinear Volterra features of EEG signals by using only one single EEG channel. The Volterra features are extracted from characteristic waves of EEG signals which can characterize different sleep stages individually. The recurrent neural classifier takes all the features extracted on 30s epochs from EEG signals and assigns them to one of the five possible stages: Wakefulness, NREM 1, NREM 2, SWS, and REM. Eight sleep recordings obtained from Caucasian males and females without any medication are utilized to validate the proposed method. Moreover, the performance of the proposed classifier in comparison with other classifiers is presented. The classification rate of the proposed classifier is better than that of the other classifier that does not use nonlinear Volterra feature. The results demonstrate that the proposed classifier with nonlinear Volterra features of the characteristic waves of EEG signals can classify sleep stages more efficiently and accurately using only a single EEG channel.

scholarly journals 0434 Clinical Application of Computer Aided Cloud Sleep Scoring System

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A166-A167
Author(s):  
W Lin ◽  
P Kuo ◽  
M Liu ◽  
C Li ◽  
C Lin ◽  
...  
Keyword(s):  
Scoring System ◽  
Sleep Stage ◽  
Sleep Onset ◽  
Onset Time ◽  
Fine Tuning ◽  
Physiological Signals ◽  
Sleep Stages ◽  
Sleep Scoring ◽  
Computer Aided ◽  
Sleep Centers

Abstract Introduction According to a survey by World Sleep Society, 45% of the population suffered from sleep disorders. The best way to diagnose these patients is to use Polysomnography (PSG), recording their physiological signals throughout the night. Mostly, sleep technologists manually score sleep stages. Manual scoring is quite subjective and time-consuming. Although the technologist’s judgments are based on scoring standards of the American Academy of Sleep Medicine, fine-tuning scoring results because of different considerations in different sleep centers may be happened. In order to assess the consistency of scoring standards in sleep technologists, we tried to establish a cloud sleep scoring system and evaluate its feasibility in 4 sleep centers in southern Taiwan. Methods We constructed a computer-aided cloud sleep scoring system. Each sleep technologist could score the same test data of PSG online without being restricted by places and hardware equipment. After comparing scoring results of all participants, the scoring system could provide the following reports, including an overall agreement, agreement of each sleep stage and each sleep index. Besides, multi-person scoring results of each epoch with displaying physiological signals were analyzed. Results Seven sleep technologists from 4 hospitals in Tainan, Taiwan joined this study. Standard deviations (SDs) of each sleep stage included 2.64 in Wake stage, 6.90 in N1, 8.31 in N2, 6.87 in N3, 1.38 in REM, respectively. SDs of sleep indexes were 2.64 in sleep efficiency, 2.14 in sleep onset time, 8.35 in wake after sleep onset time, 10.03 in total sleep time, individually. The overall agreement was 89.6%. The satisfaction of this scoring system operation was 85.7%. Conclusion With the cloud sleep scoring system assistance, it was feasible to evaluate the scoring consistency among sleep technologists in different sleep centers. Support This work is supported by the Ministry of Science and Technology, Taiwan. (MOST 108-2634-F-006-012)

scholarly journals Automated Detection of Sleep Stages Using Deep Learning Techniques: A Systematic Review of the Last Decade (2010–2020)

2020 ◽  
Vol 10 (24) ◽  
pp. 8963
Author(s):  
Hui Wen Loh ◽  
Chui Ping Ooi ◽  
Jahmunah Vicnesh ◽  
Shu Lih Oh ◽  
Oliver Faust ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Sleep Disorders ◽  
Sleep Stage ◽  
Automated Detection ◽  
Sleep Stages ◽  
Sleep Analysis ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
Eeg Recordings

Sleep is vital for one’s general well-being, but it is often neglected, which has led to an increase in sleep disorders worldwide. Indicators of sleep disorders, such as sleep interruptions, extreme daytime drowsiness, or snoring, can be detected with sleep analysis. However, sleep analysis relies on visuals conducted by experts, and is susceptible to inter- and intra-observer variabilities. One way to overcome these limitations is to support experts with a programmed diagnostic tool (PDT) based on artificial intelligence for timely detection of sleep disturbances. Artificial intelligence technology, such as deep learning (DL), ensures that data are fully utilized with low to no information loss during training. This paper provides a comprehensive review of 36 studies, published between March 2013 and August 2020, which employed DL models to analyze overnight polysomnogram (PSG) recordings for the classification of sleep stages. Our analysis shows that more than half of the studies employed convolutional neural networks (CNNs) on electroencephalography (EEG) recordings for sleep stage classification and achieved high performance. Our study also underscores that CNN models, particularly one-dimensional CNN models, are advantageous in yielding higher accuracies for classification. More importantly, we noticed that EEG alone is not sufficient to achieve robust classification results. Future automated detection systems should consider other PSG recordings, such as electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG) signals, along with input from human experts, to achieve the required sleep stage classification robustness. Hence, for DL methods to be fully realized as a practical PDT for sleep stage scoring in clinical applications, inclusion of other PSG recordings, besides EEG recordings, is necessary. In this respect, our report includes methods published in the last decade, underscoring the use of DL models with other PSG recordings, for scoring of sleep stages.

Sign in / Sign up

Export Citation Format

Share Document