scholarly journals Automated Characterization of Cyclic Alternating Pattern Using Wavelet-Based Features and Ensemble Learning Techniques with EEG Signals

Diagnostics ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1380
Author(s):  
Manish Sharma ◽  
Virendra Patel ◽  
Jainendra Tiwari ◽  
U. Rajendra Acharya
Keyword(s):  
Sleep Disorders ◽  
Single Channel ◽  
Behavior Disorder ◽  
The Body ◽  
Machine Learning Algorithms ◽  
Movement Behavior ◽  
Sleep Stages ◽  
Eeg Signals ◽  
Medical Practitioners ◽  
Cyclic Alternating Pattern

Sleep is highly essential for maintaining metabolism of the body and mental balance for increased productivity and concentration. Often, sleep is analyzed using macrostructure sleep stages which alone cannot provide information about the functional structure and stability of sleep. The cyclic alternating pattern (CAP) is a physiological recurring electroencephalogram (EEG) activity occurring in the brain during sleep and captures microstructure of the sleep and can be used to identify sleep instability. The CAP can also be associated with various sleep-related pathologies, and can be useful in identifying various sleep disorders. Conventionally, sleep is analyzed using polysomnogram (PSG) in various sleep laboratories by trained physicians and medical practitioners. However, PSG-based manual sleep analysis by trained medical practitioners is onerous, tedious and unfavourable for patients. Hence, a computerized, simple and patient convenient system is highly desirable for monitoring and analysis of sleep. In this study, we have proposed a system for automated identification of CAP phase-A and phase-B. To accomplish the task, we have utilized the openly accessible CAP sleep database. The study is performed using two single-channel EEG modalities and their combination. The model is developed using EEG signals of healthy subjects as well as patients suffering from six different sleep disorders namely nocturnal frontal lobe epilepsy (NFLE), sleep-disordered breathing (SDB), narcolepsy, periodic leg movement disorder (PLM), insomnia and rapid eye movement behavior disorder (RBD) subjects. An optimal orthogonal wavelet filter bank is used to perform the wavelet decomposition and subsequently, entropy and Hjorth parameters are extracted from the decomposed coefficients. The extracted features have been applied to different machine learning algorithms. The best performance is obtained using ensemble of bagged tress (EBagT) classifier. The proposed method has obtained the average classification accuracy of 84%, 83%, 81%, 78%, 77%, 76% and 72% for NFLE, healthy, SDB, narcolepsy, PLM, insomnia and RBD subjects, respectively in discriminating phases A and B using a balanced database. Our developed model yielded an average accuracy of 78% when all 77 subjects including healthy and sleep disordered patients are considered. Our proposed system can assist the sleep specialists in an automated and efficient analysis of sleep using sleep microstructure.

scholarly journals Detection of Sleep Apnea Based on the Analysis of Sleep Stages Data Using Single Channel EEG

2021 ◽  
Vol 38 (2) ◽  
pp. 431-436
Author(s):  
Vijayakumar Gurrala ◽  
Padmasai Yarlagadda ◽  
Padmaraju Koppireddi
Keyword(s):  
Sleep Apnea ◽  
Sleep Disorders ◽  
Single Channel ◽  
The Body ◽  
Sleep Stages ◽  
Detection Accuracy ◽  
Learning Classifier ◽  
Tree Classifier ◽  
The One ◽  
Electrocardiogram Ecg

Sleep is a basic need for a human being’s intellectual and physiological restoration and overlaying nearly one 1/3 length of a daytime. A first-rate and deep sleep is required for green regeneration of the body. Sleep disorders hamper the performance of an individual. Sleep Apnea is the one amongst the disorders that affect many. Most of Apnea related works consider Electrocardiogram (ECG) and respiratory signals /or combinations, instead of considering all Polysomnographic signals (PSG). It is evident that for the detection of Apnea related sleep disorders it is required to consider one or few signals rather considering all PSG signals. In this work, we advocate a way that might be carried out to perceive the information of sleep stages which might be crucial in diagnosing and treating sleep disorders. It differentiates sleep stages and derives new features from the sleep EEG that allows helping physicians with the analysis and treatment of associated sleep issues. This theory depends on exclusive EEG datasets from Physionet with the use of MIT-BIH polysomnographic database that have been received and described through scientists for the analysis and prognosis of sleep ranges. Experimental results on 18 records with 10197 epochs show that an Apnea detection accuracy of 95.9% obtained for Machine learning classifier with Ensemble Bagged Tree classifier.

scholarly journals Automated Identification of Sleep Disorder Types Using Triplet Half-Band Filter and Ensemble Machine Learning Techniques with EEG Signals

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1531
Author(s):  
Manish Sharma ◽  
Jainendra Tiwari ◽  
Virendra Patel ◽  
U. Rajendra Acharya
Keyword(s):  
Machine Learning ◽  
Sleep Disorders ◽  
Sleep Disorder ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Movement Behavior ◽  
Subjective Time ◽  
Eeg Signals ◽  
Automated Identification ◽  
Band Filter

A sleep disorder is a medical condition that affects an individual’s regular sleeping pattern and routine, hence negatively affecting the individual’s health. The traditional procedures of identifying sleep disorders by clinicians involve questionnaires and polysomnography (PSG), which are subjective, time-consuming, and inconvenient. Hence, an automated sleep disorder identification is required to overcome these limitations. In the proposed study, we have proposed a method using electroencephalogram (EEG) signals for the automated identification of six sleep disorders, namely insomnia, nocturnal frontal lobe epilepsy (NFLE), narcolepsy, rapid eye movement behavior disorder (RBD), periodic leg movement disorder (PLM), and sleep-disordered breathing (SDB). To the best of our belief, this is one of the first studies ever undertaken to identify sleep disorders using EEG signals employing cyclic alternating pattern (CAP) sleep database. After sleep-scoring EEG epochs, we have created eight different data subsets of EEG epochs to develop the proposed model. A novel optimal triplet half-band filter bank (THFB) is used to obtain the subbands of EEG signals. We have extracted Hjorth parameters from subbands of EEG epochs. The selected features are fed to various supervised machine learning algorithms for the automated classification of sleep disorders. Our proposed system has obtained the highest accuracy of 99.2%, 98.2%, 96.2%, 98.3%, 98.8%, and 98.8% for insomnia, narcolepsy, NFLE, PLM, RBD, and SDB classes against normal healthy subjects, respectively, applying ensemble boosted trees classifier. As a result, we have attained the highest accuracy of 91.3% to identify the type of sleep disorder. The proposed method is simple, fast, efficient, and may reduce the challenges faced by medical practitioners during the diagnosis of various sleep disorders accurately in less time at sleep clinics and homes.

scholarly journals Classification of Brainwaves for Sleep Stages by High-Dimensional FFT Features from EEG Signals

2020 ◽  
Vol 10 (5) ◽  
pp. 1797 ◽  
Author(s):  
Mera Kartika Delimayanti ◽  
Bedy Purnama ◽  
Ngoc Giang Nguyen ◽  
Mohammad Reza Faisal ◽  
Kunti Robiatul Mahmudah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sleep Stage ◽  
Machine Learning Algorithms ◽  
High Dimensional ◽  
Sleep Stages ◽  
Eeg Signals ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
Low Dimensional

Manual classification of sleep stage is a time-consuming but necessary step in the diagnosis and treatment of sleep disorders, and its automation has been an area of active study. The previous works have shown that low dimensional fast Fourier transform (FFT) features and many machine learning algorithms have been applied. In this paper, we demonstrate utilization of features extracted from EEG signals via FFT to improve the performance of automated sleep stage classification through machine learning methods. Unlike previous works using FFT, we incorporated thousands of FFT features in order to classify the sleep stages into 2–6 classes. Using the expanded version of Sleep-EDF dataset with 61 recordings, our method outperformed other state-of-the art methods. This result indicates that high dimensional FFT features in combination with a simple feature selection is effective for the improvement of automated sleep stage classification.

Forensic Sleep Medicine

2020 ◽  
pp. 497-510
Author(s):  
Kenneth J. Weiss ◽  
Clarence Watson ◽  
Mark R. Pressman
Keyword(s):  
Sleep Disorders ◽  
Expert Testimony ◽  
Violent Behavior ◽  
Behavior Disorder ◽  
Sleep Medicine ◽  
Sleep Stages ◽  
Rem Behavior Disorder ◽  
Criminal Charges ◽  
Violent Acts ◽  
Criminal Culpability

Patients with sleep disorders can exhibit behavior that includes violent acts. The behavior may occur during various sleep stages, ranges in complexity, and requires an analysis of consciousness. When the behavior harms another person and criminal charges follow, expert testimony will be required to explain the physiology of the disorder and impairments in consciousness that determine criminal culpability, that is, whether there was conscious intent behind the behavior. In this chapter, sleep-related conditions associated with violent behavior are discussed, along with guidelines for presenting scientific testimony in court. These disorders include rapid eye movement (REM) behavior disorder, somnambulism and other non-REM partial awakenings, and hypersomnolence. Feigned symptoms and malingering must be ruled out, and the clinical parameters for them are discussed. While the physiology of sleep disorders has widely been known, admissibility in court is not automatic. Standards for acceptable expert testimony are discussed.

scholarly journals Prevalence and correlates of sleep disorders in Parkinson’s disease: a polysomnographic study

2015 ◽  
Vol 73 (3) ◽  
pp. 241-245 ◽  
Author(s):  
Vanessa Alatriste-Booth ◽  
Mayela Rodríguez-Violante ◽  
Azyadeh Camacho-Ordoñez ◽  
Amin Cervantes-Arriaga
Keyword(s):  
Parkinson’S Disease ◽  
Body Mass Index ◽  
Parkinson's Disease ◽  
Sleep Disorders ◽  
Body Mass ◽  
Behavior Disorder ◽  
Equivalent Dose ◽  
Movement Behavior ◽  
Single Night ◽  
Hypopnea Syndrome

Objective Sleep disorders in Parkinson’s disease are very common. Polysomnography (PSG) is considered the gold standard for diagnosis. The aim of the present study is to assess the prevalence of nocturnal sleep disorders diagnosed by polysomnography and to determine the associated clinical factors. Method A total of 120 patients with Parkinson’s disease were included. All patients underwent a standardized overnight, single night polysomnography. Results Ninety-four (78.3%) patients had an abnormal PSG. Half of the patients fulfilled criteria for sleep apnea-hypopnea syndrome (SAHS); rapid eye movement behavior disorder (RBD) was present in 37.5%. Characteristics associated with SAHS were age (p = 0.049) and body mass index (p = 0.016). Regarding RBD, age (p < 0.001), left motor onset (p = 0.047) and levodopa equivalent dose (p = 0.002) were the main predictors. Conclusion SAHS and RBD were the most frequent sleep disorders. Higher levodopa equivalent dose and body mass index appear to be risk factors for RBD and SAHS, respectively.

scholarly journals Using time causal quantifiers to characterize sleep stages

2019 ◽  
Author(s):  
Diego M. Mateos ◽  
Jaime Gómez-Ramírez ◽  
Osvaldo A. Rosso
Keyword(s):  
Temporal Dynamics ◽  
Single Channel ◽  
Well Being ◽  
Sleep Stages ◽  
Eeg Signals ◽  
Information Theoretic ◽  
Information Theoretic Measures ◽  
Sleep Recording ◽  
Vector Approach

AbstractSleep plays substantial role in daily cognitive performance, mood and memory. The study of sleep has attracted the interest of neuroscientists, clinicans and the overall population, with increasing number of adults suffering from insufficient amounts of sleep. Sleep is an activity composed of different stages whose temporal dynamics, cycles and inter dependencies are not fully understood. Healthy body function and personal well being, however, depends on proper unfolding and continuance of the sleep cycles. The characterization of the different sleep stages can be undertaken with the development of biomarkers derived from sleep recording. For this purpose, in this work we analyzed single-channel EEG signals from 106 healthy subjects. The signals were quantified using the permutation vector approach using five different information theoretic measures: i) Shannon’s entropy, ii) MPR statistical complexity, iii) Fisher information, iv) Renyí Min-entropy and v) Lempel-Ziv complexity. The results show that all five information theory-based measures make possible to quantify and classify the underlying dynamics of the different sleep stages. In addition to this, we combine these measures to show that planes containing pairs of measures, such as the plane composed of Lempel-Ziv and Shannon, have a better performance for differentiating sleep states than measures used individually for the same purpose.

scholarly journals Differences in Sleep Disorders between HIV-Infected Persons and Matched Controls with Sleep Problems: A Matched-Cohort Study Based on Laboratory and Survey Data

2021 ◽  
Vol 10 (21) ◽  
pp. 5206
Author(s):  
Yen-Chin Chen ◽  
Chang-Chun Chen ◽  
Patrick J. Strollo ◽  
Chung-Yi Li ◽  
Wen-Chien Ko ◽  
...  
Keyword(s):  
Sleep Disorders ◽  
Eye Movement ◽  
Sleep Disturbances ◽  
Sleep Disordered Breathing ◽  
Behavior Disorder ◽  
Movement Behavior ◽  
Rapid Eye Movement ◽  
Sleep Study ◽  
Psychological Disturbances ◽  
Disordered Breathing

Objectives: Sleep disturbances are prevalent problems among human immunodeficiency virus (HIV)-infected persons. The recognition of comorbid sleep disorders in patients with HIV is currently hampered by limited knowledge of sleep-related symptoms, sleep architecture, and types of sleep disorders in this population. We aimed to compare the differences in sleep-related symptoms and polysomnography-based sleep disorders between HIV-infected persons and controls. Methods: The study evaluated 170 men with a Pittsburgh sleep quality index scores greater than 5, including 44 HIV-infected men and 126 male controls who were frequency-matched by sex, age (±3.0 years) and BMI (±3.0 kg/m2). For all participants, an overnight sleep study using a Somte V1 monitor was conducted. Differences in sleep-related symptoms and sleep disorders between HIV-infected patients and controls were examined using t-tests or chi-square tests. Results: HIV-infected persons with sleep disturbances more often had psychological disturbances (72.7% vs. 40.5%, p < 0.001) and suspected rapid eye movement behavior disorder (25.0% vs. 4.8%, p < 0.01) than controls. Sleep-disordered breathing was less common in HIV-infected persons than in controls (56.8% vs. 87.3%, p < 0.001). The mean percentage of rapid eye movement sleep was higher among HIV-infected patients than among controls (20.6% vs. 16.6%, p < 0.001). Nocturia was more common in HIV-infected persons than in controls (40.9% vs. 22.2%, p = 0.02). Conclusions: Psychological disturbances and sleep-disordered breathing can be possible explanations of sleep disturbances in HIV-infected persons in whom sleep-disordered breathing is notable. Further studies are warranted to examine the underlying factors of rapid eye movement behavior disorder among HIV-infected persons with sleep disturbances.

Sleep Health and Sleep Disorders

2020 ◽  
pp. 283-326
Author(s):  
Weili Gray
Keyword(s):  
Sleep Disorders ◽  
Behavioral Therapy ◽  
Behavior Disorder ◽  
Evaluation Process ◽  
Light Therapy ◽  
Oral Appliance ◽  
Movement Behavior ◽  
Periodic Limb Movement Disorder ◽  
Sleep Study ◽  
Integrative Therapies

This chapter reviews the architecture and functions of sleep, how to interview patients on their sleep histories, how to evaluate for sleep disorders, commonly encountered sleep disorders and their pathophysiology, and the conventional and integrative therapies for each. The evaluation process includes a conventional sleep study as well as addressing vitamin D, B, and magnesium status. Sleep disorders discussed in this chapter are obstructive sleep apnea, insomnia, restless legs syndrome, periodic limb movement disorder, rapid-eye-movement behavior disorder, circadian rhythm disorders, and narcolepsy and other central hypersomnias. The role of conventional tools and times when alternative and complementary therapies may be considered are discussed in detail. Treatment covered include continuous positive airway pressure, oral appliance, myofunctional therapy, cognitive-behavioral therapy for insomnia, physical modalities, acupuncture, light therapy, melatonin, nutraceuticals, and other supplements that aid with sleep and daytime symptoms.

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