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 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 Sleep Apnea

2017 ◽  
Vol 9 (2) ◽  
pp. 29-37
Author(s):  
Nazia Uzma ◽  
VD Reddy
Keyword(s):  
Sleep Apnea ◽  
Cognitive Function ◽  
Action Potential ◽  
The Body ◽  
Sleep Stages ◽  
Control Group ◽  
Control Subjects ◽  
Function Tests ◽  
Obstructive Sleep ◽  
Overnight Polysomnography

Background: Sleep apnea is a condition that interrupts breathing while sleeping, usually caused by an obstruction blocking the back of the throat so that the air cannot reach the lungs. The brief cessation in breath automatically forces individuals to wake up and restart breathing. This can happen many times during the night, making it hard for the body to get enough oxygen, and impacts the sleep quality. It is the most common type of sleep disorder breathing.Objectives: The present study was designed to investigate the effects of obstructive sleep apnea (OSA) on different mental, physical and nervous disorders which are manifested in such patients. This study would not only benefit in ascertaining the causes of OSA through assessment of higher mental functions of autonomic and peripheral nervous systems but also in the development of algorithm for estimation of degree of damage to the nervous system with severity of OSA.Methods: A total of 1365 consecutive participants participated in this study at the Department of Pulmonary Medicine, Deccan College of Medical Sciences, Hyderabad, Telangana State, India for suspected sleep disordered breathing (SDB) between October 2012 and February 2016. In this cohort, 1140 participants were deemed ineligible, as per the inclusion criteria. Therefore, 225 patients were considered in the study along with 75 control subjects, who were healthy individuals. The cohort was diagnosed by an experienced pulmonologist for the symptoms of   snoring and daytime somnolence. The data included documentation of age, gender, weight, height, BMI, waist and neck circumference, and clinical data such as history of apnea, insomnia, dyslipidemia, hypertension, and coronary heart disease. All participants underwent overnight polysomnography (PSG) in sleep laboratory. The cognitive function tests consisted of mini-mental state examination and by employing the depression questionnaire (Using Zung self report depression scale). The autonomic function tests were performed. Variabilities in heart rate were determined. Brain natriuretic peptide (BNP) levels in the blood were measured.Results: The study group had an AHI ≥5 per hour of sleep while the control group had AHI <5 per hour of sleep. Overall, patients in the OSA cohort were older compared to those in the Control cohort. The overnight polysomnography values indicated distinct differences among the parameters of the analysis depending upon the category of the patient (i.e., mild, moderate and severe). Oxygen saturation in blood during both REM and NREM sleep stages clearly indicated lower oxygen in patient cohort than the control group. The cognitive function tests revealed that in comparison to the control group, OSA patients had significantly impaired cognition. OSA patients had significantly higher (p ≤0.05) depression. Motor action, muscle action potential and nerve action potential was significantly lower (p ≤0.05) than that of the control group of healthy patients. The plasma BNP in OSA patients was significantly higher (p ≤0.05) than control subjects. RR intervals in the patient group were significantly shorter than in the control group. The blood pressure of the OSA patients in general was relatively higher than the control group, both during the postural response and in handgrip test.Conclusions: Among the enrolled individuals, those with severe OSA were affected in all faculties, namely, cognitive abilities and health attributes; and had high BNP levels in their blood. In aggregate, OSA patients can be alleviated from the syndrome, if accurate diagnosis is made on time. This study developed an algorithm which would aid the clinicians in early detection of OSA symptoms and mitigate the prognosis of the syndrome. Journal of Gandaki Medical CollegeVolume, 09, Number 2, July December  2016, 29-37

scholarly journals Ensemble of Deep Learning Models for Sleep Apnea Detection: An Experimental Study

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5425
Author(s):  
Debadyuti Mukherjee ◽  
Koustav Dhar ◽  
Friedhelm Schwenker ◽  
Ram Sarkar
Keyword(s):  
Experimental Study ◽  
Deep Learning ◽  
Sleep Apnea ◽  
Choquet Integral ◽  
The Body ◽  
Majority Voting ◽  
Detection Accuracy ◽  
Learning Models ◽  
Ensemble Techniques ◽  
Obstructive Sleep

Sleep Apnea is a breathing disorder occurring during sleep. Older people suffer most from this disease. In-time diagnosis of apnea is needed which can be observed by the application of a proper health monitoring system. In this work, we focus on Obstructive Sleep Apnea (OSA) detection from the Electrocardiogram (ECG) signals obtained through the body sensors. Our work mainly consists of an experimental study of different ensemble techniques applied on three deep learning models—two Convolutional Neural Network (CNN) based models, and a combination of CNN and Long Short-Term Memory (LSTM) models, which were previously proposed in the OSA detection domain. We have chosen four ensemble techniques—majority voting, sum rule and Choquet integral based fuzzy fusion and trainable ensemble using Multi-Layer Perceptron (MLP) for our case study. All the experiments are conducted on the benchmark PhysioNet Apnea-ECG Database. Finally, we have achieved highest OSA detection accuracy of 85.58% using the MLP based ensemble approach. Our best result is also able to surpass many of state-of-the-art methods.

scholarly journals Sleep Apnea Identification using HRV Features of ECG Signals

2018 ◽  
Vol 8 (5) ◽  
pp. 3940 ◽  
Author(s):  
Billy Sulistyo ◽  
Nico Surantha ◽  
Sani M. Isa
Keyword(s):  
Sleep Apnea ◽  
The Body ◽  
Classification Model ◽  
Classification Methods ◽  
Early Prevention ◽  
Ecg Signals ◽  
Long Period ◽  
Subject Specific ◽  
Specific Scheme ◽  
Electrocardiogram Ecg

Sleep apnea is a common sleep disorder that interferes with the breathing of a person. During sleep, people can stop breathing for a moment that causes the body lack of oxygen that lasts for several seconds to minutes even until the range of hours. If it happens for a long period, it can result in more serious diseases, e.g. high blood pressure, heart failure, stroke, diabetes, etc. Sleep apnea can be prevented by identifying the indication of sleep apnea itself from ECG, EEG, or other signals to perform early prevention. The purpose of this study is to build a classification model to identify sleep disorders from the Heart Rate Variability (HRV) features that can be obtained with Electrocardiogram (ECG) signals. In this study, HRV features were processed using several classification methods, i.e. ANN, KNN, N-Bayes and SVM linear Methods. The classification is performed using subject-specific scheme and subject-independent scheme. The simulation results show that the SVM method achieves higher accuracy other than three other methods in identifying sleep apnea. While, time domain features shows the most dominant performance among the HRV features.

‘If you shoulder a rifle […] let it be for Ireland’: James Connolly's War on War

2018 ◽  
Vol 13 (3) ◽  
pp. 364-381
Author(s):  
Margot Gayle Backus ◽  
Spurgeon Thompson
Keyword(s):  
World War I ◽  
The Body ◽  
Party Leader ◽  
Irish Nationalism ◽  
British Imperialism ◽  
Communications Networks ◽  
Easter Rising ◽  
Trade Unionist ◽  
The One ◽  
National Governments

As virtually all Europe's major socialist parties re-aligned with their own national governments with the outbreak of World War I, Irish socialist and trade unionist James Connolly found himself internationally isolated by his vociferous opposition to the war. Within Ireland, however, Connolly's energetic and relentless calls to interrupt the imperial transportation and communications networks on which the ‘carnival of murder’ in Europe relied had the converse effect, drawing him into alignment with certain strains of Irish nationalism. Connolly and other socialist republican stalwarts like Helena Molony and Michael Mallin made common cause with advanced Irish nationalism, the one other constituency unamenable to fighting for England under any circumstances. This centripetal gathering together of two minority constituencies – both intrinsically opposed, if not to the war itself, certainly to Irish Party leader John Redmond's offering up of the Irish Volunteers as British cannon fodder – accounts for the “remarkably diverse” social and ideological character of the small executive body responsible for the planning of the Easter Rising: the Irish Republican Brotherhood's military council. In effect, the ideological composition of the body that planned the Easter Rising was shaped by the war's systematic diversion of all individuals and ideologies that could be co-opted by British imperialism through any possible argument or material inducement. Although the majority of those who participated in the Rising did not share Connolly's anti-war, pro-socialist agenda, the Easter 1916 Uprising can nonetheless be understood as, among other things, a near letter-perfect instantiation of Connolly's most steadfast principle: that it was the responsibility of every European socialist to throw onto the gears of the imperialist war machine every wrench on which they could lay their hands.

The Woman Who Turned Into a Jaguar, and Other Narratives of Native Women in Archives of Colonial Mexico

2017 ◽  
Author(s):  
Lisa Sousa
Keyword(s):  
Gender Relations ◽  
Civil Disobedience ◽  
Indigenous Communities ◽  
The Body ◽  
Colonial Rule ◽  
Criminal Records ◽  
Native Women ◽  
Women's Lives ◽  
Important Position ◽  
The One

The Woman Who Turned Into a Jaguar examines gender relations in indigenous societies of central Mexico and Oaxaca from the 1520s to the 1750s, focusing mainly on the Nahua, Ñudzahui (Mixtec), Bènizàa (Zapotec), and Ayuk (Mixe) people. This study draws on an unusually rich and diverse corpus of original sources, including Ñudzahui- (Mixtec-), Tíchazàa- (Zapotec-), and mainly Nahuatl-language and Spanish civil and criminal records, published texts, and pictorial manuscripts. The sources come from more than 100 indigenous communities of highland Mexico. The book considers women’s lives in the broadest context possible by addressing a number of interrelated topics, including: the construction of gender; concepts of the body; women’s labor; marriage rituals and marital relations; sexual attitudes; family structure; the relationship between household and community; and women’s participation in riots and other acts of civil disobedience. The study highlights subtle transformations and overwhelming continuities in indigenous social attitudes and relationships. The book argues that profound changes following the Spanish conquest, such as catastrophic depopulation, economic pressures, and the imposition of Christian marriage, slowly eroded indigenous women’s status. Nevertheless, gender relations remained inherently complementary. The study shows how native women and men under colonial rule, on the one hand, pragmatically accepted, adopted, and adapted certain Spanish institutions, concepts, and practices, and, on the other, forcefully rejected other aspects of colonial impositions. Women asserted their influence and, in doing so, they managed to retain an important position within their households and communities across the first two centuries of colonial rule.

scholarly journals Why Do Our Cancer Patients Sleep So Badly? Sleep Disorders in Cancer Patients: A Frequent Symptom with Multiple Causes

2021 ◽  
pp. 1-7
Author(s):  
Herwig Strik ◽  
Werner Cassel ◽  
Michael Teepker ◽  
Thomas Schulte ◽  
Jorge Riera-Knorrenschild ◽  
...  
Keyword(s):  
Sleep Disorders ◽  
Cancer Patients ◽  
Treatment Options ◽  
Ethics Committee ◽  
Breathing Disorders ◽  
The One ◽  
Diagnosis Therapy ◽  
The University ◽  
Insomnia Symptoms

<b><i>Introduction:</i></b> On the one hand, sleep disorders in cancer patients are reported in 30–50% of cancer patients. On the other hand, specific causes for these sleep disorders are little known. This study was done to evaluate factors which may affect sleep of cancer patients. To our knowledge, this is the first study which includes return to work as one factor of sleep disturbance. <b><i>Methods:</i></b> 107 patients with various types of cancer treated in 2 hospitals were interviewed with a battery of questionnaires after having given informed consent. The questionnaires intended to detect abnormalities of sleep and related pain, breathing disorders, restless legs syndrome, depression, rumination, medication, and psychosocial distress. The study was approved by the ethics committee of the University of Marburg. <b><i>Results:</i></b> The analysis of the 6 sleep-related questionnaires indicated a sleep disorder of any kind in 68% of all patients. Insomnia symptoms were present in 48 patients (44.9%). Pain, depression, anxiety, and worries about the workplace were significantly related to sleep disorders. <b><i>Conclusion:</i></b> Sleep disorders are common in cancer patients. The causes are manifold and should be considered by caregivers during diagnosis, therapy, and aftercare of cancer patients. Tumour patients should actively be asked about sleep disorders. If these are present, they should be addressed, and as they have a large impact on quality of life, treatment options should be offered in cooperation with sleep specialists.

scholarly journals Capturing the Development of Internal Representations in a High-Performing Deep Network for Sleep Stage Classification

2021 ◽  
Vol 2 (4) ◽  
Author(s):  
Sarun Paisarnsrisomsuk ◽  
Carolina Ruiz ◽  
Sergio A. Alvarez
Keyword(s):  
Input Data ◽  
Single Channel ◽  
Sleep Stage ◽  
Quantitative Measure ◽  
Classification Performance ◽  
Sleep Stages ◽  
Layer Depth ◽  
Human Sleep ◽  
Stage Classification ◽  
Sleep Stage Classification

AbstractDeep neural networks can provide accurate automated classification of human sleep signals into sleep stages that enables more effective diagnosis and treatment of sleep disorders. We develop a deep convolutional neural network (CNN) that attains state-of-the-art sleep stage classification performance on input data consisting of human sleep EEG and EOG signals. Nested cross-validation is used for optimal model selection and reliable estimation of out-of-sample classification performance. The resulting network attains a classification accuracy of $$84.50 \pm 0.13\%$$ 84.50 ± 0.13 % ; its performance exceeds human expert inter-scorer agreement, even on single-channel EEG input data, therefore providing more objective and consistent labeling than human experts demonstrate as a group. We focus on analyzing the learned internal data representations of our network, with the aim of understanding the development of class differentiation ability across the layers of processing units, as a function of layer depth. We approach this problem visually, using t-Stochastic Neighbor Embedding (t-SNE), and propose a pooling variant of Centered Kernel Alignment (CKA) that provides an objective quantitative measure of the development of sleep stage specialization and differentiation with layer depth. The results reveal a monotonic progression of both of these sleep stage modeling abilities as layer depth increases.

scholarly journals Experiential ownership and body ownership are different phenomena

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Caleb Liang ◽  
Wen-Hsiang Lin ◽  
Tai-Yuan Chang ◽  
Chi-Hong Chen ◽  
Chen-Wei Wu ◽  
...  
Keyword(s):  
Conscious Experience ◽  
Distinct Type ◽  
Body Part ◽  
The Body ◽  
Body Ownership ◽  
Rubber Hand ◽  
Person Perspective ◽  
The One ◽  
First Time ◽  
Full Body

AbstractBody ownership concerns what it is like to feel a body part or a full body as mine, and has become a prominent area of study. We propose that there is a closely related type of bodily self-consciousness largely neglected by researchers—experiential ownership. It refers to the sense that I am the one who is having a conscious experience. Are body ownership and experiential ownership actually the same phenomenon or are they genuinely different? In our experiments, the participant watched a rubber hand or someone else’s body from the first-person perspective and was touched either synchronously or asynchronously. The main findings: (1) The sense of body ownership was hindered in the asynchronous conditions of both the body-part and the full-body experiments. However, a strong sense of experiential ownership was observed in those conditions. (2) We found the opposite when the participants’ responses were measured after tactile stimulations had ceased for 5 s. In the synchronous conditions of another set of body-part and full-body experiments, only experiential ownership was blocked but not body ownership. These results demonstrate for the first time the double dissociation between body ownership and experiential ownership. Experiential ownership is indeed a distinct type of bodily self-consciousness.

scholarly journals EXPRESS: Hypoxemia during Sleep and Overnight Rostral Fluid Shift in Pulmonary Arterial Hypertension: a pilot study

2021 ◽  
pp. 204589402199693
Author(s):  
Etienne-Marie Jutant ◽  
David Montani ◽  
Caroline Sattler ◽  
Sven Günther ◽  
Olivier Sitbon ◽  
...  
Keyword(s):  
Sleep Apnea ◽  
Pilot Study ◽  
Pulmonary Arterial Hypertension ◽  
Arterial Hypertension ◽  
Central Sleep Apnea ◽  
The Body ◽  
Fluid Shift ◽  
Sleep Related Breathing Disorders ◽  
Breathing Disorders ◽  
Pulmonary Arterial

Introduction. Sleep-related breathing disorders, including sleep apnea and hypoxemia during sleep, are common in pulmonary arterial hypertension (PAH), but the underlying mechanisms remain unknown. Overnight fluid shift from the legs to the upper airway and to the lungs promotes obstructive and central sleep apnea, respectively, in fluid retaining states. The main objective was to evaluate if overnight rostral fluid shift from the legs to the upper part of the body is associated with sleep-related breathing disorders in PAH. Methods. In a prospective study, a group of stable patients with idiopathic, heritable, related to drugs, toxins, or treated congenital heart disease PAH underwent a polysomnography and overnight fluid shift measurement by bioelectrical impedance in the month preceding or following a one-day hospitalization according to regular PAH follow-up schedule with a right heart catheterization. Results. Among 15 patients with PAH (women: 87%; median [25th;75th percentiles] age: 40 [32;61] years; mean pulmonary arterial pressure 56 [46;68] mmHg; pulmonary vascular resistance 8.8 [6.4;10.1] Wood units), 2 patients had sleep apnea and 8 (53%) had hypoxemia during sleep without apnea. The overnight rostral fluid shift was 168 [118;263] mL per leg. Patients with hypoxemia during sleep had a greater fluid shift (221 [141; 361] mL) than those without hypoxemia (118 [44; 178] mL, p = 0.045). Conclusion. This pilot study suggests that hypoxemia during sleep is associated with overnight rostral fluid shift in PAH.

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