scholarly journals Automatic Sleep Disorders Classification Using Ensemble of Bagged Tree Based on Sleep Quality Features

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 512 ◽  
Author(s):  
Edita Rosana Widasari ◽  
Koichi Tanno ◽  
Hiroki Tamura
Keyword(s):  
Sleep Quality ◽  
Sleep Disorders ◽  
Sleep Disorder ◽  
Sleep Stage ◽  
Computational Cost ◽  
Behavior Disorder ◽  
The Elderly ◽  
Support Vector ◽  
Ecg Signal ◽  
Quality Features

Sleep disorder is a medical disease of the sleep patterns, which commonly suffered by the elderly. Sleep disorders diagnosis and treatment are considered to be challenging due to a time-consuming and inconvenient process for the patient. Moreover, the use of Polysomnography (PSG) in sleep disorder diagnosis is a high-cost process. Therefore, we propose an efficient classification method of sleep disorder by merely using electrocardiography (ECG) signals to simplify the sleep disorders diagnosis process. Different from many current related studies that applied a five-minute epoch to observe the main frequency band of the ECG signal, we perform a pre-processing technique that suitable for the 30-seconds epoch of the ECG signal. By this simplification, the proposed method has a low computational cost so that suitable to be implemented in an embedded hardware device. Structurally, the proposed method consists of five stages: (1) pre-processing, (2) spectral features extraction, (3) sleep stage detection using the Decision-Tree-Based Support Vector Machine (DTB-SVM), (4) assess the sleep quality features, and (5) sleep disorders classification using ensemble of bagged tree classifiers. We evaluate the effectiveness of the proposed method in the task of classifying the sleep disorders into four classes (insomnia, Sleep-Disordered Breathing (SDB), REM Behavior Disorder (RBD), and healthy subjects) from the 51 patients of the Cyclic Alternating Pattern (CAP) sleep data. Based on experimental results, the proposed method presents 84.01% of sensitivity, 94.17% of specificity, 86.27% of overall accuracy, and 0.70 of Cohen’s kappa. This result indicates that the proposed method able to reliably classify the sleep disorders merely using the 30-seconds epoch ECG in order to address the issue of a multichannel signal such as the PSG.

scholarly journals The Association between Migraine and Types of Sleep Disorder

2018 ◽  
Vol 15 (12) ◽  
pp. 2648 ◽  
Author(s):  
Seung Kim ◽  
Kyu-Tae Han ◽  
Suk-Yong Jang ◽  
Ki-Bong Yoo ◽  
Sun Kim
Keyword(s):  
Sleep Disorders ◽  
Sleep Disorder ◽  
Insurance Coverage ◽  
The Elderly ◽  
National Sample ◽  
Substantial Part ◽  
Policy Makers ◽  
Cox Proportional Hazard ◽  
Cohort Study Design ◽  
Time Dependent Covariates

Background: Migraines gradually increase year by year, as does its burden. Management and prevention are needed to reduce such burdens. Previous studies have suggested that daily health behaviors can cause migraines. Sleep is a substantial part of daily life, and in South Korea, the average sleep duration is shorter than in other countries. Thus, this study focused on the increase of both diseases, and analyzed sleep disorders as a risk factor for migraines. Methods: The data used in this study was that of the national health insurance service (NHIS) national sample cohort. We used a matched cohort study design that matched non-patients based on patients with sleep disorders, and included 133,262 patients during 2012–2015. We carried out a survival analysis using a Cox proportional hazard model with time-dependent covariates to identify the association between migraines and sleep disorders. Results: Approximately 11.72% of patients were diagnosed with migraines. Sleep disorders were positively correlated with the diagnosis of migraine (Hazard Ratio, 1.591; p < 0.0001). By the types of sleep disorder, patients who were diagnosed as having insomnia, rather than other types of sleep disorder, had the greatest associations with migraine. The associations were greater for males, people with lower income, the elderly population, and patients with mild comorbid conditions. Conclusion: This study provides evidence that migraine is associated with sleep disorders, especially insomnia. Based on these findings, healthcare professionals and policy makers have to reconsider the present level of insurance coverage for sleep medicine, recognize the risk of sleep-related diseases and educate patients about the need for appropriate care.

Sleep Quality and Cognitive Function in the Elderly Population

2020 ◽  
Author(s):  
Seyed Valiollah Mousavi ◽  
Elham Montazar ◽  
Sajjad Rezaei ◽  
Shima Poorabolghasem Hosseini
Keyword(s):  
Regression Analysis ◽  
Cognitive Function ◽  
Sleep Quality ◽  
Sleep Disorders ◽  
Elderly Population ◽  
Sleep Latency ◽  
The Elderly ◽  
Poor Sleep ◽  
Subjective Sleep Quality ◽  
Subjective Sleep

Background and Objective: Physiological process of sleep is considered as one of the influential factors of human’s health and mental functions, especially in the elderly. This research aimed at studying the association between sleep quality and the cognitive functions in the elderly population. Materials and Methods: A total of 200 elderly people (65 years and older) who were the members of retirees associa-tion in Mashhad, Iran, participated in this cross-sectional study. The participants were asked to answer the questionnaire of Pittsburgh Sleep Quality Index (PSQI) and Montreal Cognitive Assessment (MoCA) test. Correlation between the total scores of PSQI and MoCA was evaluated by Pearson correlation coefficient. In order to predict the cognitive func-tion based on different aspects of PSQI, multiple regression analysis by hierarchical method was used after removing confounding variables. Results: A significant association was found between PSQI and MoCA (P < 0.001, r = -0.55) suggesting that the com-ponents of use of sleeping medication (P < 0.001, r = -0.47), sleep disorders (P < 0.001, r = -0.37), sleep latency (P < 0.001, r = -0.34), subjective sleep quality (P < 0.001, r = -0.32), sleep duration (P < 0.001, r = -0.27), sleep effi-ciency (P < 0.001, r = -0.26), and daytime dysfunction (P < 0.001, r = -0.15) had significant negative correlation with cognitive function, and the four components of subjective sleep quality (P = 0.010, β = -0.15), sleep latency (P = 0.040, β = -0.13), sleep disorders (P = 0.010, β = -0.26), and use of sleeping medication (P = 0.010, β = -0.26) played a role in prediction of cognitive function in regression analysis. Conclusion: Poor sleep quality, sleep latency, insomnia, sleep breathing disorder, and use of sleeping medication play a determining role in cognitive function of the elderly. Thus, taking care of the sleep health is necessary for the elderly.

scholarly journals The improvement of elderly sleeping quality with progressive muscle relaxation therapy

2020 ◽  
Vol 7 (5) ◽  
pp. 1684
Author(s):  
Emira Apriyeni ◽  
Helena Patricia
Keyword(s):  
Sleep Quality ◽  
Sleep Disorders ◽  
Muscle Relaxation ◽  
The Elderly ◽  
Nursing Intervention ◽  
Progressive Muscle Relaxation ◽  
Control Group ◽  
Relaxation Therapy ◽  
Before And After

Background: Sleep is one part of physiological needs and it is a basic need which is needed by all humans to be able to function optimally. However, the elderly will often experience sleep disorders. Sleep disorders in the elderly will affect the quality of sleep. One of nursing intervention that can improve the elderly sleeping quality is progressive muscle relaxation therapy. This study aims to determine the differences of sleep quality before and after having progressive muscle relaxation therapy toward the elderly with sleep disorders.Methods: This research was conducted at the Tresna Werdha Sabai Nan Aluih Social Home, Sicincin in 2019. The research was conducted for 2 weeks with one-week intervention. This research is a Quasy experiment using one group pre-test and post-test without control group design approach. This study used the sample of 16 respondents taken by purposive sampling. The analysis of data uses dependent T-test with a significance level of 95% (α 0.05).Results: The results of the study found that the average sleep quality of the elderly before being given the intervention was 13.63 and after the intervention it became 8.44 with p value of 0.000.Conclusions: The results showed that there were significant differences before and after the intervention. For this reason, it is recommended for the elderly with sleep disorders to be able to do progressive muscle relaxation therapy to improve sleep quality.  

scholarly journals Anticipating Atrial Fibrillation Signal Using Efficient Algorithm

2021 ◽  
Vol 17 (02) ◽  
pp. 106
Author(s):  
Mohand Lokman Ahmad Al-dabag ◽  
Haider Th. Salim ALRikabi ◽  
Raid Rafi Omar Al-Nima
Keyword(s):  
Atrial Fibrillation ◽  
Computational Cost ◽  
Extraction Methods ◽  
Support Vector ◽  
Ecg Signal ◽  
Heart Problem ◽  
Ecg Signals ◽  
The Right ◽  
Electrocardiogram Ecg ◽  
Low Computational Cost

One of the common types of arrhythmia is Atrial Fibrillation (AF), it may cause death to patients. Correct diagnosing of heart problem through examining the Electrocardiogram (ECG) signal will lead to prescribe the right treatment for a patient. This study proposes a system that distinguishes between the normal and AF ECG signals. First, this work provides a novel algorithm for segmenting the ECG signal for extracting a single heartbeat. The algorithm utilizes low computational cost techniques to segment the ECG signal. Then, useful pre-processing and feature extraction methods are suggested. Two classifiers, Support Vector Machine (SVM) and Multilayer Perceptron (MLP), are separately used to evaluate the two proposed algorithms. The performance of the last proposed method with the two classifiers (SVM and MLP) show an improvement of about (19% and 17%, respectively) after using the proposed segmentation method so it became 96.2% and 97.5%, respectively.

scholarly journals STUDI LITERATUR: PENGARUH TERAPI PIJAT REFLEKSI KAKI TERHADAP KUALITAS TIDUR LANJUT USIA

2021 ◽  
Vol 7 (1) ◽  
pp. 8-13
Author(s):  
Chaerun Nissa ◽  
Ashar Prima ◽  
Fauziah Hamid Wada ◽  
Puji Astuti ◽  
Salamah T Batubara
Keyword(s):  
United States ◽  
Literature Review ◽  
Sleep Quality ◽  
Sleep Disorders ◽  
Age Groups ◽  
The Elderly ◽  
Scientific Paper ◽  
The United States ◽  
Google Scholar ◽  
World Health

WHO states that Indonesia's population is the fourth largest population after China, India, and the United States. According to the 2013 World Health Statistics data, the population of China is 1.35 billion, India is 1.24 billion, the United States 313 million, and Indonesia is in fourth place with 242 million WHO population predicts that by 2020 the estimated number of Indonesia's elderly will be around 80,000,000. Cases of insomnia in the elderly are higher than in other age groups, which is 12–39%. One therapy that can overcome sleep disorders in the elderly is foot reflexology massage therapy. This literature review aims to determine the effect of foot reflexology massage in the elderly who experience sleep disorders. The design in this scientific paper is a literature review search using an electronic data base that is google scholar and pubmed. The keywords used in the search are elderly, foot reflexology, sleep of quality. The inclusion criteria used in the article are full text accessible in English and Indonesian, the year of the journal used is limited to the last ten years. The results found 1 article from Google Scholar and 2 articles from PubMed discussing the effectiveness of foot reflexology massage on improving sleep quality in the elderly. Literature review results from the three articles show that foot reflexology is effective in improving sleep quality in the elderly.  

Association Between Hypertension, Body Mass Index, and Sleep: A Cross-Sectional Study

2020 ◽  
Vol 3 (01) ◽  
pp. 11-14
Author(s):  
Aishwarya Gonzalez Cherubal ◽  
S. Pooja ◽  
Vijaya Raghavan
Keyword(s):  
Risk Factors ◽  
Body Mass Index ◽  
Sleep Quality ◽  
Sleep Disorders ◽  
Sleep Disorder ◽  
Sleep Duration ◽  
Body Mass ◽  
Quality Of Sleep ◽  
Cross Sectional

Background: Sleep disorders can act as risk factors and even aggravate underlying conditions. With prevalence of 17% in general population, hypertension is a leading cause of morbidity and mortality in India. Though hypertension has various well established risk factors like family history, sedentary lifestyle, poor diet, smoking and age, sleep is often an understudied and overlooked factor. Body mass index is another important risk factor for various physical conditions. Associations between sleep and body mass index have been documented in many studies around the world. Although a consensus is yet to be drawn, many studies highlight that BMI related disorders could be predicted by sleep duration and quality. Materials and Methods: Two hundred consecutive hypertensive patients who were attending the OPD for follow-up were included as participants in this study after obtaining an informed consent. A semi structured proforma was designed to elicit the socio demographic profile of the participants. Each participant was assessed for the presence of sleep disorders by sleep-50 questionnaire and quality of sleep by the Pittsburgh Sleep Quality Index (PSQI). Results: Results found that BMI was significantly correlated with sleep quality, sleep duration, and sleep disorder. Hypertension was not significantly correlated to sleep quality or duration but associated to sleep disorder. Conclusion: This study found that body mass index was significantly correlated with sleep variables such as sleep duration, sleep quality, and sleep disorders. Maintaining a healthy BMI could in fact impact the amount and quality of sleep an individual receives.

scholarly journals Cross-Sectional Survey on the Relationship Between Occupational Stress, Hormone Levels, and the Sleep Quality of Oilfield Workers in Xinjiang, China

2019 ◽  
Vol 16 (18) ◽  
pp. 3316 ◽  
Author(s):  
Xue Li ◽  
Xiaoyan Gao ◽  
Jiwen Liu
Keyword(s):  
Sleep Quality ◽  
Sleep Disorders ◽  
Sleep Disorder ◽  
Occupational Stress ◽  
Protective Factor ◽  
High Stress ◽  
Cross Sectional Survey ◽  
Subjective Sleep Quality ◽  
Cross Sectional ◽  
Quality Sleep

The impact of psychosocial factors on health has received increased attention. This study employed a multi-stage hierarchical cluster sampling method and a cross-sectional survey was conducted from March to August 2017. By studying 2116 oilfield workers based in Karamay, Xinjiang, the relationship between occupational stress, blood hormone levels, and sleep was analyzed. Occupational stress was measured using the internationally accepted Occupational Stress Inventory Revised Edition (OSI-R) questionnaire and sleep disorders were measured using the Pittsburgh Sleep Quality Index (PSQI) questionnaire. The study found that the sleep quality of respondents was not high and the incidence of sleep disorders was 36.67%. The higher the level of occupational stress, the higher the incidence of sleep disorders. Irregular shifts can affect sleep quality and individuals with high-level professional titles experience a higher incidence of sleep disorders than those with low-level titles. The total score of the PSQI was different among the low, medium, and high stress groups. The higher the level of stress, the higher the scores of subjective sleep quality, sleep disorder, and daytime dysfunction. The scores of the PSQI, subjective sleep quality, sleep time, sleep disturbance, and daytime dysfunction in the high-stress group were higher than those in the low stress group. A case-control study found that the concentration of glucocorticoids in the sleep disorder positive group was lower than that in the sleep disorder negative group. The results of the regression analysis showed that glucocorticoid is a protective factor for sleep disorders (OR = 0.989, 95% CI: 0.983–0.995), suggesting that the higher the level of glycosaminoglycan, the less likely the subject is to have sleep disorders. For example, in the case of high occupational stress, the interaction between low and moderate occupational stress levels and glucocorticoids is a protective factor for sleep disorders.

scholarly journals Association Between Hypertension, Body Mass Index, and Sleep: A Cross-Sectional Study

2020 ◽  
Vol 3 (01) ◽  
pp. 11-14
Author(s):  
Aishwarya Gonzalez Cherubal ◽  
S. Pooja ◽  
Vijaya Raghavan
Keyword(s):  
Risk Factors ◽  
Body Mass Index ◽  
Sleep Quality ◽  
Sleep Disorders ◽  
Sleep Disorder ◽  
Sleep Duration ◽  
Body Mass ◽  
Quality Of Sleep ◽  
Cross Sectional

Background: Sleep disorders can act as risk factors and even aggravate underlying conditions. With prevalence of 17% in general population, hypertension is a leading cause of morbidity and mortality in India. Though hypertension has various well established risk factors like family history, sedentary lifestyle, poor diet, smoking and age, sleep is often an understudied and overlooked factor. Body mass index is another important risk factor for various physical conditions. Associations between sleep and body mass index have been documented in many studies around the world. Although a consensus is yet to be drawn, many studies highlight that BMI related disorders could be predicted by sleep duration and quality. Materials and Methods: Two hundred consecutive hypertensive patients who were attending the OPD for follow-up were included as participants in this study after obtaining an informed consent. A semi structured proforma was designed to elicit the socio demographic profile of the participants. Each participant was assessed for the presence of sleep disorders by sleep-50 questionnaire and quality of sleep by the Pittsburgh Sleep Quality Index (PSQI). Results: Results found that BMI was significantly correlated with sleep quality, sleep duration, and sleep disorder. Hypertension was not significantly correlated to sleep quality or duration but associated to sleep disorder. Conclusion: This study found that body mass index was significantly correlated with sleep variables such as sleep duration, sleep quality, and sleep disorders. Maintaining a healthy BMI could in fact impact the amount and quality of sleep an individual receives.

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