Detection Of Sleep Disorders Research Articles

External Auditory Stimulation as a Non-Pharmacological Sleep Aid.

The increased demand for well-being has fueled interest in sleep. Research in technology for monitoring sleep ranges from sleep efficiency and sleep stage analysis to sleep disorder detection, centering on wearable devices such as fitness bands, and some techniques have been commercialized and are available to consumers. Recently, as interest in digital therapeutics has increased, the field of sleep engineering demands a technology that helps people obtain quality sleep that goes beyond the level of monitoring. In particular, interest in sleep aids for people with or without insomnia but who cannot fall asleep easily at night is increasing. In this review, we discuss experiments that have tested the sleep-inducing effects of various auditory stimuli currently used for sleep-inducing purposes. The auditory stimulations were divided into (1) colored noises such as white noise and pink noise, (2) autonomous sensory meridian response sounds such as natural sounds such as rain and firewood burning, sounds of whispers, or rubbing various objects with a brush, and (3) classical music or a preferred type of music. For now, the current clinical method of receiving drugs or cognitive behavioral therapy to induce sleep is expected to dominate. However, it is anticipated that devices or applications with proven ability to induce sleep clinically will begin to appear outside the hospital environment in everyday life.

Automated identification of sleep disorders using wavelet-based features extracted from electrooculogram and electromyogram signals

Sleep is imperative for a healthy life as it rejuvenates memory, cognitive performance, cell repair and eliminates waste from the muscles. Sleep-related disorders such as insomnia, narcolepsy, sleep-disordered breathing (SDB), periodic leg movement (PLM), and bruxism lead to hormonal imbalance, slower reaction time, memory problems, depression, and headaches. This adversity of sleep disorder gained the attention of many sleep researchers. To examine the reasons for sleep disorders, it is imperative to monitor and analyze the sleep of the affected patients. The conventional method of monitoring sleep and identifying the sleep disorders using polysomnographic (PSG) recording is a complicated and cumbersome task in which multiple physiological signals with multiple modalities are recorded for a long (overnight) duration. The PSG recordings are carried out in sophisticated sleep laboratories and cannot be considered suitable for real-time sleep monitoring. Thus, a simple and patient-convenient system is highly desirable to monitor and analyze the quality of sleep. We proposed an automatic detection of sleep disorders using single modal electrooculogram (EOG) and electromyogram (EMG) signals. We have used a new maximally flat multiplier-less biorthogonal filter bank for obtaining discrete wavelet transform of the signals. We computed Hjorth parameters (HOP) such as activity, mobility, and complexity from the wavelet sub-bands. Highly discriminative HOP features are fed to different machine learning classifiers to develop the model. Our results show that the developed system can classify insomnia, narcolepsy, NFLE, PLM, and REM behaviour disorder (RBD) against normal healthy subjects with an accuracy of 99.7%, 97.6%, 97.5%, 97.5%, and 98.3%, respectively using combined features from EOG and EMG signal. The proposed model has yielded an accuracy of 94.3% in classifying six classes using an ensemble bagged trees classifier (EBTC) with a 10-fold cross-validation technique. Hence, EOG and EMG-based proposed methods can be deployed in a portable home-based environment to identify the type of sleep disorders automatically.

Sleep stage based sleep disorder detection using single-channel electroencephalogram

Sleep stage based sleep disorder detection using single-channel electroencephalogram

Sleep stage based sleep disorder detection using single-channel electroencephalogram

Sleep stage based sleep disorder detection using single-channel electroencephalogram

Features of geriatric status of patients during the pandemic COVID-19

BACKGROUND: In older patients, the new coronavirus infection may present with atypical symptoms such as delirium, dizziness, and unusual fatigue. At the same time, restriction of movement and fear of infection during the COVID-19 pandemic affect decreased physical activity and increased frequency of anxiety, depression and sleep disorders. Due to these symptoms and conditions are significant falls risk factors, the risk of falls and the frequency of falls in older adults is expected to increase in the near future.
 AIM: To identify the new cases of COVID-19 infection in patients 65 and older and to determine the incidence and risk factors of falls in patients who have had COVID-19.
 MATERIALS AND METHODS: The study involved 86 patients territorially attached to the Family Medicine Center of the North-Western State Medical University named after I.I. Mechnikov. All patients who took part in the study had COVID-19 infection. 48 of them (available at the time of the study) were interviewed in order to establish falls risk factors and cases of falls. The control group consisted of 58 patients who had not had COVID-19 infection. Analysis of the outpatient records of all study participants, screening of frailty, questionnaires, assessment of emotional status, and detection of sleep disorders were performed.
 RESULTS: The frequency of neurological symptoms increased in COVID-19 infected patients: dizziness (p 0.05), unsteady gait (p 0.05), impaired or lost sense of smell (anosmia) and loss of taste (dysgeusia) (p 0.05), sleep disorders, symptoms of depression and anxiety. The proportion of patients with decreased body weight was significantly higher after the disease (p = 0.0127). Patients in the control group also had a high frequency of complaints of memory and attention disorders, decreased vision, symptoms of anxiety and depression, and difficulty moving around. The incidence of frailty symptoms in the control group was 63.1% (n = 53). Patients who have not been infected with COVID-19 were significantly more likely to report imbalanced walking 60.3% (n = 35) than patients who had COVID-19 infection 37.5% (n = 18) (p = 0.002). One-third of COVID-19 survivors had elevated plasma glucose levels for the first time, 3.4% had worsened diabetes, and 5.8% of participants were rehospitalized within 3 months of illness.
 CONCLUSIONS: The study demonstrated a high incidence of neurological symptoms in participants who had COVID-19: dizziness, unstable gait, sleep disorders, symptoms of depression and anxiety, and weight loss complaints. Patients who had not had COVID-19 infection showed a high frequency of complaints of memory and attention impairment, decreased vision, symptoms of anxiety and depression, and imbalanced walking. The syndromes and symptoms identified in both groups were risk factors for falls and the risk of developing a fear of falling.

Association Between Sleep Disorders and Cognitive Impairment in Middle Age and Older Adult Hemodialysis Patients: A Cross-Sectional Study.

Objective: The aims of the current study were to (1) explore the features of overall poor sleep and specific sleep disorders in Chinese middle age and older adult hemodialysis patients; (2) examine the association between sleep disorders and cognitive impairment (CI) in middle age and older patients undergoing hemodialysis in China.Methods: Data of patients undergoing maintenance hemodialysis were collected from the prospective cohort study of CI in Chinese patients undergoing hemodialysis (Registered in Clinical Trials.gov, ID: NCT03251573). We included 613 patients (mean age = 63.7; SD = 7.8) in this study. We assessed sleep conditions using the Pittsburgh Sleep Quality Index (PSQI) questionnaire and cognitive function by the Chinese Beijing version of the Montreal Cognitive Assessment (MoCA-BJ) scale. Then the association between sleep disorders and CI was evaluated using multivariate logistic regression analysis.Results: The prevalence of sleep disorders in this group of 613 hemodialysis patients was 77.0%. Patients with CI were more inclined to have sleep disorders in specific aspects of sleep latency, habitual sleep efficiency, sleep disturbances, and daytime dysfunction (p < 0.05). In multivariate logistic regression analyses, every 1-point increase in global PSQI score was associated with a 1.2-fold increased risk of CI (adjusted OR = 1.201; 95%CI = 1.123–1.284, p < 0.001). For each specific PSQI, every 1-point increase in sleep disturbances score was associated with a 2.6-fold increased risk of CI (adjusted OR = 2.624; 95%CI = 1.891–3.640, p < 0.001), and every 1-point increase in daytime dysfunction score was associated with a 3.7-fold increased risk of CI (adjusted OR = 3.709; 95%CI = 2.653–5.184, p < 0.001), whereas every 1-point increase in sleep duration score was associated with a decreased risk of CI (adjusted OR = 0.600; 95%CI = 0.434–0.830, p = 0.002).Conclusion: Poor sleep quality especially sleep disturbances, daytime dysfunction, and long sleep duration are associated with CI in middle age and older adult hemodialysis patients. Thus, the early detection of sleep disorders may help identify patients with cognitive impairment among hemodialysis individuals.Clinical Trial Registration: [Clinical Trials.gov], identifier [NCT03251573]

An automatic sleep disorder detection based on EEG cross-frequency coupling and random forest model

Objective. Sleep disorders are medical disorders of a subject’s sleep architecture and based on their severity, they can interfere with mental, emotional and physical functioning. The most common ones are insomnia, narcolepsy, sleep apnea, bruxism, etc. There is an increased risk of developing sleep disorders in elderly like insomnia, periodic leg movements, rapid eye movement behavior disorders, sleep disorder breathing, etc. Consequently, their accurate diagnosis and classification are important steps towards an early stage treatment that could save the life of a patient. Approach. The electroencephalographic (EEG) signal is the most sensitive and important biosignal, which is able to capture the brain sleep activity that is sensitive to sleep. In this study, we attempt to analyze EEG sleep activity via complementary cross-frequency coupling (CFC) estimates, which further feed a classifier, aiming to discriminate sleep disorders. We adopted an open EEG database with recordings that were grouped into seven sleep disorders and a healthy control. The EEG brain activity from common sensors has been analyzed with two basic types of CFC. Main results. Finally, a random forest (RF) classification model was built on CFC patterns, which were extracted from non-cyclic alternating pattern epochs. Our RFCFC model achieved a 74% multiclass accuracy. Both types of CFC, phase-to-amplitude and amplitude–amplitude coupling patterns contribute to the accuracy of the RF model, thus supporting their complementary information. Significance. CFC patterns, in conjunction with the RF classifier proved a valuable biomarker for the classification of sleep disorders.

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

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 &amp; different type of sleep disordered subjects &amp; application of signal processing on EEG data for development the algorithm for detection of sleep disorder and implementation in MATLAB.

STQS: Interpretable multi-modal Spatial-Temporal-seQuential model for automatic Sleep scoring

Sleep scoring is an important step for the detection of sleep disorders and usually performed by visual analysis. Since manual sleep scoring is time consuming, machine-learning based approaches have been proposed. Though efficient, these algorithms are black-box in nature and difficult to interpret by clinicians. In this paper, we propose a deep learning architecture for multi-modal sleep scoring, investigate the model's decision making process, and compare the model's reasoning with the annotation guidelines in the AASM manual. Our architecture, called STQS, uses convolutional neural networks (CNN) to automatically extract spatio-temporal features from 3 modalities (EEG, EOG and EMG), a bidirectional long short-term memory (Bi-LSTM) to extract sequential information, and residual connections to combine spatio-temporal and sequential features. We evaluated our model on two large datasets, obtaining an accuracy of 85% and 77% and a macro F1 score of 79% and 73% on SHHS and an in-house dataset, respectively. We further quantify the contribution of various architectural components and conclude that adding LSTM layers improves performance over a spatio-temporal CNN, while adding residual connections does not. Our interpretability results show that the output of the model is well aligned with AASM guidelines, and therefore, the model's decisions correspond to domain knowledge. We also compare multi-modal models and single-channel models and suggest that future research should focus on improving multi-modal models.

Salivary Inflammatory Molecules as Biomarkers of Sleep Alterations: A Scoping Review.

Poor sleep quality and sleep disorders are the most common problems in people, affecting health-related quality of life. Various studies show an association between sleep disorders and altered levels of stress hormones and inflammatory cytokines measured in saliva. The main objective of this article is to provide an analysis of the current evidence related to changes in inflammatory markers in the saliva and their associations with sleep quality measurement (both objective and subjective methods) in healthy subjects and in sleep-related disorders. To that end, a scoping review was carried out, following the PRISMA criteria in the bibliographic search in several databases: PubMed, EBSCO, and SCOPUS. Eleven of the articles are from the adult population and two from the child-youth population. They mainly measure the relationship between sleep and interleukin-1β (IL-1β), interleukin-6 (IL-6), and tumor necrosis factor alpha (TNFα) alpha, as well as other inflammatory markers such as myeloperoxidase (MPO) and prostaglandin-endoperoxide synthase 2. An analysis shows the relationship between these salivary biomarkers and sleep quality, especially in the case of IL-6 in both healthy subjects and several pathologies associated with sleep-disorders. The results for TNFα and IL-1β measurements are still inconclusive and the difference with IL-6 was assessed. Two studies reported interventions that result in sleep improvement and are accompanied by the normalization of inflammatory changes detected in the saliva. As it is an easy-to-apply and non-invasive method, the measurement of salivary cytokines can be very useful in chronobiology studies. Further studies are required to determine the sensitivity of salivary inflammatory markers in monitoring biological rhythms and acting as biomarkers in the detection of sleep disorders and sleep interventions.

Prevalence of Sleep Disorders among Epileptic Children Referring To Emam-Reza Clinic and Comparing With Non-Epileptic Group during 2013-2014

Introduction: The prevalence of sleep disorders are 15-42 percent in world population. Sleep disorders can be the result of physical, psychological and environmental factors. The prevalence of these disorders is higher in epileptic children. The aim of this study was identifying the rapid diagnosis of sleep disorders in epileptic children in order to reduce the disease problems. Methods: In this case-control study, 160 children in the age range of 4-16 with epilepsy and sleep disorders were evaluated. In order to data analysis SPSS version 16, chi-square test; quantitative data, Mann-Whitney non-parametric test were used. Linear regression analysis was used to determine the effect of seizure variables on the severity of sleep disorders criteria. Results: Our results indicated that the incidence of sleep disorders are significantly higher among epileptic children compared to non-epileptic ones (p&lt;0.05). In addition, we observed a significant correlation among some seizure variables such as etiology (p&lt;0.003), response (p&lt;0.01), abnormal EEG (p&lt;0.003), abnormal MRI and CT (p&lt;0.003), age (p&lt;0.003), seizure duration (p&lt;0.002) and sex (p&lt;0.04) with the incidence of sleep disorders. Conclusion: Some of the epileptic variables, which have a significant correlation with the prevalence of sleep disorders can be considered as the possible parameters for predicting faster sleep disorders in children with epilepsy. Detection of sleep disorders in these children can reduce their problems.

Evaluation of sleep hygiene and prevalence of sleep disorders in patients with monosymptomatic enuresis. Usefulness of the BEARS sleep screening tool

ObjectivesTo analyze if there is a higher prevalence of sleep disturbances and hygiene disorders in patients with monosymptomatic enuresis (MEN) with respect to general population and to patients with corrected MEN. Assess the usefulness of the BEARS sleep disorder screening tool for children with MEN. Material and methodsTransverse observational study (n = 341) classified as: MEN (n = 122), corrected MEN (≥1 one year without recurrence) (ANTENUR) (n = 47) and controls (n = 172). The BEARS childhood sleep disorder screening questionnaire was used. Clinical variables were collected, as well as variables related to sleep hygiene. Statistics: Chi-square, Student's t, ANOVA, Mann-Whitney U and Kruskal-Wallis. Significance p < 0.05. ResultsMean age 9.7 ± 3.0 years. There were no statistically significant differences in terms of age, sex, BMI, history of tonsillectomy, asthma and sleep time hours. With respect to sleep hygiene, patients with MEN presented a higher percentage of alterations than controls. As for the BEARS questionnaire, it showed a higher prevalence of sleep disorders in the group of patients with MEN, with respect to the other two groups: 60.7% (n = 74) versus 18.6% (n = 32) and 38.3% (n = 18) of controls and ANTENUR, respectively (p < 0.05). ConclusionsChildren with MEN had a higher prevalence of sleep disturbances and sleep disorders than controls. The BEARS questionnaire is a useful clinical tool in the detection of sleep disorders in children.

Evaluación de la higiene del sueño y la prevalencia de sus alteraciones en los pacientes con enuresis monosintomática. Utilidad del cuestionario BEARS

ObjectivesTo analyze if there is a higher prevalence of sleep disturbances and hygiene disorders in patients with monosymptomatic enuresis (MEN) with respect to general population and to patients with corrected MEN. Assess the usefulness of the BEARS sleep disorder screening tool for children with MEN. Material and methodsTransverse observational study (n=341) classified as: MEN (n=122), corrected MEN (≥ 1 one year without recurrence) (ANTENUR) (n=47) and controls (n=172). The BEARS childhood sleep disorder screening questionnaire was used. Clinical variables were collected, as well as variables related to sleep hygiene. Statistics: Chi-square, Student's t, ANOVA, Mann-Whitney U and Kruskal-Wallis. Significance p<.05. ResultsMean age 9.7±3.0 years. There were no statistically significant differences in terms of age, sex, BMI, history of tonsillectomy, asthma and sleep time hours. With respect to sleep hygiene, patients with MEN presented a higher percentage of alterations than controls. As for the BEARS questionnaire, it showed a higher prevalence of sleep disorders in the group of patients with MEN, with respect to the other two groups: 60.7% (n=74) versus 18.6% (n=32) and 38.3% (n=18) of controls and ANTENUR, respectively (p<.05). ConclusionsChildren with MEN had a higher prevalence of sleep disturbances and sleep disorders than controls. The BEARS questionnaire is a useful clinical tool in the detection of sleep disorders in children.

The Spectrum of Sleep Disorders Among Children: A Cross-sectional Study at a South Indian Tertiary Care Hospital.

IntroductionSleep problems during infancy and early childhood are fairly common and rarely recognized in pediatric practice. These are mostly related to the initiation and maintenance of night-time sleep. Understanding sleep patterns and disorders associated with sleep is challenging, especially in the pediatric age group. This study was done to estimate the magnitude of sleep disorders in children and to evaluate the associated risk factors.MethodsThis cross-sectional study was carried out among 450 children visiting the pediatric outpatient department of Sri Ramachandra Institute of Higher Education and Research, Chennai, India between November 2018 and June 2019. Children with chronic illnesses and a history of physical or mental trauma in the past six months were excluded. The Sleep Disturbance Scale for Children (SDSC) was used to gather information regarding sleep disorders.ResultsIt was observed that a majority of the participants (72.2%) slept 9-11 hours per day. Among 46.2% of the participants, the time lag between bedtime and sleep time was less than 15 minutes. Overall, sleep problems were present in 34% of the participants. History of sleep problems in infancy, absence of siblings, and parental presence while sleeping emerged as statistically significant risk factors for childhood sleep disorders (p: <0.05).ConclusionWe believe our study provides a basis for exploring the pattern and problems associated with sleep behavior among children. There is a need for setting up routine screening measures in pediatric outpatient departments to facilitate early detection of sleep disorders in order to avoid complications.

An Efficient Motion and Noise Artifacts Removal Method using GAIT and Machine Learning Model

obtaining an exact measurement of oxygen saturation (SpO2) using a finger-probe based pulse oximeter is dependent on both artifact-free infrared (IR) and red (R) Photoplethysmographic signals. However, in actual real-time environment condition, these Photoplethysmographic signals are corrupted due to presence of motion artifact (MA) signal that is produced due to the movement/motion from either hand or finger. To address this motion artifacts interference, the cause of the contamination of Photoplethysmographic signals by the motion artifacts signal is observed using GAIT. Motion and noise artifacts enforce constraints on the usability of the Photoplethysmographic, predominantly in the setting of sleep disorder detection and ambulatory monitoring. Motion and noise artifacts can alter Photoplethysmographic, resulting wrong approximation of physiological factors such as arterial oxygen saturation and heart rate. For overcoming issues and problems, this manuscript presented a new approach for detection of artifacts. First, present an adaptive filter and adaptive threshold model to detect artifact and obtain derivative of correlation coefficient (CC) for labelling artifacts, respectively. Lastly, Improved Support Vector Machine Model is presented to perform classification. Experiment are conducted on real-time dataset. Our approach attain significant performance in term of accuracy, sensitivity, specificity and positive prediction.

Comparing Classification via Regression and Random Committee for Automatic Sleep Stage Classification in Autism Patients

The prevalence of autism children has increased rapidly in the last few periods. There is no cure for autism. But the management and treatment of accompanying medical conditions can be done. One of the effects of his medical condition is a sleep disorder. But children with autism have difficulty communicating the disorders they experience. In medicine, the detection of sleep disorders can be done through a test called polysomnography. One of the purposes of this test is to find the patient’s sleep patterns through the sleep stage classification. But the doctors need several days to analyze each test. This study proposes an application that can classify it automatically. The method used was based on machine learning. The two classifiers were classification via regression and random committee. The both performances were compared in sleep stages classification for the autism patients. The result showed that random committees had a higher performance than classification via regression. Its performance got more than 85% for accuracy, precision, recall, and F-measure. This study also implemented resampling to overcome imbalance class problems. It can be seen that this process was useful in improving the performance of both classifiers.

Sleep disturbance scale for children as a diagnostic tool for sleep disorders in adolescents

Background. Sleep is an essential needs for every children. Sleep may cause a large effect on mental health, emotional, physical and immune system. If sleeping time is not sufficient, then sleep disturbance may occur. Objective assessment of the sleeping quality can be done by using Polysomnography and Actigraphy, while for assessing the subjectivity of sleeping quality and quantity can be used questionnaires or interviews. Sleep Disturbance Scale for Children or SDSC is a multidimensional sleeping assessment questionnaire.Objective. To determine the quality of sleeping in adolescents using SDSC.Methods. We conducted a diagnostic test study using cross sectional method, during March to April 2015 at the elementary schools in manado. The inclusion criteria were healthy adolescent aged 10 to 12 years, the parents were willing to sign the informed consent and answer the questionnaire, agreed to attach the wristactigraph. All datas were analyzed using chi square test and table 2 x 2 to showed sensitivity, specificity, positive predicitive value and negative predictive value.Results. Of 60 adolescents, 31 were female and 29 were male with the mean age 11.39 years. The results showed the sensitivity of SDSC was 80.6%, the specificity was 37.9%, the positive predictive value was 58.1% and negative predictive value was 64.7%.Conclusions. Sleep Disturbance Scale for Children is a good screening tool for early detection of sleep disorders in a adolescent.

Multi-Class Sleep Stage Analysis and Adaptive Pattern Recognition

Multimodal signal analysis based on sophisticated sensors, efficient communication systems and fast parallel processing methods has a rapidly increasing range of multidisciplinary applications. The present paper is devoted to pattern recognition, machine learning, and the analysis of sleep stages in the detection of sleep disorders using polysomnography (PSG) data, including electroencephalography (EEG), breathing (Flow), and electro-oculogram (EOG) signals. The proposed method is based on the classification of selected features by a neural network system with sigmoidal and softmax transfer functions using Bayesian methods for the evaluation of the probabilities of the separate classes. The application is devoted to the analysis of the sleep stages of 184 individuals with different diagnoses, using EEG and further PSG signals. Data analysis points to an average increase of the length of the Wake stage by 2.7% per 10 years and a decrease of the length of the Rapid Eye Movement (REM) stages by 0.8% per 10 years. The mean classification accuracy for given sets of records and single EEG and multimodal features is 88.7% ( standard deviation, STD: 2.1) and 89.6% (STD:1.9), respectively. The proposed methods enable the use of adaptive learning processes for the detection and classification of health disorders based on prior specialist experience and man–machine interaction.

Frequency of Loud Snoring and Metabolic Syndrome among Korean Adults: Results from the Health Examinees (HEXA) Study.

Studies regarding the association between snoring and metabolic abnormalities have been inconsistent. We examine whether snoring frequency and obstructive sleep apnea markers are associated with metabolic syndrome (MetS) among Koreans aged 40–69 years. A total of 72,885 subjects (24,856 men, 48,029 women) from the Health Examinees Gem study between 2009 and 2013 were included. Snoring frequency was grouped into five categories (never, 1–3/month, 1–3/week, 4–5/week, 6+/week). Obstructive sleep apnea markers included breathing interruptions and awakenings. Adjusted odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated through logistic regression. Compared with non-snorers, those who snore 6+/week were associated with increased odds for MetS (OR: 2.07, 95% CI: 1.91–2.25, p-trend < 0.0001 among men; OR: 1.45, CI: 1.33–1.58, p-trend < 0.0001 among women). Snoring frequency is associated with MetS and its components in both men and women. Snoring and obstructive sleep apnea markers are important indicators of sleep quality, which may facilitate early detection of sleep disorders and further complications such as MetS.

Actigraphy in Holter Monitoring for Assessment of Sleep Parameters and Periods

An automated algorithm for identifying periods of sleep and waking using data from the motor activity of the subject’s thoracic cage is proposed. The algorithm is based on accelerometry and is intended as a component of systems used for prolonged monitoring of cardiovascular system functions. Use of this algorithm allows trace segments to be defined as “sleep” or “waking” with accuracies of up to 77.6% in the overall cohort, 83.7% when there are no sleep disorders, and 70.5% in patients with respiratory abnormalities during sleep. Use of the algorithm in Holter monitoring systems in routine practice will help improve the accuracy of diagnostic results in cardiovascular diseases and the detection of sleep disorders.