Traditional Polysomnography Research Articles

The rate of decrease in oxygen desaturation during severe obstructive sleep apnea syndrome is correlated with subjective excessive daytime sleepiness.

To explore factors that influence subjective excessive daytime sleepiness (EDS) in patients with severe obstructive sleep apnea syndrome (OSAS). Patients with snoring seen at the Sleep Medicine Center of The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University between October 2018 and November 2019 were included in this study. All patients underwent polysomnography (PSG). Noninvasive frequency-domain analysis was used to assess the autonomic nervous system regulation of the heart, with the low frequency (LF)/high frequency (HF) power ratio used to represent the sympathetic-parasympathetic balance. Daytime sleepiness was evaluated by the Epworth sleepiness scale (ESS). Overnight apnea episodes were included for analyses. The rate of pulse oxyhemoglobin saturation (SpO2) decrease was measured as the change in the percentage of SpO2 per second after obstructive apnea and was expressed as the oxygen desaturation rate (ODR). A total of 101 patients with severe OSAS were enrolled in this study and were further divided into two groups: the EDS group (ESS > 10, n = 52) and the non-EDS group (ESS ≤ 10, n = 49). The apnea-hypopnea index (AHI), respiratory effort-related arousals (RERAs), and LF/HF power ratio were significantly higher in the EDS group than in the non-EDS group (AHI: 69.9 ± 14.5 vs. 57.9 ± 16.1 events/h; RERAs: 42.2 ± 16.7 vs. 30.4 ± 13.7 events/h; LF/HF power ratio: 2.9 ± 0.8% vs. 2.4 ± 0.9%, all p < 0.001). Multiple linear regression analyses revealed that after adjusting for covariates expected to affect this relationship, ESS scores were correlated with ODR (β = 0.520, p < 0.001) and LF/HF power ratio (β = 0.155, p = 0.028), rather than with the traditional sleep-disordered breathing parameters. Compared with the traditional PSG parameters, both ODR and an increased LF/HF power ratio were more closely related to daytime sleepiness, especially ODR.

Deep Recurrent Neural Networks for Automatic Detection of Sleep Apnea from Single Channel Respiration Signals.

Sleep apnea is a common sleep disorder that causes repeated breathing interruption during sleep. The performance of automated apnea detection methods based on respiratory signals depend on the signals considered and feature extraction methods. Moreover, feature engineering techniques are highly dependent on the experts’ experience and their prior knowledge about different physiological signals and conditions of the subjects. To overcome these problems, a novel deep recurrent neural network (RNN) framework is developed for automated feature extraction and detection of apnea events from single respiratory channel inputs. Long short-term memory (LSTM) and bidirectional long short-term memory (BiLSTM) are investigated to develop the proposed deep RNN model. The proposed framework is evaluated over three respiration signals: Oronasal thermal airflow (FlowTh), nasal pressure (NPRE), and abdominal respiratory inductance plethysmography (ABD). To demonstrate our results, we use polysomnography (PSG) data of 17 patients with obstructive, central, and mixed apnea events. Our results indicate the effectiveness of the proposed framework in automatic extraction for temporal features and automated detection of apneic events over the different respiratory signals considered in this study. Using a deep BiLSTM-based detection model, the NPRE signal achieved the highest overall detection results with true positive rate (sensitivity) = 90.3%, true negative rate (specificity) = 83.7%, and area under receiver operator characteristic curve = 92.4%. The present results contribute a new deep learning approach for automated detection of sleep apnea events from single channel respiration signals that can potentially serve as a helpful and alternative tool for the traditional PSG method.

1206 Automated Sleep Apnea Assessment Based On Machine Learning And Wearable Technology

Abstract Introduction Obstructive sleep apnea (OSA) is a condition characterized by repeated episodes of partial or complete obstruction of the respiratory passages during the sleep. Traditional polysomnography (PSG) for OSA estimation is bulky and time-consuming for daily use. Therefore, this study aims to develop a novel photoplethysmography (PPG) and accelerometer based smart watch for OSA detection, in which a high-performance and low-complexity automated OSA detection was embedded for long-term in-home measurement. Methods The developed watch measured PPG signals from wrist radial artery and body motion from accelerometer as well. 121 patients (92 males, 29 females) were recruited from the normal community and Center of Sleep, National Taiwan University Hospital, Taiwan in this study. All OSA scoring were analyzed by three registered PSG technologists. The AHI of the cohort was 10.1±18.3 (0 to 82.7). An automated OSA detection algorithm was designed based on machine-learning (ML) technique, in which was iteratively updated according to each 30-second epoch of the collected data. Subsequently, obstructive and hypopnea events were detected according to the OSA detection algorithm. Results To better valid the effectiveness, this study focused on the estimation performance of the subjects with AHI&amp;gt;15. Based on hold-out validation, the average sensitivity and precision in the AHI&amp;gt;15 cohort were 77.2% and 58.6%, respectively, with a Cohen’s kappa of 0.46. The interclass correlation between the watch and technologists was 0.81 (95%CI: 0.61-0.91). The result showed that the proposed automated OSA detection could achieve consistent result with technologist during standard sleep testing. Conclusion This study developed a wrist-based watch based on ML technique to assess OSA severity. We compared the performance with clinical technologists for the OSA detection. Further, the sensitivity and precision were generally acceptable while subjects were with AHI&amp;gt;15. The proposed wrist-based watch could provide reliable performance for OSA estimation, and may be of a light for future in-home sleep studies. Support This study was supported by Mediatek Inc.

1205 Activity Trackers As A Tool In Sleep Research: Determining Discrepancies In Trackers Vs. PSG

Abstract Introduction Fitness-based wearables and other emerging sensor technologies have the potential to track sleep across large populations longitudinally in at-home environments. To understand how these devices can inform research studies, limitations of available trackers need to be compared to traditional polysomnography (PSG). Here we assessed discrepancies in sleep staging in activity trackers vs. PSG in subjects with various sleep disorders. Methods Twelve subjects (age 41-78, 7f, 5m) wore a Fitbit Charge 3 while undergoing a scheduled sleep study. Six subjects had been previously diagnosed with a sleep disorder (5 OSA, 1 CSA). 4 subjects used CPAP throughout the night, 2 had a split night (CPAP 2nd half of the night), and 6 had a PSG only. Activity tracker staging was compared to 2 RPSGTs staging. Results Of the 12 subjects, eight subjects’ sleep was detected in the activity tracker, and compared across sleep stages to the PSG (7 female, 1 male, ages 41-78, AHI 0.3-87, RDI 0.5-94.4, sleep efficiency 74%+/-18, 4 PSG, 1 split, 3 CPAP). The activity tracker matched either tech 52% (+/- 13). The average difference in score tech and activity tracker staging for sleep onset (SO) was 16 +/- 15 minutes and wake after sleep onset was 43.5 +/- 44 minutes. Sensitivity, specificity, and balanced accuracy were found for each sleep stage. Respectively, Wake: 0.45+/-0.27, 0.97+/-0.03, 0.71+/-0.12, REM: 0.41+/-0.30, 0.90+/-0.06, 0.60+/-0.28, Light: 0.71+/-0.09, 0.58+/-0.19, 0.65+/-0.10, Deep: 0.63+/-0.52, 0.88+/-0.05, 0.59+/-0.49. Conclusion From this study of 12 subjects seen at a sleep clinic for suspected sleep disorders, activity trackers performed best in wake, REM and deep sleep specificity (&amp;gt;=88%), while they lacked sensitivity to REM and wake (&amp;lt;=45%) stages. The tracker did not detect sleep in 4 subjects who had elevated AHI or low sleep efficiency. Further analysis can identify whether discrepancies between the Fitbit and PSG can be predicted by distinct patterns in sleep staging and/or identify subject exclusion criteria for activity tracking studies. Support This project in on-going with the support of Academy Diagnostics Sleep and EEG Center and staff.

The fusion of Internet of Intelligent Things (IoIT) in remote diagnosis of obstructive Sleep Apnea: A survey and a new model

Obstructive Sleep Apnea (OSA) syndrome is one of the most widespread diseases that difficult to be detected and remedied. In particular, the examination of OSA by using the traditional Polysomnography (PSG) is one of formidable complexity as it requires full observation in a laboratory overnight. Meanwhile, the number of available laboratories and beds is minimal comparing to the number of OSA patients. What's more, the unusual environment and restricted mobility of patients may result in deficient diagnosis results. The Internet of Things (IoT) is the most appropriate solution for the previous diagnosis obstacles by allowing doctors to synchronize patient status. Besides, several studies have been introduced to consolidate the performance of IoT interoperability via the fusion with Artificial Intelligence (AI) resulting in the Internet of Intelligent Things (IoIT). This paper presents a literature survey about the intensification of IoT technologies for smart monitoring of sleep quality and OSA diagnosis. Mainly, the most recent enabling IoT and support technologies such as (smart devices, fog computing, cloud, big data, and machine learning) are covered via the discussion of more recent works of literature published from 2016 to 2019. Also, the roles of AI in optimizing the efficiency of OSA smart diagnosis are presented. Besides, a new comprehensive IoIT optimization framework is presented which employing AI for optimizing the performance of intelligent diagnosis of OSA. Finally, the open issues and challenges in this field are argued. This paper is, therefore, a major contributor to the compilation of all IoT innovative and efficient AI methods that improving the quality of OSA diagnosis.

Portable Detection of Apnea and Hypopnea Events Using Bio-Impedance of the Chest and Deep Learning.

Sleep apnea is one of the most common sleep-related breathing disorders. It is diagnosed through an overnight sleep study in a specialized sleep clinic. This setup is expensive and the number of beds and staff are limited, leading to a long waiting time. To enable more patients to be tested, and repeated monitoring for diagnosed patients, portable sleep monitoring devices are being developed. These devices automatically detect sleep apnea events in one or more respiration-related signals. There are multiple methods to measure respiration, with varying levels of signal quality and comfort for the patient. In this study, the potential of using the bio-impedance (bioZ) of the chest as a respiratory surrogate is analyzed. A novel portable device is presented, combined with a two-phase Long Short-Term Memory (LSTM) deep learning algorithm for automated event detection. The setup is benchmarked using simultaneous recordings of the device and the traditional polysomnography in 25 patients. The results demonstrate that using only the bioZ, an area under the precision-recall curve of 46.9% can be achieved, which is on par with automatic scoring using a polysomnography respiration channel. The sensitivity, specificity and accuracy are 58.4%, 76.2% and 72.8% respectively. This confirms the potential of using the bioZ device and deep learning algorithm for automatically detecting sleep respiration events during the night, in a portable and comfortable setup.

Cross-sectional association of traditional and novel polysomnography metrics with atherosclerosis in obstructive sleep apnoea

Cross-sectional association of traditional and novel polysomnography metrics with atherosclerosis in obstructive sleep apnoea

Associations between quantitative sleep EEG and subsequent cognitive decline in older women.

The pathophysiological processes of Alzheimer's dementia predate its clinical manifestation. Sleep disturbances can accelerate the aging process and are common features of dementia. This study examined whether quantitative sleep electroencephalogram changes predate the clinical development of mild cognitive impairment and/or incident dementia. We collected data from a nested case-control sample of women (mean age 83years) from the Sleep and Cognition Study, an ancillary study to the longitudinal Study of Osteoporotic Fractures, who were characterized as cognitively normal at the time of a baseline polysomnography study (Study of Osteoporotic Fractures visit 8) based on a Mini-Mental Status Exam (MMSE) score >24. Cases (n=85) were women who developed new mild cognitive impairment or dementia by objective cognitive testing 5years after polysomnography. Controls were women with no mild cognitive impairment/dementia (n=85) at baseline or at follow-up. Differences in electroencephalogram absolute and relative power density were observed between the two groups. Specifically, higher electroencephalogram power values were found in the dementia/mild cognitive impairment group, for the alpha (p=.01) and theta bands (p=.04) in non-rapid eye movement sleep, as well as alpha (p=.04) and sigma (p=.04) bands in rapid eye movement sleep. In contrast, there were no group differences in traditional polysomnography measures of sleep architecture and sleep stage distribution, as well as sleep apnea and periodic limb movement indices. Our results provide evidence for quantitative electroencephalogram changes, which precede the clinical onset of cognitive decline and the diagnosis of dementia in elderly women, and support the application of quantitative sleep electroencephalogram analysis as a promising biomarker for imminent cognitive decline.

Validation of non-invasive monitoring device to evaluate sleep quality.

This paper presents the validation of a noninvasive sleep monitoring device and sleep quality evaluation. The system is based on an array of pressure sensors positioned on the bed that can measure relevant parameters to estimate sleep quality. This device can measure time in bed (TB), body movements (BM), the thorax expansion, periods out of bed (POB) and apnea events. We compare the results of the unobtrusive pressure sensor array with traditional polysomnography (PSG). The algorithms developed for the device provide a very good detection of all the parameters required to estimate sleep quality as compared to the gold-standard data for a study of 30 patients. For the periods out of bed it presents a sensibility (Se) and specificity (Sp) of 100%. To grade the severity of sleep apnea we compare the number of events per hour or apnea-hypopnea index (AHI) obtained by the device and the PSG; obtaining a Se of 94% and a Sp of 61%. These results validate the use of the device for screening of sleep apnea disease.

Real-time obstructive sleep apnea detection from frequency analysis of EDR and HRV using Lomb Periodogram.

In this study, an effective real-time obstructive sleep apnea (OSA) detection method from frequency analysis of ECG-derived respiratory (EDR) and heart rate variability (HRV) is proposed. Compared to traditional Polysomnography (PSG) which needs several physiological signals measured from patients, the proposed OSA detection method just only use ECG signals to determine the time interval of OSA. In order to be feasible to be implemented in hardware to achieve the real-time detection and portable application, the simplified Lomb Periodogram is utilized to perform the frequency analysis of EDR and HRV in this study. The experimental results of this work indicate that the overall accuracy can be effectively increased with values of Specificity (Sp) of 91%, Sensitivity (Se) of 95.7%, and Accuracy of 93.2% by integrating the EDR and HRV indexes.

Sleep monitoring in ICU patients

Sleep disruption and deprivation is a continuing problem in the ICU. Strategies to improve sleep are confounded by difficulties in monitoring and measuring sleep in the ICU; traditional polysomnography cannot be utilized. Practical, non-intrusive diagnostic monitoring of sleep is required. The aims were to test two new ambulatory sleep diagnostic devices to monitor sleep in the ICU, compare sleep data generated by the different devices, and characterize sleep in the ICU.

Multi-scale entropy-based measures of electroencephalogram during sleep as quantitative criteria for chronic insomnia

Introduction Patterns of transient EEG correlates of sleep, such as sleep spindles, can potentially serve as fingerprint of the underlying neurological disease. Our primary goal was to explore the feasibility of adopting complexity measures of sleep architecture as quantitative criteria for insomnia diagnostics and to assess its discriminative power in comparison with traditional polysomnography (PSG) measures through Receiver Operating characteristic (ROC) analyses. Materials and methods Participants were 29 normal sleepers and 26 insomnia patients who underwent 1 overnight ambulatory PSG. Records were scored by a registered sleep technician and were finalized by a doctoral level sleep specialist. Sleep spindles were detected from EEG waveforms during NREM sleep with frequency ranges 11–15 Hz. The three entropy-based measures used: (1) univariate entropy measuring the variability of spindle events over REM cycles, (2) multi-scale entropy measuring the variability of REM periods over time (MSER) and (3) multi-scale entropy measuring the variability of spindle event intervals (MSES). ROC was conducted to compare sensitivity and specificity of primary measures at varying thresholds. Area Under the Curve (AUC) served as the primary index of accuracy. Finally, Spearman correlation between entropy scores and the sleep severity index were calculated within 26 insomnia patients. Results ROC analyses of traditional PSG-based measures show the following rank of AUC: sleep efficiency (0.60) > WASO (0.57) > sleep latency (0.56) > sleep duration = REM duration = spindle numbers (0.55). Sleep efficiency is the best discriminator, consistent with published literature on ROC analysis of PSG-based measures in insomnia, but all measures fall into the “low” discriminative class (AUC MSER (0.69) > cyclic spindles (0.57). Only the full-range spindles demonstrated significant association with sleep severity index among insomnia patients (rs = 0.410, p = 0.0466). Conclusion Our proposed multi-scale entropy measures demonstrates superior performance over traditional PSG measures in discriminating insomnia from normal sleepers by integrating the temporal patterns at multiple layers. Interestingly, entropy analysis of spindles show increased variability at all scales in insomnia patients (F-test p Acknowledgements The project described was supported by the following grants NIAMS R01AR059410 and NIDA R01DA032922 .

Accurate scoring of the apnea–hypopnea index using a simple non‐contact breathing sensor

Sleep apnea is a serious condition that afflicts many individuals and is associated with serious health complications. Polysomnography, the gold standard for assessing and diagnosing sleep apnea, uses breathing sensors that are intrusive and can disrupt the patient's sleep during the overnight testing. We investigated the use of breathing signals derived from non-contact force sensors (i.e. load cells) placed under the supports of the bed as an alternative to traditional polysomnography breathing sensors (e.g. nasal pressure, oral-nasal thermistor, chest belt and abdominal belt). The apnea-hypopnea index estimated using the load cells was not different than that estimated using standard polysomnography leads (t44 =0.37, P=0.71). Overnight polysomnography sleep studies scored using load cell breathing signals had an intra-class correlation coefficient of 0.97 for the apnea-hypopnea index and an intra-class correlation coefficient of 0.85 for the respiratory disturbance index when compared with scoring using traditional polysomnography breathing sensors following American Academy of Sleep Medicine guidelines. These results demonstrate the feasibility of using unobtrusive load cells installed under the bed to measure the apnea-hypopnea index.

Validation of Unobtrusive Pressure Sensor Array for Central Sleep Apnea Screening

A new understanding about the role and importance of sleep in health combined with rapidly aging demographics presents opportunities for research and development of new approaches in sleep monitoring. As well, challenges with the current sleep-monitoring solution can be addressed by studying the suitability of new monitoring technologies. This paper presents a small-scale validation of the unobtrusive pressure sensor array compared with traditional polysomnography (PSG), for use as a central apnea (CA) screening tool. Algorithms developed for the pressure sensor array provided a very good detection of the CAs as compared to the gold-standard data for the six patients studied whose body-mass index was appropriate for the sensor. For the retained patients, the algorithm classified CA events with an average sensitivity of 87.6%, specificity of 99.9%, and Cohen's kappa value of 0.875. This work evaluates the ability of an algorithm applied to the unobtrusive pressure sensor array to detect CAs. The sensor array was compared to three other signals: 1) expert PSG interpreters; 2) inductance plethysmography (IP) bands alone; and 3) IP bands combined with an airflow or oxygen-saturation sensor. The impact of unobtrusive CA detection on an older adult's health could be in the areas of broadening of the access to sleep monitoring, longitudinal monitoring of the disease progression, and possibly providing information on the interaction between CA and other disease processes.

Design and Implementation of a Modularized Polysomnography System

In recent years, an increasing number of people suffer from sleep disorders. Polysomnography (PSG) is commonly used in hospitals or sleep centers to diagnose sleep disorders because it continuously and simultaneously records multiple physiological signals during sleep. However, the excessive number of wired connections for conventional PSG is often a problem that leads to sleep disturbance. Due to the high cost and bulky body, traditional PSG systems are not suitable for sleep recording at home. This paper proposes the design and implementation of a modularized and distributed PSG system that is more convenient and has potential for recording at home. It is composed of multiple, tiny, low-cost, and wireless-synchronized signal acquisition nodes, and each node acquires specific physiological signals within a small body region to reduce sleep disturbance as a result of recording wires. To evaluate accuracy, the system and a commercial PSG system were mounted on subjects to simultaneously perform overnight recording, and the recorded data were compared. A two-phase sleep experiment was also performed to compare the comfortableness of these two systems. The results show that, in addition to high consistency (>; 93%) with the reference system, due to the reduction of the disturbance from recording wires, the proposed system has better comfortableness performance in terms of several objective and subjective sleep indices.

Development of Portable System for Assessment of Daytime Sleepiness&lt;sup&gt;&lt;/sup&gt;

Many studies have shown that sleepiness is a major cause of traffic accidents. Some occupations, such as airplane pilots and automobile drivers, are not suitable for patients with daytime sleepiness. Therefore, it is important to establish an objective assessment of datime sleepiness for screening commercial drivers or pilot and for diagnosis of obstructive sleep apnea. In this study we introduced a portable system and compared it with traditional polysomnography in evaluation of daytime sleepiness. Eight patients with OSA and eight normal subjects were studied. Sleep latency as judged by portable device was the same as that judged by conventional polysomnography and sleep latency measured from patients with OSA (18.12±9.14 minutes) was significantly shorter than that in normal subjects (&gt; 40minutes) , p&lt;0.01. In conclusion, portable device is as useful as conventional polysomnography in assessment of daytime sleepiness.

Sex differences in sleep after a single oral morning dose of olanzapine in healthy volunteers

Polysomnography abnormalities are frequent in schizophrenia and have been correlated with clinical variables. Because women with schizophrenia present a general better clinical outcome than men, we aimed to determine whether sex differences in antipsychotic-induced effects on sleep could contribute to this difference. Single oral morning doses of olanzapine (5 mg) were administered to 10 men and 10 women. Sleep variables were evaluated using traditional polysomnography Rechstschaffen and Kales criteria and all-night sleep electroencephalogram spectral analysis. Drug plasma concentrations were also measured. Significant sex-by-drug interactions were obtained in slow-wave sleep. After olanzapine, women showed an increase in slow-wave sleep, whereas men showed a decrease. We did not observe sex differences in olanzapine-induced hypnotic effects. Neither did we find any significant differences in pharmacokinetic parameters between sexes. Significant sex effects were observed in deep sleep, with women showing longer periods than men. Our results showed significant pharmacodynamic differences in olanzapine sleep effects between men and women. Further studies in clinical populations are needed to assess if these sex-based differences suggest that optimal treatment and doses should differ between men and women.

Obesidad y calidad de sueño: predictores de la depresión y la ansiedad en pacientes con síndrome de apnea-hipopnea del sueño

Obstructive sleep apnea syndrome (OSAS) is a chronic illness characterized by episodes of upper airway obstruction during sleep. Mood disturbance has been frequently related to OSAS, but there is no a consensus among results of different studies. AIMS. To assess depression and anxiety of patients with SAHS, and to determine which of polysomnographic variables that best predicts the mood of the patients. The sample consisted of 100 subjects: 50 patients with OSAS diagnosed with traditional poly-somnography, and 50 without OSAS symptoms. Both groups answered the State-Trait Anxiety Inventory and the State and Trait Depression Questionnaire. The results demonstrate that the patients with OSAS show higher scores in depression and anxiety than the control group. The variables that better predict the OSAS patient's mood symptoms were body mass index and total sleep time. This study shows the effect of the OSAS in the mood of the patients and confirms the complex relation between obesity, quality of sleep and mood disorder in OSAS patients.

Sleep EEG predictors and correlates of the response to cognitive behavioral therapy for insomnia.

Determine the relationship of non-rapid eye movement (NREM) electroencephalographic (EEG) spectral measures and the response to cognitive behavioral therapy (CBT) in primary insomnia (PI). Patients with PI were randomly assigned to CBT or a placebo intervention (PC). Ambulatory polysomnography was performed before and after treatment. University medical center sleep laboratory. Thirty PI patients with sleep maintenance difficulty evident in subjective sleep measures. CBT and PC. CBT led to a more rapid decline in EEG delta power over the night, compared with PC. This change was associated with subjective improvement in response to CBT. Furthermore, lower pretreatment peak EEG delta power in the first NREM cycle and a more gradual decline in delta power predicted a better response to CBT. Increased wake time during the day produced by CBT was correlated with an increase in the steepness of the slope of EEG delta power and subjective improvement. Traditional polysomnography measures were associated with the subjective CBT response to a greater degree among patients whose total sleep time estimates better approximated polysomnography-derived total sleep time. In contrast, changes in all-night averaged NREM EEG spectral indices were more strongly related to subjective improvement in individuals who underestimated total sleep time to a greater extent. CBT led to a more rapid decline in EEG delta power over the night. This change is linked to the therapeutic effect of CBT, which appears to occur in conjunction with an increase in homeostatic sleep drive. Traditional polysomnography indices and all-night averaged NREM EEG measures appear to be related to subjective improvements with CBT in subsets of patients with PI.

Processing of Signals Recorded Through Smart Devices: Sleep-Quality Assessment

In this paper, we discuss the possibility of performing a sleep evaluation from signals, which are not usually used for this purpose. In particular, we take into consideration the heart rate variability (HRV) and respiratory signals for automatic sleep staging, arousals detection, and apnea recognition. This is particularly useful for wearable or textile devices that could be employed for home monitoring of sleep. The HRV and the respiration were analyzed in the frequency domain, and the statistics on the spectral and cross-spectral parameters put into evidence the possibility of a sleep evaluation on their basis. Comparison with traditional polysomnography (PSG) revealed a classification accuracy of 89.9% in rapid eye movement (REM) non-REM sleep separation and an accuracy of 88% for sleep apnea detection. Additional information can be achieved from the number of microarousals recognized in correspondence of typical modifications in the HRV signal. The obtained results support the idea of automatic sleep evaluation and monitoring through signals that are not traditionally used in clinical PSG, but can be easily recorded at home through wearable devices (for example, a sensorized T-shirt) or systems integrated into the environment (a sensorized bed). This is a first step for the development of systems for sleep screening on large populations that can constitute a complement for the traditional clinical evaluation.