Sleep Monitoring Device Research Articles

Exploring the potential of a new wearable sleep monitoring device for clinical application

OBJECTIVESSleep is critical to health and quality of life, and the gold-standard assessment method for sleep disorders--polysomnography (PSG)--has struggled to become widely used because of its inherent shortcomings, which portable sleep-monitoring devices are expected to improve. This study aims to validate a new EEG-, EOG-, and EMG-based portable sleep-monitoring device against conventional PSG to explore its feasibility for clinical use. MethodsTwenty participants completed overnight sleep monitoring using conventional polysomnography and the Wearable PolysomnogramTM (WPSG-I). The similarity of the raw signals recorded by the two devices and the agreement rate of their sleep-staging results were compared. ResultsThe raw electroencephalogram (EEG) signals recorded by the two devices were highly similar, and the agreement rates between WPSG-I manual sleep staging and automatic sleep staging with conventional PSG manual sleep staging were 95.8 % (kappa = 0.92) and 89.7 % (kappa = 0.80), respectively. ConclusionThe agreement rate of sleep staging results between the two devices was high. The results of this study demonstrate the validity and feasibility of the WPSG-I for sleep research and clinical applications.

Efficient Sleep Stage Identification Using Piecewise Linear EEG Signal Reduction: A Novel Algorithm for Sleep Disorder Diagnosis.

Sleep is a vital physiological process for human health, and accurately detecting various sleep states is crucial for diagnosing sleep disorders. This study presents a novel algorithm for identifying sleep stages using EEG signals, which is more efficient and accurate than the state-of-the-art methods. The key innovation lies in employing a piecewise linear data reduction technique called the Halfwave method in the time domain. This method simplifies EEG signals into a piecewise linear form with reduced complexity while preserving sleep stage characteristics. Then, a features vector with six statistical features is built using parameters obtained from the reduced piecewise linear function. We used the MIT-BIH Polysomnographic Database to test our proposed method, which includes more than 80 h of long data from different biomedical signals with six main sleep classes. We used different classifiers and found that the K-Nearest Neighbor classifier performs better in our proposed method. According to experimental findings, the average sensitivity, specificity, and accuracy of the proposed algorithm on the Polysomnographic Database considering eight records is estimated as 94.82%, 96.65%, and 95.73%, respectively. Furthermore, the algorithm shows promise in its computational efficiency, making it suitable for real-time applications such as sleep monitoring devices. Its robust performance across various sleep classes suggests its potential for widespread clinical adoption, making significant advances in the knowledge, detection, and management of sleep problems.

A low-cost and open-hardware portable 3-electrode sleep monitoring device

To continue sleep research activities during the lockdown resulting from the COVID-19 pandemic, experiments that were previously conducted in laboratories were shifted to the homes of volunteers. Furthermore, for extensive data collection, it is necessary to use a large number of portable devices. Hence, to achieve these objectives, we developed a low-cost and open-source portable monitor (PM) device capable of acquiring electroencephalographic (EEG) signals using the popular ESP32 microcontroller. The device operates based on instrumentation amplifiers. It also has a connectivity microcontroller with Wi-Fi and Bluetooth that can be used to stream EEG signals. This portable single-channel 3-electrode EEG device allowed us to record short naps and score different sleep stages, such as wakefulness, non rapid eye movement sleep (NREM), stage 1 (S1), stage 2 (S2), stage 3 (S3) and stage 4 (S4). We validated the device by comparing the obtained signals to those generated by a research-grade counterpart. The results showed a high level of accurate similarity between both devices, demonstrating the feasibility of using this approach for extensive and low-cost data collection of EEG sleep recordings.

Analysis of the factors influencing of sleep quality in intensive care unit awake patients based on a structural equation model: A cross-sectional study.

The objective of this study was to construct and validate a structural equation model (SEM) to identify factors associated with sleep quality in awake patients in the intensive care unit (ICU) and to assist in the development of clinical intervention strategies. In this cross-sectional study, 200 awake patients who were cared for in the ICU of a tertiary hospital in China were surveyed via several self-report questionnaires and wearable actigraphy sleep monitoring devices. Based on the collected data, structural equation modelling analysis was performed using SPSS and AMOS statistical analysis software. The study is reported using the STROBE checklist. The fit indices of the SEM were acceptable: χ2/df = 1.676 (p < .001) and RMSEA = .058 (p < 0.080). Anxiety/depression had a direct negative effect on the sleep quality of awake patients cared for in the ICU (β = -.440, p < .001). In addition, disease-freeness progress had an indirect negative effect on the sleep quality of awake patients cared for in the ICU (β = -.142, p < .001). Analgesics had an indirect negative effect on the sleep quality of awake patients cared for in the ICU through pain and sedatives (β = -.082, p < .001). Sedation had a direct positive effect on the sleep quality of conscious patients cared for in the ICU (β = .493; p < .001). The results of the SEM showed that the sleep quality of awake patients cared for in the ICU is mainly affected by psychological and disease-related factors, especially anxiety, depression and pain, so we can improve the sleep quality of patients through psychological intervention and drug intervention.

1115 Nano-material Based Brain Computer Interface (BCI) Sleep Monitoring Device and Its Application

1115 Nano-material Based Brain Computer Interface (BCI) Sleep Monitoring Device and Its Application

Combining a wireless radar sleep monitoring device with deep machine learning techniques to assess obstructive sleep apnea severity.

The gold standard for diagnosing obstructive sleep apnea (OSA) is polysomnography (PSG). However, PSG is a time-consuming method with clinical limitations. This study aimed to create a wireless radar framework to screen the likelihood of 2 levels of OSA severity (ie, moderate-to-severe and severe OSA) in accordance with clinical practice standards. We conducted a prospective, simultaneous study using a wireless radar system and PSG in a Northern Taiwan sleep center, involving 196 patients. The wireless radar sleep monitor, incorporating hybrid models such as deep neural decision trees, estimated the respiratory disturbance index relative to the total sleep time established by PSG (RDIPSG_TST), by analyzing continuous-wave signals indicative of breathing patterns. Analyses were performed to examine the correlation and agreement between the RDIPSG_TST and apnea-hypopnea index, results obtained through PSG. Cut-off thresholds for RDIPSG_TST were determined using Youden's index, and multiclass classification was performed, after which the results were compared. A strong correlation (ρ = 0.91) and agreement (average difference of 0.59 events/h) between apnea-hypopnea index and RDIPSG_TST were identified. In terms of the agreement between the 2 devices, the average difference between PSG-based apnea-hypopnea index and radar-based RDIPSG_TST was 0.59 events/h, and 187 out of 196 cases (95.41%) fell within the 95% confidence interval of differences. A moderate-to-severe OSA model achieved an accuracy of 90.3% (cut-off threshold for RDIPSG_TST: 19.2 events/h). A severe OSA model achieved an accuracy of 92.4% (cut-off threshold for RDIPSG_TST: 28.86 events/h). The mean accuracy of multiclass classification performance using these cut-off thresholds was 83.7%. The wireless-radar-based sleep monitoring device, with cut-off thresholds, can provide rapid OSA screening with acceptable accuracy and also alleviate the burden on PSG capacity. However, to independently apply this framework, the function of determining the radar-based total sleep time requires further optimizations and verification in future work. Lin S-Y, Tsai C-Y, Majumdar A, etal. Combining a wireless radar sleep monitoring device with deep machine learning techniques to assess obstructive sleep apnea severity. J Clin Sleep Med. 2024;20(8):1267-1277.

Reliable sleep temperature regulation based on PMV model and dynamic thermal parameters

Quality of nighttime sleep is crucial. The common practice of air conditioners' sleep mode is to recommend a fixed sleep curve or suggest a pre-trained temperature curve based on geologic location. However, these models neglect the user preference and dynamic changes in the room's thermal environment, making it difficult to satisfy people of different ages and genders and resulting in discomfort during sleep. Demanding user to input their preferences or private data causes privacy issues, which leads to a bottleneck in solving this problem. Therefore, the paper proposes a new dynamic thermal parameter model for sleep, which learns the users' metabolic rate and thermal resistance of clothing and bedding through the user's undetected setting before sleep and simple feedback after waking up. These parameters fully reflect the inherent preferences of users and the thermal environment of rooms. After 1–2 rounds of feedback, the model can simulate the needs of users pretty well. In addition, we have optimized the PMV sleep model based on the dynamic thermal parameter model to regulate the temperature of air conditioner. In the controlled experiment, 84% of the participants expressed satisfaction with the regulated sleep temperature environment using the new model, which is 17% higher than that of the fixed curve. This model establishes a personalized model for users without obtaining their privacy information. In future research, wind speed control and humidity control can be introduced, and other sleep monitoring devices can be added to achieve more precise air conditioning temperature control.

Comparative analysis of sleep parameters and structures derived from wearable flexible electrode sleep patches and polysomnography in young adults.

Polysomnography (PSG) is the gold standard for clinical sleep monitoring, but its cost, discomfort, and limited suitability for continuous use present challenges. The flexible electrode sleep patch (FESP) emerges as an economically viable and patient-friendly solution, offering lightweight, simple operation, and self-applicable. Nevertheless, its utilization in young individuals remains uncertain. The objective of this study was to compare sleep data obtained by FESP and PSG in healthy young individuals and analyze agreement for sleep parameters and structure classification. Overnight monitoring with FESP and PSG recordings in 48 participants (mean age: 23 yr) was done. Correlation analysis, Bland-Altman plots, and Cohen's kappa coefficient assessed consistency. Sensitivity, specificity, and predictive values compared classification against PSG. FESP showed strong correlation and consistency with PSG for sleep monitoring. Bland-Altman plots indicated small errors and high consistency. Kappa values (0.70-0.84) suggested substantial agreement for sleep stage classification. Pearson correlation coefficient values for sleep stages (0.75-0.88) and sleep parameters (0.80-0.96) confirm that FESP has a strong application. Intraclass correlation coefficient yielded values between 0.65 and 0.97. In addition, FESP demonstrated an impressive accuracy range of 84.12-93.47% for sleep stage classification. The FESP also features a wearable self-test program with an error rate of no more than 8% for both deep sleep and wake. In young adults, FESP demonstrated reliable monitoring capabilities comparable to PSG. With its low cost and user-friendly design, FESP is a potential alternative for portable sleep assessment in clinical and research applications. Further studies involving larger populations are needed to validate its diagnostic potential.NEW & NOTEWORTHY By comparison with PSG, this study confirmed the reliability of an efficient, objective, low-cost, and noninvasive portable automatic sleep-monitoring device FESP, which provides effective information for long-term family sleep disorder diagnosis and sleep quality monitoring.

Baseline Sleep Characteristics in NCAA Division I Collegiate Athletes.

The authors report no conflicts of interest.To determine baseline sleep characteristics of male/female student-athletes across multiple sports using objective and subjective measures. Prospective study. Division I college. Eighty-two male and female Division I student-athletes. Participants completed 2 validated sleep questionnaires (Epworth Sleepiness Scale [ESS] and Single-Item Sleep Quality Scale [SISQS]) to assess subjective sleep. They also wore a validated sleep monitoring device (WHOOP 4.0 band) for at least 14 nights to collect objective data on total sleep time (TST) and sleep architecture. Overnight sleep variables, including TST, time spent awake in bed after falling asleep, time spent in light sleep, rapid eye movement (REM) sleep, and slow-wave sleep (SWS) cycles. Sleep quality and daytime sleepiness were also assessed. There were no statistical differences between male and female student-athletes in average TST, sleep architecture, sleep consistency, SISQS, and ESS scores. The average TST was 409.2 ± 36.3 minutes. Sleep architecture consisted of 25.6% REM, 19.9% SWS, and 54.4% light sleep. The average sleep consistency was 61.6% ± 8.9%. The average SISQS score was 6.48 ± 1.71, and the average ESS score was 7.57 ± 3.82. A significant difference was found in average wake time between males and females (55.0 vs 43.7 min, P = 0.020), with an overall average of 50.2 ± 16.2 minutes. College student-athletes do not typically obtain the recommended amount of sleep. Optimizing sleep can positively affect academic and athletic performance.

The value of circadian heart rate variability for the estimation of obstructive sleep apnea severity in adult males.

Heart rate variability (HRV) is becoming more prevalent as a measurable parameter in wearable sleep-monitoring devices, which are simple and effective instruments for illness evaluation. Currently, most studies on investigating OSA severity and HRV have measured heart rates during wakefulness or sleep. Therefore, the objective of this study was to investigate the circadian rhythm of HRV in male patients with OSA and its value for the estimation of OSA severity using group-based trajectory modeling. Patients with complaints of snoring were enrolled from the Sleep Center of Shandong Qianfoshan Hospital. Patients were divided into 3 groups according to apnea hypopnea index (AHI inevents/h), as follows: (<15, 15≤AHI<30, and ≥30). HRV parameters were calculated using 24 h Holter monitoring, which included time-domain and frequency-domain indices. Circadian differences in the standard deviation of normal to normal (SDNN) were evaluated for OSA severity using analysis of variance, trajectory analysis, and multinomial logistic regression. A total of 228 patients were enrolled, 47 with mild OSA, 48 moderate, and 133 severe.Patients with severe OSA exhibited reduced triangular index and higher very low frequency than those in the other groups. Circadian HRV showed that nocturnal SDNN was considerably higher than daytime SDNN in patients with severe OSA. The difference among the OSA groups was significant at 23, 24, 2, and 3 o'clock sharp between the severe and moderate OSA groups (all P<0.05). The heterogeneity of circadian HRV trajectories in OSA was strongly associated with OSA severity, including sleep structure and hypoxia-related parameters. Among the low-to-low, low-to-high, high-to-low, and high-to-high groups, OSA severity in the low-to-high group was the most severe, especially compared with the low-to-low and high-to-low SDNN groups, respectively. Circadian HRV in patients with OSA emerged as low daytime and high nocturnal in SDNN, particularly in men with severe OSA. The heterogeneity of circadian HRV revealed that trajectories with low daytime and significantly high nighttime were more strongly associated with severe OSA. Thus, circadian HRV trajectories may be useful to identify the severity of OSA.

New Medical Device and Therapeutic Approvals in Otolaryngology: State of the Art Review of 2022.

To review new drugs and devices relevant to otolaryngology approved by the Food and Drug Administration (FDA) in 2022. Publicly available FDA data on drugs and devices approved in 2022. A preliminary screen was conducted to identify drugs and devices relevant to otolaryngology. A secondary screen by members of the American Academy of Otolaryngology-Head and Neck Surgery's (AAO-HNS) Medical Devices and Drugs Committee differentiated between minor updates and new approvals. The final list of drugs and devices was sent to members of each subspecialty for review and analysis. A total of 1251 devices and 37 drugs were identified on preliminary screening. Of these, 329 devices and 5 drugs were sent to subspecialists for further review, from which 37 devices and 2 novel drugs were selected for further analysis. The newly approved devices spanned all subspecialties within otolaryngology. Many of the newly approved devices aimed to enhance patient experience, including over-the-counter hearing aids, sleep monitoring devices, and refined CPAP devices. Other advances aimed to improve surgical access, convenience, or comfort in the operating room and clinic. Many new devices and drugs are approved each year to improve patient care and care delivery. By staying up to date with these advances, otolaryngologists can leverage new innovations to improve the safety and quality of care. Given the recent approval of these devices, further studies are needed to assess long-term impact within the field of otolaryngology.

Design and Performance Evaluation of Handheld Low-Cost Sleep Monitoring Device

Design and Performance Evaluation of Handheld Low-Cost Sleep Monitoring Device

UNVEILING THE COMPLEX REALITIES: UNDERSTANDING SLEEP AND FATIGUE CHARACTERISTICS OF END-OF-LIFE CAREGIVERS

Abstract This mixed methods multi-case study explores sleep and fatigue characteristics among end-of-life caregivers. The research sample encompasses cases aiding individuals with advanced (n=4) and chronic illnesses (n=1), including various caregiver demographics such as gender, age groups (19-64 and 65+), and health conditions. Data collection involves comprehensive surveys capturing demographics, care recipient symptom intensity, caregiver depressive symptoms, caregiving stress, caregiver sleep quality, and caregiver fatigue level. The study integrates data from a 30-day Personal Sleep Monitoring Device (PSMD) and a 14-day sleep journal, enriched by insights from semi-structured interviews and field notes. The outcomes indicate a PSQI (Pittsburgh Sleep Quality Index) score of 6.1, accompanied by 7.44 hours of sleep recorded by the PSMD. There were approximately 20.5 nocturnal awakenings within 30 days, with average fatigue at 55.28 and daily fatigue at 49.66. Objective sleep outcomes were consistent among caregivers, while their own sleep ratings ranged from “Fairly Bad” to “Very Good.” Themes for each case were formulated to delve into the context of these sleep and fatigue characteristics. These themes encompass adaptive leadership, linchpin roles, emotional labor management, self-reliance, and vigilance. A prevailing overarching theme is the perception of “Not having control.” A significant finding underscores the dynamic influence of care recipients’ daily statuses on caregivers’ sleep quality and fatigue levels, leading to unpredictable outcomes. This study offers profound insights into end-of-life caregivers’ intricate journeys, highlighting individual differences and the dynamic nature of caregiving roles, while recognizing the importance of tailored interventions for enhancing caregiver’s well-being and caregiving effectiveness.

Impact of Preoperative Sleep Disturbances on Postoperative Delirium in Patients with Intracranial Tumors: A Prospective, Observational, Cohort Study.

Postoperative delirium (POD) is prevalent in craniotomy patients and is associated with high mortality. Sleep disturbances are receiving increasing attention from clinicians as associated risk factors for postoperative complications. This study aimed to determine the impact of preoperative sleep disturbances on POD in craniotomy patients. We recruited 130 patients undergoing elective craniotomy for intracranial tumors between May 1st and December 30th, 2022. Preoperative subjective sleep disturbances were assessed using the Pittsburgh Sleep Quality Index on the day of admission. We also measured objective perioperative sleep patterns using a dedicated sleep monitoring device 3 days before and 3 days after the surgery. POD was assessed twice daily using the Confusion Assessment Model for the Intensive Care Unit within the first week after craniotomy. Preoperative sleep disturbances were diagnosed in 49% of the study patients, and POD was diagnosed in 22% of all the study patients. Sleep disturbances were an independent risk factor for POD (OR: 2.709, 95% CI: 1.020-7.192, P = 0.045). Other risk factors for POD were age (OR: 3.038, 95% CI: 1.195-7.719, P = 0.020) and the duration of urinary catheterization (OR: 1.246, 95% CI: 1.025-1.513, P = 0.027). Perioperative sleep patterns (including sleep latency, deep sleep duration, frequency of awakenings, apnea-hypopnea index, and sleep efficiency) were significantly associated with POD. This study demonstrated that preoperative sleep disturbances predispose patients undergoing craniotomy to POD, also inferred a correlation between perioperative sleep patterns and POD. The targeted screening and intervention specifically for sleep disturbances during the perioperative period are immensely required.

Sleep apnoea syndrome prevalence in chronic kidney disease and end-stage kidney disease patients: a systematic review and meta-analysis.

Several studies have examined the frequency of sleep apnoea (SA) in patients with chronic kidney disease (CKD), reporting different prevalence rates. Our systematic review and meta-analysis aimed to define the clinical penetrance of SA in CKD and end-stage kidney disease (ESKD) patients. Ovid-MEDLINE and PubMed databases were explored up to 5 June 2023 to identify studies providing SA prevalence in CKD and ESKD patients assessed by different diagnostic methods, either sleep questionnaires or respiration monitoring equipment [such as polysomnography (PSG), type III portable monitors or other diagnostic tools]. Single-study data were pooled using the random-effects model. The Chi2 and Cochrane-I2 tests were used to assess the presence of heterogeneity, which was explored performing sensitivity and/or subgroup analyses. A cumulative analysis from 32 single-study data revealed a prevalence of SA of 57% [95% confidence interval (CI) 42%-71%] in the CKD population, whereas a prevalence of 49% (95% CI 47%-52%) was found pooling data from 91 studies in ESKD individuals. The prevalence of SA using instrumental sleep monitoring devices, including classical PSG and type III portable sleep monitors, was 62% (95% CI 52%-72%) and 56% (95% CI 42%-69%) in CKD and ESKD populations, respectively. Sleep questionnaires revealed a prevalence of 33% (95% CI 16%-49%) and 39% (95% CI 30%-49%). SA is commonly seen in both non-dialysis CKD and ESKD patients. Sleep-related questionnaires underestimated the presence of SA in this population. This emphasizes the need to use objective diagnostic tools to identify such a syndrome in kidney disease.

Improving sleeping quality and health under CPS: An analysis based on EEG data and support vector machine algorithm

The quality of sleep plays a crucial role in physical well-being, and individuals are becoming increasingly concerned about sleep quality and its associated health issues. Although various sleep monitoring devices exist, there remains a need for a highly accurate sleep state identification algorithm. To address this, we present a paper that utilizes machine learning techniques to identify human sleep states based on electroencephalogram (EEG) signals collected by an EEG instrument. We propose a model that incorporates two nonlinear characteristic parameters, MSE and PSE, extracted from artificially designed EEG signals as input. Additionally, we employ a Support Vector Machine (SVM) classifier algorithm to accurately identify sleep states, eliminating uncertainties associated with manually designed feature parameters. Experimental results demonstrate the superior accuracy of our proposed model for sleep state analysis, offering valuable insights for improving sleep quality and addressing related health concerns.

Monitoring Sleep and Scratch Improves Quality of Life in Patients with Atopic Dermatitis.

Atopic dermatitis itch may cause sleep disturbance and impair quality of life. For patients finding topical therapy difficult to continue, it is important to control itch and reduce scratching. This study developed algorithms to measure nocturnal sleep and scratch, using an actigraph device worn on the back of the hand, and assessed smartphone application feedback to improve adherence with therapy. In the first trial, actigraph measurements in 5 participants who wore the device were highly correlated with measurements by a sleep-monitoring device beneath the mattress. Total actigraph-measured scratching duration for each hour of sleep was highly correlated with measurements by a person rating infrared video-recording of the sleepers. In the second trial, 40 patients with atopic dermatitis were randomly allocated into an intervention group that used the actigraph and smartphone application, and a control group that did not. Both groups were instructed to use the same moisturizer. Dermatology Life Quality Index scores decreased significantly from baseline and were lower than those in the control group at week 8. It is suggested that the device and associated smartphone application reinforced therapy adherence, moisturizer use, and contributed to improved quality of life in patients with atopic dermatitis.

Consumer Wearable Sleep Trackers: Are They Ready for Clinical Use?

As the importance of good sleep continues to gain public recognition, the market for sleep-monitoring devices continues to grow. Modern technology has shifted from simple sleep tracking to a more granular sleep health assessment. We examine the available functionalities of consumer wearable sleep trackers (CWSTs) and how they perform in healthy individuals and disease states. Additionally, the continuum of sleep technology from consumer-grade to medical-grade is detailed. As this trend invariably grows, we urge professional societies to develop guidelines encompassing the practical clinical use of CWSTs and how best to incorporate them into patient care plans.

Simulator of Heart Rate and Respiratory Rate for Monitoring Accuracy of Intelligent Sleep Monitoring Devices

Invent a simulator which provides a simulation of heart rate and respiratory rate to the intelligent sleep monitoring devices based on precision pressure sensors. The simulator was composed of control part and simulated silicone doll. The simulated silicone doll contains heartbeat simulator and breathing simulation airbag. Heartbeat and breathing combination pressure signal can be produced according to frequency set values. Frequencies of pressure signal of the simulator were compared with the monitoring results of intelligent sleep monitoring devices with known accuracy to verify the frequency accuracy of pressure signal of the simulator. Verified the repeatability and stability of the simulator with a stopwatch. The heart rate of the simulator was with in ±2 beats per minute of the monitoring results of intelligent sleep monitoring devices and the respiratory rate of the simulator was with in ±2 times per minute of the monitoring results. The repeatability and stability of the simulator was better than ±5% according to results with a stopwatch. It's practicable to use the simulator which provides a simulation of heart rate and respiratory rate to the accuracy test of the intelligent sleep monitoring devices based on precision pressure sensor.

The association between sleep restlessness and neuroimaging biomarkers of Alzheimer’s Disease in mid‐life to older adults and patients

AbstractBackgroundRecent research demonstrates a bidirectional relationship between sleep and Alzheimer’s Disease (AD) pathology, with supporting evidence that changes in sleep pattern occurs from the preclinical stage of AD. The cooccurrence of sleep disturbances and amyloid‐beta (Aβ) accumulation suggests the importance of monitoring sleep patterns in adults that are at high‐risk of developing AD later in life. We first investigated whether resting heart rate (RHR) and nocturnal sleep restlessness (NSR) are different in cognitively impaired (CI) and cognitively unimpaired (CU) mid‐life to older adults. We also examined any association of these sleep characteristics with hippocampus volume (HV) and Aβ, as neuroimaging biomarkers of AD, regardless of the cognitive status.Method107 middle to old age (46‐75 years old) adults (age 61.5±6.5, 74 Females, 64 APOE ε4 carriers, and 88 CU) underwent 3T MRI and PET scans as part of the Prospective Imaging Study of Ageing (PISA) study. A ballistocardiograph sleep monitoring device was used to track their RHR and NSR for approximately 5 months after their PET scan. NSR was quantified by total time in bed, total duration and number of bed‐exits, and multi‐episodic sleep. HV was normalised using total intracranial volume, and Aβ load was calculated in Centiloid scale (CL) with Aβ positivity defined as CL&gt;20. Generalised Linear Models were used to examine the relationship between sleep and imaging biomarkers. Age, gender, BMI, APOE, and years of education were used as independent covariates.ResultCIs spent more time in bed (p = 0.002) and had more bed‐exits (p = 0.009) than CUs. However, no significant difference was found on RHR and multi‐episodic sleep between CUs and CIs. Individuals with higher Aβ load, regardless of their cognitive status, had lower RHR (p = 0.04) and more bed‐exits (p = 0.04), and spent more time in bed (p = 0.01). Also, individuals with smaller HV frequently exited the bed during the night (p = 0.03) and spent more time in bed (p = 0.003).ConclusionIndividuals with cognitive impairment experienced higher levels of NSR with frequent bed‐exits and more time in bed. Also, these sleep characteristics were significantly associated with the Centiloid value, Aβ status, and HV, regardless of the cognitive status.