A jerk-based algorithm ACCEL for the accurate classification of sleep–wake states from arm acceleration

Effect of lithium on circadian activity level and flexibility in patients with bipolar disorder: results from the Oxford Lithium Trial

BACKGROUND

Disruption of circadian rest-activity is prevalent in patients with bipolar disorder (BD). Lithium's impact on circadian rhythms has been documented in cell lines, animal models, and pharmacogenomics studies in patients with BD. However, the causal relationship between such disruption and BD remains unclear.

METHODS

We investigated the early effects of lithium on circadian rest-activity in an exploratory analysis of a randomised, placebo-controlled, double-blind six-week study on patients with BD. Participants were assigned to receive either lithium or a placebo in a 1:1 ratio. Circadian activity was monitored using actigraphy, and daily affect was assessed through ecological momentary assessment. A computational model was used to quantify different types of activity variability, and the impact of lithium on activity level, activity onset time and their variability were analysed using linear mixed models.

FINDINGS

Of the thirty-five participants who began treatment, 19 received lithium and 16 received a placebo. Lithium significantly altered circadian rest-activity patterns, including reducing daytime activity levels (after 4 weeks, below as well: Cohen's d = -0.19, p = 0.002, linear mixed model, ibid.), advancing the onset of daytime activity (Cohen's d = -0.14, p = 0.018), and increasing the volatility of both daytime activity level (Cohen's d = 0.10, p = 0.002) and its onset time (Cohen's d = 0.13, p < 0.001), independent of affective symptoms changes.

INTERPRETATION

This study establishes a causal link between lithium treatment and reduced circadian activity with advanced circadian phase, potentially via temporarily increasing their volatility (flexibility). Significant circadian changes were detected within one week of starting lithium, highlighting their potential as an early biomarker for treatment response.

FUNDING

This research was supported by the Wellcome Trust Strategic Award (CONBRIO: Collaborative Oxford Network for Bipolar Research to Improve Outcomes, reference No. 102,616/Z), NIHR Oxford Health Biomedical Research Centre and the NIHR Oxford cognitive health Clinical Research Facility.

Jerks are useful: extracting pulse rate from wrist-placed accelerometry jerk during sleep in children

STUDY OBJECTIVES

Evaluate wrist-placed accelerometry predicted heartrate compared to electrocardiogram (ECG) heartrate in children during sleep.

METHODS

Children (n = 82, 61% male, 43.9% black) wore a wrist-placed Apple Watch Series 7 (AWS7) and ActiGraph GT9X during a polysomnogram. Three-Axis accelerometry data was extracted from AWS7 and the GT9X. Accelerometry heartrate estimates were derived from jerk (the rate of acceleration change), computed using the peak magnitude frequency in short time Fourier Transforms of Hilbert transformed jerk computed from acceleration magnitude. Heartrates from ECG traces were estimated from R-R intervals using R-pulse detection. Lin's concordance correlation coefficient (CCC), mean absolute error (MAE), and mean absolute percent error (MAPE) assessed agreement with ECG estimated heart rate. Secondary analyses explored agreement by polysomnography sleep stage and a signal quality metric.

RESULTS

The developed scripts are available on Github. For the GT9X, CCC was poor at -0.11 and MAE and MAPE were high at 16.8 (SD = 14.2) beats/minute and 20.4% (SD = 18.5%). For AWS7, CCC was moderate at 0.61 while MAE and MAPE were lower at 6.4 (SD = 9.9) beats/minute and 7.3% (SD = 10.3%). Accelerometry estimated heartrate for AWS7 was more closely related to ECG heartrate during N2, N3 and REM sleep than lights on, wake, and N1 and when signal quality was high. These patterns were not evident for the GT9X.

CONCLUSIONS

Raw accelerometry data extracted from AWS7, but not the GT9X, can be used to estimate heartrate in children while they sleep. Future work is needed to explore the sources (i.e. hardware, software, etc.) of the GT9X's poor performance.

Detection of sleep apnea using only inertial measurement unit signals from apple watch: a pilot-study with machine learning approach

PURPOSE

Despite increased awareness of sleep hygiene, over 80% of sleep apnea cases remain undiagnosed, underscoring the need for accessible screening methods. This study presents a method for detecting sleep apnea using data from the Apple Watch's inertial measurement unit (IMU).

METHODS

An algorithm was developed to extract seismocardiographic and respiratory signals from IMU data, analyzing features such as breathing and heart rate variability, respiratory dips, and body movements. In a cohort of 61 adults undergoing polysomnography, we analyzed 52,337 30-second epochs, with 12,373 (23.6%) identified as apnea/hypopnea episodes. Machine learning models using five classifiers (Logistic Regression, Random Forest, Gradient Boosting, k-Nearest Neighbors, and Multi-layer Perceptron) were trained on data from 41 subjects and validated on 20 subjects.

RESULTS

The Random Forest classifier performed best in per-epoch respiratory event detection, achieving an AUC of 0.827 and an F1 score of 0.572 in the training group, and an AUC of 0.831 and an F1 score of 0.602 in the test group. The model's per-subject predictions strongly correlated with the apnea-hypopnea index (AHI) from polysomnography (r = 0.93) and identified subjects with AHI ≥ 15 with 100% sensitivity and 90% specificity.

CONCLUSION

Utilizing the widespread availability of the Apple Watch and the low power requirements of the IMU, this approach has the potential to significantly improve sleep apnea screening accessibility.

Development and Calibration of a PATCH Device for Monitoring Children's Heart Rate and Acceleration

INTRODUCTION

Current wearables that collect heart rate and acceleration were not designed for children and/or do not allow access to raw signals, making them fundamentally unverifiable. This study describes the creation and calibration of an open-source multichannel platform (PATCH) designed to measure heart rate and acceleration in children ages 3-8 yr.

METHODS

Children (N = 63; mean age, 6.3 yr) participated in a 45-min protocol ranging in intensities from sedentary to vigorous activity. Actiheart-5 was used as a comparison measure. We calculated mean bias, mean absolute error (MAE) mean absolute percent error (MA%E), Pearson correlations, and Lin's concordance correlation coefficient (CCC).

RESULTS

Mean bias between PATCH and Actiheart heart rate was 2.26 bpm, MAE was 6.67 bpm, and M%E was 5.99%. The correlation between PATCH and Actiheart heart rate was 0.89, and CCC was 0.88. For acceleration, mean bias was 1.16 mg and MAE was 12.24 mg. The correlation between PATCH and Actiheart was 0.96, and CCC was 0.95.

CONCLUSIONS

The PATCH demonstrated clinically acceptable accuracies to measure heart rate and acceleration compared with a research-grade device.

Quantitative detection of sleep apnea in adults using inertial measurement unit embedded in wristwatch wearable devices

Sleep apnea (SA) is associated with risk of cardiovascular disease, cognitive decline, and accidents due to sleepiness, yet the majority (over 80%) of patients remain undiagnosed. Inertial measurement units (IMUs) are built into modern wearable devices and are capable of long-term continuous measurement with low power consumption. We examined if SA can be detected by an IMU embedded in a wristwatch device. In 122 adults who underwent polysomnography (PSG) examinations, triaxial acceleration and triaxial gyro signals from the IMU were recorded during the PSG. Subjects were divided into a training group and a test groups (both n = 61). In the training group, an algorithm was developed to extract signals in the respiratory frequency band (0.13-0.70 Hz) and detect respiratory events as transient (10-90 s) decreases in amplitude. The respiratory event frequency estimated by the algorithm correlated with the apnea-hypopnea index (AHI) of the PSG with r = 0.84 in the test group. With the cutoff values determined in the training group, moderate-to-severe SA (AHI ≥ 15) was identified with 85% accuracy and severe SA (AHI ≥ 30) with 89% accuracy in the test group. SA can be quantitatively detected by the IMU embedded in wristwatch wearable devices in adults with suspected SA.

Recent Progress in Long-Term Sleep Monitoring Technology

Sleep is an essential physiological activity, accounting for about one-third of our lives, which significantly impacts our memory, mood, health, and children's growth. Especially after the COVID-19 epidemic, sleep health issues have attracted more attention. In recent years, with the development of wearable electronic devices, there have been more and more studies, products, or solutions related to sleep monitoring. Many mature technologies, such as polysomnography, have been applied to clinical practice. However, it is urgent to develop wearable or non-contacting electronic devices suitable for household continuous sleep monitoring. This paper first introduces the basic knowledge of sleep and the significance of sleep monitoring. Then, according to the types of physiological signals monitored, this paper describes the research progress of bioelectrical signals, biomechanical signals, and biochemical signals used for sleep monitoring. However, it is not ideal to monitor the sleep quality for the whole night based on only one signal. Therefore, this paper reviews the research on multi-signal monitoring and introduces systematic sleep monitoring schemes. Finally, a conclusion and discussion of sleep monitoring are presented to propose potential future directions and prospects for sleep monitoring.

An Automated Algorithm for Determining Sleep Using Single-Channel Electroencephalography to Detect Delirium: A Prospective Observational Study in Intensive Care Units

The relationship between polysomnography-based objective sleep and delirium in the intensive care unit (ICU) is inconsistent across studies, suggesting limitations in manually determining the sleep stage of critically ill patients. We objectively measured 24-h sleep using a single-channel electroencephalogram (SleepScope [SS]) and an under-mattress sleep monitor (Nemuri SCAN [NSCAN]), both of which have independent algorithms that automatically determine sleep and wakefulness. Eighteen patients (median age, 68 years) admitted to the ICU after valvular surgery or coronary artery bypass grafting were included, and their sleep time was measured one day after extubation. The median total sleep times (TSTs) measured by SS (TST-SS) and NSCAN were 548 (48−1050) and 1024 (462−1257) min, respectively. Two patients with delirium during the 24-h sleep measurement had very short TST-SS of 48 and 125 min, and the percentage of daytime sleep accounted for >80% in both SS and NSCAN. This preliminary case series showed marked sleep deprivation and increased rates of daytime sleeping in ICU patients with delirium. Although data accuracy from under-mattress sleep monitors is contentious, automated algorithmic sleep/wakefulness determination using a single-channel electroencephalogram may be useful in detecting delirium in ICU patients and could even be superior to polysomnography.

Analysis of wearable time series data in endocrine and metabolic research

Many hormones in the body oscillate with different frequencies and amplitudes, creating a dynamic environment that is essential to maintain health. In humans, disruptions to these rhythms are strongly associated with increased morbidity and mortality. While mathematical models can help us understand rhythm misalignment, translating this insight into personalised healthcare technologies requires solving additional challenges. Here, we discuss how combining minimally invasive, high-frequency biosampling technologies with wearable devices can assist the development of hormonal surrogates. We review bespoke algorithms that can help analyse multidimensional, noisy, time series data and identify wearable signals that could constitute clinical proxies of endocrine rhythms. These techniques can support the development of computational biomarkers to support the diagnosis and management of endocrine and metabolic conditions.

The 103,200-arm acceleration dataset in the UK Biobank revealed a landscape of human sleep phenotypes

SignificanceHuman sleep phenotypes are diversified by genetic and environmental factors, and a quantitative classification of sleep phenotypes would lead to the advancement of biomedical mechanisms underlying human sleep diversity. To achieve that, a pipeline of data analysis, including a state-of-the-art sleep/wake classification algorithm, the uniform manifold approximation and projection (UMAP) dimension reduction method, and the density-based spatial clustering of applications with noise (DBSCAN) clustering method, was applied to the 100,000-arm acceleration dataset. This revealed 16 clusters, including seven different insomnia-like phenotypes. This kind of quantitative pipeline of sleep analysis is expected to promote data-based diagnosis of sleep disorders and psychiatric disorders that tend to be complicated by sleep disorders.

Towards On-Device Dehydration Monitoring Using Machine Learning from Wearable Device's Data

With the ongoing advances in sensor technology and miniaturization of electronic chips, more applications are researched and developed for wearable devices. Hydration monitoring is among the problems that have been recently researched. Athletes, battlefield soldiers, workers in extreme weather conditions, people with adipsia who have no sensation of thirst, and elderly people who lost their ability to talk are among the main target users for this application. In this paper, we address the use of machine learning for hydration monitoring using data from wearable sensors: accelerometer, magnetometer, gyroscope, galvanic skin response sensor, photoplethysmography sensor, temperature, and barometric pressure sensor. These data, together with new features constructed to reflect the activity level, were integrated with personal features to predict the last drinking time of a person and alert the user when it exceeds a certain threshold. The results of applying different models are compared for model selection for on-device deployment optimization. The extra trees model achieved the least error for predicting unseen data; random forest came next with less training time, then the deep neural network with a small model size, which is preferred for wearable devices with limited memory. Embedded on-device testing is still needed to emphasize the results and test for power consumption.