A jerk-based algorithm ACCEL for the accurate classification of sleep-wake states from arm acceleration.
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ABSTRACT:
SUBMITTER: Ode KL
PROVIDER: S-EPMC8784328 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND:Accelerometers have become common for evaluating the efficacy of rehabilitation for patients with neurologic disorders. For example, metrics like use ratio (UR) and magnitude ratio (MR) have been shown to differentiate movement patterns of children with cerebral palsy (CP) compared to typically-developing (TD) peers. However, these metrics are calculated from "activity counts" - a measure based on proprietary algorithms that approximate movement duration and intensity from raw accelerometer data. Algorithms used to calculate activity counts vary between devices, limiting comparisons of clinical and research results. The goal of this research was to develop complementary metrics based on raw accelerometer data to analyze arm movement after neurologic injury. METHOD:We calculated jerk, the derivative of acceleration, to evaluate arm movement from accelerometer data. To complement current measures, we calculated jerk ratio (JR) as the relative jerk magnitude of the dominant (non-paretic) and non-dominant (paretic) arms. We evaluated the JR distribution between arms and calculated the 50th percentile of the JR distribution (JR50). To evaluate these metrics, we analyzed bimanual accelerometry data for five children with hemiplegic CP who underwent Constraint-Induced Movement Therapy (CIMT) and five typically developing (TD) children. We compared JR between the CP and TD cohorts, and to activity count metrics. RESULTS:The JR50 differentiated between the CP and TD cohorts (CP?=?0.578?±?0.041 before CIMT, TD?=?0.506?±?0.026), demonstrating increased reliance on the dominant arm for the CP cohort. Jerk metrics also quantified changes in arm use during and after therapy (e.g., JR50?=?0.378?±?0.125 during CIMT, 0.591?±?0.057 after CIMT). The JR was strongly correlated with UR and MR (r =?-?0.92, 0.89) for the CP cohort. For the TD cohort, JR50 was repeatable across three data collection periods with an average similarity of 0.945?±?0.015. CONCLUSIONS:Acceleration-derived jerk captured differences in motion between TD and CP cohorts and correlated with activity count metrics. The code for calculating and plotting JR is open-source and available for others to use and build upon. By identifying device-independent metrics that can quantify arm movement in daily life, we hope to facilitate collaboration for rehabilitation research using wearable technologies.
| S-EPMC7076987 | biostudies-literature
Project description:A major limitation in the study of sleep breathing disorders in mouse models of pathology is the need to combine whole-body plethysmography (WBP) to measure respiration with electroencephalography/electromyography (EEG/EMG) to discriminate wake-sleep states. However, murine wake-sleep states may be discriminated from breathing and body movements registered by the WBP signal alone. Our goal was to compare the EEG/EMG-based and the WBP-based scoring of wake-sleep states of mice, and provide formal guidelines for the latter. EEG, EMG, blood pressure and WBP signals were simultaneously recorded from 20 mice. Wake-sleep states were scored based either on EEG/EMG or on WBP signals and sleep-dependent respiratory and cardiovascular estimates were calculated. We found that the overall agreement between the 2 methods was 90%, with a high Cohen's Kappa index (0.82). The inter-rater agreement between 2 experts and between 1 expert and 1 naïve sleep investigators gave similar results. Sleep-dependent respiratory and cardiovascular estimates did not depend on the scoring method. We show that non-invasive discrimination of the wake-sleep states of mice based on visual inspection of the WBP signal is accurate, reliable and reproducible. This work may set the stage for non-invasive high-throughput experiments evaluating sleep and breathing patterns on mouse models of pathophysiology.
| S-EPMC5282481 | biostudies-literature
Project description:This study presents a thorough analysis of sleep/wake detection algorithms for efficient on-device sleep tracking using wearable accelerometric devices. It develops a novel end-to-end algorithm using convolutional neural network applied to raw accelerometric signals recorded by an open-source wrist-worn actigraph. The aim of the study is to develop an automatic classifier that: (1) is highly generalizable to heterogenous subjects, (2) would not require manual features' extraction, (3) is computationally lightweight, embeddable on a sleep tracking device, and (4) is suitable for a wide assortment of actigraphs. Hereby, authors analyze sleep parameters, such as total sleep time, waking after sleep onset and sleep efficiency, by comparing the outcomes of the proposed algorithm to the gold standard polysomnographic concurrent recordings. The relatively substantial agreement (Cohen's kappa coefficient, median, equal to 0.78 ± 0.07) and the low-computational cost (2727 floating-point operations) make this solution suitable for an on-board sleep-detection approach.
| S-EPMC7801620 | biostudies-literature
Project description:BackgroundPrevious studies demonstrated a short-term relationship between infant sleep-wake states and oral feeding performance, with state being an indication of infants' neurobehavioral readiness for feeding. However, the relationship between sleep-wake states and feeding skills has not been evaluated longitudinally during hospitalization.ObjectivesThe purpose of this study was to examine preterm infants' sleep-wake state developmental trajectories and their associations with feeding progression during hospitalization.MethodsThis descriptive and exploratory study was a secondary analysis using data from a longitudinal two-group, randomized controlled trial evaluating the effects of early and late cycled light on health and developmental outcomes among extremely preterm infants who were born ≤28 weeks of gestational age. Sleep-wake states were assessed for two 2-hour interfeeding periods per day (day and night hours), 30 weeks postmenstrual age, and every 3 weeks until discharge. Occurrences of active sleep, quiet sleep, and waking were recorded every 10 seconds. Feeding progression was assessed based on an infant's postmenstrual age at five milestones: first enteral feeding, full enteral feeding, first oral feeding, half oral feeding, and full oral feeding. Trajectory analyses were used to describe developmental changes in sleep-wake states, feeding progression patterns, and associations between feeding progression and sleep-wake trajectories.ResultsActive sleep decreased while waking, and quiet sleep increased during hospitalization. Two distinct feeding groups were identified: typical and delayed feeding progression. In infants with delayed feeding progression, rates of active and quiet sleep development during the day were delayed compared to those with typical feeding progression. We also found that infants with delayed feeding progression were more likely to be awake more often during the night compared to infants with typical feeding progression.DiscussionsFindings suggest that delays in sleep-wake state development may be associated with delays in feeding progression during hospitalization. Infants with delayed feeding skill development may require more environmental protection to further support their sleep development.
| S-EPMC6919212 | biostudies-literature
Project description:BackgroundDifferentiating nonwear time from sleep and wake times is essential for the estimation of sleep duration based on actigraphy data. To efficiently analyze large-scale data sets, an automatic method of identifying these three different states is required. Therefore, we developed a classification algorithm to determine nonwear, sleep and wake periods from accelerometer data. Our work aimed to (I) develop a new pattern recognition algorithm for identifying nonwear periods from actigraphy data based on the influence of respiration rate on the power spectrum of the acceleration signal and implement it in an automatic classification algorithm for nonwear/sleep/wake states; (II) address motion artifacts that occur during nonwear periods and are known to cause misclassification of these periods; (III) adjust the algorithm depending on the sensor position (wrist, chest); and (IV) validate the algorithm on both healthy individuals and patients with sleep disorders.MethodsThe study involved 98 participants who wore wrist and chest acceleration sensors for one day of measurements. They spent one night in the sleep laboratory and continued to wear the sensors outside of the laboratory for the remainder of the day. The results of the classification algorithm were compared to those of the reference source: polysomnography for wake/sleep and manual annotations for nonwear/wear classification.ResultsThe median kappa values for the two locations were 0.83 (wrist) and 0.84 (chest). The level of agreement did not vary significantly by sleep health (good sleepers vs. subjects with sleep disorders) (p = 0.348, p = 0.118) or by sex (p = 0.442, p = 0.456). The intraclass correlation coefficients of nonwear total time between the reference and the algorithm were 0.92 and 0.97 with the outliers and 0.95 and 0.98 after the outliers were removed for the wrist and chest, respectively. There was no evidence of an association between the mean difference (and 95% limits of agreement) and the mean of the two methods for either sensor position (wrist p = 0.110, chest p = 0.164), and the mean differences (algorithm minus reference) were 5.11 [95% LoA -15.4-25.7] and 1.32 [95% LoA -9.59-12.24] min/day, respectively, after the outliers were removed.DiscussionWe studied the influence of the respiration wave on the power spectrum of the acceleration signal for the differentiation of nonwear periods from sleep and wake periods. The algorithm combined both spectral analysis of the acceleration signal and rescoring. Based on the Bland-Altman analysis, the chest-worn accelerometer showed better results than the wrist-worn accelerometer.
| S-EPMC6942683 | biostudies-literature
Project description:Eating and sleeping represent two mutually exclusive behaviors that satisfy distinct homeostatic needs. Because an animal cannot eat and sleep at the same time, brain systems that regulate energy homeostasis are likely to influence sleep/wake behavior. Indeed, previous studies indicate that animals adjust sleep cycles around periods of food need and availability. Furthermore, hormones that affect energy homeostasis also affect sleep/wake states: the orexigenic hormone ghrelin promotes wakefulness, and the anorexigenic hormones leptin and insulin increase the duration of slow-wave sleep. However, whether neural populations that regulate feeding can influence sleep/wake states is unknown. The hypothalamic arcuate nucleus contains two neuronal populations that exert opposing effects on energy homeostasis: agouti-related protein (AgRP)-expressing neurons detect caloric need and orchestrate food-seeking behavior, whereas activity in pro-opiomelanocortin (POMC)-expressing neurons induces satiety. We tested the hypotheses that AgRP neurons affect sleep homeostasis by promoting states of wakefulness, whereas POMC neurons promote states of sleep. Indeed, optogenetic or chemogenetic stimulation of AgRP neurons in mice promoted wakefulness while decreasing the quantity and integrity of sleep. Inhibition of AgRP neurons rescued sleep integrity in food-deprived mice, highlighting the physiological importance of AgRP neuron activity for the suppression of sleep by hunger. Conversely, stimulation of POMC neurons promoted sleep states and decreased sleep fragmentation in food-deprived mice. Interestingly, we also found that sleep deprivation attenuated the effects of AgRP neuron activity on food intake and wakefulness. These results indicate that homeostatic feeding neurons can hierarchically affect behavioral outcomes, depending on homeostatic need.
| S-EPMC6279563 | biostudies-literature
Project description:Sleep is an evolutionarily conserved process that is linked to diurnal cycles and normal daytime wakefulness. Healthy sleep and wakefulness are integral to a healthy lifestyle; this occurs when an organism is able to maintain long bouts of both sleep and wake. Homer proteins, which function as adaptors for group 1 metabotropic glutamate receptors, have been implicated in genetic studies of sleep in both Drosophila and mouse. Drosophila express a single Homer gene product that is upregulated during sleep. By contrast, vertebrates express Homer as both constitutive and immediate early gene (H1a) forms, and H1a is up-regulated during wakefulness. Genetic deletion of Homer in Drosophila results in fragmented sleep and in failure to sustain long bouts of sleep, even under increased sleep drive. However, deletion of Homer1a in mouse results in failure to sustain long bouts of wakefulness. Further evidence for the role of Homer1a in the maintenance of wake comes from the CREB alpha delta mutant mouse, which displays a reduced wake phenotype similar to the Homer1a knockout and fails to up-regulate Homer1a upon sleep loss. Homer1a is a gene whose expression is induced by CREB. Sustained behaviors of the sleep/wake cycle are created by molecular pathways that are distinct from those for arousal or short bouts, and implicate an evolutionarily-conserved role for Homer in sustaining these behaviors.
| S-EPMC3332115 | biostudies-other
Project description:The usage of wrist-worn wearables to detect sleep-wake states remains a formidable challenge, particularly among individuals with disordered sleep. We developed a novel and unbiased data-driven method for the detection of sleep-wake and compared its performance with the well-established Oakley algorithm (OA) relative to polysomnography (PSG) in elderly men with disordered sleep. Overnight in-lab PSG from 102 participants was compared with accelerometry and photoplethysmography simultaneously collected with a wearable device (Empatica E4). A binary segmentation algorithm was used to detect change points in these signals. A model that estimates sleep or wake states given the changes in these signals was established (change point decoder, CPD). The CPD's performance was compared with the performance of the OA in relation to PSG. On the testing set, OA provided sleep accuracy of 0.85, wake accuracy of 0.54, AUC of 0.67, and Kappa of 0.39. Comparable values for CPD were 0.70, 0.74, 0.78, and 0.40. The CPD method had sleep onset latency error of -22.9 min, sleep efficiency error of 2.09%, and underestimated the number of sleep-wake transitions with an error of 64.4. The OA method's performance was 28.6 min, -0.03%, and -17.2, respectively. The CPD aggregates information from both cardiac and motion signals for state determination as well as the cross-dimensional influences from these domains. Therefore, CPD classification achieved balanced performance and higher AUC, despite underestimating sleep-wake transitions. The CPD could be used as an alternate framework to investigate sleep-wake dynamics within the conventional time frame of 30-s epochs.
| S-EPMC7420526 | biostudies-literature
Project description:Inertial motions may be defined in terms of acceleration and jerk, the time-derivative of acceleration. We investigated the relative contributions of these characteristics to the perceived intensity of motions. Participants were seated on a high-fidelity motion platform, and presented with 25 above-threshold 1 s forward (surge) motions that had acceleration values ranging between 0.5 and 2.5 [Formula: see text] and jerks between 20 and 60 [Formula: see text], in five steps each. Participants performed two tasks: a magnitude estimation task, where they provided subjective ratings of motion intensity for each motion, and a two-interval forced choice task, where they provided judgments on which motion of a pair was more intense, for all possible combinations of the above motion profiles. Analysis of the data shows that responses on both tasks may be explained by a single model, and that this model should include acceleration only. The finding that perceived motion intensity depends on acceleration only appears inconsistent with previous findings. We show that this discrepancy can be explained by considering the frequency content of the motions, and demonstrate that a linear time-invariant systems model of the otoliths and subsequent processing can account for the present data as well as for previous findings.
| S-EPMC7080688 | biostudies-literature
Project description:Sleep-stage classification is essential for sleep research. Various automatic judgment programs, including deep learning algorithms using artificial intelligence (AI), have been developed, but have limitations with regard to data format compatibility, human interpretability, cost, and technical requirements. We developed a novel program called GI-SleepNet, generative adversarial network (GAN)-assisted image-based sleep staging for mice that is accurate, versatile, compact, and easy to use. In this program, electroencephalogram and electromyography data are first visualized as images, and then classified into three stages (wake, NREM, and REM) by a supervised image learning algorithm. To increase its accuracy, we adopted GAN and artificially generated fake REM sleep data to equalize the number of stages. This resulted in improved accuracy, and as little as one mouse's data yielded significant accuracy. Due to its image-based nature, the program is easy to apply to data of different formats, different species of animals, and even outside sleep research. Image data can be easily understood; thus, confirmation by experts is easily obtained, even when there are prediction anomalies. As deep learning in image processing is one of the leading fields in AI, numerous algorithms are also available.
| S-EPMC8628800 | biostudies-literature