Detection and characterization of abnormalities of movement are important to develop a method for detecting early signs of Parkinson’s disease (PD). Most of the current research in detection of characteristic reduction of movements due to PD, known as parkinsonism, requires using a set of invasive sensors in a clinical or controlled environment. Actigraphy has been widely used in medical research as a non-invasive data acquisition method in free-living conditions for long periods of time. The proposed algorithm uses triaxial accelerometer data obtained through actigraphy to detect walking bouts at least 10 seconds long and characterize them using cadence and arm swing. Accurate detection of walking periods is the first step toward the characterization of movement based on gait abnormalities. The algorithm was based on a Walking Score (WS) derived using the value of the auto-correlation function (ACF) for the Resultant acceleration vector. The algorithm achieved a precision of 0.90, recall of 0.77, and F1 score of 0.83 compared to the expert scoring for walking bout detection. We additionally described a method to measure arm swing amplitude.
Introduction The apnea-hypopnea index (AHI), the current severity metric used clinically for diagnosing obstructive sleep apnea (OSA), does not correlate well to daytime sleepiness measured via the Epworth Sleepiness Scale (ESS). Here, we assessed whether a machine-learned combination of possibly independent metrics across ventilatory/hypoxic/arousal domains would be better associated with ESS than the AHI using data from 3 large cohorts. Methods Polysomnography data were analyzed from The Sleep Heart Health Study (SHHS), The Multi-Ethnic Study of Atherosclerosis (MESA), and The Osteoporotic Fractures in Men (MrOS) Study. A total of N=6618 (39.9% female; age 68.7±6.6) subjects had valid data (ESS and good quality airflow/EEG/SpO2). Ventilatory burden was evaluated using a derived flow signal that utilized the sum of thoracic and abdominal effort signals for SHHS and was evaluated using the Nasal Cannula/Pressure Transducer signal for the MrOS and MESA data. Hypoxic burden was calculated as the area between the baseline and the SpO2 trace for any episode with >= 3% desaturation. Arousal burden was defined as the manually scored arousal index (number/hr.). Based on a cut-off of ESS(ESS>= 10), sleepiness was coded present or absent and was the primary outcome. Data were analyzed in two ways: using all 3 cohorts as train (70%) and test (30%), and by permutations and combinations of the 3 cohorts (70/30 split; e.g., SHHS for training, MESA and MrOS for test). Model performance metrics were the area under the receiver operating characteristic curve (AUROC) and %accuracy. For comparison, a logistic regression model using AHI3a (3% desaturation and/or EEG arousal) was fit. Results The logistic regression model (AHI3a) classified sleepiness with an AUROC of 0.51±0.07. The random forest model trained on 70% of all 3 cohorts achieved the highest AUROC of 0.88±0.07 (mean accuracy of 85.1± 2.13%), whereas the permutations and combinations of the 3 datasets resulted in an average AUROC of 0.63 ±0.12 (mean accuracy of 76.4±6.57%). Conclusion The machine-learned combination of ventilatory/hypoxic/arousal burdens classifies daytime sleepiness in OSA better than AHI3a across data from 3 large cohorts. These results suggest that OSA severity measured using machine-learned combination of ventilatory/hypoxic/arousal burdens better explains the variability in daytime sleepiness. Support (if any)
Introduction Sleep fragmentation is thought to be associated with adverse long-term consequences including mortality. However, conventional metrics of sleep fragmentation such as time spent in non-REM, REM sleep stages have been inconsistently associated with long-term consequences of sleep fragmentation. Recent studies suggest that morphological features of K-complexes might better describe sleep fragmentation as they are sensitive to effects of sleep disruption and better correlate with short-term outcomes such as daytime sleepiness. We previously demonstrated that the slow wave activity (SWA) surrounding K-complexes (∆SWAK) was a strong predictor of sleepiness in a cohort of sleep apnea subjects, mediated in part by its association with sleep fragmentation, however its association with long-term consequences of sleep fragmentation is not well known. Methods We analyzed nocturnal polysomnography (NPSG) data from Sleep Heart Health Study (1995-1998), a well-characterized cohort examining the cardiovascular consequences of sleep-disordered breathing. The primary quantitative EEG (C3-A2) metrics were: SWA (%relative power in 0.5-4 Hz), K-complex density (number/min of N2), and ∆SWAK. K-complexes were detected automatically using previously published open-source method (DETOKS, Parekh et. al., 2015) during stage N2 of sleep only. All EEG metrics were divided into quartiles to explore potential non-linear associations. Death from any cause up until 2011 (mean follow up of 11 years) was the primary outcome. Cox-regression models were used to assess the relationship between EEG metrics and all-cause mortality. Results After accounting for missing data, 3,909 NPSG’s with at least 6h of usable EEG were available for analysis (age=64±11 yrs., 53.4% female). In multivariate Cox-regression models adjusted for age, gender, race, severity of sleep apnea, total sleep time, time spent in stage N2, and smoking, all-cause mortality was significantly associated with the highest quartile of SWA (HR_q4=0.77 [0.6-0.9], p=0.01), highest two quartiles of ∆SWAK (HR_q3=0.8 [0.6-0.9], p=0.04; HR_q4=0.6 [0.5-0.8], p< 0.001). Conclusion Quantitative EEG measures predict mortality in a large community-dwelling cohort. Specifically, slow-wave activity surrounding K-complexes appear to be strongly associated with all-cause mortality beyond the effect of known covariates. Combined with our previous studies, our data suggest that slow-wave specific EEG measures are predictive of short- and long-term consequences of sleep fragmentation. Support (if any) AASM Foundation BS-233-20, NIH-K25HL151912, R21HL165320
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