Electrodermal activity patterns in sleep stages and their utility for sleep versus wake classification.

As the prevalence of sleep disorders is increasing, new methods for ambulatory sleep measurement are required. This paper presents electrodermal activity in different sleep stages and a sleep detection algorithm based on electrodermal activity. We analysed electrodermal activity and polysomnographic data of 43 healthy subjects and 48 patients with sleep disorders. Electrodermal activity was measured using an ambulatory device worn at the wrist. Two parameters to describe electrodermal activity were defined based on previous literature: EDASEF (electrodermal activity-smoothed feature) as parameter for skin conductance level; and EDAcounts (number of electrodermal activity-peaks) as skin conductance responses. Analysis of variance indicated significant EDASEF differences between the sleep stages wake versus N1, wake versus N2, wake versus slow-wave sleep, and wake versus rapid eye movement. The analysis of EDAcounts also showed significant differences, especially in the stages slow-wave sleep versus rapid eye movement. Between healthy subjects and patients, a significant disparity of EDAcounts was revealed in stage N1. Furthermore, the variances of EDASEF and EDAcounts in N1, N2 slow-wave sleep and rapid eye movement were higher in the patient group (p [F test] < .05). Next, an electrodermal activity-based sleep/wake discriminating algorithm was constructed. The optimized algorithm achieved an average sensitivity and specificity for sleep detection of 97% and 75%. The epoch agreement rate (average accuracy) was 86%. These outcomes are comparative to sleep detection algorithms based on actigraphy or heart rate variability. The results of this study indicate that electrodermal activity is not only a robust parameter for describing sleep, but also a potential suitable method for ambulatory sleep monitoring.

Sedentary behavior in everyday life relates negatively to mood: An ambulatory assessment study.

Empirical evidence shows that physical behavior positively impacts human health. Recently, researchers have started to differentiate between physical activity and sedentary behavior showing independent effects on somatic health. However, whether this differentiation is also relevant for mood dimensions is largely unknown. For investigating the dynamic relationships between sedentary behavior and mood dimensions in daily life, ambulatory assessment (AA) has become the state-of-the-art methodology. To investigate whether sedentary behaviors influence mood dimensions, we conducted an AA study in the everyday life of 92 university employees over 5 days. We continuously measured sedentary behavior via accelerometers and assessed mood repeatedly 10 times each day on smartphone diaries. To optimize our sampling strategy, we used a sophisticated sedentary-triggered algorithm. We employed multilevel modeling to analyze the within-subject effects of sedentary behavior on mood. Sedentary time (15-minute intervals prior to each e-diary assessment) and sedentary bouts (30-minute intervals of uninterrupted sedentary behavior) negatively influenced valence and energetic arousal (all Ps < 0.015). In particular, the more participants were sedentary in their everyday life, the less they felt well and energized. Exploratory analyses of the temporal course of these effects supported our findings. Sedentary behavior can be seen as a general risk factor because it impacts both somatic and mental health. Most importantly, physical activity and sedentary behavior showed independent effects on mood dimensions. Accordingly, future studies should consider the two sides of the physical behavior coin: How should physical activity be promoted? and How can sedentary behavior be reduced?

Ambulatory sleep scoring using accelerometers-distinguishing between nonwear and sleep/wake states.

BACKGROUND: Differentiating 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. METHODS: The 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. RESULTS: The 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. DISCUSSION: We 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.

Using DWT for ECG motion artifact reduction with noise-correlating signals.

Dealing with motion artifacts in long-term ECG recordings is a big issue. The frequency spectrum of motion artifacts is similar to the frequencies of the QRS complex--the wanted signal in the ECG. The deletion of motion artifacts often leads to a deformation of QRS complexes, too. These risks can be minimized by using a noise-correlating signal as a second channel for artifact reduction. This paper presents an approach using the electrode-skin impedance as a second channel for the reduction of motion artifacts. Using the discrete wavelet transform, motion artifacts can be deleted time and frequency selective. This filter approach leads to an improvement of the automatic QRS detection and decreases the number of false detections by 35 %.

Context-aware mobile health monitoring: evaluation of different pattern recognition methods for classification of physical activity.

There are various applications of physical activity monitoring for medical purposes, such as therapeutic rehabilitation, fitness enhancement or the use of physical activity as context information for evaluation of other vital data. Physical activity can be estimated using acceleration sensor-systems fixed on a person's body. By means of pattern recognition methods, it is possible to identify with certain accuracy which movement is being performed. This work presents a comparison of different methods for recognition of daily-life activities, which will serve as basis for the development of an online activity monitoring system.

Reliable motion artifact detection for ECG monitoring systems with dry electrodes.

Reliable signals are the basic prerequisite for most mobile ECG monitoring applications. Especially when signals are analyzed automatically, capable motion artifact detection algorithms are of great importance. This article presents different artifact detection algorithms for ECG systems with dry electrodes. The algorithms are based on the measurement of additional parameters that are correlated with the artifacts. We describe a mobile measurement system and the procedure used for the evaluation of these algorithms. The algorithms are assessed based upon their effect on QRS detection. The best algorithm improved sensitivity (Se) from 98.7% to 99.8% and positive predictive value (+P) from 98.3% to 99.9%, while 15% of the signal was marked as artifact. This corresponds to a decrease in false positive and false negative detected beats by 89.9%. Different metrics to evaluate the performance of an artifact detection algorithm are presented.