A balanced sleep/wakefulness classification method based on actigraphic data in adolescents

Abstract

Several research groups have developed automated sleep-wakefulness classifiers for night wrist actigraphic (ACT) data. These classifiers tend to be unbalanced, with a tendency to overestimate the detection of sleep, at the expense of poorer detection of wakefulness. The reason for this is that the measure of success in previous works was the maximization of the overall accuracy, disregarding the balance between sensitivity and specificity. The databases were usually sleep recordings, hence the over-representation of sleep samples. In this work an Artificial Neural Network (ANN), sleep-wakefulness classifier is presented. ACT data was collected every minute. An 11-min moving window was used as observing frame for data analysis, as applied in previous sleep ACT studies. However, our feature set adds new variables such as the time of the day, the median and the median absolute deviation. Sleep and Wakefulness data were balanced to improve the system training. A comparison with previous studies can still be done, by choosing the point in the ROC curve associated with the corresponding data balance. Our results are compared with a polysomnogram-based hypnogram as golden standard, rendering an accuracy of 92.8%, a sensitivity of 97.6% and a specificity of 73.4%. Geometric mean between sensitivity and specificity is 84.9%.

Improving Sleep Quality Assessment Using Wearable Sensors by Including Information From Postural/Sleep Position Changes and Body Acceleration: A Comparison of Chest-Worn Sensors, Wrist Actigraphy, and Polysomnography

“…Traditionally, the threshold is identified based on accuracy rate (ACCU) as suggested by sleep studies. 4,6,7,10,19,20,24 This identification of the threshold method yields the highest accuracy rate. We also used the Matthew correlation coefficients (MCC) 25 approach to identify the best threshold, which is insensitive to inter-subject difference for the number of sleep/wake epochs.…”

“…PreProcessing: The raw three-dimensional acceleration data were processed with a band-pass filter at cutoff frequencies of 0.1953 Hz and 12.5 Hz to remove the gravity component and high-frequency noise that was not originated from human body movements. 20,21 Figure S1 in the supplemental material describes the flowchart of body acceleration estimation. In summary, a vector magnitude (VM) of the filtered acceleration data was calculated for each sample (sample frequency: 100 Hz).…”

“…7 To avoid outliers, values below the 5th percentile value (0.02 g) and greater than the 95th percentile value (0.2 g) were replaced with 0.02 g and 0.2 g, respectively. 20 Then the outputs were normalized to range from zero to 1 (NmVM). The body acceleration (BAcc) is defined as the sum of NmVM values at every minute ( Figure S1).…”

Behavioural Curves Analysis Using Near-Infrared-Iris Image Sequences

“…The Alertplus, Alermeter [35], [36]. Jawbone, Fitbit [37]- [41].The Smartcap [42]- [44], among others.…”

BTP: A Bedtime Predicting Algorithm via Smartphone Screen Status

“…Activity trackers like Fitbit wristband and smartwatches like Apple Watch make it possible to collect and analyse wrist movement data of users. Reference [15] employed an artificial neural network to classify sleep and wakefulness based on night wrist actigraph data. Reference [16] integrated unsupervised machine learning algorithms and domain knowledge heuristics to detect sleep or wake status.…”