Performance comparison between wrist and chest actigraphy in combination with heart rate variability for sleep classification. (1st October 2017)

• Record Type: Journal Article
• Authors: Aktaruzzaman, Md; Rivolta, Massimo Walter; Karmacharya, Ruby; Scarabottolo, Nello; Pugnetti, Luigi; Garegnani, Massimo; Bovi, Gabriele; Scalera, Giovanni; Ferrarin, Maurizio; Sassi, Roberto
• Is Part Of: Computers in biology and medicine. Volume 89(2017)
• Page Start: 212
• Page End: 221
• Publication Date: 2017-10-01
• Subjects: Sleep scoring -- Heart rate variability -- Actigraphy -- SVM classifier -- Wearable sensors; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2017.08.006
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: The concurrent usage of actigraphy and heart rate variability (HRV) for sleep efficiency quantification is still matter of investigation. This study compared chest (CACT) and wrist (WACT) actigraphy (actigraphs positioned on chest and wrist, respectively) in combination with HRV for automatic sleep vs wake classification. Accelerometer and ECG signals were collected during polysomnographic studies (PSGs) including 18 individuals (25–53 years old) with no previous history of sleep disorders. Then, an experienced neurologist performed sleep staging on PSG data. Eleven features from HRV and accelerometry were extracted from series of different lengths. A support vector machine (SVM) was used to automatically distinguish sleep and wake. We found 7 min as the optimal signal length for classification, while maximizing specificity (wake detection). CACT and WACT provided similar accuracies (78% chest vs 77% wrist), larger than what yielded by HRV alone (66%). The addition of HRV to CACT reduced slightly the accuracy, while improving specificity (from 33% to 51%, p < 0.05). On the contrary, the concurrent usage of HRV and WACT did not provide statistically significant improvements over WACT. Then, a subset of features (3 from HRV + 1 from actigraphy) was selected by reducing redundancy using a strategy based on Spearman's correlation and area under the ROC curve. The usage of the reduced set of features and SVM classifier gave only slightly reduced classification performances, which did not differ from the full sets of features. The study opens interesting possibilities in the design of wearable devices for long-term monitoring of sleep at home. Highlights: Context of the study is automatic sleep vs wake classification with wearable sensors. We tested chest and wrist actigraphy in combination with heart rate variability (HRV). A support vector machine (SVM) was used to automatically distinguish sleep and wake. The addition of HRV to chest actigraphy led to better detection of wake epochs. HRV and chest actigraphy can be easily embedded in a single compact wearable sensor.