A Deep Learning Approach for Sleep-Wake Detection from HRV and Accelerometer Data

Sleep-wake classification is important for measuring the sleep quality. In this paper, we propose a novel deep learning framework for sleep-wake detection by using acceleration and heart rate variability (HRV) data. Firstly, considering the high sampling rate of acceleration data with temporal dependency, we propose a local feature based long short-term memory (LF-LSTM) approach to learn high-level features. Meanwhile, we manually extract representative features from HRV data, as HRV data has a distinct format with acceleration data. Then, a unified framework is developed to combine the features learned by the LF-LSTM from acceleration data and the features extracted from HRV data for sleep-wake detection. We use real data to evaluate the performance of the proposed framework and compare it with some benchmark approaches. The results show that the proposed approach achieves a superior performance over all the benchmark approaches for sleep-wake detection.

[1]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[2]  Yeng Chai Soh,et al.  Building Occupancy Estimation with Environmental Sensors via CDBLSTM , 2017, IEEE Transactions on Industrial Electronics.

[3]  Yanqing Zhang,et al.  SVMs Modeling for Highly Imbalanced Classification , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[4]  Shuohang Wang,et al.  Learning Natural Language Inference with LSTM , 2015, NAACL.

[5]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[6]  Zhongwei Jiang,et al.  Sleep-wake stages classification and sleep efficiency estimation using single-lead electrocardiogram , 2012, Expert Syst. Appl..

[7]  Hong Cao,et al.  Modeling perceived stress via HRV and accelerometer sensor streams , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[8]  M. Kothare,et al.  Algorithms for sleep–wake identification using actigraphy: a comparative study and new results , 2009, Journal of sleep research.

[9]  Michael Catt,et al.  A Novel, Open Access Method to Assess Sleep Duration Using a Wrist-Worn Accelerometer , 2015, PloS one.

[10]  Wei Cui,et al.  WiFi CSI Based Passive Human Activity Recognition Using Attention Based BLSTM , 2019, IEEE Transactions on Mobile Computing.

[11]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[12]  Dario Floreano,et al.  Sleep and Wake Classification With ECG and Respiratory Effort Signals , 2009, IEEE Transactions on Biomedical Circuits and Systems.

[13]  Xi Long,et al.  Sleep and Wake Classification With Actigraphy and Respiratory Effort Using Dynamic Warping , 2014, IEEE Journal of Biomedical and Health Informatics.

[14]  Mehrdad Nourani,et al.  Sleep state classification using pressure sensor mats , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[15]  S. Shea,et al.  Adverse Metabolic Consequences in Humans of Prolonged Sleep Restriction Combined with Circadian Disruption , 2012, Science Translational Medicine.

[16]  Akane Sano,et al.  Multimodal ambulatory sleep detection , 2017, 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI).

[17]  Elke Naujokat,et al.  Sleep/wake detection based on cardiorespiratory signals and actigraphy , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[18]  Shai Fine,et al.  Actigraphy-based Sleep/Wake Pattern Detection using Convolutional Neural Networks , 2018, ArXiv.