A Novel Ensemble Deep Learning Approach for Sleep-Wake Detection Using Heart Rate Variability and Acceleration

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

[2]  Hua Wang,et al.  EEG Sleep Stages Analysis and Classification Based on Weighed Complex Network Features , 2021, IEEE Transactions on Emerging Topics in Computational Intelligence.

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

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

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

[6]  Sarah G. Woo,et al.  Role of sleep continuity and total sleep time in executive function across the adult lifespan. , 2014, Psychology and aging.

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

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

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

[10]  Xiaoli Liu,et al.  Sleep Stage Classification Using Bidirectional LSTM in Wearable Multi-sensor Systems , 2019, IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[11]  Manuel Schabus,et al.  About the Accuracy and Problems of Consumer Devices in the Assessment of Sleep , 2019, Sensors.

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

[13]  Yachuan Pu,et al.  Heart rate variability, sleep and sleep disorders. , 2012, Sleep medicine reviews.

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

[15]  John R. Shambroom,et al.  Validation of an automated wireless system to monitor sleep in healthy adults , 2012, Journal of sleep research.

[16]  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).

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

[18]  Sabine Van Huffel,et al.  An Evaluation of Cardiorespiratory and Movement Features With Respect to Sleep-Stage Classification , 2014, IEEE Journal of Biomedical and Health Informatics.

[19]  Ruili Wang,et al.  A Survey on an Emerging Area: Deep Learning for Smart City Data , 2019, IEEE Transactions on Emerging Topics in Computational Intelligence.

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

[21]  Xi Long,et al.  Cardiorespiratory Sleep Stage Detection Using Conditional Random Fields , 2017, IEEE Journal of Biomedical and Health Informatics.

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

[23]  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.

[24]  Homer Nazeran,et al.  Automatic sleep staging using empirical mode decomposition, discrete wavelet transform, time-domain, and nonlinear dynamics features of heart rate variability signals , 2013, Comput. Methods Programs Biomed..

[25]  Yanan Sui,et al.  Automatic Sleep Stage Classification Based on Subthalamic Local Field Potentials , 2019, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

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

[27]  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).

[28]  Dirk Van,et al.  Ensemble Methods: Foundations and Algorithms , 2012 .

[29]  Carolina Ruiz,et al.  Deep Learning for Automated Feature Discovery and Classification of Sleep Stages , 2020, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[30]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[31]  Le Zhang,et al.  Ensemble deep learning for regression and time series forecasting , 2014, 2014 IEEE Symposium on Computational Intelligence in Ensemble Learning (CIEL).

[32]  M. Littner,et al.  Practice parameters for the indications for polysomnography and related procedures: an update for 2005. , 2005, Sleep.

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

[34]  Fei Wang,et al.  Deep learning for healthcare: review, opportunities and challenges , 2018, Briefings Bioinform..

[35]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[36]  Min Wu,et al.  A Deep Learning Approach for Sleep-Wake Detection from HRV and Accelerometer Data , 2019, 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI).

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

[38]  Thomas Penzel,et al.  Comparison of detrended fluctuation analysis and spectral analysis for heart rate variability in sleep and sleep apnea , 2003, IEEE Transactions on Biomedical Engineering.