Sleep monitoring approach based on belief rule-based systems with pulse oxygen saturation and heart rate

This paper aims to propose a sleep and wake classification approach based on Belief Rule-Based (BRB) systems (called BRB classifier) to address the challenge of daily personal sleep monitoring at home. The BRB classifier infers sleep and wake states with two signals, pulse oxygen saturation and heart rate which can be obtained from wearable devices. The actual sleep datasets collected by Sleep Heart Health Study group are investigated to testify the efficiency of BRB classifier. In this case study, several experiments are conducted to determine the settings of BRB systems. By comparing with Linear Discriminate classifier and Neural Network classifier, the proposed BRB classifier has shown superior performance in sleep/wake classification with pulse oxygen saturation and heart rate.

[1]  Jian-Bo Yang,et al.  On the inference and approximation properties of belief rule based systems , 2013, Inf. Sci..

[2]  Jiang Jiang,et al.  A Bayesian approach for sleep and wake classification based on dynamic time warping method , 2017, Multimedia Tools and Applications.

[3]  Matteo Matteucci,et al.  Sleep staging from Heart Rate Variability: time-varying spectral features and Hidden Markov Models , 2010 .

[4]  E. Wolpert.  A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects. , 1969 .

[5]  Jian-Bo Yang,et al.  Online Updating Belief-Rule-Base Using the RIMER Approach , 2011, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[6]  Bingfeng Ge,et al.  Automatic sleep and wake classifier with heart rate and pulse oximetry: Derived dynamic time warping features and logistic model , 2016, 2016 Annual IEEE Systems Conference (SysCon).

[7]  Shanlin Yang,et al.  An evidential reasoning based consensus model for multiple attribute group decision analysis problems with interval-valued group consensus requirements , 2012, Eur. J. Oper. Res..

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

[9]  Claudio A. Perez,et al.  Extracting Fuzzy Rules From Polysomnographic Recordings for Infant Sleep Classification , 2006, IEEE Transactions on Biomedical Engineering.

[10]  Jian-Bo Yang,et al.  On the evidential reasoning algorithm for multiple attribute decision analysis under uncertainty , 2002, IEEE Trans. Syst. Man Cybern. Part A.

[11]  Miad Faezipour,et al.  Efficient sleep stage classification based on EEG signals , 2014, IEEE Long Island Systems, Applications and Technology (LISAT) Conference 2014.

[12]  Zhao Hongyao.  Study of measuring heart rate and respiration rate based on PPG , 2011 .

[13]  Matteo Matteucci,et al.  Sleep Staging Based on Signals Acquired Through Bed Sensor , 2010, IEEE Transactions on Information Technology in Biomedicine.

[14]  Dario Floreano,et al.  Adaptive Sleep–Wake Discrimination for Wearable Devices , 2011, IEEE Transactions on Biomedical Engineering.

[15]  Chang-Hua Hu,et al.  A New BRB-ER-Based Model for Assessing the Lives of Products Using Both Failure Data and Expert Knowledge , 2016, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[16]  Jian-Bo Yang,et al.  Environmental impact assessment using the evidential reasoning approach , 2006, Eur. J. Oper. Res..

[17]  Jian-Bo Yang,et al.  A New Prediction Model Based on Belief Rule Base for System's Behavior Prediction , 2011, IEEE Transactions on Fuzzy Systems.

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

[19]  Xuan Li,et al.  Weapon System Capability Assessment under uncertainty based on the evidential reasoning approach , 2011, Expert Syst. Appl..

[20]  Jian-Bo Yang,et al.  A methodology to generate a belief rule base for customer perception risk analysis in new product development , 2011, Expert Syst. Appl..

[21]  Jian-Bo Yang,et al.  Belief rule-base inference methodology using the evidential reasoning Approach-RIMER , 2006, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[22]  Zhi-Jie Zhou,et al.  Belief rule based expert system for classification problems with new rule activation and weight calculation procedures , 2016, Inf. Sci..

[23]  Bonnie K. Lind,et al.  Methods for obtaining and analyzing unattended polysomnography data for a multicenter study. Sleep Heart Health Research Group. , 1998, Sleep.