Monitoring Obstructive Sleep Apnea by means of a real-time mobile system based on the automatic extraction of sets of rules through Differential Evolution

Real-time Obstructive Sleep Apnea (OSA) episode detection and monitoring are important for society in terms of an improvement in the health of the general population and of a reduction in mortality and healthcare costs. Currently, to diagnose OSA patients undergo PolySomnoGraphy (PSG), a complicated and invasive test to be performed in a specialized center involving many sensors and wires. Accordingly, each patient is required to stay in the same position throughout the duration of one night, thus restricting their movements. This paper proposes an easy, cheap, and portable approach for the monitoring of patients with OSA, which collects single-channel ElectroCardioGram (ECG) data only. It is easy to perform from the patient's point of view because only one wearable sensor is required, so the patient is not restricted to keeping the same position all night long, and the detection and monitoring can be carried out in any place through the use of a mobile device. Our approach is based on the automatic extraction, from a database containing information about the monitored patient, of explicit knowledge in the form of a set of IF…THEN rules containing typical parameters derived from Heart Rate Variability (HRV) analysis. The extraction is carried out off-line by means of a Differential Evolution algorithm. This set of rules can then be exploited in the real-time mobile monitoring system developed at our Laboratory: the ECG data is gathered by a wearable sensor and sent to a mobile device, where it is processed in real time. Subsequently, HRV-related parameters are computed from this data, and, if their values activate some of the rules describing the occurrence of OSA, an alarm is automatically produced. This approach has been tested on a well-known literature database of OSA patients. The numerical results show its effectiveness in terms of accuracy, sensitivity, and specificity, and the achieved sets of rules evidence the user-friendliness of the approach. Furthermore, the method is compared against other well known classifiers, and its discrimination ability is shown to be higher.

[1]  R. Storn,et al.  Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) , 2005 .

[2]  Ivanoe De Falco,et al.  Discovering interesting classification rules with genetic programming , 2002, Appl. Soft Comput..

[3]  Robert C. Holte,et al.  Very Simple Classification Rules Perform Well on Most Commonly Used Datasets , 1993, Machine Learning.

[4]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[5]  Carlo Combi,et al.  Data mining with Temporal Abstractions: learning rules from time series , 2007, Data Mining and Knowledge Discovery.

[6]  Joanne W Y Chung,et al.  Evaluation of the performance of using mean absolute amplitude analysis of thoracic and abdominal signals for immediate indication of sleep apnoea events. , 2008, Journal of clinical nursing.

[7]  J. Kaprio,et al.  Snoring as a risk factor for ischaemic heart disease and stroke in men. , 1987, British medical journal.

[8]  M. Hilton,et al.  Validation of British Thoracic Society guidelines for the diagnosis of the sleep apnoea/hypopnoea syndrome: can polysomnography be avoided? , 1995, Thorax.

[9]  Chwan-Lu Tseng,et al.  A NEW APPROACH FOR IDENTIFYING SLEEP APNEA SYNDROME USING WAVELET TRANSFORM AND NEURAL NETWORKS , 2006 .

[10]  Pat Langley,et al.  Estimating Continuous Distributions in Bayesian Classifiers , 1995, UAI.

[11]  N. de Vries,et al.  Risks of general anaesthesia in people with obstructive sleep apnoea , 2004, BMJ : British Medical Journal.

[12]  Hlaing Minn,et al.  An Improved Approach for Real-time Detection of Sleep Apnea , 2011, BIOSIGNALS.

[13]  J. Victor Marcos,et al.  Spectral analysis of electroencephalogram and oximetric signals in obstructive sleep apnea diagnosis , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[14]  John G. Cleary,et al.  K*: An Instance-based Learner Using and Entropic Distance Measure , 1995, ICML.

[15]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

[16]  H. Altay Güvenir,et al.  Classification by Voting Feature Intervals , 1997, ECML.

[17]  Finn Verner Jensen,et al.  Introduction to Bayesian Networks , 2008, Innovations in Bayesian Networks.

[18]  David W. Aha,et al.  Instance-Based Learning Algorithms , 1991, Machine Learning.

[19]  V. M. Wadhai,et al.  APNEA Detection on Smart Phone , 2012 .

[20]  M. H. Asyali,et al.  Sleep stage and obstructive apneaic epoch classification using single-lead ECG , 2010, Biomedical engineering online.

[21]  Khaled M. Elleithy,et al.  Detection of obstructive sleep apnea through ECG signal features , 2012, 2012 IEEE International Conference on Electro/Information Technology.

[22]  Pericles A. Mitkas,et al.  Applying Machine Learning Techniques on Air Quality Data for Real-Time Decision Support , 2003 .

[23]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[24]  Hlaing Minn,et al.  Apnea MedAssist: Real-time Sleep Apnea Monitor Using Single-Lead ECG , 2011, IEEE Transactions on Information Technology in Biomedicine.

[25]  Conor Heneghan,et al.  Automated processing of the single-lead electrocardiogram for the detection of obstructive sleep apnoea , 2003, IEEE Transactions on Biomedical Engineering.

[26]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[27]  Yuval Shahar,et al.  Medical Temporal-Knowledge Discovery via Temporal Abstraction , 2009, AMIA.

[28]  Ian H. Witten,et al.  Generating Accurate Rule Sets Without Global Optimization , 1998, ICML.

[29]  Michael J. Chappell,et al.  Screening for obstructive sleep apnoea based on the electrocardiogram-the computers in cardiology challenge , 2000, Computers in Cardiology 2000. Vol.27 (Cat. 00CH37163).

[30]  W. McNicholas,et al.  Sleep-related breathing disorders: definitions and measurements. , 2000, The European respiratory journal.

[31]  Yoshiharu Yonezawa,et al.  A wearable, mobile phone-based respiration monitoring system for sleep apnea syndrome detection. , 2005, Biomedical sciences instrumentation.

[32]  Giuseppe De Pietro,et al.  A Mobile Reasoning System for Supporting the Monitoring of Chronic Diseases , 2011, MobiHealth.

[33]  Mahsan Rofouei,et al.  A Non-invasive Wearable Neck-Cuff System for Real-Time Sleep Monitoring , 2011, 2011 International Conference on Body Sensor Networks.

[34]  Johannes Fürnkranz,et al.  Foundations of Rule Learning , 2012, Cognitive Technologies.

[35]  Paul Compton,et al.  Knowledge in Context: A Strategy for Expert System Maintenance , 1990, Australian Joint Conference on Artificial Intelligence.

[36]  Ron Kohavi,et al.  Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid , 1996, KDD.

[37]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[38]  Vitaliy Feoktistov,et al.  Differential Evolution: In Search of Solutions (Springer Optimization and Its Applications) , 2006 .

[39]  Geoffrey E. Hinton,et al.  Learning representations by back-propagation errors, nature , 1986 .

[40]  Fadi A. Aloul,et al.  Sleep Apnea Monitoring using mobile phones , 2012, 2012 IEEE 14th International Conference on e-Health Networking, Applications and Services (Healthcom).

[41]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[42]  Nuria Oliver,et al.  HealthGear: Automatic Sleep Apnea Detection and Monitoring with a Mobile Phone , 2007, J. Commun..

[43]  Willis J. Tompkins,et al.  A Real-Time QRS Detection Algorithm , 1985, IEEE Transactions on Biomedical Engineering.

[44]  G. Moody,et al.  The apnea-ECG database , 2000, Computers in Cardiology 2000. Vol.27 (Cat. 00CH37163).

[45]  M.H. Hassoun,et al.  Fundamentals of Artificial Neural Networks , 1996, Proceedings of the IEEE.

[46]  Ivanoe De Falco,et al.  Differential Evolution for automatic rule extraction from medical databases , 2013, Appl. Soft Comput..

[47]  G. Castellanos-Dominguez,et al.  Detection of obstructive sleep apnea in ECG recordings using time-frequency distributions and dynamic features , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.