Apnea MedAssist II: A smart phone based system for sleep apnea assessment

We have developed a real-time sleep apnea monitoring system called “Apnea MedAssist II”. This has three independent sensors: ECG, SpO2 and Breath sensor. These sensors are connected to a smartphone via Bluetooth. The signal processing of the sensor signals and automated classification of a sleep epoch as an apnea event or a non-apnea event are done in a smart phone. The soft ware for signal processing and automated classification can be imported to an Android OS based smart phones as an `app' that we have developed. The system has accuracy well over 97% when tested on Physionet database. The system is being readied for limited patient trial.

[1]  Mark A. Hall,et al.  Correlation-based Feature Selection for Machine Learning , 2003 .

[2]  K. Behbehani,et al.  A noninvasive technique for detecting obstructive and central sleep apnea , 1997, IEEE Transactions on Biomedical Engineering.

[3]  F. Yasuma,et al.  Respiratory sinus arrhythmia: why does the heartbeat synchronize with respiratory rhythm? , 2004, Chest.

[4]  JunichiroHayano,et al.  Respiratory Sinus Arrhythmia , 1996, Encyclopedia of Evolutionary Psychological Science.

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

[6]  Marimuthu Palaniswami,et al.  Support Vector Machines for Automated Recognition of Obstructive Sleep Apnea Syndrome From ECG Recordings , 2009, IEEE Transactions on Information Technology in Biomedicine.

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

[8]  Richard B Berry,et al.  Positive airway pressure treatment for obstructive sleep apnea. , 2007, Chest.

[9]  Patrick Oonincx,et al.  Second generation wavelets and applications , 2005 .

[10]  S. Akselrod,et al.  Electrocardiogram derived respiration during sleep , 2005, Computers in Cardiology, 2005.

[11]  Andreas Christmann,et al.  Support vector machines , 2008, Data Mining and Knowledge Discovery Handbook.

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

[13]  Daniel J Buysse,et al.  Sleep–Related Breathing Disorders in Adults: Recommendations for Syndrome Definition and Measurement Techniques in Clinical Research , 2000 .

[14]  Nuria Oliver,et al.  HealthGear: a real-time wearable system for monitoring and analyzing physiological signals , 2006, International Workshop on Wearable and Implantable Body Sensor Networks (BSN'06).

[15]  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).

[16]  Marti A. Hearst Trends & Controversies: Support Vector Machines , 1998, IEEE Intell. Syst..

[17]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[18]  T. Yorozu,et al.  Electron Spectroscopy Studies on Magneto-Optical Media and Plastic Substrate Interface , 1987, IEEE Translation Journal on Magnetics in Japan.

[19]  A. Tewfik,et al.  Monitoring of Obstructive Sleep Apnea in Heart Failure Patients , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.