The research mHealth platform for ECG monitoring

Nowadays, mobile terminal applications rapidly transform the meaning and context of health care services around the globe. Continuous monitoring of physical parameters is very important for improvement of the patient's life quality. Detection of ECG RR interval and QRS complex is crucial for diagnosis of heart diseases. In this paper, we present Bluetooth ECG Sensor Emulator in Matlab. We have tested the computation performances of mobile phones for monitoring and analysis of ECG signals. Three different peak detection algorithms were implemented using Java ME. MIT-BIH Arrythmia database signals were used for testing. Experimental results are discussed in order to determine their applicability to the real-time ECG telemonitoring.

[1]  R. Istepanian,et al.  M-Health: Emerging Mobile Health Systems , 2006 .

[2]  W.J. Tompkins,et al.  ECG beat detection using filter banks , 1999, IEEE Transactions on Biomedical Engineering.

[3]  Thomas Norgall,et al.  Body area network--a key infrastructure element for patient-centered telemedicine. , 2004, Studies in health technology and informatics.

[4]  S. Suppappola,et al.  Nonlinear transforms of ECG signals for digital QRS detection: a quantitative analysis , 1994, IEEE Transactions on Biomedical Engineering.

[5]  Z Dokur,et al.  Detection of ECG waveforms by neural networks. , 1997, Medical engineering & physics.

[6]  Eduardo Casilari,et al.  Development of wireless body area network based on J2ME for m-health applications , 2008 .

[7]  S.P. Nelwan,et al.  Ubiquitous mobile access to real-time patient monitoring data , 2002, Computers in Cardiology.

[8]  Emmanuel Skordalakis Syntactic ECG processing: A review , 1986, Pattern Recognit..

[9]  R. Fensli,et al.  A wireless ECG system for continuous event recording and communication to a clinical alarm station , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[10]  C. Li,et al.  Detection of ECG characteristic points using wavelet transforms. , 1995, IEEE transactions on bio-medical engineering.

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

[12]  R. Orglmeister,et al.  The principles of software QRS detection , 2002, IEEE Engineering in Medicine and Biology Magazine.

[13]  Rune Fensli,et al.  A wearable ECG-recording system for continuous arrhythmia monitoring in a wireless tele-home-care situation , 2005, 18th IEEE Symposium on Computer-Based Medical Systems (CBMS'05).

[14]  H. T. Nagle,et al.  A comparison of the noise sensitivity of nine QRS detection algorithms , 1990, IEEE Transactions on Biomedical Engineering.

[15]  I. Cosic,et al.  ECG R-R Peak Detection on Mobile Phones , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[16]  Majid Sarrafzadeh,et al.  Wireless health and the smart phone conundrum , 2009, SIGBED.

[17]  Alf Helge Omre,et al.  Bluetooth Low Energy: Wireless Connectivity for Medical Monitoring , 2010, Journal of diabetes science and technology.

[18]  P. Keeratiwintakorn,et al.  The three-lead wireless ECG in sensor networks for mobile patients , 2008, 2008 SICE Annual Conference.

[19]  Arantza Illarramendi,et al.  A Wireless Application That Monitors ECG Signals On-Line: Architecture and Performance , 2004, ICEIS.