A Real-Time Cardiac Arrhythmia Classification System with Wearable Electrocardiogram

Long term continuous monitoring of electrocardiogram (ECG) in a free living environment provides valuable information for prevention on the heart attack and other high risk diseases. A design of a real-time wearable ECG monitoring system with cardiac arrhythmia classification is proposed in this paper. One of the striking advantages is that ECG analog front-end and on-node digital processing are designed to remove most of the noise and bias. In addition, a novel layered hidden Markov model is seamlessly integrated to classify multiple cardiac arrhythmias in real time. Last, human activities by an accelerometer can be identified to reduce the chance of false alarm in classification due to the motion artifacts.

[1]  Marcelo R. Risk,et al.  Beat detection and classification of ECG using self organizing maps , 1997, Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 'Magnificent Milestones and Emerging Opportunities in Medical Engineering' (Cat. No.97CH36136).

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

[3]  Mohammad Bagher Shamsollahi,et al.  Robust Detection of Premature Ventricular Contractions Using a Wave-Based Bayesian Framework , 2010, IEEE Transactions on Biomedical Engineering.

[4]  Geng Yang,et al.  A novel wearable ECG monitoring system based on active-cable and intelligent electrodes , 2008, HealthCom 2008 - 10th International Conference on e-health Networking, Applications and Services.

[5]  John G. Webster,et al.  Driven-right-leg circuit design , 1983, IEEE Transactions on Biomedical Engineering.

[6]  Matt Welsh,et al.  CodeBlue: An Ad Hoc Sensor Network Infrastructure for Emergency Medical Care , 2004 .

[7]  A. Savitzky,et al.  Smoothing and Differentiation of Data by Simplified Least Squares Procedures. , 1964 .

[8]  F.M. Ham,et al.  Classification of cardiac arrhythmias using fuzzy ARTMAP , 1996, IEEE Transactions on Biomedical Engineering.

[9]  G Bortolan,et al.  Premature ventricular contraction classification by the Kth nearest-neighbours rule , 2005, Physiological measurement.

[10]  Wan-Young Chung,et al.  A wireless sensor network compatible wearable u-healthcare monitoring system using integrated ECG, accelerometer and SpO2 , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[11]  Anwar Vahed.  3-Lead Wireless ECG , 2009 .

[12]  Jindong Tan,et al.  BioLogger: A wireless physiological sensing and logging system with applications in poultry science , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[13]  D. Mozaffarian,et al.  Heart disease and stroke statistics--2010 update: a report from the American Heart Association. , 2010, Circulation.

[14]  Philip de Chazal,et al.  Automatic classification of heartbeats using ECG morphology and heartbeat interval features , 2004, IEEE Transactions on Biomedical Engineering.

[15]  Benoît Frénay,et al.  Supervised ECG Delineation Using the Wavelet Transform and Hidden Markov Models , 2009 .

[16]  François Charpillet,et al.  A Multi-HMM Approach to ECG Segmentation , 2006, 2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06).

[17]  Moncef Gabbouj,et al.  Automated patient-specific classification of premature ventricular contractions , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[18]  G.B. Moody,et al.  PhysioNet: a Web-based resource for the study of physiologic signals , 2001, IEEE Engineering in Medicine and Biology Magazine.

[19]  Pablo Laguna,et al.  A wavelet-based ECG delineator: evaluation on standard databases , 2004, IEEE Transactions on Biomedical Engineering.

[20]  Gregory T. A. Kovacs,et al.  Robust Neural-Network-Based Classification of Premature Ventricular Contractions Using Wavelet Transform and Timing Interval Features , 2006, IEEE Transactions on Biomedical Engineering.

[21]  Gérard Dreyfus,et al.  Automatic ECG wave extraction in long-term recordings using Gaussian mesa function models and nonlinear probability estimators , 2007, Comput. Methods Programs Biomed..

[22]  Liang-Yu Shyu,et al.  Using wavelet transform and fuzzy neural network for VPC detection from the holter ECG , 2004, IEEE Transactions on Biomedical Engineering.

[23]  Sang-Joong Jung,et al.  A wireless sensor network compatible wearable u-healthcare monitoring system using integrated ECG, accelerometer and SpO2. , 2008, Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference.

[24]  P F Angelino,et al.  [Computers in cardiology]. , 1980, Minerva medica.