An innovative wireless Cardiac Rhythm Management (iCRM) system

In this paper, we propose a wireless Communicator to manage and enhance a Cardiac Rhythm Management System. The system includes: (1) an on-body wireless Electrocardiogram (ECG), (2) an Intracardiac Electrogram (EGM) embedded inside an Implantable Cardioverter/Defibrillator, and (3) a Communicator (with a resident Learning System). The first two devices are existing technology available in the market and are emulated using data from the Physionet database, while the Communicator was designed and implemented by our research team. The value of the information obtained by combining the information supplied by (1) and (2), presented to the Communicator, improves decision making regarding use of the actuator or other actions. Preliminary results show a high level of confidence in the decisions made by the Communicator. For example, excellent accuracy is achieved in predicting atrial arrhythmia in 8 patients using only external ECG when we used a neural network.

[1]  Amar Kachenoura,et al.  Surface Electrocardiogram Reconstruction From Intracardiac Electrograms Using a Dynamic Time Delay Artificial Neural Network , 2013, IEEE Transactions on Biomedical Engineering.

[2]  T. Denton,et al.  Comprehensive electrocardiology : theory and practice in health and disease , 1990 .

[3]  A. Ishida,et al.  Stress assessment based on ECG using neural networks , 1996, Proceedings of 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[4]  A. Kadish,et al.  Heart failure devices: implantable cardioverter-defibrillators and biventricular pacing therapy. , 2005, Circulation.

[5]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[6]  Izzah Amani Tarmizi,et al.  A journal of real peak recognition of electrocardiogram (ECG) signals using neural network , 2012, 2012 Second International Conference on Digital Information and Communication Technology and it's Applications (DICTAP).

[7]  อนิรุธ สืบสิงห์,et al.  Data Mining Practical Machine Learning Tools and Techniques , 2014 .

[8]  Yukinori Suzuki.  Self-organizing QRS-wave recognition in ECG using neural networks , 1995, IEEE Trans. Neural Networks.

[9]  J. Zaidan.  Implantable cardioverter-defibrillators. , 1999, Journal of cardiothoracic and vascular anesthesia.

[10]  Hansen Yee,et al.  Neural network classification of intracardiac ECG's , 1991, [Proceedings] 1991 IEEE International Joint Conference on Neural Networks.

[11]  Ulrich Amsel,et al.  Comprehensive Electrocardiology Theory And Practice In Health And Disease , 2016 .

[12]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[13]  G Stuart Mendenhall,et al.  Implantable and surface electrocardiography: complementary technologies. , 2010, Journal of electrocardiology.

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