Real-time algorithm for a mobile cardiac monitoring system to detect life-threatening arrhythmias

This paper presents a real-time algorithm for a mobile cardiac monitoring system to detect life-threatening arrhythmias. This detection algorithm focuses on two life-threatening arrhythmias ventricular tachycardia and fibrillation (VT/VF), which are detected through the application of pre-detection processing and main detection processing. In pre-detection processing, applies a statistical method to detect VT/VF. In contrast, a neural fuzzy network is applied to detect VT/VF in main detection processing. The neural fuzzy network's input features are obtained by wavelet transform and several effective extraction methods. This real-time detection algorithm outperform Amann's algorithm, with 92% accuracy and 93% sensitivity. It has been implemented as a cardiac monitoring system in a mobile phone. This system meets heart patient's requirements of early detection and out-of-hospital rehabilitation.

[1]  J. Moraes,et al.  A real time QRS complex classification method using Mahalanobis distance , 2002, Computers in Cardiology.

[2]  Wang Zhizhong,et al.  Discrimination of VF and VT with method of detrended fluctuation analysis , 2004, 2004 International Conference on Intelligent Mechatronics and Automation, 2004. Proceedings..

[3]  J.L. Rojo-Alvarez,et al.  Discriminating between supraventricular and ventricular tachycardias from EGM onset analysis , 2002, IEEE Engineering in Medicine and Biology Magazine.

[4]  H. Nakajima,et al.  Real-time discrimination of ventricular tachyarrhythmia with Fourier-transform neural network , 1999, IEEE Transactions on Biomedical Engineering.

[5]  Sang-Hong Lee,et al.  Extracting Fuzzy Rules for Detecting Ventricular Arrhythmias Based on NEWFM , 2009, PAKDD.

[6]  Zhen-Xing Zhang,et al.  Detecting ventricular arrhythmias by NEWFM , 2008, 2008 IEEE International Conference on Granular Computing.

[7]  Karl Unterkofler,et al.  Detecting Ventricular Fibrillation by Time-Delay Methods , 2007, IEEE Transactions on Biomedical Engineering.

[8]  J. Folgueras,et al.  Validation of a set of algorithms for ventricular fibrillation detection: experimental results , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[9]  Dianhui Wang,et al.  A neuro-fuzzy approach for diagnosis of antibody deficiency syndrome , 2006, Neurocomputing.

[10]  Ho Joon Kim,et al.  Feature Selection for Specific Antibody Deficiency Syndrome by Neural Network with Weighted Fuzzy Membership Functions , 2005, FSKD.

[11]  José Carlos Teixeira de Barros Moraes,et al.  Ventricular fibrillation detection using a leakage/complexity measure method , 2002, Computers in Cardiology.

[12]  Zhen-Xing Zhang,et al.  Comparison of feature selection methods in ECG signal classification , 2010, ICUIMC '10.

[13]  Joon S. Lim,et al.  Finding Features for Real-Time Premature Ventricular Contraction Detection Using a Fuzzy Neural Network System , 2009, IEEE Transactions on Neural Networks.