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| Diagnosis of Congestive Heart Failure Based on BP Network with Multiple Heart Rate Variability Parameters |
1 Institute for Biomedical Electronic Engineering, School of Electronic Science and Engineering,Nanjing University, Nanjing 210093, China
2 Information Management Teaching and Research Center, Department of Basic Science, China Pharmaceutical University, Nanjing 210009, China |
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Abstract Information delivered by a single heart rate variability (HRV) index is inadequate and it is very difficult to completely classify the congestive heart failure (CHF) patients from healthy people by using a single index. Artificial neural network，inspired by biological nervous systems, is composed of simple elements operating in parallel and open up very broad vistas in the field of pattern recognition. Based on composing timedomain analysis, frequencydomain analysis and nonlinear analysis of HRV signals, several indices were extracted as the feature parameters for the diagnosis of CHF. Then BP network was trained and applied to the diagnosis of congestive heart failure. After 10000 times of training, validating and testing, the optimal recognition rate achieved 99.14% with 86.97% on average. The results suggest that the conjunctive application of linear and nonlinear methods of HRV can extract more information underlying the complex dynamic systems.
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