Heart rate variability pattern recognition in ambulatory environments

Coronary Heart Disease (CHD) is the leading cause of death in the world. Heart Rate Variability (HRV) has been known as a measure of cardiac electrophysiology that independently predicts sudden death in CHD patients. In this paper, we develop a Poincaré encoding based HRV patterns discovering Incremental Artificial neural Network (PHIAN). Long-term Electrocardiogram (ECG) recordings are made in various daily activities under the controlled heart rate and breathing frequency. Incremental training is exploited to achieve the ability to train the classifier model with new data without destroying the previously learned patterns. The hidden layer of the network is updated by new node insertions based on local errors. However, the insertion in the overlapping decision areas has to be stopped when it on longer improves the performance of the classification model. The algorithm effectiveness is finally assessed in terms of classification error in relation to the data set structure and network structure. The results manifest that PHIAN with a satisfactory number of nodes outperforms the previous techniques.