A patient-adaptive neural network ECG patient monitoring algorithm

A new, patient-adaptive ECG patient monitoring algorithm is described. The algorithm combines a patient-independent neural network classifier with a three-parameter patient model. The patient model is used to modulate the patient-independent classifier via multiplicative connections. Adaptation is carried out by gradient descent in the patient model parameter space. The patient-adaptive classifier was compared with a well-established baseline algorithm on six major databases, consisting of over 3 million heartbeats. When trained on an initial 77 records and tested on an additional 382 records, the patient-adaptive algorithm was found to reduce the number of Vn errors on one channel by a factor of 5, and the number of Nv errors by a factor of 10. We conclude that patient adaptation provides a significant advance in classifying normal vs. ventricular beats for ECG patient monitoring.