Inter-patient ECG classification with i-vector based unsupervised patient adaptation

This paper proposes an unsupervised patient adaptation approach to creating patient-specific deep neural network (DNN) classifiers for inter-patient ECG classification. The method exploits the information embedded in the patient-specific i-vectors derived from some unlabeled patient-specific ECG. The adaptation process comprises two stages of backpropagation (BP) fine-tuning, using the i-vector of a target patient as an auxiliary input to a middle layer of the DNN. In the first stage, labeled ECG data from a general population are used for creating a patient-adaptive DNN. Then, in the second stage, unlabeled ECG data from the target patient are used for further BP fine-tuning, using the labels hypothesized by the patient-adaptive DNN as the desired outputs. To ensure that only reliable data are used for adaptation, an information-theoretic heartbeat selector is employed to select the patients’ ECG with high-confidence hypothesized labels. Evaluations on the MIT-BIH arrhythmia dataset show that the proposed unsupervised adaptation leads to patient-specific ECG classifiers that outperform existing patient-specific models. The classifiers also perform comparably to patient-specific models obtained via supervised adaption. This unsupervised adaptation approach can fully automate patient adaptation, making personalized ECG classification more practical.