Semi-supervised learning for electrocardiography signal classification.

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An electrocardiogram (ECG) is a cardiology test that provides information about the structure and function of the heart. The size of the ECG data collected from patients can be very large, and the data analysis is tedious. Inspired by human learning, in this thesis we propose a new semi-supervised training framework for deep neural network to classify ECG data. The idea is to reward the valid associations that belong to the same class after a round trip during cross-matching of supervised and unsupervised learning, while penalizing the incorrect associations. The implementation of our framework can be easily integrated with any existing training setup. With data preprocessing, the detection of heart disease is improved.

Semi-supervised learning, Electrocardiography, pattern recognition