Semi-Supervised Consensus Clustering for ECG Pathology Classification

Pervasive technology is changing the paradigm of healthcare, by empowering users and families with the means for self-care and general health management. However, this requires accurate algorithms for information processing and pathology detection. Accordingly, this paper presents a system for electrocardiography (ECG) pathology classification, relying on a novel semi-supervised consensus clustering algorithm, which finds a consensus partition among a set of baseline clusterings that have been collected for the data under consideration. In contrast to typical unsupervised scenarios, our solution allows exploiting partial prior knowledge of a subset of data points. Our method is built upon the evidence accumulation framework to efficaciously sidestep the cluster correspondence problem. Computationally, the consensus partition is sought by exploiting a result known as Baum-Eagon inequality in the probability domain, which allows for a step-size-free optimization. Experiments on standard benchmark datasets show the validity of our method over the state-of-the-art. In the real world problem of ECG pathology classification, the proposed method achieves comparable performance to supervised learning methods using as few as 20% labeled data points.