Beat discovery from dimensionality reduced perspective streams of electrocardiogram signal data

Spectral characteristics of ECG traces have identified a stochastic component in the inter-beat interval for triggering a new cardiac cycle. Yet the stream consistently shows impressive reproducibility of the inherent core waveform. Respectively, the presence of close to deterministic structures firmly contends for representing a single cycle ECG wave by a state vector in a low dimensional embedding space. Rather than performing arrhythmia clustering directly on the high dimensional state space, our work first reduces the dimensionality of the extracted raw features. Analysis of heartbeat irregularities becomes then more tractable computationally, and thus claims more relevance to run on emerging wearable and IoT devices that are severely resource and power constraint. In contrast to prior work that searches for a two dimensional embedding space, we project feature vectors onto a three dimensional coordinate frame. This merits an essential depth perception facet to a specialist that qualifies cluster memberships, and furthermore, by removing stream noise, we managed to retain a high percentile level of source energy. We performed extensive analysis and classification experiments on a large arrhythmia dataset, and report robust results to support the intuition of expert neutral similarity.