Author:
Avi Bleiweiss
Affiliation:
Intel Corporation, United States
Keyword(s):
Electrocardiogram, Cardiac Arrhythmia, Spectral Decomposition, Dimensionality Reduction, Clustering.
Related
Ontology
Subjects/Areas/Topics:
Design and Implementation of Signal Processing Systems
;
Mobile Multimedia (GSM, GPRS, RF, Wi-Fi,…)
;
Multidimensional Signal Processing
;
Multimedia
;
Multimedia and Communications
;
Multimedia Signal Processing
;
Multimedia Systems and Applications
;
Telecommunications
Abstract:
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.
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