13 March 2017 Machine learning for cardiac ultrasound time series data
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Abstract
We consider the problem of identifying frames in a cardiac ultrasound video associated with left ventricular chamber end-systolic (ES, contraction) and end-diastolic (ED, expansion) phases of the cardiac cycle. Our procedure involves a simple application of non-negative matrix factorization (NMF) to a series of frames of a video from a single patient. Rank-2 NMF is performed to compute two end-members. The end members are shown to be close representations of the actual heart morphology at the end of each phase of the heart function. Moreover, the entire time series can be represented as a linear combination of these two end-member states thus providing a very low dimensional representation of the time dynamics of the heart. Unlike previous work, our methods do not require any electrocardiogram (ECG) information in order to select the end-diastolic frame. Results are presented for a data set of 99 patients including both healthy and diseased examples.
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Baichuan Yuan , Sathya R. Chitturi, Geoffrey Iyer, Nuoyu Li, Xiaochuan Xu, Ruohan Zhan, Rafael Llerena, Jesse T. Yen, Andrea L. Bertozzi, "Machine learning for cardiac ultrasound time series data", Proc. SPIE 10137, Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging, 101372D (13 March 2017); doi: 10.1117/12.2254704; https://doi.org/10.1117/12.2254704
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