Journal article
Generalized precursor pattern discovery for biomedical signals
Conference proceedings (IEEE Engineering in Medicine and Biology Society. Conf.), Vol.2012, pp.2198-2201
2012
Handle:
https://hdl.handle.net/2376/115134
PMID: 23366359
Abstract
With the advent of low-cost, high-fidelity, and long lasting sensors in recent years, it has become possible to acquire biomedical signals cheaply and remotely over a prolonged period of time. Oftentimes different types of sensors are deployed in the hope of capturing precursor patterns that are highly correlated to a particular clinical episode, such as seizure, congestive heart failure etc. While there have been several studies that successfully identify patterns as reliable precursors for specific medical conditions, most of them require domain-specific knowledge and expertise. The developed algorithms are also unlikely to be applicable to other medical conditions. In this paper we present a generalized algorithm that discovers potential precursor patterns without prior knowledge or domain expertise. The algorithm makes use of wavelet transform and information theory to extract generic features, and it is also classifier agnostic. Based on experiment results using three distinct datasets collected from real-world patients, our algorithm has attained performance comparable to those obtained from previous studies that rely heavily on domain-expert knowledge. Furthermore, the algorithm also discovers non-trivial knowledge in the process.
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Details
- Title
- Generalized precursor pattern discovery for biomedical signals
- Creators
- Mars Lan - Computer Science Department, University of California, Los Angeles, CA 90034, USA. marslan@cs.ucla.eduHassan GhasemzadehMajid Sarrafzadeh
- Publication Details
- Conference proceedings (IEEE Engineering in Medicine and Biology Society. Conf.), Vol.2012, pp.2198-2201
- Academic Unit
- Electrical Engineering and Computer Science, School of
- Publisher
- United States
- Identifiers
- 99900547417901842
- Language
- English
- Resource Type
- Journal article