TY - GEN
T1 - Generalized precursor pattern discovery for biomedical signals
AU - Lan, Mars
AU - Ghasemzadeh, Hassan
AU - Sarrafzadeh, Majid
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84880962775&partnerID=8YFLogxK
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U2 - 10.1109/EMBC.2012.6346398
DO - 10.1109/EMBC.2012.6346398
M3 - Conference contribution
C2 - 23366359
AN - SCOPUS:84880962775
SN - 9781424441198
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 2198
EP - 2201
BT - 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2012
T2 - 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012
Y2 - 28 August 2012 through 1 September 2012
ER -