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https://hdl.handle.net/2142/46508
Description
Title
Acoustic Heart Monitoring System
Author(s)
Uppal, Karan
Contributor(s)
Hasegawa-Johnson, Mark
Issue Date
2012-05
Keyword(s)
heart monitoring
acoustic hear monitoring
heartbeat classification
machine learning algorithms
clustering algorithms
Abstract
This thesis explores the use of clustering algorithms in acoustic heart monitoring
systems to detect the points of occurrence for a heartbeat. The proposed technique
recovers heartbeats from an acoustically recorded heartbeat signal using
unsupervised machine learning algorithms such as K-means clustering to cluster
the provided data into different classes and identify the heartbeats from it. The K-means
algorithm used to cluster the data is based on squared Euclidean distance.
Experiments were conducted to determine the correct type of features, distance
and number of clusters to use. Silhouette values were used to derive the
appropriate number of clusters. To confirm our algorithm, datasets provided by
Salutron Inc. (Menlo Park, CA) were used and the clustered data was used as a
training set to train hidden Markov processes.
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