Machine Learning
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Neural network classification of homomorphic segmented heart sounds
Applied Soft Computing
Detecting time series motifs under uniform scaling
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Computers in Biology and Medicine
Heartbeat time series classification with support vector machines
IEEE Transactions on Information Technology in Biomedicine - Special section on biomedical informatics
Mining approximate motifs in time series
DS'06 Proceedings of the 9th international conference on Discovery Science
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The aim of this work is to describe an exploratory study on the use of a SAX-based Multiresolution Motif Discovery method for Heart Sound Classification. The idea of our work is to discover relevant frequent motifs in the audio signals and use the discovered motifs and their frequency as characterizing attributes. We also describe different configurations of motif discovery for defining attributes and compare the use of a decision tree based algorithm with random forests on this kind of data. Experiments were performed with a dataset obtained from a clinic trial in hospitals using the digital stethoscope DigiScope. This exploratory study suggests that motifs contain valuable information that can be further exploited for Heart Sound Classification.