@inproceedings{61a643d36652476fb7ed964d5a2b308f,
title = "Heart murmur detection with SVM classification",
abstract = "This paper presents an approach to detect low frequency vibrations from the human chest and correlate them to cardiac conditions. Our system which includes data acquisition via a TekScan FlexiForce sensor, signal processing, and hardware/software interfacing is developed and tested through clinical trials. A Support Vector Machine (SVM) learning algorithm is used to train and classify signals. Our results show that a SVM is able to separate and distinguish signals between normal and abnormal cardiac conditions.",
keywords = "Clinical trials, Data acquisition, Heart, Support vector machines, Testing, Vibrations",
author = "Yang, {Jiann Shiou}",
year = "2015",
month = aug,
day = "20",
doi = "10.1109/ICCE-TW.2015.7216869",
language = "English (US)",
series = "2015 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "228--229",
booktitle = "2015 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2015",
note = "2nd IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2015 ; Conference date: 06-06-2015 Through 08-06-2015",
}