Title :
Heart murmur detection with SVM classification
Author :
Jiann-Shiou Yang
Author_Institution :
Dept. of Electr. Eng., Univ. of Minnesota, Duluth, MN, USA
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 :
cardiology; data acquisition; medical signal detection; medical signal processing; signal classification; support vector machines; SVM classification; TekScan FlexiForce sensor; data acquisition; frequency vibrations; hardware-software interfacing; heart murmur detection; signal processing; support vector machine learning algorithm; Clinical trials; Data acquisition; Heart; Support vector machines; Testing; Vibrations;
Conference_Titel :
Consumer Electronics - Taiwan (ICCE-TW), 2015 IEEE International Conference on
Conference_Location :
Taipei
DOI :
10.1109/ICCE-TW.2015.7216869
