TY - GEN
T1 - A Support Vector Machine (SVM) classification approach to heart murmur detection
AU - Rud, Samuel
AU - Yang, Jiann-Shiou
PY - 2010
Y1 - 2010
N2 - This paper focuses on the study of detecting low frequency vibrations from the human chest and correlate them to cardiac conditions using new devices and techniques, custom software, and the Support Vector Machine (SVM) classification technique. Several new devices and techniques of detecting a human heart murmur have been developed through the extraction of vibrations primarily in the range of 10 - 150 Hertz (Hz) on the human chest. The devices and techniques have been tested on different types of simulators and through clinical trials. Signals were collected using a Kardiac Infrasound Device (KID) and accelerometers integrated with a custom MATLAB software interface and a data acquisition system. Using the interface, the data was analyzed and classified by an SVM approach. Results show that the SVM was able to classify signals under different testing environments. For clinical trials, the SVM distinguished between normal and abnormal cardiac conditions and between pathological and non-pathological cardiac conditions. Finally, using the various devices, a correlation between heart murmurs and normal hearts was observed from human chest vibrations.
AB - This paper focuses on the study of detecting low frequency vibrations from the human chest and correlate them to cardiac conditions using new devices and techniques, custom software, and the Support Vector Machine (SVM) classification technique. Several new devices and techniques of detecting a human heart murmur have been developed through the extraction of vibrations primarily in the range of 10 - 150 Hertz (Hz) on the human chest. The devices and techniques have been tested on different types of simulators and through clinical trials. Signals were collected using a Kardiac Infrasound Device (KID) and accelerometers integrated with a custom MATLAB software interface and a data acquisition system. Using the interface, the data was analyzed and classified by an SVM approach. Results show that the SVM was able to classify signals under different testing environments. For clinical trials, the SVM distinguished between normal and abnormal cardiac conditions and between pathological and non-pathological cardiac conditions. Finally, using the various devices, a correlation between heart murmurs and normal hearts was observed from human chest vibrations.
KW - Hear murmur detection
KW - support vector machine
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U2 - 10.1007/978-3-642-13318-3_7
DO - 10.1007/978-3-642-13318-3_7
M3 - Conference contribution
AN - SCOPUS:77954411666
SN - 3642133177
SN - 9783642133176
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 52
EP - 59
BT - Advances in Neural Networks - ISNN 2010 - 7th International Symposium on Neural Networks, ISNN 2010, Proceedings
T2 - 7th International Symposium on Neural Networks, ISNN 2010
Y2 - 6 June 2010 through 9 June 2010
ER -