Classification of heart sounds using time-frequency method and artificial neural networks.
In: Enderle J.D, , (ed.)
Annu Int Conf IEEE Eng Med Biol Proc.
(pp. 988 - 991).
: Chicago, IL.
Full text not available from this repository.
||Classification of heart sounds using time-frequency method and artificial neural networks
||Inst. of Sound and Vibration Res., University of Southampton, Southampton, SO17 1BJ, United Kingdom
||Conference code: 58179 Cited By (since 1996): 12 Export Date: 27 April 2010 Source: Scopus CODEN: CEMBA Language of Original Document: English Correspondence Address: Leung, T.S.; Inst. of Sound and Vibration Res., University of Southampton, Southampton, SO17 1BJ, United Kingdom; email: email@example.com Digitally recorded pathological and non-pathological phonocardiograms (PCGs) were characterised by a time-frequency (TF) method known as the trimmed mean spectrogram (TMS). Features were extracted from the TMS containing the distribution of the systolic and diastolic signatures in the TF domain. Together with the acoustic intensities in systole and diastole, these features were used as inputs to the probability neural networks (PNNs) for classification. A total of 56 PCGs were employed to train the PNNs including 21 non-pathological and 35 pathological PCGs. The PNNs were then tested with a different group of 18 non-pathological and 37 pathological PCGs. The system provided a sensitivity of 97.3% (36/37) and a specificity of 94.4% (17/18) in detecting pathological systolic murmurs. The results show that the system offers a promising methodology for classifying murmurs.
||Artificial neural networks, Heart sounds, Phonocardiogram, Time-frequency method, Biological organs, Biomedical engineering, Cardiovascular system, Medical computing, Neural networks, Pathology, Haert sound, Phonocardiogram, Time frequency method, Cardiology
||UCL > School of BEAMS > Faculty of Engineering Science > Medical Physics and Bioengineering
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