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  • 1.
    Ghareh Baghi, Arash
    et al.
    Linköping University, Linköping, Sweden.
    Borga, Magnus
    Linköping University, Linköping, Sweden.
    Janerot Sjöberg, Birgitta
    Linköping University, Linköping, Sweden.
    Ask, Per
    Linköping University, Linköping, Sweden.
    A novel method for discrimination between innocent and pathological heart murmurs2015Inngår i: Medical Engineering and Physics, ISSN 1350-4533, E-ISSN 1873-4030, Vol. 37, nr 7, s. 674-682Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This paper presents a novel method for discrimination between innocent and pathological murmurs using the growing time support vector machine (GTSVM). The proposed method is tailored for characterizing innocent murmurs (IM) by putting more emphasis on the early parts of the signal as IMs are often heard in early systolic phase. Individuals with mild to severe aortic stenosis (AS) and IM are the two groups subjected to analysis, taking the normal individuals with no murmur (NM) as the control group. The AS is selected due to the similarity of its murmur to IM, particularly in mild cases. To investigate the effect of the growing time windows, the performance of the GTSVM is compared to that of a conventional support vector machine (SVM), using repeated random sub-sampling method. The mean value of the classification rate/sensitivity is found to be 88%/86% for the GTSVM and 84%/83% for the SVM. The statistical evaluations show that the GTSVM significantly improves performance of the classification as compared to the SVM.

  • 2.
    Ghareh Baghi, Arash
    et al.
    Linköping University, Linköping, Sweden.
    Dutoit, Thierry
    Mons University, Mons, Belgium.
    Ask, Per
    Linköping University, Linköping, Sweden.
    Sörnmo, Leif
    Lund University, Lund, Sweden .
    Detection of systolic ejection click using time growing neural network2014Inngår i: Medical Engineering and Physics, ISSN 1350-4533, E-ISSN 1873-4030, Vol. 36, nr 4, s. 477-483Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    In this paper, we present a novel neural network for classification of short-duration heart sounds: the time growing neural network (TGNN). The input to the network is the spectral power in adjacent frequency bands as computed in time windows of growing length. Children with heart systolic ejection click (SEC) and normal children are the two groups subjected to analysis. The performance of the TGNN is compared to that of a time delay neural network (TDNN) and a multi-layer perceptron (MLP), using training and test datasets of similar sizes with a total of 614 normal and abnormal cardiac cycles. From the test dataset, the classification rate/sensitivity is found to be 97.0%/98.1% for the TGNN, 85.1%/76.4% for the TDNN, and 92.7%/85.7% for the MLP. The results show that the TGNN performs better than do TDNN and MLP when frequency band power is used as classifier input. The performance of TGNN is also found to exhibit better immunity to noise.

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