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- Gharehbaghi, Arash, et al.
(author)
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Detection of systolic ejection click using time growing neural network
- 2014
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In: Medical Engineering and Physics. - : Elsevier. - 1350-4533 .- 1873-4030. ; 36:4, s. 477-483
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Journal article (peer-reviewed)abstract
- 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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| 2. |
- Gharehbaghi, Arash, et al.
(author)
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An Automatic Tool for Pediatric Heart Sounds Segmentation
- 2011
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In: Computing in Cardiology. vol. 28. - 9781457706127 ; , s. 37-40
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Conference paper (other academic/artistic)abstract
- In this paper, we present a novel algorithm for pediatric heart sound segmentation, incorporated into a graphical user interface. The algorithm employs both the Electrocardiogram (ECG) and Phonocardiogram (PCG) signals for an efficient segmentation under pathological circumstances. First, the ECG signal is invoked in order to determine the beginning and end points of each cardiac cycle by using wavelet transform technique. Then, first and second heart sounds within the cycles are identified over the PCG signal by paying attention to the spectral properties of thesounds. The algorithm is applied on 120 recordings of normal and pathological children, totally containing 1976 cardiac cycles. The accuracy of thesegmentation algorithm is 97% for S1 and 94% for S2 identification while all the cardiac cycles are correctly determined.
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