Detection of systolic ejection click using time growing neural network.

Arash Gharehbaghi, Thierry Dutoit, Per Ask, Leif Sörnmo

Research output: Contribution to journalArticlepeer-review

22 Citations (SciVal)

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.
Original languageEnglish
Pages (from-to)477-483
JournalMedical Engineering & Physics
Volume36
Issue number4
DOIs
Publication statusPublished - 2014

Subject classification (UKÄ)

  • Electrical Engineering, Electronic Engineering, Information Engineering

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