Detecting acute myocardial infarction in the 12-lead ECG using Hermite expansions and neural networks

@article{Haraldsson2004DetectingAM,
  title={Detecting acute myocardial infarction in the 12-lead ECG using Hermite expansions and neural networks},
  author={Henrik Haraldsson and Lars Edenbrandt and Mattias Ohlsson},
  journal={Artificial intelligence in medicine},
  year={2004},
  volume={32 2},
  pages={127-36}
}
We use artificial neural networks (ANNs) to detect signs of acute myocardial infarction (AMI) in ECGs. The 12-lead ECG is decomposed into Hermite basis functions, and the resulting coefficients are used as inputs to the ANNs. Furthermore, we present a case-based method that qualitatively explains the operation of the ANNs, by showing regions of each ECG critical for ANN response. Key ingredients in this method are: (i) a cost function used to find local ECG perturbations leading to the largest… CONTINUE READING
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