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Detection of Myocardial Ischaemia based on Artificial Neural Networks and Skin Sympathetic Nerve Activity

EasyChair Preprint 2165

4 pagesDate: December 13, 2019

Abstract

In this study, we propose a new technique which detects the anomalies in skin sympathetic nerve activity (SKNA) recorded from the chest wall by using the state-of-the-art signal processing and machine learning methods for the robust detection of myocardial ischaemia (AMI). For this purpose, a preprocessing technique that obtains SKNA from the wideband recordings on STAFF III database, which are non-invasively recorded from the skin surface of the chest wall by using an equipment that has a wide frequency bandwidth and high sampling rate, is developed. By using the data that is obtained as a result of preprocessing, a novel feature extraction technique which obtains SKNA features that are critical for the reliable detection of AMI is developed. By using the critical SKNA features, a supervised learning technique based on artificial neural networks (ANN) which performs the robust detection of AMI is developed. The performance results of the proposed technique obtained from a considerable number of patients with coronary artery disease on STAFF III database indicate that the technique provides highly reliable detection of AMI.

Keyphrases: Artificial Neural Networks, Back propagation algorithm, Classification, ECG, Myocardial ischaemia, Sympathetic nerve activity, anomaly detection, coronary artery disease, feature extraction, skin sympathetic nerve activity

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
@booklet{EasyChair:2165,
  author    = {Merve Begüm Terzi and Makbule Kübra Korkmaz and Orhan Arıkan and Salih Topal and Adnan Abaci},
  title     = {Detection of Myocardial Ischaemia based on Artificial Neural Networks and Skin Sympathetic Nerve Activity},
  howpublished = {EasyChair Preprint 2165},
  year      = {EasyChair, 2019}}
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