Systematic Analysis and Classification of Cardiac Rate Variability using Artificial Neural Network

Electrocardiogram (ECG) is acquisition of electrical activity signals in cardiology. It contains important information about the condition and diseases of heart. An ECG wave, pattern, size, shape and the time interval between different peaks of P-QRS-T wave provide useful information about the diseases which afflict heart. Heart rate signals vary and this variation contains important indicators of cardiac diseases. To assess autonomic nervous system, heart rate variability is popular and non-invasive tool. These indicators contained in ECG wave appear all the day or occur randomly in the day. So, computer based information is much useful over day long interval to diagnose heart disease. Thus, this paper deals with classification of heart diseases on the basis of heart rate variability using artificial neural network. Feed forward neural network is considered to be almost correct 85% of the test results.

@article{Kakar2018,
  title     = {Systematic Analysis and Classification of Cardiac Rate Variability using Artificial Neural Network},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {9},
  number    = {11},
  year      = {2018},
  publisher = {The Science and Information Organization},
  author    = {Azizullah Kakar and Naveed Sheikh and Bilal Ahmed and Saleem Iqbal},
  doi       = {10.14569/IJACSA.2018.0911106},
  url       = {https://doi.org/10.14569/IJACSA.2018.0911106}
}