Noninvasive diagnosis of coronary artery disease using a neural network algorithm
Biological Cybernetics
Detection of seizure activity in EEG by an artificial neural network: a preliminary study
Computers and Biomedical Research
A New Method for Diagnosis of Cirrhosis Disease: Complex-valued Artificial Neural Network
Journal of Medical Systems
Fuzzy clustering complex-valued neural network to diagnose cirrhosis disease
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
This research is concentrated on the diagnosis of occlusion disease through the analysis of femoral artery Doppler signals with the help of Artificial Neural Network (ANN). Doppler femoral artery signals belong to occlusion patient and healthy subjects were recorded. Afterwards, power spectral densities (PSD) of these signals were obtained using Welch method and Autoregressive (AR) modeling. Multilayer feed forward ANN trained with a Levenberg Marquart (LM) backpropagation algorithm was implemented to these PSD. The designed classification structure has about 98% sensitivity, 97-100% specifity and correct classification is calculated to be 98-99% (for AR modeling and Welch method respectively). The end results are classified as healthy and diseased. Testing results were found to be compliant with the expected results that are derived from the physician's direct diagnosis. The end benefit would be to assist the physician to make the final decision without hesitation.