Automated sleep stage scoring using hybrid rule- and case-based reasoning
Computers and Biomedical Research
Managing uncertainty in diagnosis of acute coronaric ischemia
Artificial Intelligence in Medicine
Knowledge acquisition for multi-channel electroencephalogram interpretation
Artificial Intelligence in Medicine
Artificial Intelligence in Medicine
Combinations of case-based reasoning with other intelligent methods
International Journal of Hybrid Intelligent Systems - CIMA-08
Expert Systems with Applications: An International Journal
Feature extraction of forearm EMG signals for prosthetics
Expert Systems with Applications: An International Journal
Feature reduction and selection for EMG signal classification
Expert Systems with Applications: An International Journal
Fractal analysis features for weak and single-channel upper-limb EMG signals
Expert Systems with Applications: An International Journal
Introduction of a combination vector to optimise the interpolation of numerical phantoms
Expert Systems with Applications: An International Journal
Performance index assessment of intelligent computing methods in EMG-based neuromuscular diseases
International Journal of Knowledge Engineering and Soft Data Paradigms
Case-Based Reasoning adaptation of numerical representations of human organs by interpolation
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
Intelligent computing system (ICS) and knowledge-based system (KBS) have been widely used in the detection and interpretation of EMG (electromyography) based diseases. Heuristic-based detection methods of EMG parameters for a particular disease have also been reported in the literature but little effort has been made by researchers to combine rule-based reasoning (RBR) and case-based reasoning of KBS, and ANN (artificial neural nets) of ICS. Integrating the methods in KBS and ICS improves the computational and reasoning efficiency of the problem-solving strategy. We have developed an integrated model of CBR and RBR for generating cases, and ANN for matching cases for the interpretation and diagnosis of neuromuscular diseases. We have hierarchically structured the neuromuscular diseases in terms of their physio-pyscho (muscular, cognitive and psychological) parameters and EMG based parameters (amplitude, duration, phase etc.). Cumulative confidence factor is computed at different node from lowest to highest level of hierarchal structure in the process of diagnosis of the neuromuscular diseases. The diseases considered are Duchenne muscular dystrophy, Polymyostits, Endocrine myopathy, Metabolic myopathy, Neuropathy, Poliomyletis and Myasthenia gravis. The basic objective of this work is to develop an integrated model of RBR, CBR and ANN in which RBR is used to hierarchically correlate the sign and symptom of the disease and also to compute cumulative confidence factor (CCF) of the diseases. CBR is used for diagnosing the neuromuscular diseases and to find the relative importance of sign and symptoms of a diseases to other diseases and ANN is used for matching process in CBR.