Building expert systems
Verification and Validation of Knowledge-Based Systems
IEEE Transactions on Knowledge and Data Engineering
IEEE Expert: Intelligent Systems and Their Applications
Design of a Case-Based Reasoning System Applied to Neuropathy Diagnosis
EWCBR '94 Selected papers from the Second European Workshop on Advances in Case-Based Reasoning
Integrating Rule-Based and Case-Based Decision Making in Diabetic Patient Management
ICCBR '99 Proceedings of the Third International Conference on Case-Based Reasoning and Development
A Causal-Functional Model Applied to EMG Diagnosis
AIME '97 Proceedings of the 6th Conference on Artificial Intelligence in Medicine in Europe
Use of Support Vector Machines and Neural Network in Diagnosis of Neuromuscular Disorders
Journal of Medical Systems
Classification of EMG Signals Using PCA and FFT
Journal of Medical Systems
Temporal management of RFID data
VLDB '05 Proceedings of the 31st international conference on Very large data bases
A two-stage method for MUAP classification based on EMG decomposition
Computers in Biology and Medicine
An integrated intelligent computing model for the interpretation of EMG based neuromuscular diseases
Expert Systems with Applications: An International Journal
Knowledge and intelligent computing system in medicine
Computers in Biology and Medicine
A novel method for automated EMG decomposition and MUAP classification
Artificial Intelligence in Medicine
International Journal of Knowledge Engineering and Soft Data Paradigms
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In the medical systems, there is a lack of determining and assessing the performance measure of the intelligent computing methods ICM deployed in the diagnosis of bioelectric signals EEG/EMG/ECG-based diseases. There have been few attempts for performance measure of mathematical models in medical computing. In this paper, we have developed a heuristic method for the assessment of performance measure in the diagnosis of EMG-based neuromuscular diseases. Firstly, we review the various ICM then we perform qualitative assessment of mathematical, algorithmic and heuristic content, data acquisition cost as well as medical consultancy cost of various parameter of EMG and non-EMG psychological, cognitive and muscular. The computational overhead CO of EMG parameters, overall computational overhead OCO and clinical consultancy cost CC are determined. Finally, performance index PI is computed based on the two overheads and clinical cost. A graph showing the comparative view of CO of EMG parameters, OCO and CC is plotted and PI is shown for all the methods.