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首页> 外文期刊>Expert systems with applications >An Integrated Intelligent Computing Model For The Interpretation Of Emg Based Neuromuscular Diseases
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An Integrated Intelligent Computing Model For The Interpretation Of Emg Based Neuromuscular Diseases

机译：用于解释基于Emg的神经肌肉疾病的集成智能计算模型

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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.
机译：智能计算系统（ICS）和基于知识的系统（KBS）已被广泛用于基于EMG（肌电图）的疾病的检测和解释。文献中也报道了针对特定疾病的基于EMG参数的启发式检测方法，但是研究人员很少进行努力，将基于规则的推理（RBR）和基于案例的KBS推理以及ANN（人工神经网络）相结合）。将方法集成到KBS和ICS中可提高问题解决策略的计算和推理效率。我们已经开发了用于生成病例的CBR和RBR以及用于神经肌肉疾病的解释和诊断的匹配病例的ANN的集成模型。我们已根据神经肌肉疾病的生理性精神障碍（肌肉，认知和心理）参数和基于EMG的参数（振幅，持续时间，相位等）对神经肌肉疾病进行了层次结构设计。在神经肌肉疾病的诊断过程中，从最低到最高层次结构的不同节点计算累积置信度。所考虑的疾病是杜氏肌营养不良症，多肌性肌炎，内分泌肌病，代谢性肌病，神经病，脊髓灰质炎和重症肌无力。这项工作的基本目标是建立RBR，CBR和ANN的集成模型，其中RBR用于层次关联疾病的征兆和症状，并计算疾病的累积置信度（CCF）。 CBR用于诊断神经肌肉疾病并发现疾病的体征和症状与其他疾病的相对重要性，而ANN用于CBR中的匹配过程。

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