title: Interpretation of surface EMGs in children with cerebral palsy: An initial study using a fuzzy expert system.
abstract: Surface EMG detected simultaneously at different muscles has become an important tool for analysing the gait of children with cerebral palsy (CP), as it offers essential information about muscular coordination. However, the interpretation of surface EMG is a difficult task that assumes extensive knowledge and experience. As such, this noninvasive procedure is not frequently used in the general clinical routine. An Artificial Intelligence (AI) system for interpreting surface EMG signals and the resulting muscular coordination patterns could overcome these limitations. To support such interpretation, an expert system based on fuzzy inference methodology was developed. The knowledge-base of the system implemented 15 rules, from which the fuzzy inference methodology performs a prediction of the effectiveness of the muscular coordination during gait. Our aim was to assess the feasibility and value of such an expert system in clinical applications. Surface EMG signals were recorded from the tibialis anterior, soleus muscle, and gastrocnemius muscles of children with CP to assess muscular coordination patterns of ankle movement during gait. Nineteen children underwent 114 surface EMG measurements. Simultaneously, the gait cycles of each patient were determined using foot switches and videotapes. From the EMG signals, the effectiveness of the ankle movement was predicted by the expert system, and predictions were classified using a three-point ordinal scale. In 91 cases (80%), the clinical findings matched the predictions of the expert system. In 23 cases (20%) the predictions of the expert system differed from the clinical findings with 12 cases revealing worse and 11 cases revealing better results in comparison to the clinical findings. As this study is a first attempt to verify the feasibility and correctness of this expert system, the results are promising. Further study is required to assess the correlation with the kinematic data and to include the whole leg.
authors.affiliation: Department of Orthopaedics, Aachen University Hospital, Pauwel Strasse 30, D-52074 Aachen, Germany. bernhard.schmidt@post.rwth-aachen.de
authors.forename: Bernhard
authors.initials: B
authors.lastname: Schmidt-Rohlfing
authors.forename: Ferdinand
authors.initials: F
authors.lastname: Bergamo
authors.forename: Sybele
authors.initials: S
authors.lastname: Williams
authors.forename: Hans J
authors.initials: HJ
authors.lastname: Erli
authors.forename: Günther
authors.initials: G
authors.lastname: Rau
authors.forename: Fritz U
authors.initials: FU
authors.lastname: Niethard
authors.forename: Catherine
authors.initials: C
authors.lastname: Disselhorst-Klug
mesh_terms: D002547:Cerebral Palsy / Q000150:complications / Q000175:diagnosis / Q000503:physiopathology; D002648:Child; D002675:Child, Preschool; D004576:Electromyography / Q000379:methods*; D005103:Expert Systems; D005240:Feasibility Studies; D005260:Female; D017143:Fuzzy Logic; D020233:Gait Disorders, Neurologic / Q000175:diagnosis / Q000209:etiology / Q000503:physiopathology; D006801:Humans; D008297:Male; D018482:Muscle, Skeletal / Q000503:physiopathology*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results