Muscle activation patterns in gait with cerebral palsy

• Applicant: Professor Dr. Sebastian Wolf
• Subject Area: Orthopaedics, Traumatology, Reconstructive Surgery ; Medical Informatics and Medical Bioinformatics
• Term: since 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 495955395

People suffering from neurological gait disorders such as cerebral palsy (CP) demonstrate gait patterns differing from those of typically developing subjects. Many attempts have been undertaken to classify gait patterns of patients with CP using 3D gait analysis, video and clinical exams and recently also with artificial intelligence tools. Several studies have been performed for developing computational techniques to enable the classification between typical and pathological gait. The main issue with these studies is that they attempt to improve and interpret EMG signals using mathematical and computing techniques only. There are few studies that actually have monitored the relationship between EMG and other mechanical and clinical gait parameters. However, the EMG data, among researchers and clinicians has been considered as a regular secondary information source that could be used along with the other parameters with potentially having less influence on treatment decision making. Our hypothesis is that determining and extracting EMG features throughout the gait cycle and then analyzing and recognizing the pattern of the EMG data in parallel with the other gait parameters during a treatment process can provide useful and critical recommendations for the clinicians in relation to that treatment. Our aim in this project is therefore to develop global measures for EMG assessment similar to global gait indexes for characterizing a degree of neurologic motor involvement as well as a feature-oriented analysis framework for setting EMG data in context with clinical measures of joint ranges of motion, muscle strength, and spasticity as well as with gait features. This ultimately may help in establishing rule-oriented orthopedic treatment decision trees in patients with CP. Accessing to a large database is an essential part of succeeding with this project. Our archive consists of data from the time 1993-2020 derived from more than 1250 patients with bilateral spastic CP and more than 300 patients with unilateral CP of which we have in total more than 2550 EMG exams available. Our work flow program includes 6 work-packages (WP). WP1 (Data preparation) will organize and customize the data for the purpose of this project. WP2 (Subjective assessment) will assess EMG signals in collaboration with clinical experts. WP3 (EMG pattern recognition) will evaluate the EMG data in a cross-sectional study for classifying phenotypes. In longitudinal studies, EMG changes with age and orthopedic intervention will be monitored (WP4). In WP5 a summary EMG index will be developed. WP6 (Treatment decision trees) will formulate treatment paths according to the clinical documents and the EMG data for improved decision making in the management of CP.

• DFG Programme: Research Grants