Development of Neuro-Degenerative Diseases' Gait Classification Algorithm Using Convolutional Neural Network and Wavelet Coherence Spectrogram of Gait Synchronization

Objective: A neurodegenerative disease (NDD) detection algorithm using a convolutional neural network (CNN) and wavelet coherence spectrogram of gait synchronization was developed to classify NDD based on gait force signals. The main purpose of this research was to help physicians with screening for NDD for early diagnosis, efficient treatment planning, and monitoring of disease progression. Methods: The NDD detection algorithm was evaluated using the existing online database from Physionet by Hausdorff et al., called gait in neurodegenerative disease database, comprised of windowing, feature transformation, and classification processes. Force pattern variations among healthy control (HC) and patients with ALS, HD, and PD were distinctly observed from feature-extracted wavelet coherence spectrogram images. Results: HC was balanced because their left and right feet supported each other when walking. In patients with ALS, the left-right foot correlation was weaker than that in HC. In patients with HD, walking velocity varied, which indicated that only one foot (right or left) was dominant and sustained the entire body's balance during movement. The left and right feet of patients with PD were correlated and coordinated in terms of supporting lower-body movements. The right foot was always on the ground to support the entire body when walking. Conclusion: The proposed NDD detection algorithm effectively differentiates gait patterns on the basis of a time-frequency spectrogram of gait force signals between HC and NDD patients with an overall sensitivity of 94.34%, specificity of 96.98%, accuracy of 96.37%, and AUC value of 0.97 using 5-fold cross-validation.