The impact of feature extraction for the classification of amyotrophic lateral sclerosis among neurodegenerative diseases and healthy subjects

Abstract

Abstract Neurodegenerative diseases (NDD) including Amyotrophic Lateral Sclerosis (ALS), Parkinson’s disease (PD) and Huntington disease (HD) can be defined as the degeneration in the structure of neurons in human body. It is mentioned in the related literature that NDD may cause various clinical symptoms disrupting gait dynamics. The characterization of gait analysis is crucial for early diagnosis, efficient treatment planning and monitoring of ALS progression and other NDD. The database consisting of 64 one-minute recordings of Compound Force Signal (CFS) obtained from 13 ALS, 15 PD, 20 HD and 16 healthy subjects was used in the study. CFS is the composition of force signals for both left and right feet of each subject during the gait. CFS was decomposed for determination of features using 6-level Discrete Wavelet Transform (DWT) with different wavelets in the study. The obtained features were evaluated using the means of 20-trials for five-fold cross-validation (FFCV) in Linear Discriminant Analysis (LDA) and Naive Bayesian Classifier (NBC). As a result, D5 (4.6875–9.375 Hz) in all classifications, D4 (9.375–18.75 Hz) in ALS vs. PD, ALS vs. PD + HD and ALS vs. Co + PD + HD classifications while D2 (37.5–75 Hz) and D6 (2.3438-4.6875 Hz) in ALS vs. Co. and ALS vs. HD classifications were determined as the most significant frequency bands in CFS for discrimination of ALS among healthy and other NDD subjects in the end of the study.