title: Recognition of amyotrophic lateral sclerosis disease using factorial hidden Markov model.
abstract: Amyotrophic lateral sclerosis (ALS) is a common disease among neurological disorders that can change the pattern of gait in human. One of the effective methods for recognition and analysis of gait patterns in ALS patients is utilizing stride interval time series. With proper preprocessing for removing unwanted artifacts from the raw stride interval times and then extracting meaningful features from these data, the factorial hidden Markov model (FHMM) was used to distinguish ALS patients from healthy subjects. The results of classification accuracy evaluated using the leave-one-out (LOO) cross-validation algorithm showed that the FHMM method provides better recognition of ALS and healthy subjects compared to standard HMM. Moreover, comparing our method with a state-of-the art method named least square support vector machine (LS-SVM) showed the efficiency of the FHMM in distinguishing ALS subjects from healthy ones.
authors.forename: Abed
authors.initials: A
authors.lastname: Khorasani
authors.forename: Mohammad Reza
authors.initials: MR
authors.lastname: Daliri
authors.forename: Mohammad
authors.initials: M
authors.lastname: Pooyan
mesh_terms: D000465:Algorithms; D000690:Amyotrophic Lateral Sclerosis / Q000150:complications / Q000175:diagnosis*; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005163:Factor Analysis, Statistical; D020233:Gait Disorders, Neurologic / Q000175:diagnosis* / Q000209:etiology; D006801:Humans; D000069550:Machine Learning; D008390:Markov Chains; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D051598:Whole Body Imaging / Q000379:methods*