title: Discriminating Between Patients With Parkinson's and Neurological Diseases Using Cepstral Analysis.
abstract: In this paper, we wanted to discriminate between two groups of patients (patients who suffer from Parkinson's disease and patients who suffer from other neurological disorders). We collected a variety of voice samples from 50 subjects using different recording devices in different conditions. Subsequently, we analyzed and extracted features from these samples using three different Cepstral techniques; Mel frequency cepstral coefficients (MFCC), perceptual linear prediction (PLP), and ReAlitive SpecTrAl PLP (RASTA-PLP). For classification we used leave one subject out validation scheme along with five different supervised learning classifiers. The best obtained result was 90% using the first 11 coefficients of the PLP and linear SVM kernels.
authors.forename: Achraf
authors.initials: A
authors.lastname: Benba
authors.forename: Abdelilah
authors.initials: A
authors.lastname: Jilbab
authors.forename: Ahmed
authors.initials: A
authors.lastname: Hammouch
mesh_terms: D000328:Adult; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D003937:Diagnosis, Differential; D016002:Discriminant Analysis; D005260:Female; D005583:Fourier Analysis; D006801:Humans; D008297:Male; D008875:Middle Aged; D009422:Nervous System Diseases / Q000150:complications / Q000175:diagnosis*; D010300:Parkinson Disease / Q000150:complications / Q000175:diagnosis*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013018:Sound Spectrography / Q000379:methods*; D013064:Speech Disorders / Q000000981:diagnostic imaging* / Q000209:etiology