Can we predict who is at risk of facing cognitive issues in PD and address them earlier? These are the questions being pursued by Dr. Goldman of the PDF Research Center at Rush University Medical Center.
PDF Grant Programs
Are you interested in furthering Parkinson's science? View PDF's open grant programs.
Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings.
PDF's targeted PubMed search provides you with access to journal articles from the last 90 days that may be pertinent to Parkinson's disease research.
Not what you're looking for? Do you need informational publications about Parkinson's targeted for people living with Parkinson's, caregivers and family members? Please browse PDF's educational materials and programs - which are all available electronically or in print. Order for yourself, a loved one or in bulk for your patients or support group.
IEEE J Biomed Health Inform 2013 Jul; 17(4):828-34
Authors: Betul Erdogdu Sakar, M Erdem Isenkul, C Okan Sakar, Ahmet Sertbas, Fikret Gurgen, Sakir Delil, Hulya Apaydin, Olcay Kursun
There has been an increased interest in speech pattern analysis applications of Parkinsonism for building predictive telediagnosis and telemonitoring models. For this purpose, we have collected a wide variety of voice samples, including sustained vowels, words, and sentences compiled from a set of speaking exercises for people with Parkinson's disease. There are two main issues in learning from such a dataset that consists of multiple speech recordings per subject: 1) How predictive these various types, e.g., sustained vowels versus words, of voice samples are in Parkinson's disease (PD) diagnosis? 2) How well the central tendency and dispersion metrics serve as representatives of all sample recordings of a subject? In this paper, investigating our Parkinson dataset using well-known machine learning tools, as reported in the literature, sustained vowels are found to carry more PD-discriminative information. We have also found that rather than using each voice recording of each subject as an independent data sample, representing the samples of a subject with central tendency and dispersion metrics improves generalization of the predictive model.
PMID: 25055311 [PubMed - as supplied by publisher]