Quality Control of Voice Recordings in Remote Parkinson’S Disease Monitoring Using the Infinite Hidden Markov Model


The performance of voice-based systems for remote monitoring of Parkinson’s disease is highly dependent on the degree of adherence of the recordings to the test protocols, which probe for specific symptoms. Identifying segments of the signal that adhere to the protocol assumptions is typically performed manually by experts. This process is costly, time consuming, and often infeasible for large-scale data sets. In this paper, we propose a method to automatically identify the segments of signals that violate the test protocol with a high accuracy. In our approach, the signal is first split into variable duration segments by fitting an infinite hidden Markov model (iHMM) to the frames of the signals in the mel-frequency cepstral domain. The complexity of the iHMM is capable of growing jointly with the data allowing us to infer a potentially large (asymptotically infinite) number of different phenomena segmented into different hidden states. Then, we identify the segments that adhere to the test protocol by applying a multinomial naive Bayes classifier to the state indicators of segments. The experimental results show that even by using a small amount of training data, we can achieve around 96% accuracy in identifying short-term protocol violations with a 0.2 s resolution.

Publication DOI: https://doi.org/10.1109/ICASSP.2019.8682523
Divisions: College of Engineering & Physical Sciences
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Additional Information: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords: Bayesian Nonparametric,Parkinson's disease,infinite HMM,quality control,segmentation,Software,Signal Processing,Electrical and Electronic Engineering
Publication ISSN: 2379-190X
Last Modified: 17 Apr 2024 07:16
Date Deposited: 25 Apr 2019 12:37
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Related URLs: https://ieeexpl ... cument/8682523/ (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Conference article
Published Date: 2019-04-17
Accepted Date: 2019-04-01
Authors: Poorjam, Amir Hossein
Raykov, Yordan P. (ORCID Profile 0000-0003-0753-717X)
Badawy, Reham
Jensen, Jesper Rindom
Christensen, Mads Grasboll
Little, Max A. (ORCID Profile 0000-0002-1507-3822)



Version: Accepted Version

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