Novel speech signal processing algorithms for high-accuracy classification of Parkinson's disease

There has been considerable recent research into the connection between Parkinson's disease (PD) and speech impairment. Recently, a wide range of speech signal processing algorithms (dysphonia measures) aiming to predict PD symptom severity using speech signals have been introduced. In this paper, we test how accurately these novel algorithms can be used to discriminate PD subjects from healthy controls. In total, we compute 132 dysphonia measures from sustained vowels. Then, we select four parsimonious subsets of these dysphonia measures using four feature selection algorithms, and map these feature subsets to a binary classification response using two statistical classifiers: random forests and support vector machines. We use an existing database consisting of 263 samples from 43 subjects, and demonstrate that these new dysphonia measures can outperform state-of-the-art results, reaching almost 99% overall classification accuracy using only ten dysphonia features. We find that some of the recently proposed dysphonia measures complement existing algorithms in maximizing the ability of the classifiers to discriminate healthy controls from PD subjects. We see these results as an important step toward noninvasive diagnostic decision support in PD

A Review of the Assessment Methods of Voice Disorders in the Context of Parkinson's Disease

In recent years, a significant progress in the field of research dedicated to the treatment of disabilities has been witnessed. This is particularly true for neurological diseases, which generally influence the system that controls the execution of learned motor patterns. In addition to its importance for communication with the outside world and interaction with others, the voice is a reflection of our personality, moods and emotions. It is a way to provide information on health status, shape, intentions, age and even the social environment. It is also a working tool for many, but an important element of life for all. Patients with Parkinson’s disease (PD) are numerous and they suffer from hypokinetic dysarthria, which is manifested in all aspects of speech production: respiration, phonation, articulation, nasalization and prosody. This paper provides a review of the methods of the assessment of speech disorders in the context of PD and also discusses the limitations

Accurate telemonitoring of Parkinson's disease symptom severity using nonlinear speech signal processing and statistical machine learning

This study focuses on the development of an objective, automated method to extract clinically useful information from sustained vowel phonations in the context of Parkinson’s disease (PD). The aim is twofold: (a) differentiate PD subjects from healthy controls, and (b) replicate the Unified Parkinson’s Disease Rating Scale (UPDRS) metric which provides a clinical impression of PD symptom severity. This metric spans the range 0 to 176, where 0 denotes a healthy person and 176 total disability. Currently, UPDRS assessment requires the physical presence of the subject in the clinic, is subjective relying on the clinical rater’s expertise, and logistically costly for national health systems. Hence, the practical frequency of symptom tracking is typically confined to once every several months, hindering recruitment for large-scale clinical trials and under-representing the true time scale of PD fluctuations. We develop a comprehensive framework to analyze speech signals by: (1) extracting novel, distinctive signal features, (2) using robust feature selection techniques to obtain a parsimonious subset of those features, and (3a) differentiating PD subjects from healthy controls, or (3b) determining UPDRS using powerful statistical machine learning tools. Towards this aim, we also investigate 10 existing fundamental frequency (F_0) estimation algorithms to determine the most useful algorithm for this application, and propose a novel ensemble F_0 estimation algorithm which leads to a 10% improvement in accuracy over the best individual approach. Moreover, we propose novel feature selection schemes which are shown to be very competitive against widely-used schemes which are more complex. We demonstrate that we can successfully differentiate PD subjects from healthy controls with 98.5% overall accuracy, and also provide rapid, objective, and remote replication of UPDRS assessment with clinically useful accuracy (approximately 2 UPDRS points from the clinicians’ estimates), using only simple, self-administered, and non-invasive speech tests. The findings of this study strongly support the use of speech signal analysis as an objective basis for practical clinical decision support tools in the context of PD assessment.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

New Statistical Transfer Learning Models for Health Care Applications

abstract: Transfer learning is a sub-field of statistical modeling and machine learning. It refers to methods that integrate the knowledge of other domains (called source domains) and the data of the target domain in a mathematically rigorous and intelligent way, to develop a better model for the target domain than a model using the data of the target domain alone. While transfer learning is a promising approach in various application domains, my dissertation research focuses on the particular application in health care, including telemonitoring of Parkinson’s Disease (PD) and radiomics for glioblastoma. The first topic is a Mixed Effects Transfer Learning (METL) model that can flexibly incorporate mixed effects and a general-form covariance matrix to better account for similarity and heterogeneity across subjects. I further develop computationally efficient procedures to handle unknown parameters and large covariance structures. Domain relations, such as domain similarity and domain covariance structure, are automatically quantified in the estimation steps. I demonstrate METL in an application of smartphone-based telemonitoring of PD. The second topic focuses on an MRI-based transfer learning algorithm for non-invasive surgical guidance of glioblastoma patients. Limited biopsy samples per patient create a challenge to build a patient-specific model for glioblastoma. A transfer learning framework helps to leverage other patient’s knowledge for building a better predictive model. When modeling a target patient, not every patient’s information is helpful. Deciding the subset of other patients from which to transfer information to the modeling of the target patient is an important task to build an accurate predictive model. I define the subset of “transferrable” patients as those who have a positive rCBV-cell density correlation, because a positive correlation is confirmed by imaging theory and the its respective literature. The last topic is a Privacy-Preserving Positive Transfer Learning (P3TL) model. Although negative transfer has been recognized as an important issue by the transfer learning research community, there is a lack of theoretical studies in evaluating the risk of negative transfer for a transfer learning method and identifying what causes the negative transfer. My work addresses this issue. Driven by the theoretical insights, I extend Bayesian Parameter Transfer (BPT) to a new method, i.e., P3TL. The unique features of P3TL include intelligent selection of patients to transfer in order to avoid negative transfer and maintain patient privacy. These features make P3TL an excellent model for telemonitoring of PD using an At-Home Testing Device.Dissertation/ThesisDoctoral Dissertation Industrial Engineering 201