Publication: PHYSIOLOGICALLY BASED FEATURES RELATED TO VOCAL HYPERFUNCTION: FROM LABORATORY TO AMBULATORY DATA
Loading...
Date
2020-01
Authors
CORTÉS SOTOMAYOR, JUAN PABLO
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
The following thesis proposal describes a framework for the analysis of signal-
based features related to vocal pathologies, namely phonotraumatic vocal hyper-
function (PVH) and non-phonotraumatic vocal hyperfunction (NPVH), using an
accelerometer attached to the neck-skin in an ambulatory setting. The first stage
consists of extracting physiologically relevant features that are associated with
PVH on a daily basis. A clinical set-up (In Lab) that captures key components
of vocal function, such as acoustics (microphone) and aerodynamics (oral airflow)
from a reading passage, provides a set of model parameters to characterize vocal function. An impedance-based inverse filtering (IBIF) technique is used to es-
timate glottal airflow and related features from the accelerometer signal and to
obtain the same features for the ambulatory data (In Field). An in-depth analysis
of IBIF aerodynamic measures is done in the context of machine learning classi-
fiers. Subsequently, an adaptive version of the IBIF filter (i.e., Kalman smoother)
is proposed in order to estimate the airflow signal, incorporating modeling and
observation noise. The Kalman smoother is compared to the original IBIF fil-
ter with In Lab and In Field data within a classification task to determine the
efficiency and relevance of both approaches. Additional efforts are presented to
provide insights on the capabilities of machine learning tools to be used on PVH
and NPVH patients when compared to their matched-controls. First, a case study
with 4 pairs of PVH and controls is used to determine how the variability of the
IBIF parameters can affect the classification performance with In Lab data. Later,
classical machine learning algorithms are used to investigate the nuances in the
classification of NPVH subjects vs. controls, while a final effort explores the use
of wavelets with deep learning to separate Pre vs Post therapy in NPVH patients.
The main contributions of this thesis are: 1) to develop a machine learning frame-
work for the analysis and classification of neck-surface acceleration signals using
aerodynamic features; 2) to propose an alternative filtering scheme to IBIF based
on adaptive filtering; and 3) to support the first two contributions by exploring
pilot studies on salient features for NPVH, IBIF parameter uncertainty, and therapy effects on NPVH. Discussions and conclusions are included in each chapter
to interconnect the ambulatory analysis of glottal flow with machine learning,
to establish the potential benefits and limitations of these approaches in clinical
settings.
Description
Keywords
VOCAL HYPERFUNCTION , MACHINE LEARNING , AMBULATORY