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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017., Cataloged from PDF version of thesis., Includes bibliographical references (pages 149-161)., Quantitative approaches to psychiatric assessment beyond the qualitative descriptors in the Diagnostic and Statistical Manual of Mental Disorders could transform mental health care. However, objective neurocognitive state estimation and tracking demands robust, scalable indicators of a disorder. A person's speech is a rich source of neurocognitive information because speech production is a complex sensorimotor task that draws upon many cortical and subcortical regions. Furthermore, the ease of collection makes speech a practical, scalable candidate for assessment of mental health. One aspect of speech production that has shown sensitivity to neuropsychological disorders is phoneme rate, the rate at which individual consonants and vowels are spoken. Our aim in this thesis is to characterize phoneme rate as an indicator of depression and to improve our use of phoneme rate as a feature through both brain imaging and neurocomputational modeling. This thesis proposes that psychiatric assessment can be enhanced using a neurocomputational model of speech motor control to estimate unobserved parameters as latent descriptors of a disorder. We use depression as our model disorder and focus on motor control of speech phoneme rate. First, we investigate the neural basis for phoneme rate modulation in healthy subjects uttering emotional sentences and in depression using functional magnetic resonance imaging. Then, we develop a computational model of phoneme rate to estimate subject-specific parameters that correlate with individual phoneme rate. Finally, we apply these and other features derived from speech to distinguish depressed from healthy control subjects., by Gregory Alan Ciccarelli., Ph. D.

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017., Cataloged from PDF version of thesis., Includes bibliographical references (pages 149-161)., Quantitative approaches to psychiatric assessment beyond the qualitative descriptors in the Diagnostic and Statistical Manual of Mental Disorders could transform mental health care. However, objective neurocognitive state estimation and tracking demands robust, scalable indicators of a disorder. A person's speech is a rich source of neurocognitive information because speech production is a complex sensorimotor task that draws upon many cortical and subcortical regions. Furthermore, the ease of collection makes speech a practical, scalable candidate for assessment of mental health. One aspect of speech production that has shown sensitivity to neuropsychological disorders is phoneme rate, the rate at which individual consonants and vowels are spoken. Our aim in this thesis is to characterize phoneme rate as an indicator of depression and to improve our use of phoneme rate as a feature through both brain imaging and neurocomputational modeling. This thesis proposes that psychiatric assessment can be enhanced using a neurocomputational model of speech motor control to estimate unobserved parameters as latent descriptors of a disorder. We use depression as our model disorder and focus on motor control of speech phoneme rate. First, we investigate the neural basis for phoneme rate modulation in healthy subjects uttering emotional sentences and in depression using functional magnetic resonance imaging. Then, we develop a computational model of phoneme rate to estimate subject-specific parameters that correlate with individual phoneme rate. Finally, we apply these and other features derived from speech to distinguish depressed from healthy control subjects., by Gregory Alan Ciccarelli., Ph. D.

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017., Cataloged from PDF version of thesis., Includes bibliographical references (pages 149-161)., Quantitative approaches to psychiatric assessment beyond the qualitative descriptors in the Diagnostic and Statistical Manual of Mental Disorders could transform mental health care. However, objective neurocognitive state estimation and tracking demands robust, scalable indicators of a disorder. A person's speech is a rich source of neurocognitive information because speech production is a complex sensorimotor task that draws upon many cortical and subcortical regions. Furthermore, the ease of collection makes speech a practical, scalable candidate for assessment of mental health. One aspect of speech production that has shown sensitivity to neuropsychological disorders is phoneme rate, the rate at which individual consonants and vowels are spoken. Our aim in this thesis is to characterize phoneme rate as an indicator of depression and to improve our use of phoneme rate as a feature through both brain imaging and neurocomputational modeling. This thesis proposes that psychiatric assessment can be enhanced using a neurocomputational model of speech motor control to estimate unobserved parameters as latent descriptors of a disorder. We use depression as our model disorder and focus on motor control of speech phoneme rate. First, we investigate the neural basis for phoneme rate modulation in healthy subjects uttering emotional sentences and in depression using functional magnetic resonance imaging. Then, we develop a computational model of phoneme rate to estimate subject-specific parameters that correlate with individual phoneme rate. Finally, we apply these and other features derived from speech to distinguish depressed from healthy control subjects., by Gregory Alan Ciccarelli., Ph. D.

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017., Cataloged from PDF version of thesis., Includes bibliographical references (pages 149-161)., Quantitative approaches to psychiatric assessment beyond the qualitative descriptors in the Diagnostic and Statistical Manual of Mental Disorders could transform mental health care. However, objective neurocognitive state estimation and tracking demands robust, scalable indicators of a disorder. A person's speech is a rich source of neurocognitive information because speech production is a complex sensorimotor task that draws upon many cortical and subcortical regions. Furthermore, the ease of collection makes speech a practical, scalable candidate for assessment of mental health. One aspect of speech production that has shown sensitivity to neuropsychological disorders is phoneme rate, the rate at which individual consonants and vowels are spoken. Our aim in this thesis is to characterize phoneme rate as an indicator of depression and to improve our use of phoneme rate as a feature through both brain imaging and neurocomputational modeling. This thesis proposes that psychiatric assessment can be enhanced using a neurocomputational model of speech motor control to estimate unobserved parameters as latent descriptors of a disorder. We use depression as our model disorder and focus on motor control of speech phoneme rate. First, we investigate the neural basis for phoneme rate modulation in healthy subjects uttering emotional sentences and in depression using functional magnetic resonance imaging. Then, we develop a computational model of phoneme rate to estimate subject-specific parameters that correlate with individual phoneme rate. Finally, we apply these and other features derived from speech to distinguish depressed from healthy control subjects., by Gregory Alan Ciccarelli., Ph. D.

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013., Cataloged from PDF version of thesis., Includes bibliographical references (p. 161-166)., Early signs of patient deterioration have been documented in the medical literature. Recognition of such signs offers the possibility of treatment with sufficient lead time to prevent irreversible organ damage and death. Pediatric hospitals currently utilize simple, human evaluated rubrics called early warning scores to detect early signs of patient deterioration. These scores comprise subjective (patient behavior, clinician's impression) and objective (vital signs) components to assess patient health and are computed intermittently by the nursing staff. At Boston Children's Hospital (BCH), early warning scores are evaluated at least every four hours for each patient. Many hospitals monitor inpatients continuously to alert caregivers to changes in physiological status. At BCH, each hospital bed is equipped with a bedside monitor that continuously collects and archives vital sign data, such as heart rate, respiration rate, and arterial oxygen saturation. Continuous access to these physiological variables allows for the definition of a continuously evaluated early warning score on a reduced rubric. This thesis quantitatively assesses the performance of BCH's current Children's Hospital Early Warning Score (CHEWS). We also apply several standard machine learning approaches to investigate the utility of automatically collected bedside monitoring trend data for prediction of patient deterioration. Our results suggest that CHEWS offers at least a 6-hour warning with sensitivity 0.78 and specificity 0.90 but only with a prohibitively large uncertainty (48 hours) surrounding the time of transfer. Performance using only standard bedside trend data is no better than chance; improvement may require exploiting additional intra-beat features of monitored waveforms. The full CHEWS appears to capture significant clinical features that are not present in the monitoring data used in this study., by Gregory Alan Ciccarelli., S.M.