Vocal modulation features in the prediction of major depressive disorder severity

Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014."September 2014." Cataloged from PDF version of thesis.Includes bibliographical references (pages 113-115).This thesis develops a model of vocal modulations up to 50 Hz in sustained vowels as a basis for biomarkers of neurological disease, particularly Major Depressive Disorder (MDD). Two model components contribute to amplitude modulation (AM): AM from respiratory muscles and from interaction between formants and frequency modulation in the fundamental frequency harmonics. Based on the modulation model, we test three methods to extract the envelope of the third formant from which features are extracted using sustained vowels from the 2013 AudioNisual Emotion Challenge. Using a Gaussian-Mixture-Model-based predictor, we evaluate performance of each feature in predicting subjects' Beck MDD severity score by the root mean square error (RMSE), mean absolute error (MAE), and Spearman correlation between the actual Beck score and predicted score. Our lowest MAE and RMSE values are 8.46 and 10.32, respectively (Spearman correlation=0.487, p<0.001), relative to the mean MAE of 10.05 and mean RMSE of 11.86.by Rachelle L. Horwitz.S.M

Acoustic features of impaired articulation due to amyotrophic lateral sclerosis

Thesis: Ph. D. in Biomedical Engineering, Harvard-MIT Program in Health Sciences and Technology, 2017.Cataloged from PDF version of thesis.Includes bibliographical references (pages 213-227).Progressive bulbar motor deterioration resulting from amyotrophic lateral sclerosis (ALS) leads to speech impairment. Despite the devastating consequences of speech impairment to life quality, few options are available to objectively assess speech motor involvement. The overarching goal of this research was to derive objective measures of speech acoustics that can be used to support clinical decision making. To achieve this goal, we obtained 121 speech samples from 33 patients with ALS who repeated the phrase "Buy Bobby a puppy" five times in succession. In total, 342 acoustic features were semi-automatically extracted from each speech recording. Pearson correlations were computed between each feature and three metrics of overall speech severity: sentence intelligibility, speaking rate, and communication efficiency. The findings were grounded within a physiologic framework where acoustic features were grouped into one of three domains that when combined, were hypothesized to broadly characterize articulatory performance: articulatory specification, articulatory coupling, and articulatory consistency. To obtain the most accurate prediction of ALS with the features we extracted, we compared two machine learning algorithms: linear regression and random forest. In shuffle-split cross-validation, the strongest mean Pearson correlations we obtained between actual and predicted intelligibility, speaking rate, and communication efficiency were 0.67, 0.74, and 0.77, respectively (SD=0.077, 0.050, and 0.059, respectively). Of the three domains, the specificity features were the most strongly associated with intelligibility impairments (mean r=0.68), and coupling was the most strongly associated with slower speaking rate (mean r=0.73). Specificity and coupling yielded similar performances in communication efficiency prediction. Other contributions of this thesis are that it is the first to implement a framework of dysarthric speech in terms of three domains: specification, coupling, and consistency; the first to validate automated formant tracking in dysarthric speech; and the first to perform an in-depth investigation into physiologically-inspired acoustic features that describe articulatory impairments of patients with ALS. Novel findings include the presence of abnormal formant coupling patterns, which may suggest greater tonguejaw coupling, in patients with more severe dysarthria due to ALS. Areas of future research involve further feature discovery, improved analysis methods, and a deeper understanding of relations to articulatory kinematics.by Rachelle L. Horwitz-Martin.Ph. D. in Biomedical Engineerin

Design of an Automated Experimentation and Data Processing Software Suite for the ADiR Sensor

Analog Devices, Inc., Limerick, Ireland, has developed an infrared sensor for non-contact thermometry applications. We developed a suite of software that automated and expedited testing and data processing procedures for four experiments conducted on the sensor. For the pressure and qualification experiments, we reduced the human-machine interaction time by 92% and 90%, respectively. The experimentation software we created for the angular response and lens focusing experiments enabled engineers to conduct experiments that had previously been impracticable

Critical Care Tower: Improving the Healthcare of Children for the Future

The Children's Hospital Costa Rica Foundation (CHCRF), the organization for which we worked, is raising more than $22 million to fund the construction and equipment for the new Critical Care Tower in the Hospital Nacional de Ninos in Costa Rica. A Critical Care Tower is necessary because currently, the Hospital Nacional de Ninos is overcrowded and lacks crucial equipment. The goal of this project was to assist the CHCRF in improving the quality and quantity of healthcare provided to children. Our objectives were to prepare a list of potential funders; write a business case, which will serve as a blueprint for brochures; make recommendations for the brochure, which included devising methods of breaking down the Tower's total cost; and defining the social implications of improved health care as a result of the new Critical Care Tower

A Device for Measuring the Severity of Peripheral Edema

Peripheral edema causes swelling in extremities and often indicates the presence of a serious medical condition. The subjective methods that clinicians currently use to assess edema lead to inconsistencies in medical treatment. To eliminate this subjectivity, we have developed a handheld device that uses a manually depressed probe to measure the depth of tissue displacement and the force required to depress the tissue. These measurements allow clinicians to objectively monitor a patient's progress over time