Dataset Information

Using machine learning of computerized vocal expression to measure blunted vocal affect and alogia.

ABSTRACT: Negative symptoms are a transdiagnostic feature of serious mental illness (SMI) that can be potentially "digitally phenotyped" using objective vocal analysis. In prior studies, vocal measures show low convergence with clinical ratings, potentially because analysis has used small, constrained acoustic feature sets. We sought to evaluate (1) whether clinically rated blunted vocal affect (BvA)/alogia could be accurately modelled using machine learning (ML) with a large feature set from two separate tasks (i.e., a 20-s "picture" and a 60-s "free-recall" task), (2) whether "Predicted" BvA/alogia (computed from the ML model) are associated with demographics, diagnosis, psychiatric symptoms, and cognitive/social functioning, and (3) which key vocal features are central to BvA/Alogia ratings. Accuracy was high (>90%) and was improved when computed separately by speaking task. ML scores were associated with poor cognitive performance and social functioning and were higher in patients with schizophrenia versus depression or mania diagnoses. However, the features identified as most predictive of BvA/Alogia were generally not considered critical to their operational definitions. Implications for validating and implementing digital phenotyping to reduce SMI burden are discussed.

SUBMITTER: Cohen AS

PROVIDER: S-EPMC7519104 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Using machine learning of computerized vocal expression to measure blunted vocal affect and alogia.

Cohen Alex S AS Cox Christopher R CR Le Thanh P TP Cowan Tovah T Masucci Michael D MD Strauss Gregory P GP Kirkpatrick Brian B

NPJ schizophrenia 20200925 1

Negative symptoms are a transdiagnostic feature of serious mental illness (SMI) that can be potentially "digitally phenotyped" using objective vocal analysis. In prior studies, vocal measures show low convergence with clinical ratings, potentially because analysis has used small, constrained acoustic feature sets. We sought to evaluate (1) whether clinically rated blunted vocal affect (BvA)/alogia could be accurately modelled using machine learning (ML) with a large feature set from two separate ...[more]

PMID: 32978400

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Project description:BackgroundDespite the efforts of research groups to develop and implement at least partial automation, cough counting remains impractical. Analysis of 24-h cough frequency is an established regulatory endpoint which, if addressed in an automated manner, has the potential to ease cough symptom evaluation over multiple 24-h periods in a patient-centric way, supporting the development of novel treatments for chronic cough, an unmet clinical need.ObjectivesIn light of recent technological advancements, we propose a system based on the use of smartphones for objective continuous sound collection, suitable for automated cough detection and analysis. Two capabilities were identified as necessary for naturalistic cough assessment: (1) recording sound in a continuous manner (sound collection), and (2) detection of coughs from the recorded sound (cough detection).MethodsThis work did not involve any human subject testing or trials. For sound collection, we designed, built, and verified technical parameters of a smartphone application for sound collection. Our cough detection work describes the development of a mathematical model for sound analysis and cough identification. Performance of the model was compared to previously published results of commercially available solutions and to human raters. The compared solutions use the following methods to automatically or semi-automatically assess cough: 24-h sound recording with an ambulatory device with multiple microphones, automatic silence removal, and manual recording review for cough count.ResultsSound collection: the application demonstrated the ability to continuously record sounds using the phone's internal microphone; the technical verification informed the configuration of the technical and user experience parameters. Cough detection: our cough recognition sensitivity to cough as determined by human listeners was 90 at 99.5% specificity preset and 75 at 99.9% specificity preset for a dataset created from publicly available data.ConclusionsSound collection: the application reliably collects sound data and uploads them securely to a remote server for subsequent analysis; the developed sound data collection application is a critical first step toward future incorporation in clinical trials. Cough detection: initial experiments with cough detection techniques yielded encouraging results for application to patient-collected data from future studies.

Dataset Information

Using machine learning of computerized vocal expression to measure blunted vocal affect and alogia.

Publications

Using machine learning of computerized vocal expression to measure blunted vocal affect and alogia.

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