Continuous Sound Collection Using Smartphones and Machine Learning to Measure Cough.

BACKGROUND: Despite 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. OBJECTIVES: In 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). METHODS: This 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. RESULTS: Sound 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. CONCLUSIONS: Sound 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.

Tablet-Based Application for Objective Measurement of Motor Fluctuations in Parkinson Disease.

BACKGROUND: The motor subscale of the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS-III) has limited applicability for the assessment of motor fluctuations in the home setting. METHODS: To assess whether a self-administered, tablet-based application can reliably quantify differences in motor performance using two-target finger tapping and forearm pronation-supination tasks in the ON (maximal dopaminergic medication efficacy) and OFF (reemergence of parkinsonian deficits) medication states, we recruited 11 Parkinson disease (PD) patients (age, 60.6 ± 9.0 years; disease duration, 12.8 ± 4.1 years) and 11 healthy age-matched controls (age, 62.5 ± 10.5 years). The total number of taps, tap interval, tap duration, and tap accuracy were algorithmically calculated by the application, using the more affected side in patients and the dominant hand in healthy controls. RESULTS: Compared to the OFF state, PD patients showed a higher number of taps (84.2 ± 20.3 vs. 54.9 ± 26.9 taps; p = 0.0036) and a shorter tap interval (375.3 ± 97.2 vs. 708.2 ± 412.8 ms; p = 0.0146) but poorer tap accuracy (2,008.4 ± 995.7 vs. 1,111.8 ± 901.3 pixels; p = 0.0055) for the two-target task in the ON state, unaffected by the magnitude of coexistent dyskinesia. Overall, test-retest reliability was high (r >0.75) and the discriminatory ability between OFF and ON states was good (0.60 ≤ AUC ≤ 0.82). The correlations between tapping data and MDS-UPDRS-III scores were only moderate (-0.55 to 0.55). CONCLUSIONS: A self-administered, tablet-based application can reliably distinguish between OFF and ON states in fluctuating PD patients and may be sensitive to additional motor phenomena, such as accuracy, not captured by the MDS-UPDRS-III.