Prototypical System to Detect Anxiety Manifestations by Acoustic Patterns in Patients with Dementia

Abstract

INTRODUCTION: Dementia is a syndrome characterised by a decline in memory, language, and problem-solving thataffects the ability of patients to perform everyday activities. Patients with dementia tend to experience episodes of anxietyand remain for extended periods, which affects their quality of life.OBJECTIVES: To design AnxiDetector, a system capable of detecting patterns of sounds associated before and during themanifestation of anxiety in patients with dementia.METHODS: We conducted a non-participatory observation of 70 diagnosed patients in-situ, and conducted semi-structuredinterviews with four caregivers at a residential centre. Using the findings from our observation and caregiver interviews, wedeveloped the AnxiDetector prototype and tested this in an experimental setting where we defined nine classes of audio torepresent two groups of sounds: (i) Disturbance which includes audio files that characterise sounds that trigger anxiety inpatients with dementia, and (ii) Expression which includes audio files that characterise sounds expressed by the patientsduring episodes of anxiety. We conducted two experimental classifications of sounds using (i) a Neural Network modeltrained and (ii) a Support Vector Machine model. The first evaluation consists of a binary discriminating between the twogroups of sounds; the second evaluation discriminates the nine classes of audio. The audio resources were retrieved frompublicly available datasets.RESULTS: The qualitative results present the views of the caregivers on the adoption of AnxiDetector. The quantitativeresults from our binary discrimination show a classification accuracy of 98.1% and 99.2% for the Deep Neural Network andSupport Vector Machine models, respectively. When classifying the nine classes of sound, our model shows a classificationaccuracy of 92.2%. Whereas, the Support Vector Machine model yielded an overall classification accuracy of 93.0%.CONCLUSION: In this paper, we presented the outcomes from an observational study in-site at a residential care centre,qualitative findings from interviews with caregivers, the design of AnxiDetector, and preliminary qualitative results of amethodology devised to detect relevant acoustic events associated with anxiety in patients with dementia. We conclude bysignalling future plans to conduct in-situ validation of the effectiveness of AnxiDetector for anxiety detection.

Keywords

• smart microphones
• anxiety
• dementia
• sound recognition