A Novel Methodology to Remotely and Early Diagnose Sleep Bruxism by Leveraging on Audio Signals and Embedded Machine Learning

Abstract

Nowadays, more and more people suffer from sleep bruxism, involving repetitive jaw-muscle activity characterised by teeth clenching or teeth grinding during night. Albeit it is not a life-threatening disease, its timely diagnosis is fundamental to prevent teeth wear and preserve quality of life. However, this condition is usually detected by dentists when its effects are overt, and its remote and early diagnosis is challenging and almost unfeasible. Nevertheless, sleep bruxism entails sounds related to teeth grinding or teeth chattering. Therefore, they can be exploited to detect occurrences of sleep bruxism, resulting in a first stage screening favouring its quick diagnosis. To this end, this paper proposes an innovative methodology for the remote assessment and diagnosis of sleep bruxism. Specifically, a Machine Learning (ML) model is devised which classifies audio signals in order to distinguish bruxism-related sounds. The classifier is carried out by making use of a Convolutional Neural Network (CNN), while the overall ML model is designed to be deployed on an embedded device featuring a microcontroller and a microphone. In so doing, a tiny, portable and ready to use embedded ML-enable device is set up, that is able to detect bruxism phenomena directly at patients home.