Dental loop signals: Image-to-signal processing for mandibular electromyography

Abstract

Dental Loop Signals (DLS) offers a unique approach to biomedical signal-processing, employing deep learning to convert archived images of mandibular muscle activity during dynamic functions into signal data. DLS, processed through unsupervised learning, introduces a cluster-centric signal processing method, enhancing data normalisation for broad applicability. The modular design of the software facilitates customisable use in Temporomandibular Joint (TMJ) and orthopaedic clinics for long-term patient follow-ups and retrospective research. The software’s robustness increases with a larger dataset of electromyographic muscle activities, promising versatility across devices, clinics, and timeframes.