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Sleep bruxism is an oromotor parafunction characterized by clenching or grinding the teeth during sleep time that can lead to severe dental attrition, muscle hypertrophy, headaches and migraines,and even dental destruction. Research has estimated a common prevalence rate of bruxism at 8% to 10% among adults and 10% to 50% among children. While researchers have several ways of diagnosing and monitoring bruxism, accurate methods such as polysomnography are either too expensive or invasive for long term studies, while other methods of assessment such as surveys and clinical examinations can sometimes lead to inaccurate assessments. Carra et al noted that the future direction for sleep bruxism assessment would be to develop a handy tool that can directly, reliably, and rapidly measure ongoing sleep bruxism activity and that can be used in both clinical (for diagnosis, treatment outcome evaluation, and follow-up) and research settings [10]. Having a device that enables patients and researchers to track duration and frequency of sleep bruxism on a wider scale will be helpful both in gathering a greater population of data for research and for individuals who want to monitor their own condition. While recent developments of devices such as the Grindcare® and BiteStrip® have made it more affordable for individuals to diagnose and monitor bruxism, their sole reliance on EMG for diagnosis reduces the accuracy of assessment. In this thesis, we examine the possibility of developing an application for mobile phones to help diagnose and monitor sleep bruxism using three channels: audio, video, and electromyography (EMG). Through the use of the channels, we were able to capture sleep bruxism activity throughout the night. In addition, we found evidence that EMG and audio activity follow similar consistent patterns which may indicate that audio signals may potentially be used in lieu of EMG, reducing invasiveness. Our application has the potential to become easily accessible and aff

Sleep bruxism is an oromotor parafunction characterized by clenching or grinding the teeth during sleep time that can lead to severe dental attrition, muscle hypertrophy, headaches and migraines,and even dental destruction. Research has estimated a common prevalence rate of bruxism at 8% to 10% among adults and 10% to 50% among children. While researchers have several ways of diagnosing and monitoring bruxism, accurate methods such as polysomnography are either too expensive or invasive for long term studies, while other methods of assessment such as surveys and clinical examinations can sometimes lead to inaccurate assessments. Carra et al noted that the future direction for sleep bruxism assessment would be to develop a handy tool that can directly, reliably, and rapidly measure ongoing sleep bruxism activity and that can be used in both clinical (for diagnosis, treatment outcome evaluation, and follow-up) and research settings [10]. Having a device that enables patients and researchers to track duration and frequency of sleep bruxism on a wider scale will be helpful both in gathering a greater population of data for research and for individuals who want to monitor their own condition. While recent developments of devices such as the Grindcare® and BiteStrip® have made it more affordable for individuals to diagnose and monitor bruxism, their sole reliance on EMG for diagnosis reduces the accuracy of assessment. In this thesis, we examine the possibility of developing an application for mobile phones to help diagnose and monitor sleep bruxism using three channels: audio, video, and electromyography (EMG). Through the use of the channels, we were able to capture sleep bruxism activity throughout the night. In addition, we found evidence that EMG and audio activity follow similar consistent patterns which may indicate that audio signals may potentially be used in lieu of EMG, reducing invasiveness. Our application has the potential to become easily accessible and aff