1. Please give a summary of your research
Sleep apnoea is a sleep related breathing disorder that involves a decrease or complete halt of airflow. Snoring can be a symptom of sleep apnoea. It is a serious and prevalent health issue affecting quality of life associated with a high rate of mortality and morbidity. Unfortunately, 80 to 90% of the sufferers remain undiagnosed and untreated. If it remains untreated, the consequences are immense such as cardiac disorders, heart failure and physiological, psychological and financial sever costs. It is estimated that anywhere up to 15% of the population have sleep apnoea and many suffer from daytime sleepiness and fatigue which may be a symptom of disturbed sleep pattern.
One of the main indicators and symptoms of sleep apnoea is snoring and almost everyone has an experience with a family member or friend suffering from snoring. Since starting my PhD in 2014, my goal was to enhance the diagnosis and treatment for sleep disorders and increase the public awareness toward the importance of sleep and sleep disorders and their consequences on mental, cognitive and physiological health and quality of life.
We aim to monitor sleep, respiratory and cardiovascular disorders using wearable sensors such as pulse oximetry (to detect blood oxygen level) and ECG (to monitor heart signal). My solution is a software which uses the signals recorded using the widely used sensors available in market and/or are already installed in clinical centres and hospitals. It derives respiratory and sleep quality information as well as features for arrhythmia detection and implement pattern recognition techniques using different methods of data science and machine learning algorithms. It is anticipated that an innovative measure for linking respiratory and sleep analysis to cardiovascular disorders will be discovered during this project.
2. Please include any additional details you would like to share
Long waiting time, costly and inconvenient diagnosis of sleep respiratory and cardiovascular disorders are the main problems of current sleep diagnosis tests. The current home-based sleep tests are either not convenient enough for the patients or not accurate enough to avoid the follow up hospital sleep test. Most importantly, they are not able to predict developing serious cardiovascular disorders such as heart failure.
The cost of cardiovascular disorders associated with chronic, undiagnosed and/or untreated sleep breathing disorders is enormous on the community. Thus, predicting AF and heart failure and treating the sleep and/or breathing disorder will reduce the long-term cost of the heart failure, hospitalization and follow ups. Also, it will be accessible in rural areas and aboriginal communities and will reduce the referrals and transportation costs to clinical centres and bigger cities.
The main objectives and value proposition are using wearable sensors to monitor cardio-respiratory and sleep disorders, predict cardiovascular conditions and arrhythmias from respiratory and sleep analysis, reducing various costs such as late cardiovascular disorders diagnosis and diagnosis in medical centres, potential for remote diagnosis in rural areas and aboriginal communities.
Nadi is a Biomedical Engineer working at the Sleep Research group of Charles Perkins Centre, the University of Sydney. She finished her PhD at the University of Sydney on developing minimally invas...