Implementing machine learning algorithms to detect and screen obstructive sleep apnoea episodes using a headband

Can machine learning algorithms be used to detect obstructive sleep apnoea with high accuracy and precision? How do machine learning algorithms compare to the traditional method (polysomnography) of diagnosing obstructive sleep apnoea? Implement machine learning algorithms to detect obstructive sleep apnoea episodes in patients who are suspected of obstructive sleep apnoea. It is costly and labour-intensive for a clinician to manually go through a patient's sleep data to detect obstructive sleep apnoea. Especially because there is more than one aspect that needs to be looked at. For example, sound data (snoring) is not enough on its own to be able to detect obstructive sleep apnoea. Also, the frequency of certain events is important. Therefore, the clinician needs to go through a lot of data to be able to diagnose the patient with obstructive sleep apnoea. It is likely that the human error will result from this laborious activity. Therefore, it can be difficult to make decisions and diagnose the patients if important events have been missed during the analysis. Sensor noise can also mask certain events and signals making it hard to make decisions. The general aim is to reduce the burden on clinical staff that manually sleep score patients. So, learning algorithms will be used to automatically highlight the obstructive sleep apnoea episodes. It is a mass screening tool rather than a diagnostic test. A sleep score will be generated, so a clinician can decide whether a PSG is required. The machine learning tool will use data for multiple sensors. This is to prevent the collected data being redundant if one of the sensors fail or a connection is lost etc. The algorithm will take into account multiple nights of data which will have an advantage over the expensive PSG. The band that is to be used includes multiple sensors that are essential for the detection of obstructive sleep apnoea. The band can be taken home, and it is not necessary for the patient to be at a clinic or a hospital. Therefore, the extracted data is more reliable because patients being at hospital do not feel comfortable and they might not sleep as they would at home. Different machine learning algorithms have been researched and reviewed as to which is more accurate and suitable for detection of obstructive sleep apnoea. However, there has been little implementation into an actual device.

Impact on Health and Care
The CDT primarily addresses the most pressing needs of nations such as the UK - namely the growth of expenditure on long term health conditions. These conditions (e.g. diabetes, depression, arthritis) cost the NHS over £70Bn a year (~70% of its budget). As our populations continue to age these illnesses threaten the nation's health and its finances.

Digital technologies transforming our world - from transport to relationships, from entertainment to finance - and there is consensus that digital solutions will have a huge role to play in health and care. Through the CDT's emphasis on multidisciplinarity, teamwork, design and responsible innovation, it will produce future leaders positioned to seize that opportunity.

Impact on the Economy
The UK has Europe's 2nd largest medical technology industry and a hugely strong track record in health, technology and societal research. It is very well-placed to develop digital health and care solutions that meet the needs of society through the creation of new businesses.

Achieving economic impact is more than a matter of technology. The CDT has therefore been designed to ensure that its graduates are team players with deep understanding of health and social care systems, good design and the social context within which a new technology is introduced.

Many multinationals have been keen to engage the CDT (e.g. Microsoft, AstraZeneca, Lilly, Biogen, Arm, Huawei ) and part of the Director's role will be to position the UK as a destination for inwards investment in Digital Health. CDT partners collectively employ nearly 1,000,000 people worldwide and are easily in a position to create thousands of jobs in the UK.

The connection to CDT research will strongly benefit UK enterprises such as System C and Babylon, along with smaller companies such as Ayuda Heuristics and Evolyst.

Impact on the Public
When new technologies are proposed to collect and analyse highly personal health data, and are potentially involved in life or death decisions, it is vital that the public are given a voice. The team's experience is that listening to the public makes research better, however involving a full spectrum of the community in research also has benefits to those communities; it can be empowering, it can support the personal development of individuals within communities who may have little awareness of higher education and it can catalyse community groups to come together around key health and care issues.

Policy Makers
From the team's conversations with the senior leadership of the NHS, local leaders of health and social care transformation (see letters from NHS and Bristol City Council) and national reports, it is very apparent that digital solutions are seen as vital to the delivery of health and care. The research of the CDT can inform policy makers about the likely impact of new technology on future services.

Partner organisation Care & Repair will disseminate research findings around independent living and have a track record of translating academic research into changes in practice and policy.

Carers UK represent the role of informal carers, such as family members, in health and social care. They have a strong voice in policy development in the UK and are well-placed to disseminate the CDTs research to policy makers.

STEM Education
It has been shown that outreach for school age children around STEM topics can improve engagement in STEM topics at school. However female entry into STEM at University level remains dramatically lower than males; the reverse being true for health and life sciences. The CDT outreach leverages this fact to focus STEM outreach activities on digital health and care, which can encourage young women into computer science and impact on the next generation of women in higher education.

For academic impact see "Academic Beneficiaries" section.