[7A2] A smart sleep apnoea detection service
R Barika, A Shenfield, H Razaghi and O Faust
Sheffield Hallam University, UK
Over the last decades, sleep apnoea has become one of the most prevalent healthcare problems. Diagnosis and treatment monitoring are key elements when it comes to addressing this public health crisis. A problem for diagnosis and treatment monitoring is a chronic lack of specialised lab facilities, which results in long waiting times or the absence of such services. This can delay appropriate treatment, which might prolong living with sleep apnoea and thereby lead to health issues due to poor sleep.
We address this problem with a smart sleep apnoea detection service based on heart rate variability (HRV) analysis. The service incorporates Internet of Medical Things (IoMT), mobile technology (MT) and advanced artificial intelligence (AI). The measured signals are relayed by a smartphone into a cloud server via IoMT protocols. Once the data is stored in the cloud server, a deep learning (DL) algorithm is used to detect sleep apnoea events. Detecting these events can trigger a warning message, which is sent to care givers.
The smart sleep apnoea detection service is beneficial for patients who find it difficult to access specialised lab facilities for diagnosis or treatment monitoring. Furthermore, the system prolongs the observation period, which can improve the diagnosis accuracy. The resource requirements for the proposed service are lower when compared to clinical facilities, which might lead to significant cost savings for healthcare providers.
We address this problem with a smart sleep apnoea detection service based on heart rate variability (HRV) analysis. The service incorporates Internet of Medical Things (IoMT), mobile technology (MT) and advanced artificial intelligence (AI). The measured signals are relayed by a smartphone into a cloud server via IoMT protocols. Once the data is stored in the cloud server, a deep learning (DL) algorithm is used to detect sleep apnoea events. Detecting these events can trigger a warning message, which is sent to care givers.
The smart sleep apnoea detection service is beneficial for patients who find it difficult to access specialised lab facilities for diagnosis or treatment monitoring. Furthermore, the system prolongs the observation period, which can improve the diagnosis accuracy. The resource requirements for the proposed service are lower when compared to clinical facilities, which might lead to significant cost savings for healthcare providers.
