New algorithms for screening cardiac autonomic neuropathy (CAN) in diabetic patients

Project description

The prevalence of diabetes mellitus is currently estimated at 200 million people worldwide, exceeding 360 million patients by 2030 [Bax 2006]. The majority of these patients will have type 2 diabetes. As many as 22% of people with type 2 diabetes suffer from  cardiovascular autonomic neuropathy (CAN), which leads to impaired regulation of blood pressure, heart rate and heart rate variability (HRV). Silent ischaemia is significantly more frequent in patients with CAN - 38% as opposed to 5% for those without CAN [Johnston 2003]. Around 75% of people with diabetes die from cardiovascular disease such as heart attack and stroke [Australian Diabetes Society]. Early sub-clinical detection of CAN and intervention are of prime importance for risk stratification in preventing the potentially serious consequences of CAN. However, existing CAN screening techniques using cardiovascular reflex responses are subject dependent and likely to detect CAN at the advanced stage, once symptoms are visible, rather than at an earlier stage of CAN progression.  The aim of this project is, therefore, to develop sophisticated but simple algorithms for early detection of CAN in diabetic patients and monitor the progression. This project is expected to make an important contribution to screening techniques for diabetes in Australia and elsewhere, providing a low cost tool to primary health care professionals and indigenous health care workers.

Project team

Leader: Ahsan Khandoker

Staff: Marimuthu Palaniswami

Students: Hasan Imam

Collaborators: Herbert Jelinek (Charles Sturt University)

Other projects

Convergence of engineering and IT with the life sciences projects

Research Centre

ARC Research Network on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)


Biomedical Engineering


Convergence of engineering and IT with the life sciences


biomedical engineering; disease; electrocardiogram ECG; heart