Potential Research Student Projects

Professor P. A. Kyriacou

The vision of the proposed research programme lies in the design, development and implementation of non-invasive sensing technologies with wide applications, as will be described below, in both the clinical and the home setting. Such sensing technologies could be used for monitoring, prognostic (screening), diagnostic and therapeutic purposes.  The global sensors market is estimated at over $44billion and with sensors being integrated into an increasingly diverse array of applications, smaller, smarter, lighter and cheaper sensors have more growth potential. Modern applications of sensing systems require the establishment of reliable, low-power wireless networks of sensors which are data-centric, with sensor nodes communicating only when necessary to transport data.  The development of such technologies are in line with the strategic plan of NHS and other similar organisations, which is to drive the more mundane delivery of health care, outside the hospitals and in the community. Technologies for screening, monitoring, etc. will alleviate the congestion of health care centres and it will enable them to concentrate in critical care interventions. Recording of physiological and psychological variables in real-life conditions could be especially useful in management of chronic disorders or other health problems e.g. for high blood pressure, diabetes, anorexia nervosa, chronic pain or severe obesity, stress, epilepsy, depression and many others.

Professor P. A. Kyriacou, Dr J. Phillips, Dr I. Triantis, Dr M. Hickey

The focus of this project is to develop non-invasive optical rapid measurements and/or continuous monitoring of biological variables currently only measurable via blood samples or invasive techniques (i.e. concentrations of chromophores, gases, hormones, ionic salts, enzymes, lipids, and other biomarkers relating to organ function, stress, mood disorders, etc., within biological tissues and blood). This will be made possible by applying optical and electrical impedance spectroscopic techniques.

Professor P. A. Kyriacou, Dr J. Phillips, Dr I. Triantis, Dr M. Hickey

The main objectives of this project are to develop multi-parameter sensors, and signal processing techniques for the non-invasive and continuous mapping of physiological and haemodynamic parameters. The output will be the non-invasive screening of pathologies relating to cardiovascular diseases (peripheral vascular (artery/vein) disease, lower limb claudication, atherosclerosis, carotid artery disease, hypertension, stroke, etc.). Some of the targeted parameters will be pulse transit time (PTT), pulse wave velocity (PWV), blood flow, blood volume, arterial and venous oxygen saturation, blood pressure, etc.

Professor P. A. Kyriacou, Dr J. Phillips, Dr I. Triantis, Dr M. Hickey

This work proposes the development of new sensitive and accurate point-of-care (POC) diagnostic and monitoring portable devices for use by mood disorder patients at home. These technologies will allow for the quantification of (a) pharmaceutical blood levels ensuring that patients maintain a therapeutic state and are not at danger of toxicity-related complications, and (b) stress biomarkers which correlate with possible depressive relapses in mood disorder patients, allowing for timely intervention. An inter-disciplinary team with expertise in MEMS, biomedical sensors, biochemistry and psychopharmacology has been established to provide these solutions. The POC devices will include miniaturised sensor platforms for depositing drops of blood/saliva, optical and electrical impedance sensors to interrogate the sample, portable instrumentation, and algorithms to rapidly determine concentration levels. Such devices will have a significant impact on clinical decisions and health outcomes for mood disorder patients.

Professor P. A. Kyriacou, Dr J. Phillips, Dr M. Hickey

This project supports fundamental research in sensing targeted specifically at the diagnosis of dementias and the quantitative measurement of disease progression. The aim is to deliver non-invasive and cost effective solutions with applications making a clear contribution to one or more of the following:

• Technologies/techniques which more accurately and easily differentiate dementia sub-types. Applications which focus on prodromal, presymptomatic stages of the disease are particularly welcomed.
• Technologies/techniques which enable quantitative measurement of disease progression of dementia.

Dr I. Triantis

Develop peripheral nervous system implants that will utilise sensory neural pathways as probes into the brain and the spinal cord, while minimising the effects on the periphery. Examples include Vagus nerve stimulators that can be used for a range of diseases including epilepsy, Alzheimer’s, depression and Parkinson’s disease. The methods developed will have a range of benefits, from the neuroscience perspective, gaining invaluable insights into the neural pathways that provide access to deep brain structures; to clinical practices, where signals from the brain are used as triggers for preventive stimulation providing fully automated symptom suppression.

Dr I. Triantis, Professor P. A. Kyriacou

Rapid healing of wounds, including those that relate to surgery or injury, as well as those that occur due to a disease, e.g. diabetic ulcers. The benefits will be enormous both for patients and for the health system, by respectively reducing mortality and infection and reducing the time of hospitalisation and the enormous use of resources necessary for conventional treatment. Application specific optical spectroscopy and electrical stimulation will be utilised in combination with “smart” dressings that will feature embedded miniaturised multi-modal sensors for real time monitoring of the condition of the wound.

Professor P. A. Kyriacou

The recent advancements in physiological sensors (optical and electrical) have enabled the acquisition of physiological data non-invasively which was not possible in the past. With the help of such a multiparameter dataset it might be possible, utilising advance linear and non-linear signal processing techniques such as Time-Frequency Distribution (TFD), Empirical Mode Decomposition (EMD) etc., to extract features that will provide useful physiological information related to the haemodynamic and cardiovascular state of a person. The combined information obtained from different non-invasive modalities such as Electrocardiograph (ECG), Respiration, Photoplethysmograph (PPG), Blood pressure (BP) and Near-infrared Spectroscopy (NIRS) can be beneficial and has a significant impact in various fields such as anaesthesia management, paediatric care and sports medicine. This work is in collaboration with national and international partners including St Bartholomew’s Hospital, The Royal London Hospital, Great Ormond street hospital for Children and Yale School of medicine.

Dr Constantino Carlos Reyes-Aldasoro

Abnormal growth of cells inside the brain is relatively rare, but serious and life threatening when it leads to brain tumours. Recent research has shown that certain features of certain tumours are probably important in predicting their response to treatment, but the relationship between the different features is so complicated that this information cannot yet be used to help patients. This multidisciplinary proposal will investigate in detail how various characteristics of brain tumours impact on patient outcome. The aims of this project are to obtain quantitative measurements from the images of tumour specimens and evaluate which measurements, or sets of measurements, correlate best with patient outcomes such as life expectancy and response to treatment.

This project requires programming image analysis algorithms in Matlab to segment the different cells in tumours stained by immunohistochemical techniques. The ideal student should have good programming skills in Matlab, should have a good knowledge of image analysis and interest in biomedical imaging.  Previous biological knowledge is not required but an interest to learn is essential.

Dr Constantino Carlos Reyes-Aldasoro

This project is a collaboration with the laboratory of social insects of the University of Sussex (http://www.sussex.ac.uk/lasi). The aims of this project are to extract information regarding the complex behaviour of bees from their movements from videos, in order to do that it is necessary to develop segmentation and tracking algorithms. This is a very challenging problem as the images of the bees are complex and difficult to analysed through algorithms. This project requires programming image analysis algorithms in Matlab to segment and track the movements of the Bees. The ideal student should have good programming skills in Matlab, should have a good knowledge of image analysis and interest in biomedical imaging.