The research focuses on the application of engineering and physical science principles to human biosignals.
The primary aim of the research is to use biosignal analysis methods as diagnostic indicators of clinical problems by addressing two key issues:
- a) understanding the physiology of human cardiovascular, respiratory and neural control in a variety of clinical diagnostic settings and
- b) developing innovative physiological measurement techniques by using biosignal processing, computational intelligence and low-cost instrumentation technology.
Project title: Cardiovascular (CVD) Diagnostic Engineering
Summary:
Electrocardiography is still widely accepted as the gold standard for early diagnosing CHD, that if required can be supplemented by coronary artery angiography. However, single system diagnostics has been shown miss comorbidities associated with CVD or CHD and is not available routinely in rural communities or as a self-help diagnostic tool leading to potential CVD patients not presenting at health clinics for examination. Several important diagnostic tools, such as stress ECG, computed tomography (CT) and magnetic resonance imaging (MRI) in CHD risk patients are limited and expensive. Monitoring is usually via a 3- month follow up examination at hospitals. Because CHD progresses slowly and may develop over a patient's lifetime, and be the results of alterations in diet, exercise, living conditions, or medication, CHD may not be identified in the asymptomatic stage. The current gap in knowledge is that CHDs are not clinically diagnosed where there are no symptoms or mild symptoms, nor are high risk groups monitored for comorbidities, when immediate treatments could have been more effective. Also, patient lifestyles and psychosocial factors are not considered in the routine cardiac screening of CHD patients. However, multisystem screening of cardiac patients takes a huge amount of time and is prone to subjective bias in assessment across diverse medical disciplines. Moreover, there is a severe lack of programs that offer a multisystem screening (all-in-one health screening) and peer mentoring to address low self-efficacy associated with poor medication compliance, exercise technique and poor nutrition at home as well as monitoring of multisystem pathology such is the case with CHD and depression as an example. Thus, the missing part in current clinical practice is the combined monitoring technology of physiological markers across a multiple physiological systems and behavioral attitudes of CHD patients, which includes monitoring of continuous acquired cardiac vital signals (ECG and pulse rate), temperature and respiration, behavioral attributes (body movements, daily steps, dietary habits, smoking cessation, substance consumption and stressful situation) and psychosocial factors. There is therefore a need to develop automated computational models that combine routine clinical characteristics and daily behavioral features for determining not only CHD progression from being asymptomatic to more severe presentations of CHD but to include signs and signals that characterize comorbidities. Such a model could help physicians refine their knowledge of the stratification of CHD risk, provide better care and improve patient health and quality of life.
Aims
- Develop and test non-invasive physiological recording techniques and tools to predict and diagnose cardiovascular disease and its comorbidities (mainly CHD, Stroke, depression) at early stages when intervention and rehabilitation are most effective.
- Introduce a radically novel approach of continuous monitoring (CM) of heart health inclusive within a physiological multisystem approach that will ensure a paradigm shift towards empowering patients to more effectively self-manage their CVD and comorbidities, set within a collaborative care context with health professionals.
- Establish novel machine learning paradigms for interpreting biosignals that characterize network pathophysiology, and for combining these with other clinical data in the diagnostic process.
Funding
July 2018- June 2021 Healthcare Engineering Innovation Center Khalifa University Type 2 Research Center Theme (Diagnostic Engineering) AED5,500,000 [Theme leader: Ahsan Khandoker; Project leaders: Leontios Hadjileontiadis, Herbert Jelinek]
July 2018- June 2020 Care4MyHeart: Personalized management of Cardiovascular Diseases via technology-enabled behavioral change Department of Education and Knowledge (ADEK) award for Research Excellence. AED 300,000. Lead Principal Investigator (Leontios Hadjileontiadis)
Collaborators:
Dr Andreas Voss (University of Applied Sciences, Jena, Germany)
Dr Jean-Philippe Couderc (University of Rochester, USA)
Dr Luca Mainardi (Polytechnic Milano, Italy)
Project title: Developing a new integrated abdominal Electrography system for noninvasively detecting fetal brain signals
Summary:
Fetal brain damage is a common contributing cause of preventable death and adverse neurological outcome (e.g. cerebral palsy) all over the world. At present there is no device (invasive or non-invasive) in the market to measure fetal brain electrical activity. In this project we, therefore, will first examine the patterns of fetal brain waves in pregnant animal model (mice) and then investigate a new method for non-invasive measurement of human fetal brain development (32 weeks’ gestation till birth) by abdominal surface electrical signal with inexpensive gel electrodes, comparing with fetal fMRI images of fetal brain and direct lead scalp electroencephagram during labor. The industry partner’s cash contributions for three years that can ultimately be incorporated into their existing monitoring device with clear potential for commercialization. The outcomes of this project would make fundamental as well as translational research outputs for fetal neurological screening and its potential to reduce fetal deaths.
External Collaborators:
• Dr Yoshitaka Kimura (Professor of Obstetrics, Tohoku University Hospital, Japan)Â
- Dr Marimuthu Palaniswami (The University of Melbourne, Australia)
• Dr Shamsa Al Awar (Chair of Ob/Gyn UAE University, UAE)
• Dr Farah Asghar (Consultant Obstetrician, Al Mafraq Hospital, Abu Dhabi, UAE)
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July 2019 – June 2022 Khalifa University Competitive Internal Research Award (CIRA). Developing a new integrated abdominal Electrography system for noninvasively detecting fetal brain signals (AED2,700,000) Lead Principal Investigator (Ahsan Khandoker)
Project title: Personalized smart management test bed for Cardiovascular and Mental Diseases via Technology-enabled Physiological and Behavioral changes (MindMyHeart)
Summary:
Cardiovascular disease (CVD) is the major cause of death in the UAE/Korea, causing one in every five deaths. Some lifestyle factors (such as an unhealthy diet, lack of exercise, and smoking), known to contribute to a higher risk of CVD, are also common in people with mental health issues (such as depression). Integrated Cardiac and Mental Rehabilitation can significantly improve mortality and morbidity rates, leading to longer independent living and a reduced use of healthcare resources. The proposed collaborative project, namely MindMyHeart, sets as an overall goal to introduce a personalized home-based rehabilitation program, enabling lifestyle behavioral change towards increased quality of life with personalized management for CVD and mental health issues. The overall concept of MindMyHeart platform is easily transferable to address other diseases providing market opportunities for the commercialization. MindMyHeart will be realized in collaboration with the KAIST, Korean Hospitals and Cleveland Clinic Abu Dhabi through research visits.
External Collaborators:
Dr Uichin Lee (KAIST, Korea)
Dr Yong Jeong (KAIST, Korea)
Dr Taoufik Al Saadi (American Center for Psychiatry and Neurology, Abu Dhabi, UAE)
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July 2019 – June 2022 KU-KAIST Joint Research Center grant [AED 2,700,000] on “MindMyHeart: Personalized smart management test bed for Cardiovascular and Mental Diseases via Technology-enabled Physiological and Behavioral changes. Principal Investigator (Ahsan Khandoker)
