Skip to Content

Scalable Heart-Rate-Variability Analysis for Individualized Secure Health Monitoring

PhD project title

Scalable Heart-Rate-Variability Analysis for Individualized Secure Health Monitoring 

Outline description, including interdisciplinary, intersectoral and international dimensions (300 words max)

In the past decade, the prevalence of unhealthy lifestyles and globalization have increased the number of people living with cardiovascular disorders, asthma and diabetes, while making them more prone to outbreaks of respiratory diseases. Such health conditions can affect each person’s life and can even shut down key aspects of the economy as we have recently experienced due to the global COVID-19 outbreak. Fortunately, wireless body sensor networks (WBSNs) have emerged in the past decade offering the capability to remotely record and analyse vital body signals, which is essential for the monitoring of each patient’s condition and even for the diagnosis of the onset of critical symptoms. The analysis of the electrocardiograms (ECGs) has been key in the monitoring and early detection of not only heart related disorders but also flu like symptoms. Currently, most of the ECG analysis is limited on time domain due to the high complexity of the power spectral analysis (PSA) of heart-rate in frequency-domain, which is recognized as a powerful tool for evaluating many autonomous nervous system activities.  The primary aim of this project is the development of a scalable PSA system of heart-rate variability for health monitoring as well as person’s authentication.  The developed PSA algorithm will be able to scale its effort depending on the required vital features that could be associated with the disease type and severity as well as the available hardware resources at the edge or cloud. The new features extracted in the frequency domain will be used to train machine learning based algorithms for the evaluation of patient’s conditions and the detection of critical health conditions such as arrythmias. Finally, the extracted heart features will also be used for authenticating each person and enhancing the efficacy of existing ECG based security algorithms which currently use only time domain information.                             

The project is interdisciplinary and intersectoral bringing together experts on low power biomedical system design from the ECIT-GRI and on diabetes and cardiovascular disease detection and prevention from the Wellcome-Wolfson Institute for Experimental Medicine as well as on Artificial Intelligence and wellness monitoring from B-Secur. The project is squarely aligned with the scalable-secure-intelligence theme of the ECIT GRI, which forms a central part of the Belfast City-Deal as well as the aimed smart health monitoring solutions. The project will enhance the rapidly growing area of Artificial-Intelligence in Northern-Ireland as detailed in the recent report from the Matrix-panel and will further support the targeted creation of a regional Centre of Excellence on AI. The project has the potential to enhance the capabilities of WBSNs and reveal unique complex features and enable their analysis on portable devices that was impossible up-to now helping in the handling of diseases and epidemics.  It is expected that the project results will be published in publications in international top-tier venues, while potentially resulting to more patents with commercial value and industrial interest like the one invented by the supervisor.    

Key words/descriptors

Heath monitoring, ECGs, Heart-Rate-Variability, Energy-Efficient Systems, Embedded-Systems, Wearables, Artificial-Intelligence, Security, Smart City   

Fit to CITI-GENS theme(s)

The project is aligned with most CITIGENS themes, since it targets design of a portable health monitoring system that is central in emerging smart city and smart health applications. A novel aspect lie son the fact that it will not only enable the portable monitoring of complex heart features but it will also seek to utilize such features for person authentication that is unique worldwide as the partner B-Secur has already demonstrated. The PhD project is in particular aligned with the Information Technology and the Life Sciences themes of CITI-GENS.

Supervisor Information



First Supervisor: Georgios Karakonstantis                                                         School: EEECS, ECIT

Second Supervisor:  Chris Watson                                                                       School: SMDBS, WWIEM

Third Supervisor:    Andrian Condon                                                                    Company: B-Secur  


Name of non-HEI partner(s)

B-Secur has invested over 15 years of scientific research in ECG to become a world leader in the development and integration of ECG technology, partnering with some of the world’s leading technology companies. B-Secur has harnessed the power of ECG to create HeartKey, a unique technology that offers advanced physiological monitoring with intrinsic data protection.  B-Secur collaborates with leading technology companies, including Analog Devices and Cypress Semiconductor. It is recognised by Forrester as a key disruptor in the global ECG market an dparticipates in  the London Stock Exchange ELITE programme, nurturing the UK’s highest growth companies. B-Secur was named one of the UK’s top 37 fastest-growing tech companies, part of the Upscale 2018 programme from TechCity UK.

There is also another non-HEI company that is interested on trying out the technologies within their platforms used in hospital ICUs and for the monitor of respiratory diseases, called SmartCardia in Switzerland. They are also willing to support the student and the project thus enhancing the mobility and  international elements of the project. An internship of at least 3 months will also be scheduled to take place on their premises.


Contribution of non-HEI partner(s) to the project:





The project is a unique opportunity for a PhD student to develop an energy-efficient power spectral analysis system of heart rate variability and apply it on health monitoring and security applications enhancing industrial expertise that is unique worldwide on the use of ECG features for authentication and health monitoring. B-Secur has developed a suite of powerful ECG algorithms and analytics for user identification, wellness and health, which can be integrated into leading devices and systems. The student will be provided access to such a framework and will be given the opportunity to integrate the developed low power spectral analysis system on B-Secur’s platforms.  This will be enabled through a 6-month internship as well as continued supervision throughout the PhD.

Overall, the Fellow will gain skills on an industry related research project on applications that have high commercial value and interest. The project will help develop a unique PSA system that can scale depending on the available hardware resources and required monitoring intensity of features. This will give B-Secur a market advantage especially in the foreseen deployments at the Edge. It is expected that joint publications will be produced which in combination with public presentations will help the Fellow develop his presentation skills, while enhancing the research image of B-Secur. The project will establish a long-term relationship between ECIT and B-Secure and will enable the identification of research challenges and the subsequent preparation of follow up funding proposals. Finally, the developed material are going to enhance current courses on Artificial-Intelligence and Cloud in EEECS, while potentially providing the basis for developing a course on smart health monitoring systems.

Research centre / School

Queen’s ECIT-GRI and Wellcome-Wolfson Institute for Experimental Medicine

Subject area

Electrical Engineering, Computer Science, Medicine and Biomedical Science