Early Career Researcher Spotlight – Chloe Hinchliffe

We are launching a new segment to present the work of Early Career Researchers in IDEA-FAST.  In this first instalment, we talk to Chloe Hinchliffe, a  postdoctoral researcher in the Brain and Movement (BAM) Research Group at the Translational and Clinical Research Institute of Newcastle University.


Chloe received her integrated master’s degree in Medical Engineering (MEng) at the University of Surrey in 2018, with her final project titled “Electroencephalogram Analysis with Advanced Signal Processing Techniques for the Characterisation of Seizures”. She continued working with her master’s thesis supervisors, Dr Abasolo and Dr Yogarajah, at the University of Surrey and completed her PhD in Biomedical Engineering in 2022. Her PhD thesis was titled “Application of Machine Learning to Electroencephalograms and Electrocardiograms for the Differential Diagnosis of Psychogenic Non-epileptic Seizures and Epilepsy”. 

From October 2022 she has been a postdoctoral researcher at Newcastle University, where she is working as part of IDEA-FAST project in at the Translational and Clinical Research Institute of Newcastle University. Her area of expertise is in biomedical signal processing with a focus on feature engineering and machine/deep learning.

We asked Chloe about her research, her passions in science, as well as her work within IDEA-FAST.

      • What is your major focus as a researcher, and why were you drawn to this path?

    I am biomedical engineer, and my focus is biomedical signal processing with machine learning. During my master’s at the University of Surrey, my favourite module was biomedical signal processing with Dr Daniel Abasolo, so for my final year project I wanted to work with him. My project was seizure classification using measures of electroencephalogram (EEG) complexity, where I worked with Dr Abasolo and Dr Mahinda Yogarajah from St George’s Hospital in London. I enjoyed the project so much that I continued this work for a PhD, expanding the analysis to include electrocardiograms (ECGs), a wealth of other EEG features, and machine and deep learning. During my PhD, I developed a true passion for biomedical signal processing and machine and deep learning.

        • What is IDEA-FAST, and why is it important? How can it change the future of chronically ill patients?

      IDEA-FAST is a multinational project aimed to identify objective, digital evaluations of fatigue, sleep, and activities for individuals with neurodegenerative and autoimmune disorders. The project’s focused to improve assessments of patient’s activity and daily living to improve therapeutic intervention, using low-cost and highly accessible methods.

          • What challenges have you encountered so far within the project? How have you overcome these impediments?

        In the few short months since I joined the project, my mains challenges have been adapting to this new field and data and meeting of tight deadlines for multiple submissions to prestigious conferences. But with support from the whole team, I have been able to thrive and meet those deadlines with work that I am extremely  proud of.

            • What does the future have in store for this project and you as a researcher?

          I look forward to continuing to work to develop novel features and machine learning models that improve the objective identification of fatigue and daytime sleepiness. Later in my career I hope to continue to push the boundaries of science and engineering to improve healthcare and access for all.  

              • What is your specific role within IDEA-FAST? What do your tasks include?

            My role in the project is working with triaxial accelerometer data to develop novel features and signal processing methods from free-living walking data. I am working with data from a MoveMonitor device placed on the lower back of participants, from which I can analyse their gait and gait characteristics. These characteristics include macros, such as number of steps or time spent walking, and gait micro characteristics, such as step time or step length. I have also been investigating gait variability: the regularity of participants’ walking. With these features, I have been investigating their associations with patient reported fatigue and daytime sleepiness using statistical and machine learning techniques.


            Stay tuned! Chloe will present her work at the ISPGR World Congress 2023 with an oral presentation on “Are measures of gait variability associated with sleepiness and fatigue in immune and neurodegenerative disorders? Insights from the IDEA-FAST feasibility study. ”