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The use of Artificial Intelligence Approaches to the Analysis of Heart Rate Variability Data (Advert Ref: SF22/EE/CIS/SICE)


   Faculty of Engineering and Environment

  Dr P Sice  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

Importance: Heart rate variability (HRV), or the variation in the time interval between consecutive heartbeats, is a proven measure for assessing changes in Autonomic Nervous System(ANS) activity. The ANS is made up of two branches, the sympathetic nervous system(SNS) and the parasympathetic nervous system(PNS). The SNS is responsible for upregulating our body (e.g. by increasing heart rate, breathing rate, blood pressure) according to the body’s physiological needs to prepare it for action; the extreme of which is the Fight and Flight response. The PNS on the other hand is responsible for the Rest and Digest processes in the body, and the decrease in heart rate, breathing rate and blood pressure and increase in salivary juices and digestion(Rauch et al, 2019; Sice et al, 2022).The organism’s survival and health are sustained by a balanced functioning of the SNS and the PNS to maintain internal homeostasis within a changing environment. Interventions able to restore dysregulated ANS balance, potentially have many health and wellbeing benefits (Bentley et al, 2019.) Such interventions typically facilitate disengagement of the bodily Fight and Flight reactivity and  facilitate transitioning to the more heart healthy Rest and Digest response mediated via the vagus nerve (Sice et al, 2022). We aim to develop a framework of reference to integrate HRV measures with qualitative self-reporting evaluations by implementing novel and innovative analytical tools using artificial intelligence (AI) models (e.g. Self-Organising Map -SOM, Generative Topographic Map-GTM, convolution networks) to identify patterns and assess changes in HRV, respiratory rate, and blood pressure individually, and also explore dynamical relationships between them using mutual information score (a non-linear alternative to the linear metrics e.g. Pearson correlation) (Bentley et al, 2019; Lim et al, 2021; Moorton et al, 2021; Sergi, 2022).

We are looking for candidates committed to pursue PhD in computer science, signal processing, AI.

For informal enquiries please contact

Eligibility and How to Apply: 

Please note eligibility requirement:  

·               Academic excellence of the proposed student i.e. 2:1 (or equivalent GPA from non-UK universities [preference for 1st class honours]); or a Masters (preference for Merit or above); or APEL evidence of substantial practitioner achievement. 

·               Appropriate IELTS score, if required. 

For further details of how to apply, entry requirements and the application form, see 

https://www.northumbria.ac.uk/research/postgraduate-research-degrees/how-to-apply/  

Please note: Applications that do not include a research proposal of approximately 1,000 words (not a copy of the advert), or that do not include the advert reference (e.g. SF22/…) will not be considered. 

Start Date: 1 October 2022 


Funding Notes

Please note this is a self-funded project and does not include tuition fees or stipend.

References

Bentley, E., Rauch, L., Sice, P. and Patel, P., (2019). Using the Self-Organising Maps to distinguish between stress and non-stress state heart rate. SYSTEMIST, December 2019.
Moorton Z., Kurt Z., Woo W.L., (2021). Is the use of Deep Learning and Artificial Intelligence an appropriate means to locate debris in the ocean without harming aquatic wildlife? arXiv preprint arXiv:2112.00190, 2021 - arxiv.org
Kurt Z., Barrere-Cain R., Laguardia J., (2018). "Tissue-specific pathways and networks underlying sexual dimorphism in non-alcoholic fatty liver disease", Biology of Sex Differences 2018, vol. 9:46. doi: 10.1186/s13293-018-0205-7.
Krishnan KC.*, Kurt Z*., (2017) Barrere-Cain R, et al. “Integration of Multi-omics Data from Mouse Diversity Panel Highlights Mitochondrial Dysfunction in Non-Alcoholic Fatty Liver Disease”, Cell Systems 2017. doi: 10.1016/j.cels.2017.12.006. *Shared (co-first) authors.
Koh, B., Lim, C., Rahimi, H., Woo, W., Gao, B., (2021) Deep Temporal Convolution Network for Time Series Classification, 16 Jan 2021, In: Sensors.
Rauch, L., Sice, P. Zink, C. (2022) Feasibility study of the effect of Taijiquan Based Movement Training in soccer players; a link between the Autonomic Nervous System and Patterned Movement, (under review) Frontiers in Neuroscience, section Autonomic Neuroscience
Sergi, Arian, “Using generative topographic map algorithm on heart rate variability data and track the stress level change after exercise”, MSc Thesis supervised by Dr Zeyneb Kurt, (completion date: February 2022), CIS department, Northumbria University.
Sice, P., Rauch, L., Elvin, G., Riachy, Shang Y., (2022) Impact of Online Guided Rhythmic Movement Practice on Integrative wellbeing, SYSTEMIST, January, 2022.

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