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DARTH group mission

Ever-increasing healthcare provision demands strain national health systems globally, which struggle to meet patients' needs. There is a growing unmet clinical need to develop accurate, robust, practical decision support tools to facilitate diagnosis, assessment, and symptom monitoring to improve patient health and care.

We are a dynamic research group based at the Usher Institute, Edinburgh Medical SchoolUniversity of Edinburgh working at the interface of engineering, mathematics, informatics, and medicine. We passionately believe we can design and provide effective solutions which will revolutionize contemporary healthcare delivery through capitalizing technology and harnessing data.

We develop and apply signal processing and statistical machine learning algorithms to explore data and decipher complicated concealed statistical relationships. Our algorithms are directly driven by and validated on complicated real-world problems, aiming to facilitate interpretation of the underlying key mechanisms of the modelled system. Our work is inherently multi-disciplinary and we collaborate with industrial partners and researchers worldwide. We tackle challenging problems in healthcare domains from neurodegenerative disorders and mental health, to asthma, cardiovascular disease, and neonatal monitoring.

News

30 March 2021


Evi wins the SPR MD/PhD student research award in the Pediatrics conference

Evi has won this award at the Society for Pediatrics Research conference (one of the biggest international academic meetings in the area of Pediatrics) for her research paper: “Machine Learning for Stratification of children at risk of language delay following Preterm Birth". This is work that Evi publishes as lead author, and is a multi-centre collaboration with colleagues across the University of Edinburgh. She will be presenting her work at the conference in May.

17 February 2021


New journal article accepted for publication in Frontiers in Human Neuroscience

"A neuromotor to acoustical jaw-tongue projection model with application in Parkinson’s disease hypokinetic dysarthria". We explored concurrent speech, surface electromyography, and three-dimensional accelerometry signals to assess the neuromotor activity of the massseter-jaw-tongue articulation in Parkinson's disease (PD). We demonstrate that there are important cross-correlations which may serve as useful acoustic biomarkers of hypokinetic dysarthria in PD.

13 February 2021


Thanasis won the Biosignals 2021 best paper award

"Assessing Parkinson’s disease speech signal generalization of clustering results across three countries: findings in the Parkinson’s voice initiative". We demonstrated that we can identify four robust clusters of Parkinson's disease using sustained vowels which generalize well on three cohorts collected across three countries as part of the Parkinson's Voice Initiative. These findings have important implications for cost-effective group membership assignment.   

1 February 2021


Minhong has successfully defended her PhD thesis 

"New insights into probabilistic pattern formation of embryonic stem cells using agent-based modelling”. Minhong proposed an agent-based modelling approach to identify biologically plausible rules acting at the meso-scale within stem cell collectives that may explain spontaneous patterning. She introduced a new distance-based metric to assess the deviation between probabilistic ground truth and probabilistic simulated outcomes. Her work provides some useful key insights into stem cell understanding, facilitating further investigations towards the development of novel treatments.

27 January 2021


New journal article accepted for publication in IEEE Access

"Smartphone speech testing for symptom assessment in rapid eye movement sleep behavior disorder and Parkinson's disease”. We analyzed 4242 smartphone voice recordings from people diagnosed with REM sleep behavior disorder and Parkinson's disease, and contrasted findings with data from controls. We demonstrate the potential of our approach towards a novel digital biomarker facilitating intervention in the early and prodromal stages of Parkinson's disease.

11 January 2021


New journal article accepted for publication in Biomedical Signal Processing and Control

"Acoustic to kinematic projection in Parkinson’s disease dysarthria”. We investigated the effects of neuromotor activity during muscular exertion that translates formant acoustics into speech articulatory movements affected by hypokinetic dysarthria in Parkinson’s Disease (PD). The study provides new mechanistic insights into the biomechanical system of voice production and the understanding of PD effects.

Previous years in review
2020
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2018

© Data Analytics Research and Technology in Healthcare (DARTH) group, 2020