© Data Analytics Research and Technology in Healthcare (DARTH) group, 2019

Projects

This page summarizes some of the key research areas that members of the group work on. Most of these projects build on national and international collaborations with academic and industrial partners, and are by nature inter-disciplinary.
Biomedical speech signal processing

We are so used to speaking from a young age, that is easy to underestimate the complexity of this convoluted process. Speech requires the delicate co-ordination of a large number of muscles and organs, starting from the lungs, the vocal folds, and the vocal tract. Viewed under this prism, it is then hardly surprising that speech signals may convey considerable information which can be capitalized for biomedical applications.

We have been developing novel signal processing algorithms to characterize speech. For example, we have demonstrated that we can differentiate people with Parkinson's disease from healthy controls with almost 99% accuracy, we can track Parkinson's disease symptom severity more accurately than the difference between two trained specialists (inter-rater variability), and we can automate the process for self-monitoring voice rehabilitation with about 90% accuracy

More recently, we have demonstrated that the developed methodology can be applied to assist in forensic phonetic analysis, where we can probabilistically very accurately determine whether a suspect is the culprit: remarkably, we have demonstrated this is possible merely using voice fillers.

Longitudinal monitoring of neurodegenerative disorders

We had previously demonstrated that we can accurately differentiate healthy controls from people with Parkinson's disease, and also replicate UPDRS, the standard Parkinson's disease symptom severity metric, with accuracy which is less than the difference between expert neurologists (inter-rater variability) using speech signals. Although extremely promising, these results 'only' manage to demonstrate that we can automatically replicate the clinicians' assessments, and do not capture the true underlying fluctuation of symptoms on a daily basis which really matter to patients.

We are currently working towards integrating additional wearable sensors and smartphones to elicit daily responses from people with Parkinson's disease and motor neurone disease, in an attempt to understand better the daily fluctuations of motor and non-motor symptoms in Parkinson's disease.

 

We envisage collecting a high-resolution dataset which may facilitate key new insights into Parkinson's disease symptom trajectory, whilst providing a platform to associate self-perception and clinical assessment of symptoms.

Mental health assessment using wearable sensors and smartphones

Mental health problems are on the rise globally. Clinical diagnosis and assessment is challenging, subjective, and relies on highly specialized experts, whereas treatment effects are very difficult to be quantified and objectively monitored. 

We have developed a novel approach combining Patient Reported Outcome Measures (PROMs), where patients themselves self-monitor their symptom daily, and the use of wearable sensors and smartphones. The self-reported PROMs are recorded on an Android smartphone app we developed in house, time-stamped, and processed offline to assess trends and symptom variability: this enabled us to gain key new insights which are otherwise missed in interim patient visits to the clinic. Furthermore, we reported new insights into diurnal variability in people diagnosed with bipolar disorder or borderline personality disorder using electrocardiography signals, and also demonstrated we can assess depression by analyzing geolocation patterns collected from smartphone data. Collectively, these tools provide a better day-to-day monitoring of patients. 

We are currently extending this prior work, also expanding on additional disorders.

Asthma characterization and monitoring

Asthma is a chronic disease characterized by inflammation of the airways to the lungs, estimated to affect 235 million people worldwide.  Despite both the availability of, and high spending on, asthma treatments, the burden of asthma mortality and morbidity is high. We are working in close collaboration with AUKCAR and the team of Aziz Sheikh to study practical aspects of asthma.

Specifically, we mine electronic health records and investigate medication non-adherence. Failing to assess non-adherence as a causal factor for poor asthma control may lead to inappropriate dose escalation and underestimated incidence of adverse effects. We are working towards characterizing adherence taking into account patient specific characteristics, including history of asthma attacks, and drug prescriptions.

Furthermore, asthma is increasingly recognized as an umbrella term used to describe a group of distinct diseases leading to similar symptoms. Numerous groupings of asthma have been suggested (e.g. early-onset atopic, severe) but at present none fully account for the heterogeneity observed across asthma patients. We are working on developing a new robust clustering approach which will identify clinically interpretable subtypes.

Cardiovascular disease assessment

In early days we developed a comprehensive closed loop model of the cardiovascular system to understand the interactions of lumped parts, and study the effect of fitness, smoking, drugs, and cardiac arrest.

More recently, we have investigated the use of high sensitivity troponin, a biomarker used to assess heart attack (formally known as myocardial infarction), to explore whether we can improve clinical outcome assessment.

We are currently working with some of the world's top experts in this field within Nick Mill's team at the University of Edinburgh to extend these findings aiming to provide better patient subtyping through clustering algorithms, and automating the process of myocardial infarction assessment using a range of routinely collected clinical tests and state-of-the-art statistical machine learning algorithms.