Mainstream technology as an occupational therapy tool: technophobe or technogeek?

Occupational therapists need to embrace the use of mainstream technology in their quest to ensure that therapy remains current and meaningful to their clients. Technology can be useful to improve both functional independence and occupational performance. This opinion piece introduces how occupational therapists can apply mainstream technologies, including information and communication technologies such as the internet, computer software, portable devices and computer games, in their everyday interventions.

Artefact detection and removal algorithms for EEG diagnostic systems

The electroencephalogram (EEG) is a medical technology that is used in the monitoring of the brain and in the diagnosis of many neurological illnesses. Although coarse in its precision, the EEG is a non-invasive tool that requires minimal set-up times, and is suitably unobtrusive and mobile to allow continuous monitoring of the patient, either in clinical or domestic environments. Consequently, the EEG is the current tool-of-choice with which to continuously monitor the brain where temporal resolution, ease-of- use and mobility are important. Traditionally, EEG data are examined by a trained clinician who identifies neurological events of interest. However, recent advances in signal processing and machine learning techniques have allowed the automated detection of neurological events for many medical applications. In doing so, the burden of work on the clinician has been significantly reduced, improving the response time to illness, and allowing the relevant medical treatment to be administered within minutes rather than hours. However, as typical EEG signals are of the order of microvolts (μV ), contamination by signals arising from sources other than the brain is frequent. These extra-cerebral sources, known as artefacts, can significantly distort the EEG signal, making its interpretation difficult, and can dramatically disimprove automatic neurological event detection classification performance. This thesis therefore, contributes to the further improvement of auto- mated neurological event detection systems, by identifying some of the major obstacles in deploying these EEG systems in ambulatory and clinical environments so that the EEG technologies can emerge from the laboratory towards real-world settings, where they can have a real-impact on the lives of patients. In this context, the thesis tackles three major problems in EEG monitoring, namely: (i) the problem of head-movement artefacts in ambulatory EEG, (ii) the high numbers of false detections in state-of-the-art, automated, epileptiform activity detection systems and (iii) false detections in state-of-the-art, automated neonatal seizure detection systems. To accomplish this, the thesis employs a wide range of statistical, signal processing and machine learning techniques drawn from mathematics, engineering and computer science. The first body of work outlined in this thesis proposes a system to automatically detect head-movement artefacts in ambulatory EEG and utilises supervised machine learning classifiers to do so. The resulting head-movement artefact detection system is the first of its kind and offers accurate detection of head-movement artefacts in ambulatory EEG. Subsequently, addtional physiological signals, in the form of gyroscopes, are used to detect head-movements and in doing so, bring additional information to the head- movement artefact detection task. A framework for combining EEG and gyroscope signals is then developed, offering improved head-movement arte- fact detection. The artefact detection methods developed for ambulatory EEG are subsequently adapted for use in an automated epileptiform activity detection system. Information from support vector machines classifiers used to detect epileptiform activity is fused with information from artefact-specific detection classifiers in order to significantly reduce the number of false detections in the epileptiform activity detection system. By this means, epileptiform activity detection which compares favourably with other state-of-the-art systems is achieved. Finally, the problem of false detections in automated neonatal seizure detection is approached in an alternative manner; blind source separation techniques, complimented with information from additional physiological signals are used to remove respiration artefact from the EEG. In utilising these methods, some encouraging advances have been made in detecting and removing respiration artefacts from the neonatal EEG, and in doing so, the performance of the underlying diagnostic technology is improved, bringing its deployment in the real-world, clinical domain one step closer.

Mesenchymal stem cell therapy for the treatment of myocardial infarction

Mesenchymal stem cells (MSCs) are currently under investigation as repair agents in the preservation of cardiac function following myocardial infarction (MI). However concerns have emerged regarding the safety of acute intracoronary (IC) MSC delivery specifically related to mortality, micro-infarction and microvascular flow restriction post cell therapy in animal models. This thesis aimed to firstly identify an optimal dose of MSC that could be tolerated when delivered via the coronary artery in a porcine model of acute MI (AMI). Initial dosing studies identified 25x106 MSC to be a safe MSC cell dose, however, angiographic observations from these studies recognised that on delivery of MSC there was a significant adverse decrease in distal blood flow within the artery. This observation along with additional supportive data in the literature (published during the course of this thesis) suggested MSC may be contributing to such adverse events through the propagation of thrombosis. Therefore further studies aimed to investigate the innate prothrombotic activity of MSC. Expression of the initiator of the coagulation cascade initiator tissue factor (TF) on MSC was detected in high levels on the surface of these cells. MSC-derived TF antigen was catalytically active, capable of supporting thrombin generation in vitro and enhancing platelet-driven thrombus deposition on collagen under flow. Infusion of MSC via IC route was associated with a decreased coronary flow reserve when delivered but not when coadministered with an antithrombin agent heparin. Heparin also reduced MSC-associated in situ thrombosis incorporating platelets and VWF in the microvasculature. Heparin-assisted MSC delivery reduced acute apoptosis and significantly improved infarct size, left ventricular ejection fraction, LV volumes, wall motion and scar formation at 6 weeks post AMI. In addition, this thesis investigated the paracrine factors secreted by MSC, in particular focusing on the effect on cardiac repair of a novel MSC-paracrine factor SPARCL1. In summary this work provides new insight into the mechanism by which MSC may be deleterious when delivered by an IC route and a means of abrogating this effect. Moreover we present new data on the MSC secretome with elucidation of the challenges encountered using a single paracrine factor cardiac repair strategy.