256 results found
Serban A-I, Soreq E, Barnaghi P, et al., 2022, The effect of COVID-19 on the home behaviours of people affected by dementia, npj Digital Medicine, Vol: 5, ISSN: 2398-6352
The COVID-19 pandemic has dramatically altered the behaviour of most of the world's population, particularly affecting the elderly, including people living with dementia (PLwD). Here we use remote home monitoring technology deployed into 31 homes of PLwD living in the UK to investigate the effects of COVID-19 on behaviour within the home, including social isolation. The home activity was monitored continuously using unobtrusive sensors for 498 days from 1 December 2019 to 12 April 2021. This period included six distinct pandemic phases with differing public health measures, including three periods of home 'lockdown'. Linear mixed-effects modelling is used to examine changes in the home activity of PLwD who lived alone or with others. An algorithm is developed to quantify time spent outside the home. Increased home activity is observed from very early in the pandemic, with a significant decrease in the time spent outside produced by the first lockdown. The study demonstrates the effects of COVID-19 lockdown on home behaviours in PLwD and shows how unobtrusive home monitoring can be used to track behaviours relevant to social isolation.
Bethlehem RAI, Seidlitz J, White SR, et al., 2022, Brain charts for the human lifespan (vol 604, pg 525, 2022), NATURE, Vol: 610, Pages: E6-E6, ISSN: 0028-0836
Soreq E, Kolanko M, Guruswamy Ravindran KK, et al., 2022, Longitudinal assessment of sleep/wake behaviour in dementia patients living at home, Publisher: BMJ PUBLISHING GROUP, ISSN: 0022-3050
Parker T, Zimmerman K, Laverse E, et al., 2022, ACTIVE ELITE RUGBY PARTICIPATION PREDICTS ALTERATIONS IN CORTICAL THICKNESS, Publisher: BMJ PUBLISHING GROUP, ISSN: 0022-3050
Parkinson M, Curtis F, Dani M, et al., 2022, EXPLORING INTERACTIONS BETWEEN TRAUMATIC BRAIN INJURY AND COGNITIVE CO-MORBIDITY: DESCRIPTIVE CASE ANALYSIS FROM REAL-WORLD MONITORING, Publisher: BMJ PUBLISHING GROUP, ISSN: 0022-3050
Parkinson M, Curtis F, Dani M, et al., 2022, MTBI PREDICT: A PROSPECTIVE BIOMARKER STUDY TO PREDICT OUTCOMES IN MILD TRAUMATIC BRAIN INJURY, Publisher: BMJ PUBLISHING GROUP, ISSN: 0022-3050
Bourke N, Demarchi C, De Simoni S, et al., 2022, Brain volume abnormalities and clinical outcomes following paediatric traumatic brain injury, Brain: a journal of neurology, Vol: 145, Pages: 2920-2934, ISSN: 0006-8950
Long-term outcomes are difficult to predict after paediatric traumatic brain injury. The presence or absence of focal brain injuries often do not explain cognitive, emotional and behavioural disabilities that are common and disabling. In adults, traumatic brain injury produces progressive brain atrophy that can be accurately measured and is associated with cognitive decline. However, the effect of paediatric traumatic brain injury on brain volumes is more challenging to measure because of its interaction with normal brain development. Here we report a robust approach to the individualised estimation of brain volume following paediatric traumatic brain injury and investigate its relationship to clinical outcomes. We first used a large healthy control dataset (N>1200, age 8-22) to describe the healthy development of white and grey matter regions through adolescence. Individual estimates of grey and white matter regional volume were then generated for a group of moderate/severe traumatic brain injury patients injured in childhood (N=39, mean age 13.53±1.76, median time since injury = 14 months, range 4 – 168 months) by comparing brain volumes in patients to age matched controls. Patients were individually classified as having low or normal brain volume. Neuropsychological and neuropsychiatric outcomes were assessed using standardised testing and parent/carer assessments. Relative to head size, grey matter regions decreased in volume during normal adolescence development whereas white matter tracts increased in volume. Traumatic brain injury disrupted healthy brain development, producing reductions in both grey and white matter brain volumes after correcting for age. Of the 39 patients investigated, 11 (28%) had at least one white matter tract with reduced volume and seven (18%) at least one area of grey matter with reduced volume. Those classified as having low brain volume had slower processing speed compared to healthy controls, emotional impairments
Li LM, Dilley MD, Carson A, et al., 2022, Response to: Management of traumatic brain injury: practical development of a recent proposal, CLINICAL MEDICINE, Vol: 22, Pages: 358-359, ISSN: 1470-2118
Gil Rosa B, Akingbade OE, Guo X, et al., 2022, Multiplexed immunosensors for point-of-care diagnostic applications, Biosensors and Bioelectronics, Vol: 203, ISSN: 0956-5663
Accurate, reliable, and cost-effective immunosensors are clinically important for the early diagnosis and monitoring of progressive diseases, and multiplexed sensing is a promising strategy for the next generation of diagnostics. This strategy allows for the simultaneous detection and quantification of multiple biomarkers with significantly enhanced reproducibility and reliability, whilst requiring smaller sample volumes, fewer materials, and shorter average analysis time for individual biomarkers than individual tests. In this opinionated review, we compare different techniques for the development of multiplexed immunosensors. We review the state-of-the-art approaches in the field of multiplexed immunosensors using electrical, electrochemical, and optical methods. The barriers that prevent translating this sensing strategy into clinics are outlined together with the potential solutions. We also share our vision on how multiplexed immunosensors will continue their evolution in the coming years.
Duckworth H, Azor A, Wischmann N, et al., 2022, A finite element model of cerebral vascular injury for predicting microbleeds location, Frontiers in Bioengineering and Biotechnology, Vol: 10, ISSN: 2296-4185
Finite Element (FE) models of brain mechanics have improved our understanding of the brain response to rapid mechanical loads that produce traumatic brain injuries. However, these models have rarely incorporated vasculature, which limits their ability to predict the response of vessels to head impacts. To address this shortcoming, here we used high-resolution MRI scans to map the venous system anatomy at a submillimetre resolution. We then used this map to develop an FE model of veins and incorporated it in an anatomically detailed FE model of the brain. The model prediction of brain displacement at different locations was compared to controlled experiments on post-mortem human subject heads, yielding over 3,100 displacement curve comparisons, which showed fair to excellent correlation between them. We then used the model to predict the distribution of axial strains and strain rates in the veins of a rugby player who had small blood deposits in his white matter, known as microbleeds, after sustaining a head collision. We hypothesised that the distribution of axial strain and strain rate in veins can predict the pattern of microbleeds. We reconstructed the head collision using video footage and multi-body dynamics modelling and used the predicted head accelerations to load the FE model of vascular injury. The model predicted large axial strains in veins where microbleeds were detected. A region of interest analysis using white matter tracts showed that the tract group with microbleeds had 95th percentile peak axial strain and strain rate of 0.197 and 64.9 s−1 respectively, which were significantly larger than those of the group of tracts without microbleeds (0.163 and 57.0 s−1). This study does not derive a threshold for the onset of microbleeds as it investigated a single case, but it provides evidence for a link between strain and strain rate applied to veins during head impacts and structural damage and allows for future work to generate threshold valu
Al-Diwani A, Theorell J, Damato V, et al., 2022, Cervical lymph nodes and ovarian teratomas as germinal centres in NMDA receptor-antibody encephalitis, BRAIN, Vol: 145, Pages: 2742-2754, ISSN: 0006-8950
Popescu SG, Sharp DJ, Cole JH, et al., 2022, Distributional Gaussian Processes Layers for Out-of-Distribution Detection, Journal of Machine Learning for Biomedical Imaging
Machine learning models deployed on medical imaging tasks must be equippedwith out-of-distribution detection capabilities in order to avoid erroneouspredictions. It is unsure whether out-of-distribution detection models relianton deep neural networks are suitable for detecting domain shifts in medicalimaging. Gaussian Processes can reliably separate in-distribution data pointsfrom out-of-distribution data points via their mathematical construction.Hence, we propose a parameter efficient Bayesian layer for hierarchicalconvolutional Gaussian Processes that incorporates Gaussian Processes operatingin Wasserstein-2 space to reliably propagate uncertainty. This directlyreplaces convolving Gaussian Processes with a distance-preserving affineoperator on distributions. Our experiments on brain tissue-segmentation showthat the resulting architecture approaches the performance of well-establisheddeterministic segmentation algorithms (U-Net), which has not been achieved withprevious hierarchical Gaussian Processes. Moreover, by applying the samesegmentation model to out-of-distribution data (i.e., images with pathologysuch as brain tumors), we show that our uncertainty estimates result inout-of-distribution detection that outperforms the capabilities of previousBayesian networks and reconstruction-based approaches that learn normativedistributions. To facilitate future work our code is publicly available.
Parkinson M, Doherty R, Curtis C, et al., 2022, Exploring interactions between traumatic brain injury, Association of British Neurologists
Ibitoye R, Mallas E-J, Bourke N, et al., 2022, The human vestibular cortex: functional anatomy of OP2, its connectivity and the effect of vestibular disease, Cerebral Cortex, ISSN: 1047-3211
Area OP2 in the posterior peri-sylvian cortex has been proposed to be the core human vestibular cortex. We investigated the functional anatomy of OP2 and adjacent areas (OP2+) using spatially constrained independent component analysis of functional MRI data from the Human ConnectomeProject. Ten ICA-derived subregions were identified. OP2+ responses to vestibular and visual-motion were analysed in 17 controls and 17 right-sided vestibular neuritis patients who had previously undergone caloric and optokinetic stimulation during functional MRI. In controls, a posterior part of right OP2+ showed: (a) direction-selective responses to visual motion; and (b) activation during caloric stimulation that correlated positively with perceived self-motion, and negatively with visual dependence and peak slow phase nystagmus velocity. Patients showed abnormal OP2+ activity, with an absence of visual or caloric activation of the healthy ear and no correlations with vertigo or visual dependence – despite normal slow-phase nystagmus responses to caloric stimulation. Activity in a lateral part of right OP2+ correlated with chronic visually-induced dizziness in patients. In summary, distinct functional subregions of right OP2+ show strong connectivity to other vestibular areas and a profile of caloric and visual responses suggesting a central role for vestibular function in health and disease.
Bourke N, Trender W, Hampshire A, et al., 2022, Assessing prospective and retrospective metacognitive accuracy following traumatic brain injury remotely across cognitive domains, Neuropsychological Rehabilitation, ISSN: 0960-2011
The ability to monitor one's behaviour is frequently impaired following TBI, impacting on patients’ rehabilitation. Inaccuracies in judgement or self-reflection of one’s performance provides a useful marker of metacognition. However, metacognition is rarely measured during routine neuropsychology assessments and how it varies across cognitive domains is unclear. A cohort of participants consisting of 111 TBI patients [mean age = 45.32(14.15), female = 29] and 84 controls [mean age = 31.51(12.27), female = 43] was studied. Participants completed cognitive assessments via a bespoke digital platform on their smartphones. Included in the assessment were a prospective evaluation of memory and attention, and retrospective confidence judgements of task performance. Metacognitive accuracy was calculated from the difference between confidence judgement of task performance and actual performance. Prospective judgment of attention and memory was correlated with task performance in these domains for controls but not patients. TBI patients had lower task performance in processing speed, executive functioning and working memory compared to controls, maintaining high confidence, resulting in overestimation of cognitive performance compared to controls. Additional judgments of task performance complement neuropsychological assessments with little additional time–cost. These results have important theoretical and practical implications for evaluation of metacognitive impairment in TBI patients and neurorehabilitation.
Baker C, Martin P, Wilson M, et al., 2022, The relationship between road traffic collision dynamics and traumatic brain injury pathology, Brain Communications, Vol: 4, ISSN: 2632-1297
Road traffic collisions are a major cause of traumatic brain injury. However, the relationship between road traffic collision dynamics and traumatic brain injury risk for different road users is unknown. We investigated 2,065 collisions from Great Britain’s Road Accident In-depth Studies collision database involving 5,374 subjects (2013-20). 595 subjects sustained a traumatic brain injury (20.2% of 2,940 casualties), including 315 moderate-severe and 133 mild-probable. Key pathologies included skull fracture (179, 31.9%), subarachnoid haemorrhage (171, 30.5%), focal brain injury (168, 29.9%) and subdural haematoma (96, 17.1%). These results were extended nationally using >1,000,000 police-reported collision casualties. Extrapolating from the in-depth data we estimate that there are ~20,000 traumatic brain injury casualties (~5,000 moderate-severe) annually on Great Britain’s roads, accounting for severity differences. Detailed collision investigation allows vehicle collision dynamics to be understood and the change-in-velocity (known as delta-V) to be estimated for a subset of in-depth collision data. Higher delta-V increased the risk of moderate-severe brain injury for all road users. The four key pathologies were not observed below 8km/h delta-V for pedestrians/cyclists and 19km/h delta-V for car occupants (higher delta-V threshold for focal injury in both groups). Traumatic brain injury risk depended on road user type, delta-V and impact direction. Accounting for delta-V, pedestrians/cyclists had a 6-times higher likelihood of moderate-severe brain injury than car occupants. Wearing a cycle helmet was protective against overall and mild-to-moderate-severe brain injury, particularly skull fracture and subdural haematoma. Cycle helmet protection was not due to travel or impact speed differences between helmeted and non-helmeted cyclist groups. We additionally examined the influence of delta-V direction. Car occupants exposed to a higher latera
Hadi Z, Pondeca Y, Calzolari E, et al., 2022, The human brain networks mediating the vestibular sensation of self-motion, Publisher: Cold Spring Harbor Laboratory
Vestibular Agnosia - where peripheral vestibular activation triggers the usual reflex nystagmus response but with attenuated or no self-motion perception - is found in brain disease with disrupted cortical network functioning, e.g. traumatic brain injury (TBI) or neurodegeneration (Parkinson’s Disease). Patients with acute focal hemispheric lesions (e.g. stroke) do not manifest vestibular agnosia. Thus brain network mapping techniques, e.g. resting state functional MRI (rsfMRI), are needed to interrogate functional brain networks mediating vestibular agnosia. Whole-brain rsfMRI was acquired from 39 prospectively recruited acute TBI patients with preserved peripheral vestibular function, along with self-motion perceptual thresholds during passive yaw rotations in the dark. Following quality-control checks, 25 patient scans were analyzed. TBI patients were classified as having vestibular agnosia (n = 11) or not (n = 14) via laboratory testing of self-motion perception. Using independent component analysis, we found altered functional connectivity in the right superior longitudinal fasciculus and left rostral prefrontal cortex in vestibular agnosia. Moreover, regions of interest analyses showed both inter-hemispheric and intra-hemispheric network disruption in vestibular agnosia. In conclusion, our results show that vestibular agnosia is mediated by bilateral anterior and posterior network dysfunction and reveal the distributed brain mechanisms mediating vestibular self-motion perception.
Rosnati M, Soreq E, Monteiro M, et al., 2022, Automatic Lesion Analysis for Increased Efficiency in Outcome Prediction of Traumatic Brain Injury, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol: 13596 LNCS, Pages: 135-146, ISSN: 0302-9743
The accurate prognosis for traumatic brain injury (TBI) patients is difficult yet essential to inform therapy, patient management, and long-term after-care. Patient characteristics such as age, motor and pupil responsiveness, hypoxia and hypotension, and radiological findings on computed tomography (CT), have been identified as important variables for TBI outcome prediction. CT is the acute imaging modality of choice in clinical practice because of its acquisition speed and widespread availability. However, this modality is mainly used for qualitative and semi-quantitative assessment, such as the Marshall scoring system, which is prone to subjectivity and human errors. This work explores the predictive power of imaging biomarkers extracted from routinely-acquired hospital admission CT scans using a state-of-the-art, deep learning TBI lesion segmentation method. We use lesion volumes and corresponding lesion statistics as inputs for an extended TBI outcome prediction model. We compare the predictive power of our proposed features to the Marshall score, independently and when paired with classic TBI biomarkers. We find that automatically extracted quantitative CT features perform similarly or better than the Marshall score in predicting unfavourable TBI outcomes. Leveraging automatic atlas alignment, we also identify frontal extra-axial lesions as important indicators of poor outcome. Our work may contribute to a better understanding of TBI, and provides new insights into how automated neuroimaging analysis can be used to improve prognostication after TBI.
Ibitoye RT, Castro P, Cooke J, et al., 2022, A link between frontal white matter integrity and dizziness in cerebral small vessel disease, NEUROIMAGE-CLINICAL, Vol: 35, ISSN: 2213-1582
Li B, Xu L, Ramadan S, et al., 2021, Detection of glial fibrillary acidic protein in patient plasma using on-chip graphene field-effect biosensors, in comparison with ELISA and single molecule array, ACS Sensors, Vol: 7, Pages: 253-262, ISSN: 2379-3694
Glial fibrillary acidic protein (GFAP) is a discriminative blood biomarker for many neurological diseases, such as traumatic brain injury. Detection of GFAP in buffer solutions using biosensors has been demonstrated, but accurate quantification of GFAP in patient samples has not been reported, yet in urgent need. Herein, we demonstrate a robust on-chip graphene field-effect transistor (GFET) biosensing method for sensitive and ultrafast detection of GFAP in patient plasma. Patients with moderate–severe traumatic brain injuries, defined by the Mayo classification, are recruited to provide plasma samples. The binding of target GFAP with the specific antibodies that are conjugated on a monolayer GFET device triggers the shift of its Dirac point, and this signal change is correlated with the GFAP concentration in the patient plasma. The limit of detection (LOD) values of 20 fg/mL (400 aM) in buffer solution and 231 fg/mL (4 fM) in patient plasma have been achieved using this approach. In parallel, for the first time, we compare our results to the state-of-the-art single-molecule array (Simoa) technology and the classic enzyme-linked immunosorbent assay (ELISA) for reference. The GFET biosensor shows competitive LOD to Simoa (1.18 pg/mL) and faster sample-to-result time (<15 min), and also it is cheaper and more user-friendly. In comparison to ELISA, GFET offers advantages of total detection time, detection sensitivity, and simplicity. This GFET biosensing platform holds high promise for the point-of-care diagnosis and monitoring of traumatic brain injury in GP surgeries and patient homes.
We propose a new framework for estimating neuroimaging-derived “brain-age” at a local level within the brain, using deep learning. The local approach, contrary to existing global methods, provides spatial information on anatomical patterns of brain ageing. We trained a U-Net model using brain MRI scans from n = 3,463 healthy people (aged 18–90 years) to produce individualised 3D maps of brain-predicted age. When testing on n = 692 healthy people, we found a median (across participant) mean absolute error (within participant) of 9.5 years. Performance was more accurate (MAE around 7 years) in the prefrontal cortex and periventricular areas. We also introduce a new voxelwise method to reduce the age-bias when predicting local brain-age “gaps.” To validate local brain-age predictions, we tested the model in people with mild cognitive impairment or dementia using data from OASIS3 (n = 267). Different local brain-age patterns were evident between healthy controls and people with mild cognitive impairment or dementia, particularly in subcortical regions such as the accumbens, putamen, pallidum, hippocampus, and amygdala. Comparing groups based on mean local brain-age over regions-of-interest resulted in large effects sizes, with Cohen's d values >1.5, for example when comparing people with stable and progressive mild cognitive impairment. Our local brain-age framework has the potential to provide spatial information leading to a more mechanistic understanding of individual differences in patterns of brain ageing in health and disease.
David M, Barnaghi P, Nilforooshan R, et al., 2021, Home monitoring of vital signs and generation of alerts in a cohort of people living with dementia, Alzheimer's & dementia : the journal of the Alzheimer's Association, Vol: 17
BACKGROUND: People with dementia (PwD) are at increased risk of adverse medical events (e.g. infections and falls). These often cause clinical deterioration, and potentially preventable admissions. Remote home monitoring of vital signs using internet-of-things technology can identify risk factors for these events - something particular pertinent during the COVID-19 pandemic. We present data from an on-going UK Dementia Research Institute and Technology Integrated Health Management (DRI-TIHM) project. We aim to define algorithms that generate automated alerts, like the hospital-based NEWS system (Morgan, 1997, Clin Intensive Car), and provide more proactive care for PwD. METHOD: PwD recorded their systolic/diastolic blood pressure (SBP/DBP), heart rate (HR), temperature (BTM), bodyweight (BW) and oxygen saturation (Sats) daily (figure 1) and data were collected centrally. A 'monitoring team' followed algorithms in response to alerts and advised medical attention if required. Events such as infections, were logged and correlated with the data. The dataset was then used to calculate the number of alerts that would have been raised if different thresholds were used. RESULT: 52 PwD living at home were included. Patients had a range of dementia diagnoses, most commonly Alzheimer's disease. In total, 89,894 measurements were collected over 556 days (figure 2). During the pandemic, a sub-group were given Sats probes and these produced a significant number of false positive results (figure 4f). On sub-group analysis, PwD with Parkinson's disease dementia had significantly lower SBP and DBP (p=0.004 and p=0.01 respectively, Mann-Whitney U) (figure 3a), one of whom suffered from repeated falls (figure 3b). Pilot data were used to set alert thresholds. Average values and number of alerts for each domain are shown (table 1). We then modelled the effect of different thresholds, based on NEWS, to the number of alerts generated (figure 4, table 2). This is informative in optimising
Wairagkar M, De Lima MR, Harrison M, et al., 2021, Conversational artificial intelligence and affective social robot for monitoring health and well-being of people with dementia., Alzheimers & Dementia, Vol: 17 Suppl 11, Pages: e053276-e053276, ISSN: 1552-5260
BACKGROUND: Social robots are anthropomorphised platforms developed to interact with humans, using natural language, offering an accessible and intuitive interface suited to diverse cognitive abilities. Social robots can be used to support people with dementia (PwD) and carers in their homes managing medication, hydration, appointments, and evaluating mood, wellbeing, and potentially cognitive decline. Such robots have potential to reduce care burden and prolong independent living, yet translation into PwD use remains insignificant. METHOD: We have developed two social robots - a conversational robot and a digital social robot for mobile devices capable of communicating through natural language (powered by Amazon Alexa) and facial expressions that ask PwD daily questions about their health and wellbeing and also provide digital assistant functionality. We record data comprising of PwD's responses to daily questions, audio speech and text of conversations with Alexa to automatically monitor their health and wellbeing using machine learning. We followed user-centric development processes by conducting focus groups with 13 carers, 2 PwD and 5 clinicians to iterate the design. We are testing social robot with 3 PwD in their homes for ten weeks. RESULT: We received positive feedback on social robot from focus group participants. Ease of use, low maintenance, accessibility, assistance with medication, supporting with health and wellbeing were identified as the key opportunities for social robots. Based on responses to a daily questionnaire, our robots generate a report detailing PwD wellbeing that is automatically sent via email to family members or carers. This information is also stored systematically in a database that can help clinicians monitor their patients remotely. We use natural language processing to analyse conversations and identify topics of interest to PwD such that robot behaviour could be adapted. We process speech using signal processing and machine lear
Fletcher-Lloyd N, Soreq E, Wilson D, et al., 2021, Home monitoring of daily living activities and prediction of agitation risk in a cohort of people living with dementia., Alzheimers & Dementia, Vol: 17, Pages: 1-1, ISSN: 1552-5260
BACKGROUND: People living with dementia (PLWD) have an increased susceptibility to developing adverse physical and psychological events. Internet of Things (IoT) technologies provides new ways to remotely monitor patients within the comfort of their homes, particularly important for the timely delivery of appropriate healthcare. Presented here is data collated as part of the on-going UK Dementia Research Institute's Care Research and Technology Centre cohort and Technology Integrated Health Management (TIHM) study. There are two main aims to this work: first, to investigate the effect of the COVID-19 quarantine on the performance of daily living activities of PLWD, on which there is currently little research; and second, to create a simple classification model capable of effectively predicting agitation risk in PLWD, allowing for the generation of alerts with actionable information by which to prevent such outcomes. METHOD: A within-subject, date-matched study was conducted on daily living activity data using the first COVID-19 quarantine as a natural experiment. Supervised machine learning approaches were then applied to combined physiological and environmental data to create two simple classification models: a single marker model trained using ambient temperature as a feature, and a multi-marker model using ambient temperature, body temperature, movement, and entropy as features. RESULT: There are 102 PLWD total included in the dataset, with all patients having an established diagnosis of dementia, but with ranging types and severity. The COVID-19 study was carried out on a sub-group of 21 patient households. In 2020, PLWD had a significant increase in daily household activity (p = 1.40e-08), one-way repeated measures ANOVA). Moreover, there was a significant interaction between the pandemic quarantine and patient gender on night-time bed-occupancy duration (p = 3.00e-02, two-way mixed-effect ANOVA). On evaluating the models using 10-fold cross validation, both th
Rezvani R, Kouchaki S, Nilforooshan R, et al., 2021, Analysing behavioural changes in people with dementia using in-home monitoring technologies., Alzheimers & Dementia, Vol: 17 Suppl 11, Pages: e052181-e052181, ISSN: 1552-5260
BACKGROUND: Behavioural changes and neuropsychiatric symptoms such as agitation are common in people with dementia. These symptoms impact the quality of life of people with dementia and can increase the stress on caregivers. This study aims to identify the likelihood of having agitation in people affected by dementia (i.e., patients and carers) using routinely collected data from in-home monitoring technologies. We have used a digital platform and analytical methods, developed in our previous study, to generate alerts when changes occur in the digital markers collected using in-home sensing technologies (i.e., vital signs, environmental and activity data). A care monitoring team use the platform and interact with participants and caregivers when an alert is generated. METHOD: We have used connected sensory devices to collect environmental markers, including Passive Infra-Red (PIR), smart power plugs for monitoring home appliance use, motion and door sensors. The environmental marker data have been aggregated within each hour and used to train an agitation risk analysis model. We have trained a model using data collected from 88 homes (∼6 months of data from each home). The proposed model has two components: a self-supervised transformation learning and an ensemble classification model for agitation likelihood. Ten different neural network encoders are learned to create pseudo-labels using the samples from the unlabelled data. We use these pseudo-labels to train a classification model with a convolutional block and a decision layer. The trained convolutional block is then used to learn a latent representation of the data for an ensemble classification block. RESULTS: Comparing with baseline models such as LSTM network, Bidirectional LSTM (BiLSTM) network, VGG, ResNet, Inception, Random Forest (RF), Support Vector Machine (SVM) and Gaussian Process (GP) classifiers, the proposed model performs better in sensitivity (recall) and area under the precision-recall curv
Schubert JJ, Veronese M, Scott G, et al., 2021, Evidence of blood-to-cerebrospinal fluid alterations in traumatic brain injury, Publisher: SAGE PUBLICATIONS INC, Pages: 87-88, ISSN: 0271-678X
Ibitoye R, Castro P, Cooke J, et al., 2021, Frontal white matter integrity and idiopathic dizziness in cerebral small vessel disease, 25th World Congress of Neurology (WCN), Publisher: ELSEVIER, ISSN: 0022-510X
Ibitoye R, Mallas E-J, Bourke N, et al., 2021, The human vestibular cortex: Functional anatomy, connectivity and the effect of peripheral vestibular disease, 25th World Congress of Neurology (WCN), Publisher: ELSEVIER, ISSN: 0022-510X
Ibitoye R, Castro P, Cooke J, et al., 2021, Frontal white matter integrity and idiopathic dizziness in cerebral small vessel disease, 25th World Congress of Neurology (WCN), Publisher: ELSEVIER, ISSN: 0022-510X
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