Predicting Risk of Post-Operative Morbidity and Mortality following Gynaecological Oncology Surgery (PROMEGO) : A Global Gynaecological Oncology Surgical Outcomes Collaborative Led Study

Acknowledgments We acknowledge support provided by a number of stakeholders, including GO SOAR collaborators and the University of Aberdeen. Conflicts of Interest F.G. declares funding from the NHS Grampian Endowment Fund, Medtronic, Karl Storz, the British Gynaecological Cancer Society outside of this work, and an honorarium from Astra Zeneca. M.I.K. declares funding in the field of artificial intelligence, provided by the Analytical Center for the Government of the Russian Federation, in accordance with the subsidy agreement (agreement identifier 000000D730321P5Q0002) and the agreement with the Ivannikov Institute for System Programming of the Russian Academy of Sciences dated 2 November 2021, No. 70-2021-00142. O.B. declares funding from Barts Charity (G-001522). All other authors declare no conflicts of interest.Peer reviewe

Machine Learning Applied to Video Monitoring of Sleep

Sleep quality is an important determinant of human health and wellbeing and impacts on many aspects of health, ranging from everyday fitness and general alertness to the rate of recovery from a serious illness. Two important indicators of sleep quality are body posture and movements during sleep. Currently, clinical diagnosis of sleep disorders such as sleep apnea or rapid eye movement (REM) sleep behavior disorder (RBD) requires patients to undergo a polysomnography (PSG)-based assessment in a dedicated clinical sleep unit. This involves attaching multiple electrodes to the head and body, which can themselves impact on sleep quality. Additionally, the current standard manual or automated scoring of sleep state lacks a comprehensive quantification of body position during sleep.However, non-contact camera-based sleep monitoring offers an alternative approach to sleep quality assessment that addresses these shortcomings. Nevertheless, these approaches have not been validated against the PSG gold standard or other clinical methods. Such evaluations to date have relied mainly on simulated sleep rather than natural sleep or require subjects to avoid any occlusion from the bedding. These aspects constitute significant limitations for the routine implementation of video monitoring to measure body position and movements during sleep.In this thesis, the design and development of a non-contact monitoring system that automatically analyses body posture and movement by using video data captured from actual sleep using a blanket bed covering are presented. Experimental data are compared to gold standard methods of PSG and manual expert annotation of the video data. Starting with simulated sleep data, a variety of different methodologies were explored based on deep learning for automatic sleep pose detection, including a 4-layer convolutional neural network (CNN) network, two-step deep learning, and combining deep learning with tensor factorisation for the detection of body poses during sleep when poses were occluded by a blanket.Nocturnal sleep was quantified in 12 healthy participants using recordings of the IR camera as well as PSG data in the Surrey sleep laboratory. Using a transfer learning approach applied to IR camera data, supervised machine learning strategies successfully quantified sleep poses of participants covered by a blanket. This represents the first occasion that such a machine learning approach has been used to successfully detect four predefined poses and the empty bed state during 8-10 hour overnight sleep episodes. The Markov Chain transition matrix was also used to quantify sleep behaviour and perform another comparison method against PSG, and manual sleep pose scoring. In a cohort of 12 healthy participants, we found that fine-tuning a ResNet-152 pre-trained CNN network achieved the best performance compared with the standard end-to-end CNN and other pre-trained CNN networks. The method outperforms other video-based methods with an accuracy of 95.1% for sleep pose estimation and outperforms the clinical standard for pose estimation using a PSG position sensor.Unsupervised data-driven pose analysis has also been investigated as a potential avenue of quantifying personalised sleep behaviour. This approach revealed that a participant may have as many as 17 distinct sleep poses during a nocturnal sleep episode. Pilot analysis of the correlation between the sleep poses as well as movement with sleep physiology such as sleep stage, heart rate, and heart rate variability has also been performed. This demonstrates that while sleep pose based on four standard poses and sleep physiology only show low levels of correlation, unsupervised clustering when combined with physiology such as heart rate may offer an alternative approach to resolving sleep states using non-contact strategies. The results of these studies indicate that it is feasible to use video data and machine learning to quantify sleep behaviour

Validation of technology to monitor sleep and bed occupancy in older men and women

BackgroundNocturnal disturbance is frequently observed in dementia and is a major contributor to institutionalisation. Unobtrusive technology that can quantify sleep/wake and determine bed occupancy during the major nocturnal sleep episode may be beneficial for long-term clinical monitoring and the carer. Such technologies have, however, not been validated in older people. Here we assessed the performance of the Withings Sleep Mattress (WSM) in a heterogenous older population to ensure external validity.MethodEighteen participants (65 – 80 years, 10M:8F) completed 7-12 days of sleep/wake monitoring at home prior to an overnight laboratory session. WSM performance was compared to gold-standard (laboratory polysomnography [PSG] with video) and silver standard (actiwatch [AWS] and sleep diary at home). WSM data were downloaded from a third party API and the minute-to-minute sleep/wake timeseries extracted and time-ordered to create a sleep profile. Discontinuities in the timeseries were labelled as ‘missing data’ events.ResultsParticipants contributed 107 nights with WSM and PSG or AWS data. In the laboratory, the overall epoch to epoch agreement (accuracy) of sleep/wake detection of WSM compared to PSG was 0.71 (sensitivity 0.8; specificity 0.45) and to AWS was 0.74 (sensitivity 0.77; specificity 0.53). Visual inspection of video recordings demonstrated that 20 of 21 ‘missing data’ events were true ‘out of bed’ events. These events were always associated with an increase in activity (AWS). At home, all 97 WSM ‘missing data’ events that occurred within the major nocturnal sleep episode defined by sleep diary data, were associated with an increase in activity levels in the AWS data and 36 of these events were also associated with an increase in light levels, indicating that the participant had left the bed. In several participants, data recorded by the WSM during daytime coincided with reported naps in the sleep diary.ConclusionAlthough WSM cannot reliably distinguish between sleep and wake, the presence/absence of data in WSM seem to be an accurate representation of whether older people are in or out of bed (bed occupancy). Thus, in dementia, this contactless, low-burden technology may be able to provide information about nocturnal disturbances and daytime naps in bed

Validation of technology to monitor sleep and bed occupancy in older men and women

Nocturnal disturbance is frequently observed in dementia and is a major contributor to institutionalisation. Unobtrusive technology that can quantify sleep/wake and determine bed occupancy during the major nocturnal sleep episode may be beneficial for long-term clinical monitoring and the carer. Such technologies have, however, not been validated in older people. Here we assessed the performance of the Withings Sleep Mattress (WSM) in a heterogenous older population to ensure external validity.</p

Using home monitoring technology to study the effects of traumatic brain injury on older multimorbid adults: protocol for a feasibility study

Introduction The prevalence of traumatic brain injury (TBI) among older adults is increasing exponentially. The sequelae can be severe in older adults and interact with age-related conditions such as multimorbidity. Despite this, TBI research in older adults is sparse. Minder, an in-home monitoring system developed by the UK Dementia Research Institute Centre for Care Research and Technology, uses infrared sensors and a bed mat to passively collect sleep and activity data. Similar systems have been used to monitor the health of older adults living with dementia. We will assess the feasibility of using this system to study changes in the health status of older adults in the early period post-TBI.Methods and analysis The study will recruit 15 inpatients (&gt;60 years) with a moderate-severe TBI, who will have their daily activity and sleep patterns monitored using passive and wearable sensors over 6 months. Participants will report on their health during weekly calls, which will be used to validate sensor data. Physical, functional and cognitive assessments will be conducted across the duration of the study. Activity levels and sleep patterns derived from sensor data will be calculated and visualised using activity maps. Within-participant analysis will be performed to determine if participants are deviating from their own routines. We will apply machine learning approaches to activity and sleep data to assess whether the changes in these data can predict clinical events. Qualitative analysis of interviews conducted with participants, carers and clinical staff will assess acceptability and utility of the system.Ethics and dissemination Ethical approval for this study has been granted by the London-Camberwell St Giles Research Ethics Committee (REC) (REC number: 17/LO/2066). Results will be submitted for publication in peer-reviewed journals, presented at conferences and inform the design of a larger trial assessing recovery after TBI