Under the Cover Infant Pose Estimation using Multimodal Data

Infant pose monitoring during sleep has multiple applications in both healthcare and home settings. In a healthcare setting, pose detection can be used for region of interest detection and movement detection for noncontact based monitoring systems. In a home setting, pose detection can be used to detect sleep positions which has shown to have a strong influence on multiple health factors. However, pose monitoring during sleep is challenging due to heavy occlusions from blanket coverings and low lighting. To address this, we present a novel dataset, Simultaneously-collected multimodal Mannequin Lying pose (SMaL) dataset, for under the cover infant pose estimation. We collect depth and pressure imagery of an infant mannequin in different poses under various cover conditions. We successfully infer full body pose under the cover by training state-of-art pose estimation methods and leveraging existing multimodal adult pose datasets for transfer learning. We demonstrate a hierarchical pretraining strategy for transformer-based models to significantly improve performance on our dataset. Our best performing model was able to detect joints under the cover within 25mm 86% of the time with an overall mean error of 16.9mm. Data, code and models publicly available at https://github.com/DanielKyr/SMa

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

Non-Contact Sleep Monitoring

"The road ahead for preventive medicine seems clear. It is the delivery of high quality, personalised (as opposed to depersonalised) comprehensive medical care to all." Burney, Steiger, and Georges (1964) This world's population is ageing, and this is set to intensify over the next forty years. This demographic shift will result in signicant economic and societal burdens (partic- ularly on healthcare systems). The instantiation of a proactive, preventative approach to delivering healthcare is long recognised, yet is still proving challenging. Recent work has focussed on enabling older adults to age in place in their own homes. This may be realised through the recent technological advancements of aordable healthcare sen- sors and systems which continuously support independent living, particularly through longitudinally monitoring deviations in behavioural and health metrics. Overall health status is contingent on multiple factors including, but not limited to, physical health, mental health, and social and emotional wellbeing; sleep is implicitly linked to each of these factors. This thesis focusses on the investigation and development of an unobtrusive sleep mon- itoring system, particularly suited towards long-term placement in the homes of older adults. The Under Mattress Bed Sensor (UMBS) is an unobstrusive, pressure sensing grid designed to infer bed times and bed exits, and also for the detection of development of bedsores. This work extends the capacity of this sensor. Specically, the novel contri- butions contained within this thesis focus on an in-depth review of the state-of-the-art advances in sleep monitoring, and the development and validation of algorithms which extract and quantify UMBS-derived sleep metrics. Preliminary experimental and community deployments investigated the suitability of the sensor for long-term monitoring. Rigorous experimental development rened algorithms which extract respiration rate as well as motion metrics which outperform traditional forms of ambulatory sleep monitoring. Spatial, temporal, statistical and spatiotemporal features were derived from UMBS data as a means of describing movement during sleep. These features were compared across experimental, domestic and clinical data sets, and across multiple sleeping episodes. Lastly, the optimal classier (built using a combina- tion of the UMBS-derived features) was shown to infer sleep/wake state accurately and reliably across both younger and older cohorts. Through long-term deployment, it is envisaged that the UMBS-derived features (in- cluding spatial, temporal, statistical and spatiotemporal features, respiration rate, and sleep/wake state) may be used to provide unobtrusive, continuous insights into over- all health status, the progression of the symptoms of chronic conditions, and allow the objective measurement of daily (sleep/wake) patterns and routines

Sleep dependent memory consolidation in mild cognitive impairment subtypes

Sleep plays a crucial role in the overnight consolidation of newly learnt information in young adults, however the sleep-memory relationship in older adults is less understood. Age-associated memory decline as well as sleep disturbances are a concern for up to 60% of older people. Greater non-rapid eye movement (NREM) sleep neurophysiology such as slow waves and spindles have been postulated to be important for overnight memory consolidation, however, these associations are unclear in those at greater risk of dementia, namely in Mild Cognitive Impairment (MCI). Furthermore, it is unclear whether structural brain integrity for regions important for sleep and memory in ageing such as the hippocampus and medial prefrontal cortex, are associated with OMC in this ‘at-risk’ population. The overall aims of this study were to determine if there are differences in memory consolidation in older adults with and without MCI (and their subtypes), and examine associations between overnight memory consolidation with NREM sleep neurophysiology, and structural brain integrity using neuroimaging. Using a 256-channel high density EEG and a novel task of spatial navigation memory, the implications of these findings speak to the design of clinical trials targeting sleep in older adults, to determine the impact and functions of sleep as a modifiable risk factor for cognitive decline

Utilidad de las señales de oximetría y flujo aéreo en el diagnóstico simplificado de la apnea obstructiva del sueño. Diseño de un test automático domiciliario

Obstructive Sleep Apnea (OSA) is a respiratory disorder characterized by recurrent episodes of total (apnea) or partial (hypopnea) absence of airflow during sleep. Untreated OSA produces a significant decrease in quality of life and is associated with the main causes of mortality in industrialized countries.However, OSA is considered an underdiagnosed chronic disease. Continuous positive airway pressure (CPAP) is the most common therapeutic option. Nocturnal polysomnography (PSG) in a specialized sleep unit is the reference diagnostic method, although it has low availability and accessibility. Consequently, in recent years there has been a significant demand for abbreviated methods, most of them at home, to reduce waiting lists. The fundamental hypothesis that the use of automatic processing techniques based on machine learning tools could allow maximizing the diagnostic accuracy of a reduced set of combined biomedical signals: overnight oximetry and airflow recorded at patient&#8217;s home. The main objective was to evaluate whether the joint analysis by means of machine learning algorithms of unsupervised SpO2 and AF signals acquired at patient's home leads to a significant increase in diagnostic performance compared to single-channel approaches. A prospective observational study was carried out in which a population referred consecutively to the Sleep Unit showing moderate-to-high clinical suspicion of having OSA was analyzed.All patients underwent an unsupervised PSG at home(gold standard) from which the SpO2 and AF signals were extracted, which were subsequently processed offline.The apnea-hypopnea index(AHI) derived from the PSG was used to confirm or rule out the presence of the disease.Three different approaches for screening patients with suspected OSA were assessed in terms of the source of information used: single-channel based on SpO2, single-channel based on AF, and two-channel combining information from both SpO2 and AF.The automatic processing of the SpO2 and AF signals was developed in 4 stages: preprocessing, feature extraction, feature selection, and pattern recognition. Unsupervised SpO2 and AF recordings were parameterized using the fast correlation-based filter(FCBF)algorithm.The following machine learning methods were used: linear regression(MLR), multilayer perceptron neural networks(MLP) and support vector machines(SVM). The population was divided into independent training and test groups. Agreement between the estimated and the actual AHIderived from at-home PSG was assessed, and typical OSA cutoff points(5, 15, and 30 events/h) were applied. A total of 299 unattended PSGs were performed at home, with a validity percentage of 85.6%. The highest agreement between the estimated AHI and the PSG AHI was reached by the SVMSpO2+AF model, with an CCI 0.93 and a 4-class kappa index 0.71, as well as with an overall accuracy for the 4 OSA severity categories equal to 81.25%, significantly higher than the individual analysis of the SpO2 signal and the airflow signal.The SVMSpO2+AF model achieved the highest diagnostic performance of all algorithms for the detection of severe OSA, with an accuracy of 95.83% and AUC ROC 0.98. In addition, the AUC ROC of the dual-channel models was significantly higher (p<0.01) than that achieved by all the single-channel approaches for the cutoff of 15events/h. The proposed methodology based on the joint automatic analysis of the SpO2 and AF signals acquired at home showed a high complementarity that led to a remarkable increase in diagnostic performance compared to single-channel approaches. The automatic models outperformed the conventional indices(desaturation and airflow-derived indexes) both in terms of correlation and concordance with the AHI from PSG, as well as in terms of overall diagnostic accuracy, providing a moderate increase in diagnostic performance, particularly in the detection of moderate-to-severe OSA.Our findings suggest that the joint analysis of oximetry and airflow signals by means of machine learning methods allows a simplified as well as accurate screening of OSA at patient's home.La Apnea Obstructiva del Sueño (AOS) es un trastorno respiratorio crónico infradiagnosticado caracterizado por la repetición recurrente de episodios de ausencia total (apnea) o parcial (hipopnea) del flujo aéreo (FA) durante el sueño, que disminuye la calidad de vida y aumenta la mortalidad. La CPAP es el tratamiento más habitual, no invasivo, eficaz y coste-efectivo, por lo que favorecer el proceso de diagnóstico es fundamental. La PSG nocturna es el método diagnóstico de referencia, presentando baja disponibilidad y accesibilidad, lo que ha contribuido a desbordar los recursos disponibles, retrasando el diagnóstico y el tratamiento. En contexto de la simplificación diagnóstica portátil, en auge, el uso de únicamente una (monocanal) o dos (bi-canal) señales, como las de SpO2 y FA ha sido ampliamente explorado, aunque la mayoría en entornos hospitalarios controlados. La hipótesis se fundamenta en que las técnicas de procesado automático basadas en machine learning podrían maximizar la precisión diagnóstica de un conjunto reducido de señales combinadas. El objetivo consistió en evaluar si el análisis conjunto mediante algoritmos de aprendizaje automático de las señales de SpO2 y FA no supervisadas adquiridas en el domicilio aumenta el rendimiento diagnóstico en comparación con los enfoques de un solo canal. Se llevó a cabo un estudio observacional prospectivo en pacientes con sospecha moderada-alta de AOS. Se realizó una PSG no supervisada en su domicilio (gold standard de referencia), de la que se extrajeron las señales de SpO2 y FA, procesadas offline posteriormente. El índice de apnea-hipopnea (IAH) derivado de la PSG se empleó para confirmar o descartar la presencia de la enfermedad. Se implementaron y compararon 3 metodologías de screening en función de la fuente de información empleada: (1) monocanal basado en SpO2, (2) monocanal basado en FA, (3) bi-canal combinando SpO2 y FA. El procesado automático de las señales de SpO2 y FA se desarrolló en 4 etapas: preprocesado, extracción de características, selección de características (mediante fast correlation-based filter, FCBF) y reconocimiento de patrones. Cada enfoque de screening se empleó para estimar automáticamente el IAH utilizando los siguientes métodos de machine learning: (1) regresión lineal múltiple (MLR), (2) redes neuronales perceptrón multicapa (MLP) y (3) máquinas vector soporte (SVM). La población se dividió en grupos independientes de entrenamiento (60%) y test (40%). Se realizaron un total de 299 PSGs domiciliarias. Los modelos de enfoque combinado bi-canal alcanzaron valores de concordancia entre el IAH estimado y el IAH de la PSG domiciliaria y de rendimiento diagnóstico para todos los puntos de corte típicos de AOS (5, 15 y 30 e/h) superiores al enfoque monocanal. La mayor concordancia fue alcanzada por el modelo SVMSpO2+FA (CCI 0.93, kappa4 clases 0.71, precisión global 81.25%), significativamente superior a los análisis individuales. El modelo SVMSpO2+FA alcanzó el mayor rendimiento diagnóstico de todos los algoritmos para la detección de AOS grave (precisión 95.83% y AUC ROC 0.98). Además, el AUC ROC de los modelos bi-canal fue superior (p <0.01) al de los enfoques monocanal para el punto de corte de 15 e/h. La metodología propuesta basada en el análisis automático conjunto de las señales de SpO2 y FA adquiridas en el domicilio mostró una alta complementariedad y un notable aumento del rendimiento diagnóstico en comparación con los enfoques monocanal. Los modelos automáticos superaron globalmente a los índices clásicos (de desaturación y de eventos de flujo aéreo), aportando un incremento moderado del rendimiento diagnóstico particularmente en la detección de AOS moderado-grave. Los resultados obtenidos indican que el análisis conjunto de las señales de oximetría y flujo mediante métodos de aprendizaje automático permite un screening simplificado a la vez que preciso de la AOS en el domicilio del paciente.Escuela de DoctoradoDoctorado en Investigación en Ciencias de la Salu

Potential Role of OERP as Early Marker of Mild Cognitive Impairment

Olfactory impairment is present in up to 90% of patients with Alzheimer’s disease (AD) and is present in certain cases of mild cognitive impairment (MCI), a transient phase between normal aging and dementia. Subjects affected by MCI have a higher risk of developing dementia compared to the general population, and studies have found that olfactory deficits could be an indicator of whether such a conversion might happen. Following these assumptions, aim of this study was to investigate olfactory perception in MCI patients. We recruited 12 MCI subjects (mean age 70 ± 6.7 years) through the Alzheimer Assessment Unit (UVA Unite) of ASL Lecce (Italy), and 12 healthy geriatric volunteers (HS) as the control group (mean age 64 ± 6.0 years), all of whom were first evaluated via a panel of neuropsychological tests. Subjects were asked to perform an olfactory recognition task involving two scents: rose and eucalyptus, administrated in the context of an oddball task during EEG recordings. Olfactory event-related potential (OERP) components N1 and Late Positive Potential (LPC) were then analyzed as measures of the sensorial and perceptive aspects of the olfactory response, respectively. It was determined that, in the MCI group, both the N1 and LPC components were significantly different compared to those of the HS group during the execution of the oddball task. In particular, the N1 amplitude, was reduced, while the LPC amplitude was increased, indicating that a degree of perceptive compensation can occur when sensorial function is impaired. Further, a correlation analysis, involving OERP components and neuropsychological battery scores, indicated that impairment of olfactory perception may share common pathways with impairments of the spatial system and long-term memory processing

Review of Wearable Devices and Data Collection Considerations for Connected Health

Wearable sensor technology has gradually extended its usability into a wide range of well-known applications. Wearable sensors can typically assess and quantify the wearer’s physiology and are commonly employed for human activity detection and quantified self-assessment. Wearable sensors are increasingly utilised to monitor patient health, rapidly assist with disease diagnosis, and help predict and often improve patient outcomes. Clinicians use various self-report questionnaires and well-known tests to report patient symptoms and assess their functional ability. These assessments are time consuming and costly and depend on subjective patient recall. Moreover, measurements may not accurately demonstrate the patient’s functional ability whilst at home. Wearable sensors can be used to detect and quantify specific movements in different applications. The volume of data collected by wearable sensors during long-term assessment of ambulatory movement can become immense in tuple size. This paper discusses current techniques used to track and record various human body movements, as well as techniques used to measure activity and sleep from long-term data collected by wearable technology devices