Unobtrusive Monitoring of Heart Rate and Respiration Rate during Sleep

Sleep deprivation has various adverse psychological and physiological effects. The effects range from decreased vigilance causing an increased risk of e.g. traffic accidents to a decreased immune response causing an increased risk of falling ill. Prevalence of the most common sleep disorder, insomnia can be, depending on the study, as high as 30 % in adult population. Physiological information measured unobtrusively during sleep can be used to assess the quantity and the quality of sleep by detecting sleeping patterns and possible sleep disorders. The parameters derived from the signals measured with unobtrusive sensors may include all or some of the following: heartbeat intervals, respiration cycle lengths, and movements. The information can be used in wellness applications that include self-monitoring of the sleep quality or it can also be used for the screening of sleep disorders and in following-up of the effect of a medical treatment. Unobtrusive sensors do not cause excessive discomfort or inconvenience to the user and are thus suitable for long-term monitoring. Even though the monitoring itself does not solve the sleeping problems, it can encourage the users to pay more attention on their sleep. While unobtrusive sensors are convenient to use, their common drawback is that the quality of the signals they produce is not as good as with conventional measurement methods. Movement artifacts, for example, can make the detection of the heartbeat intervals and respiration impossible. The accuracy and the availability of the physiological information extracted from the signals however depend on the measurement principle and the signal analysis methods used. Three different measurement systems were constructed in the studies included in the thesis and signal processing methods were developed for detecting heartbeat intervals and respiration cycle lengths from the measured signals. The performance of the measurement systems and the signal analysis methods were evaluated separately for each system with healthy young adult subjects. The detection of physiological information with the three systems was based on the measurement of ballistocardiographic and respiration movement signals with force sensors placed under the bedposts, the measurement of electrocardiographic (ECG) signal with textile electrodes attached to the bed sheet, and the measurement of the ECG signal with non-contact capacitive electrodes. Combining the information produced by different measurement methods for improving the detection performance was also tested. From the evaluated methods, the most accurate heartbeat interval information was obtained with contact electrodes attached to the bed sheet. The same method also provided the highest heart rate detection coverage. This monitoring method, however, has a limitation that it requires a naked upper body, which is not necessarily acceptable for everyone. For respiration cycle length detection, better results were achieved by using signals recorded with force sensors placed under a bedpost than when extracting the respiration information from the ECG signal recorded with textile bed sheet electrodes. From the data quality point of view, an ideal night-time physiological monitoring system would include a contact ECG measurement for the heart rate monitoring and force sensors for the respiration monitoring. The force sensor signals could also be used for movement detection

Quality of heart rate variability features obtained from ballistocardiograms

Doctor of PhilosophyDepartment of Electrical and Computer EngineeringDavid E. ThompsonHeartbeat intervals (HBIs) vary over time, and that variance can be quantified as heart rate variability (HRV). HRV has several health-related applications including long-term health monitoring and sleep quality assessment. The focus of this research is obtaining HRV from ballistocardiograms (BCGs), force signals caused by micro-movements of the human body in response to blood ejections. This method of HRV estimation is attractive because it does not require direct attachment of any sensor to the body. However, the HBIs and corresponding HRV measured with BCGs are different than those obtained via electrocardiograms (ECGs), signals obtained by attaching electrodes to the body to detect electrical heart activity. Because ECG-based HRV is typically considered ground truth, differences in BCG-based versus ECG-based parameters are referred to as HBI and HRV errors. This research investigates the effects of HBI error on HRV feature quality. While a few studies have used BCG-based HBIs to estimate HRV features for sleep staging, the effects of HBI error on the quality of the resulting HRV features seem to have been overlooked. As a result, an acceptable HBI error range has not been defined. One contribution of this work is the development of such an acceptable error range. This dissertation work (i) develops a hardware and software system necessary to record BCGs and to perform BCG peak detection to obtain HBIs with the least possible error, (ii) determines an allowable range for HBI error by studying the effects of this error on HRV quality in the context of HRV-based sleep staging, and (iii) compares the determined acceptable HBI error range to the HBI error of our final system. The inherent error in BCG-based HBI determination due to physiological and platform effects is also taken into account in this comparison. A minimum HBI error of 20 ms was obtained from the system developed in (i), and the allowable error range was determined to be 30 ms based on the investigations conducted in (ii). The combined physiological and platform effects led to an error of 8.8 ms on average. Based on the comparisons conducted in (iii), the developed system is suitable for long-term sleep quality assessment. In addition, the effects of the HBI errors introduced by this system on the resulting HRV features are negligible in the sleep staging context

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

Exploration of sleep as a specific risk factor for poor cardiometabolic and mental health & the comparison of subjective and objective assessments of sleep

Ph. D. ThesisNumerous studies have attempted to evaluate the impact of sleep on cardiometabolic and mental health, although most of the population-based studies utilised self-reported sleep assessment and health status. Therefore, the main aim of this project is to explore the relationship between accelerometer measured sleep and cardiometabolic and mental health amongst the UK Biobank participants. The UK Biobank collected extensive information of the general UK population. They have also collected accelerometry data allowing the extraction of sleep duration and quality. Disease status was obtained from their primary care record. Out of the 84,411 participants with available processable accelerometry data, 17.3% slept 8 hours/night. Short sleep duration was significantly associated with the male gender, older age, high body mass index, social deprivation and ethnic minority group (p<0.001). A significant ‘U-shaped’ association was found between sleep duration and metabolic disease status. Both short and long sleep durations were also associated with negative mood and worse cognitive performances including slower reaction time and worse visual memory (p<0.001). These findings showed the importance of sleep in maintaining health. However, sleep misperception was found to be common leading to a discrepancy between subjective and objective measurements of sleep. A cross-sectional analysis was conducted on 28 human participants (11 controls and 17 patients). Sleep was assessed using a paper sleep diary, wrist-worn tri-axial accelerometer and laboratory-based polysomnography. The level of cortisol, melatonin, mitochondrial DNA damage and gene expression was measured using saliva, urine, skin swab and hair samples, respectively. An overestimation of sleep duration was observed in this study which is consistent with the UK Biobank analysis. Patients were found to have a longer sleep duration, but a lower sleep efficiency. Moreover, patients were found to have a lower level of melatonin and cortisol. A ‘U-shaped’ association was found between sleep duration and mitochondrial DNA damage level. Finally, circadian rhythm and mitochondria-related pathways have been identified in the gene expression analysis. However, these associations were not found to be statistically significant. Therefore, it is proposed that larger sample size should be considered in future studies informed in part by further power calculations based upon the findings presented in the current thesis.BBSRC and Unileve

Physics-Based Model-Guided Machine Learning Analysis of Wrist Ballistocardiography for Cuff-Less Blood Pressure Monitoring

Cuff-less blood pressure (BP) monitoring technology is being widely pursued today. In this research we investigated the wrist ballistocardiogram (BCG) as a limb BCG, to develop a scientific basis to use the limb BCG to for cuff-less BP monitoring. In our study, we pursue two alternative approaches to the use of wrist BCG signal for BP monitoring: (1) use of the wrist BCG as proximal timing in pulse transit time (PTT) based methods; (2) use of wrist BCG wave features for BP monitoring. In this regard, the physics-based model is developed to elucidate the mechanism responsible for the generation of the BCG signal at the body’s extremity limb locations. The developed and experimentally validated mathematical model can predict the limb BCG in responses to the arterial BP waves in the aorta. The model suggests that the limb BCG waveform reveals the timings and amplitudes associated with the aortic BP waves and it exhibits meaningful morphological changes in response to the alterations in the CV risk predictors. Such understanding combined with machine learning techniques has helped us to extract viable features, and construct predictive models that can estimate BP. The findings of this study show that limb BCG has the potential to realize convenient cuff-less BP monitoring. First, it is a strong candidate to extract the proximal timing for PTT based methods. Second, BCG wave features are associated with BP and it could be used for BP monitoring. Third, we can combine the PTT with BCG wave features to achieve more comprehensive prediction models with superior performance

Wearable and Nearable Biosensors and Systems for Healthcare

Biosensors and systems in the form of wearables and “nearables” (i.e., everyday sensorized objects with transmitting capabilities such as smartphones) are rapidly evolving for use in healthcare. Unlike conventional approaches, these technologies can enable seamless or on-demand physiological monitoring, anytime and anywhere. Such monitoring can help transform healthcare from the current reactive, one-size-fits-all, hospital-centered approach into a future proactive, personalized, decentralized structure. Wearable and nearable biosensors and systems have been made possible through integrated innovations in sensor design, electronics, data transmission, power management, and signal processing. Although much progress has been made in this field, many open challenges for the scientific community remain, especially for those applications requiring high accuracy. This book contains the 12 papers that constituted a recent Special Issue of Sensors sharing the same title. The aim of the initiative was to provide a collection of state-of-the-art investigations on wearables and nearables, in order to stimulate technological advances and the use of the technology to benefit healthcare. The topics covered by the book offer both depth and breadth pertaining to wearable and nearable technology. They include new biosensors and data transmission techniques, studies on accelerometers, signal processing, and cardiovascular monitoring, clinical applications, and validation of commercial devices