Acoustic sensing as a novel approach for cardiovascular monitoring at the wrist

Cardiovascular diseases are the number one cause of deaths globally. An increased cardiovascular risk can be detected by a regular monitoring of the vital signs including the heart rate, the heart rate variability (HRV) and the blood pressure. For a user to undergo continuous vital sign monitoring, wearable systems prove to be very useful as the device can be integrated into the user's lifestyle without affecting the daily activities. However, the main challenge associated with the monitoring of these cardiovascular parameters is the requirement of different sensing mechanisms at different measurement sites. There is not a single wearable device that can provide sufficient physiological information to track the vital signs from a single site on the body. This thesis proposes a novel concept of using acoustic sensing over the radial artery to extract cardiac parameters for vital sign monitoring. A wearable system consisting of a microphone is designed to allow the detection of the heart sounds together with the pulse wave, an attribute not possible with existing wrist-based sensing methods. Methods: The acoustic signals recorded from the radial artery are a continuous reflection of the instantaneous cardiac activity. These signals are studied and characterised using different algorithms to extract cardiovascular parameters. The validity of the proposed principle is firstly demonstrated using a novel algorithm to extract the heart rate from these signals. The algorithm utilises the power spectral analysis of the acoustic pulse signal to detect the S1 sounds and additionally, the K-means method to remove motion artifacts for an accurate heartbeat detection. The HRV in the short-term acoustic recordings is found by extracting the S1 events using the relative information between the short- and long-term energies of the signal. The S1 events are localised using three different characteristic points and the best representation is found by comparing the instantaneous heart rate profiles. The possibility of measuring the blood pressure using the wearable device is shown by recording the acoustic signal under the influence of external pressure applied on the arterial branch. The temporal and spectral characteristics of the acoustic signal are utilised to extract the feature signals and obtain a relationship with the systolic blood pressure (SBP) and diastolic blood pressure (DBP) respectively. Results: This thesis proposes three different algorithms to find the heart rate, the HRV and the SBP/ DBP readings from the acoustic signals recorded at the wrist. The results obtained by each algorithm are as follows: 1. The heart rate algorithm is validated on a dataset consisting of 12 subjects with a data length of 6 hours. The results demonstrate an accuracy of 98.78%, mean absolute error of 0.28 bpm, limits of agreement between -1.68 and 1.69 bpm, and a correlation coefficient of 0.998 with reference to a state-of-the-art PPG-based commercial device. A high statistical agreement between the heart rate obtained from the acoustic signal and the photoplethysmography (PPG) signal is observed. 2. The HRV algorithm is validated on the short-term acoustic signals of 5-minutes duration recorded from each of the 12 subjects. A comparison is established with the simultaneously recorded electrocardiography (ECG) and PPG signals respectively. The instantaneous heart rate for all the subjects combined together achieves an accuracy of 98.50% and 98.96% with respect to the ECG and PPG signals respectively. The results for the time-domain and frequency-domain HRV parameters also demonstrate high statistical agreement with the ECG and PPG signals respectively. 3. The algorithm proposed for the SBP/ DBP determination is validated on 104 acoustic signals recorded from 40 adult subjects. The experimental outputs when compared with the reference arm- and wrist-based monitors produce a mean error of less than 2 mmHg and a standard deviation of error around 6 mmHg. Based on these results, this thesis shows the potential of this new sensing modality to be used as an alternative, or to complement existing methods, for the continuous monitoring of heart rate and HRV, and spot measurement of the blood pressure at the wrist.Open Acces

An energy-efficient hardware system for robust and reliable heart rate monitoring

Cardiac arrhythmia, one of the most common causes of death in the world today, is not always effectively detected by regular examinations, as it usually occurs infrequently and suddenly. Therefore, real-time, continuous monitoring of the heart rate is needed to detect arrhythmia problems sooner and prevent their severe consequences. To make continuous monitoring possible and give it widespread acceptance, a portable heart rate monitoring system must have three key characteristics: (1) accuracy, (2) portability, and (3) long battery life. Previous studies have focused on addressing these problems separately, either improving the accuracy of the monitoring algorithm or the efficiency of the underlying hardware. This thesis proposes a robust and reliable heart rate monitoring system (RRHMS), in which both algorithm accuracy and hardware efficiency are considered. As a result, algorithmic optimizations are exploited to enable further hardware efficiency. In the RRHMS, robust heart rate monitoring is achieved by extracting heart rates from both electrocardiogram (ECG) and arterial blood pressure (ABP) signals and fusing them based on the signal qualities. Therefore, accurate heart rate data can be provided continuously, even when one signal is severely corrupted. Algorithmic optimizations are applied to merge the separate ECG and ABP processing steps into shared ones, which allows shared hardware modules and hence low-area (portable) hardware design. Also, an embedded hardware architecture framework is proposed for the design of the RRHMS hardware system. Coarse-grained functional units (FUs) can be easily added or removed in this framework, allowing for application-specific hardware optimization. Further, the application invariant properties are used to achieve low-overhead fault tolerance in the FUs to enhance reliability. Both ASIC and FPGA implementations of the RRHMS are able to accurately detect heart rates in real time while consuming only 1/2870 and 1/923 of the energy required by the Android implementation

Bio-signal data gathering, management and analysis within a patient-centred health care context

The healthcare service is under pressure to do more with less, and changing the way the service is modelled could be the key to saving resources and increasing efficacy. This change could be possible using patient-centric care models. This model would include straightforward and easy-to-use telemonitoring devices and a flexible data management structure. The structure would maintain its state by ingesting many sources of data, then tracking this data through cleaning and processing into models and estimates to obtaining values from data which could be used by the patient. The system can become less disease-focused and more health-focused by being preventative in nature and allowing patients to be more proactive and involved in their care by automating the data management. This work presents the development of a new device and a data management and analysis system to utilise the data from this device and support data processing along with two examples of its use. These are signal quality and blood pressure estimation. This system could aid in the creation of patient-centric telecare systems

Sensing stress: stress detection from physiological variables in controlled and uncontrolled conditions

Recently, general concern about work-related stress is increasing. Chronic stress induces a number of mental and physical health problems that impact personal life, organizations and society. Timely detection and reduction of stress could prevent these health problems and their negative effects. Stress causes rapid activation of the autonomic nervous system and this activation can be measured in a number of physiological variables. The goal of this thesis is:\ud \ud to assess the feasibility of constructing personal models for the relation between mental stress and physiological variables, for use in ambulatory stress management systems.\ud \ud Four studies were performed in which physiological variable were measured, as well as self-reported stress measures and context variables. Stress induced reactions in the physiological variables, but the pattern of the reactions varies from person to person.\ud The main conclusions of this thesis are that using physiological variables for mental stress detection is feasible, personalization is necessary due to large variations among persons, and that ambulatory measurements are feasible if an unobtrusive and low-power sensor is available. The most common features used in stress estimation are blood pressure, heart rate and skin conductance. Other features such as heart rate variability, EMG and temperature are relevant for some subjects, but not for others. Respiration rate could be a useful feature but is heavily influenced by speech.\ud The main difficulty in the research field that needs attention in future work is that there is no recognized reference measure available that is known to resemble actual mental stress level accurately

Causes and consequences of autonomic dysfunction in Chronic Fatigue Syndrome

PhD ThesisChronic Fatigue Syndrome (CFS) is an incapacitating condition characterised by extreme fatigue. In the absence of an objective diagnostic test CFS remains a clinical diagnosis based on a broad spectrum of symptoms, including autonomic dysfunction and cognitive impairment. This has given rise to significant challenges, not least the development of multiple sets of diagnostic criteria that may represent different disease phenotypes. This thesis examines autonomic and cognitive features between subgroups that meet different diagnostic criteria to better understand this possibility. It also examines the overlap between symptoms of CFS and depression, a potential confounder. Methods A subset of data from a larger Medical Research Council funded observational study Understanding the pathogenesis of autonomic dysfunction in CFS and its relationship with cognitive impairment was examined. Patients were screened using the SCID-I assessment tool to exclude major depression prior to the main study. Depressive symptoms were compared to CFS Fukuda criteria. The DePaul Symptom Questionnaire (DSQ) was used to differentiate between diagnostic criteria. COMPASS and COGFAIL questionnaires were administered for self-reported autonomic and cognitive features respectively. The Task Force® Monitor was used for autonomic assessment and a battery of neuropsychological tests administered for objective cognitive assessment. Results Subjective autonomic and cognitive symptoms were significantly greater in CFS subjects compared to controls. There were no statistically significant differences in objective autonomic measures between CFS and controls. There were clinically significant differences between DSQ subgroups on objective autonomic testing. Psychomotor speed was significantly slower in CFS compared to controls. Visuospatial memory, verbal memory and psychomotor speed were significantly different between DSQ subgroups. Conclusion The findings indicate phenotypic differences between DSQ subsets and suggest that elucidating the symptoms seen in CFS, or its disease spectrum, will support research into its underlying pathophysiology and enable more tailored treatment. The absence of significant differences in objective autonomic function between CFS and controls in this cohort contrasts to findings of some other studies and may reflect study exclusion for depression. Together with the overlap between CFS and depressive symptoms, this reinforces the need to better understand the underpinning causality to allow appropriate identification and management.Medical Research Counci

Telemedicine

Telemedicine is a rapidly evolving field as new technologies are implemented for example for the development of wireless sensors, quality data transmission. Using the Internet applications such as counseling, clinical consultation support and home care monitoring and management are more and more realized, which improves access to high level medical care in underserved areas. The 23 chapters of this book present manifold examples of telemedicine treating both theoretical and practical foundations and application scenarios

Modern Telemetry

Telemetry is based on knowledge of various disciplines like Electronics, Measurement, Control and Communication along with their combination. This fact leads to a need of studying and understanding of these principles before the usage of Telemetry on selected problem solving. Spending time is however many times returned in form of obtained data or knowledge which telemetry system can provide. Usage of telemetry can be found in many areas from military through biomedical to real medical applications. Modern way to create a wireless sensors remotely connected to central system with artificial intelligence provide many new, sometimes unusual ways to get a knowledge about remote objects behaviour. This book is intended to present some new up to date accesses to telemetry problems solving by use of new sensors conceptions, new wireless transfer or communication techniques, data collection or processing techniques as well as several real use case scenarios describing model examples. Most of book chapters deals with many real cases of telemetry issues which can be used as a cookbooks for your own telemetry related problems

State of the art of audio- and video based solutions for AAL

Working Group 3. Audio- and Video-based AAL ApplicationsIt is a matter of fact that Europe is facing more and more crucial challenges regarding health and social care due to the demographic change and the current economic context. The recent COVID-19 pandemic has stressed this situation even further, thus highlighting the need for taking action. Active and Assisted Living (AAL) technologies come as a viable approach to help facing these challenges, thanks to the high potential they have in enabling remote care and support. Broadly speaking, AAL can be referred to as the use of innovative and advanced Information and Communication Technologies to create supportive, inclusive and empowering applications and environments that enable older, impaired or frail people to live independently and stay active longer in society. AAL capitalizes on the growing pervasiveness and effectiveness of sensing and computing facilities to supply the persons in need with smart assistance, by responding to their necessities of autonomy, independence, comfort, security and safety. The application scenarios addressed by AAL are complex, due to the inherent heterogeneity of the end-user population, their living arrangements, and their physical conditions or impairment. Despite aiming at diverse goals, AAL systems should share some common characteristics. They are designed to provide support in daily life in an invisible, unobtrusive and user-friendly manner. Moreover, they are conceived to be intelligent, to be able to learn and adapt to the requirements and requests of the assisted people, and to synchronise with their specific needs. Nevertheless, to ensure the uptake of AAL in society, potential users must be willing to use AAL applications and to integrate them in their daily environments and lives. In this respect, video- and audio-based AAL applications have several advantages, in terms of unobtrusiveness and information richness. Indeed, cameras and microphones are far less obtrusive with respect to the hindrance other wearable sensors may cause to one’s activities. In addition, a single camera placed in a room can record most of the activities performed in the room, thus replacing many other non-visual sensors. Currently, video-based applications are effective in recognising and monitoring the activities, the movements, and the overall conditions of the assisted individuals as well as to assess their vital parameters (e.g., heart rate, respiratory rate). Similarly, audio sensors have the potential to become one of the most important modalities for interaction with AAL systems, as they can have a large range of sensing, do not require physical presence at a particular location and are physically intangible. Moreover, relevant information about individuals’ activities and health status can derive from processing audio signals (e.g., speech recordings). Nevertheless, as the other side of the coin, cameras and microphones are often perceived as the most intrusive technologies from the viewpoint of the privacy of the monitored individuals. This is due to the richness of the information these technologies convey and the intimate setting where they may be deployed. Solutions able to ensure privacy preservation by context and by design, as well as to ensure high legal and ethical standards are in high demand. After the review of the current state of play and the discussion in GoodBrother, we may claim that the first solutions in this direction are starting to appear in the literature. A multidisciplinary 4 debate among experts and stakeholders is paving the way towards AAL ensuring ergonomics, usability, acceptance and privacy preservation. The DIANA, PAAL, and VisuAAL projects are examples of this fresh approach. This report provides the reader with a review of the most recent advances in audio- and video-based monitoring technologies for AAL. It has been drafted as a collective effort of WG3 to supply an introduction to AAL, its evolution over time and its main functional and technological underpinnings. In this respect, the report contributes to the field with the outline of a new generation of ethical-aware AAL technologies and a proposal for a novel comprehensive taxonomy of AAL systems and applications. Moreover, the report allows non-technical readers to gather an overview of the main components of an AAL system and how these function and interact with the end-users. The report illustrates the state of the art of the most successful AAL applications and functions based on audio and video data, namely (i) lifelogging and self-monitoring, (ii) remote monitoring of vital signs, (iii) emotional state recognition, (iv) food intake monitoring, activity and behaviour recognition, (v) activity and personal assistance, (vi) gesture recognition, (vii) fall detection and prevention, (viii) mobility assessment and frailty recognition, and (ix) cognitive and motor rehabilitation. For these application scenarios, the report illustrates the state of play in terms of scientific advances, available products and research project. The open challenges are also highlighted. The report ends with an overview of the challenges, the hindrances and the opportunities posed by the uptake in real world settings of AAL technologies. In this respect, the report illustrates the current procedural and technological approaches to cope with acceptability, usability and trust in the AAL technology, by surveying strategies and approaches to co-design, to privacy preservation in video and audio data, to transparency and explainability in data processing, and to data transmission and communication. User acceptance and ethical considerations are also debated. Finally, the potentials coming from the silver economy are overviewed.publishedVersio

A Photoplethysmography System Optimised for Pervasive Cardiac Monitoring

Photoplethysmography is a non-invasive sensing technique which infers instantaneous cardiac function from an optical measurement of blood vessels. This thesis presents a photoplethysmography based sensor system that has been developed speci fically for the requirements of a pervasive healthcare monitoring system. Continuous monitoring of patients requires both the size and power consumption of the chosen sensor solution to be minimised to ensure the patients will be willing to use the device. Pervasive sensing also requires that the device be scalable for manufacturing in high volume at a build cost that healthcare providers are willing to accept. System level choice of both electronic circuits and signal processing techniques are based on their sensitivity to cardiac biosignals, robustness against noise inducing artefacts and simplicity of implementation. Numerical analysis is used to justify the implementation of a technique in hardware. Circuit prototyping and experimental data collection is used to validate a technique's application. The entire signal chain operates in the discrete-time domain which allows all of the signal processing to be implemented in firmware on an embedded processor which minimised the number of discrete components while optimising the trade-off between power and bandwidth in the analogue front-end. Synchronisation of the optical illumination and detection modules enables high dynamic range rejection of both AC and DC independent light sources without compromising the biosignal. Signal delineation is used to reduce the required communication bandwidth as it preserves both amplitude and temporal resolution of the non-stationary photoplethysmography signals allowing more complicated analytical techniques to be performed at the other end of communication channel. The complete sensing system is implemented on a single PCB using only commercial-off -the-shelf components and consumes less than 7.5mW of power. The sensor platform is validated by the successful capture of physiological data in a harsh optical sensing environment