Pulse Rate Variability for the Assessment of Cardiovascular Changes

Pulse rate variability (PRV) describes the way pulse rate changes through time and is measured from pulsatile signals such as the photoplethysmogram (PPG). It has been proposed as a surrogate for heart rate variability (HRV). Nonetheless, the relationship between these variables is not entirely clear, probably due to both physiological and technical aspects involved in the extraction of PRV. Moreover, the effects of cardiovascular changes on PRV have not been elucidated. In this thesis, four studies were performed to (1) determine the best combination of some technical aspects for the extraction of PRV from PPG signals; (2) evaluate the relationship between PRV and HRV under different cardiovascular conditions; and (3) explore the effects of cardiovascular changes on PRV. First, PRV extraction gave lower errors when (1) signals were acquired for at least 120 s with a 256 Hz sampling rate and filtered with lower low cut-off frequencies and elliptic, equiripple or Parks-McClellan filter; (2) cardiac cycles were determined using the D2max algorithm and the a fiducial points; and (3) the Fast Fourier Transform was applied to obtain frequency spectra. Secondly, the relationship between HRV and PRV was found to be affected by cold exposure and changes in blood pressure, while PRV was found to be different at different body sites. Finally, PRV was affected by haemodynamic changes, such as target flow, stroke rate and blood pressure, both in an in-vitro model and in-vivo data. Additionally, PRV was found to be a potential tool for the estimation of blood pressure, with errors as low as 1:54 ± 0:17 mmHg, 1:07 ± 0:06 mmHg and 1:22 ± 0:09 mmHg for the estimation of systolic, diastolic and mean arterial pressure. Although more studies are needed to fully understand PRV and its clinical potential, PRV should not be regarded as the same as HRV, and it could be consider as a potential valuable biomarker for cardiovascular health

Advances in Clinical Neurophysiology

Including some of the newest advances in the field of neurophysiology, this book can be considered as one of the treasures that interested scientists would like to collect. It discusses many disciplines of clinical neurophysiology that are, currently, crucial in the practice as they explain methods and findings of techniques that help to improve diagnosis and to ensure better treatment. While trying to rely on evidence-based facts, this book presents some new ideas to be applied and tested in the clinical practice. Advances in Clinical Neurophysiology is important not only for the neurophysiologists but also for clinicians interested or working in wide range of specialties such as neurology, neurosurgery, intensive care units, pediatrics and so on. Generally, this book is written and designed to all those involved in, interpreting or requesting neurophysiologic tests

Source reconstruction of the neural correlates of ongoing pain using magnetoencephalography

Pain is a pervasive, complex, and subjective phenomenon that can be described by many features and researched using many paradigms; chronic pain has a significant impact on the quality of life of patients experiencing it and constitutes a large burden on the National Health Service. Discovering neural biomarkers for ongoing pain and pain sensitivity has the potential to elucidate underlying mechanisms, evaluate therapy effectiveness, and identify regions of interest within the brain for further study or intervention; something that is possible with functional imaging of brain activity. Magnetoencephalography (MEG) is a non-invasive technique that records brain activity through magnetic fields unobstructed by tissue of the head. This thesis utilises modern source reconstruction of MEG data to explore brain activity that characterises tonic pain conditions, and explores the future of tonic pain research by evaluating the utility of the PATHWAY Contact Heat Evoked Potentials Stimulator (CHEPS) – a tool used both as an experimental pain stimulus, and a clinical evaluation method in chronic pain – in current and future MEG research. A systematic review of studies exploring the CHEPS and MEG, which highlights the paucity of the literature combining the two despite the potential benefits of each, is presented within. Study one investigates the brain activity changes resulting from paraesthesia-based Spinal Cord Stimulation for chronic pain: significant enhancements in synchrony for theta and delta frequency bands during SCS-on resting-state are demonstrated, and a significant reduction in Somatosensory Evoked Potential (SSEP) power spectra in the SCS-on condition – providing evidence that conventional SCS influences resting and ascending processing in the brain, but does not necessarily suppress the field strength of SSEPs. Study two compared the neural activity of participants with high and low pain sensitivity during the Cold Pressor Test, and identifies regions of interest for future study. Study three is a methodological chapter which attempts to mitigate the methodological challenges involved in utilising the PATHWAY CHEPS in MEG research: The thorough exploration of independent component analysis, signal space separation and beamforming parameters demonstrates that it is possible to suppress the artefacts generated by the non-fMRI compatible CHEPS’ thermode with the application of signal attenuation techniques, but only in an empty room dataset; the implications of this for future research are discussed

Intelligent Biosignal Processing in Wearable and Implantable Sensors

This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine

Cortical and Brainstem Circuits Responsible for Pain Modulatory Responses in Healthy Humans

The human ability to regulate our own pain is governed by specific sites and circuits within the brain which can powerfully inhibit or enhance nociception. Placebo analgesia and nocebo hyperalgesia are the modulatory phenomena which leverage these circuits in the presence of a pharmacologically inert treatment to cause perceived changes in pain. The principal aim of this thesis was to utilize recent advancements in high field human brain imaging to assess the responsibility of descending pain-modulatory circuits within the brainstem, as well as the cortical connections which recruit these circuits in the generation of placebo analgesia and nocebo hyperalgesia. Chapter 2 establishes the brainstem’s role in both phenomena. We utilized a response conditioning model and a brainstem-specific imaging pipeline to reveal how activation within discrete nuclei altered depending on the intensity of placebo and nocebo responses. Building on this work, Chapter 3 presents a dual network model of the human cortical sites which regulate brainstem output in the context of placebo analgesia. Relative to chapter 2, this work included a larger sample size, a higher placebo response rate, and analyses sensitive to how cortical connections to the brainstem change across time. Chapter 4 bridges function and biochemistry, circumventing limitations in functional magnetic resonance imaging by incorporating proton magnetic resonance spectroscopy (1H-MRS) to investigate how metabolite concentrations within the dorsolateral prefrontal cortex (dlPFC) - a primary node in the cortical pain system - play a role in the generation of placebo analgesia. I conclude by discussing the clinical and experimental implications of our three studies, with a focus on how further interrogation of the circuits revealed could aid and assist in the development of new approaches that treat chronic pain, by leveraging the neural mechanisms of placebo analgesia and nocebo hyperalgesia