Multi-electrode array recording and data analysis methods for molluscan central nervous systems

In this work the use of the central nervous system (CNS) of the aquatic snail Lymnaea stagnalis on planar multi-electrode arrays (MEAs) was developed and analysis methods for the data generated were created. A variety of different combinations of configurations of tissue from the Lymnaea CNS were explored to determine the signal characteristics that could be recorded by sixty channel MEAs. In particular, the suitability of the semi-intact system consisting of the lips, oesophagus, CNS, and associated nerve connectives was developed for use on the planar MEA. The recording target area of the dorsal surface of the buccal ganglia was selected as being the most promising for study and recordings of its component cells during fictive feeding behaviour stimulated by sucrose were made. The data produced by this type of experimentation is very high volume and so its analysis required the development of a custom set of software tools. The goal of this tool set is to find the signal from individual neurons in the data streams of the electrodes of a planar MEA, to estimate their position, and then to predict their causal connectivity. To produce such an analysis techniques for noise filtration, neural spike detection, and group detection of bursts of spikes were created to pre-process electrode data streams. The Kohonen self-organising map (SOM) algorithm was adapted for the purpose of separating detected spikes into data streams representing the spike output of individual cells found in the target system. A significant addition to SOM algorithm was developed by the concurrent use of triangulation methods based on current source density analysis to predict the position of individual cells based on their spike output on more than one electrode. The likely functional connectivity of individual neurons identified by the SOM technique were analysed through the use of a statistical causality method known as Granger causality/causal connectivity. This technique was used to produce a map of the likely connectivity between neural sources

Resource efficient on-node spike sorting

Current implantable brain-machine interfaces are recording multi-neuron activity by utilising multi-channel, multi-electrode micro-electrodes. With the rapid increase in recording capability has come more stringent constraints on implantable system power consumption and size. This is even more so with the increasing demand for wireless systems to increase the number of channels being monitored whilst overcoming the communication bottleneck (in transmitting raw data) via transcutaneous bio-telemetries. For systems observing unit activity, real-time spike sorting within an implantable device offers a unique solution to this problem. However, achieving such data compression prior to transmission via an on-node spike sorting system has several challenges. The inherent complexity of the spike sorting problem arising from various factors (such as signal variability, local field potentials, background and multi-unit activity) have required computationally intensive algorithms (e.g. PCA, wavelet transform, superparamagnetic clustering). Hence spike sorting systems have traditionally been implemented off-line, usually run on work-stations. Owing to their complexity and not-so-well scalability, these algorithms cannot be simply transformed into a resource efficient hardware. On the contrary, although there have been several attempts in implantable hardware, an implementation to match comparable accuracy to off-line within the required power and area requirements for future BMIs have yet to be proposed. Within this context, this research aims to fill in the gaps in the design towards a resource efficient implantable real-time spike sorter which achieves performance comparable to off-line methods. The research covered in this thesis target: 1) Identifying and quantifying the trade-offs on subsequent signal processing performance and hardware resource utilisation of the parameters associated with analogue-front-end. Following the development of a behavioural model of the analogue-front-end and an optimisation tool, the sensitivity of the spike sorting accuracy to different front-end parameters are quantified. 2) Identifying and quantifying the trade-offs associated with a two-stage hybrid solution to realising real-time on-node spike sorting. Initial part of the work focuses from the perspective of template matching only, while the second part of the work considers these parameters from the point of whole system including detection, sorting, and off-line training (template building). A set of minimum requirements are established which ensure robust, accurate and resource efficient operation. 3) Developing new feature extraction and spike sorting algorithms towards highly scalable systems. Based on waveform dynamics of the observed action potentials, a derivative based feature extraction and a spike sorting algorithm are proposed. These are compared with most commonly used methods of spike sorting under varying noise levels using realistic datasets to confirm their merits. The latter is implemented and demonstrated in real-time through an MCU based platform.Open Acces

Tensor based singular spectrum analysis for automatic scoring of sleep EEG

A new supervised approach for decomposition of single channel signal mixtures is introduced in this paper. The performance of the traditional singular spectrum analysis (SSA) algorithm is significantly improved by applying tensor decomposition instead of traditional singular value decomposition (SVD). As another contribution to this subspace analysis method, the inherent frequency diversity of the data has been effectively exploited to highlight the subspace of interest. As an important application, sleep EEG has been analysed and the stages of sleep for the subjects in normal condition, with sleep restriction, and with sleep extension have been accurately estimated and compared with the results of sleep scoring by clinical experts

Unsupervised neural spike identification for large-scale, high-density micro-electrode arrays

This work deals with the development and evaluation of algorithms that extract sequences of single neuron action potentials from extracellular recordings of superimposed neural activity - a task commonly referred to as spike sorting. Large ($>10^3$ electrodes) and dense (subcellular spatial sampling) CMOS-based micro-electrode-arrays allow to record from hundreds of neurons simultaneously. State of the art algorithms for up to a few hundred sensors are not directly applicable to this type of data. Promising modern spike sorting algorithms that seek the statistically optimal solution or focus on real-time capabilities need to be initialized with a preceding sorting. Therefore, this work focused on unsupervised solutions, in order to learn the number of neurons and their spike trains with proper resolution of both temporally and spatiotemporally overlapping activity from the extracellular data alone. Chapter (1) informs about the nature of the data, a model based view and how this relates to spike sorting in order to understand the design decisions of this thesis. The main materials and methods chapter (2) bundles the infrastructural work that is independent of but mandatory for the development and evaluation of any spike sorting method. The main problem was split in two parts. Chapter (3) assesses the problem of analyzing data from thousands of densely integrated channels in a divide-and-conquer fashion. Making use of the spatial information of dense 2D arrays, regions of interest (ROIs) with boundaries adapted to the electrical image of single or multiple neurons were automatically constructed. All ROIs could then be processed in parallel. Within each region of interest the maximum number of neurons could be estimated from the local data matrix alone. An independent component analysis (ICA) based sorting was used to identify units within ROIs. This stage can be replaced by another suitable spike sorting algorithm to solve the local problem. Redundantly identified units across different ROIs were automatically fused into a global solution. The framework was evaluated on both real as well as simulated recordings with ground truth. For the latter it was shown that a major fraction of units could be extracted without any error. The high-dimensional data can be visualized after automatic sorting for convenient verification. Means of rapidly separating well from poorly isolated neurons were proposed and evaluated. Chapter (4) presents a more sophisticated algorithm that was developed to solve the local problem of densely arranged sensors. ICA assumes the data to be instantaneously mixed, thereby reducing spatial redundancy only and ignoring the temporal structure of extracellular data. The widely accepted generative model describes the intracellular spike trains to be convolved with their extracellular spatiotemporal kernels. To account for the latter it was assessed thoroughly whether convolutive ICA (cICA) could increase sorting performance over instantaneous ICA. The high computational complexity of cICA was dealt with by automatically identifying relevant subspaces that can be unmixed in parallel. Although convolutive ICA is suggested by the data model, the sorting results were dominated by the post-processing for realistic scenarios and did not outperform ICA based sorting. Potential alternatives are discussed thoroughly and bounded from above by a supervised sorting. This work provides a completely unsupervised spike sorting solution that enables the extraction of a major fraction of neurons with high accuracy and thereby helps to overcome current limitations of analyzing the high-dimensional datasets obtained from simultaneously imaging the extracellular activity from hundreds of neurons with thousands of electrodes

A Computational Framework to Support the Automated Analysis of Routine Electroencephalographic Data

Epilepsy is a condition in which a patient has multiple unprovoked seizures which are not precipitated by another medical condition. It is a common neurological disorder that afflicts 1% of the population of the US, and is sometimes hard to diagnose if seizures are infrequent. Routine Electroencephalography (rEEG), where the electrical potentials of the brain are recorded on the scalp of a patient, is one of the main tools for diagnosing because rEEG can reveal indicators of epilepsy when patients are in a non-seizure state. Interpretation of rEEG is difficult and studies have shown that 20-30% of patients at specialized epilepsy centers are misdiagnosed. An improved ability to interpret rEEG could decrease the misdiagnosis rate of epilepsy. The difficulty in diagnosing epilepsy from rEEG stems from the large quantity, low signal to noise ratio (SNR), and variability of the data. A usual point of error for a clinician interpreting rEEG data is the misinterpretation of PEEs (paroxysmal EEG events) ( short bursts of electrical activity of high amplitude relative to the surrounding signals that have a duration of approximately .1 to 2 seconds). Clinical interpretation of PEEs could be improved with the development of an automated system to detect and classify PEE activity in an rEEG dataset. Systems that have attempted to automatically classify PEEs in the past have had varying degrees of success. These efforts have been hampered to a large extent by the absence of a \gold standard\u27 data set that EEG researchers could use. In this work we present a distributed, web-based collaborative system for collecting and creating a gold standard dataset for the purpose of evaluating spike detection software. We hope to advance spike detection research by creating a performance standard that facilitates comparisons between approaches of disparate research groups. Further, this work endeavors to create a new, high performance parallel implementation of ICA (independent component analysis), a potential preprocessing step for PEE classification. We also demonstrate tools for visualization and analysis to support the initial phases of spike detection research. These tools will first help to develop a standardized rEEG dataset of expert EEG interpreter opinion with which automated analysis can be trained and tested. Secondly, it will attempt to create a new framework for interdisciplinary research that will help improve our understanding of PEEs in rEEG. These improvements could ultimately advance the nuanced art of rEEG interpretation and decrease the misdiagnosis rate that leads to patients suering inappropriate treatment

Retinal Repair for Macular Degeneration

Current treatments for macular degeneration, such as gene therapy, pharmacological approaches and neuroprotective approaches require the present of the target cells, the photoreceptors, in order to achieve a successful outcome. This leaves several patients, where the retinal degeneration is too advanced, without any viable therapeutic alternative. Photoreceptor transplantation aims to replace the lost cells, providing a potential option for such cases. For photoreceptor replacement to succeed, transplanted photoreceptors must mature and establish synaptic connection with the host retina. The remodeling of the host retina must be considered, as its interneurons and synaptic circuits rearrange following photoreceptor degeneration. Additionally, an ethical and renewable source is required. Studies have shown that mouse and human embryonic stem cells (ESC) can be differentiated to photoreceptor and transplanted into models of retinal dystrophy. However, no unambiguous evidence of synaptic connectivity and rescue of vision have been achieved. Here a detailed characterization of the remodeling events following photoreceptor lost in Aipl1-/- animals is described. Aipl1-/- were chosen due to the severe and fast emerging phenotype. As expected, typical features of remodeling were identified in these retinas and potential morphological elements were selected for analysis, following transplantation. The transplantation conditions, specifically the number of transplanted cells, were optimized using mouse ESC-derived photoreceptors. Here was no evident cell maturation or integration in the host’s synaptic circuit, following transplantation. Interestingly, when transplanting human (h) ESC-derived cones indication of such events was seen. To increase the period post-transplantation Aipl1-/- and Rd1, another well-established model of end-stage retinal degeneration, mice were crossed with immuno-compromised animals. Twelve weeks following transplantation into immuno-compromised Rd1 mice, hESC-derived cones matured and established functional synapses with the host retina, achieving rescue of vision. Alternative explanations for the rescue seen can be excluded due to the use non-functional human induced pluripotent (hiPS)-derived cones as a sham control

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

Approximate credibility intervals on electromyographic decomposition algorithms within a Bayesian framework

This thesis develops a framework to uncover the probability of correctness of algorithmic results. Specifically, this thesis is not concerned with the correctness of these algorithms, but with the uncertainty of their results arising from existing uncertainty in their inputs. This is achieved using a Bayesian approach. This framework is then demonstrated using independent component analysis with electromyographic data. Blind source separation (BSS) algorithms, such as independent component analysis (ICA), are often used to solve the inverse problem arising when, for example, attempting to retrieve the activation patterns of motor units (MUs) from electromyographic (EMG) data. BSS, or similar algorithms, return a result but do not generally provide any indication on the quality of that result or certainty one can have in it being the actual original pattern and not one strongly altered by the noise/errors in the input. This thesis uses Bayesian inference to extend ICA both to incorporate prior physiological information, thus making it in effect a semi-blind source separation (SBSS) algorithm, and to quantify the uncertainties around the values of the sources as estimated by ICA. To this end, this thesis also presents a way to put a prior on a mixing matrix given a physiological model as well as a re-parametrisation of orthogonal matrices which is helpful in pre-empting floating point errors when incorporating this prior of the mixing matrix into an algorithm which estimates the un-mixing matrix. In experiments done using EMG data, it is found that the addition of the prior is of benefit when the input is very noisy or very short in terms of samples or both. The experiments also show that the information about the certainty can be used as a heuristic for feature extraction or general quality control provided an appropriate baseline has been determined

The role of ATP and adenosine in nociception and inflammatory pain

The development of novel analgesics would be facilitated if the mechanisms underlying nociception and inflammatory pain were fully understood. Adenosine 5'triphosphate (ATP) and adenosine can cause pain in humans when applied to a blister base, but the algogenic mechanism of action is still unclear. Cells contain millimolar concentrations of ATP, which is released into the extracellular space when the cells are damaged, and is subsequently metabolised to adenosine. Consequently, levels of the purines are increased in damaged, inflamed or ischemic tissues and this makes them ideal candidates to signal the presence of tissue injury. It is thought that ATP and adenosine might be involved in the initiation of pain by directly or indirectly activating distinct subtypes of P2 or P 1 receptors respectively. In this thesis, behavioural, electrophysiological, and immunohistochemical techniques were used to test the hypothesis that ATP and adenosine are involved in the initiation of pain by directly and /or indirectly activating nociceptors innervating the cornea and the knee joint in vivo.ATP and ATP analogues were administered to the normal cat cornea and the normal rat knee joint under pentobarbitone anaesthesia and their effects on the discharge of nociceptors innervating these tissues were recorded. The effects of inflammation caused by photorefractive keratectomy of the cornea or Freund's adjuvant induced monoarthritis of the knee joint on the sensitivity to the purines was also determined. In behavioural studies, ATP analogues were instilled into the eyes of conscious rats and any changes in behaviour indicative of pain were assessed. To establish whether the P2X3 receptor subtype for ATP was expressed in the cell bodies of mouse corneal and rat knee joint neurones in the trigeminal and dorsal root iii ganglia respectively, these cells were retrogradely labelled using fluorogold and subsequently examined for co- localisation of fluorogold fluorescence with P2X3 immunoreactivity. Adenosine and adenosine analogues were also administered to the normal and arthritic rat knee joint and, in behavioural studies, the effect of adenosine agonists, adenosine antagonists and increasing the levels of endogenous adenosine on the pain and inflammation associated with experimental arthritis were determined.Immunoreactivity to P2X3 receptors was found in cell bodies of mouse corneal nociceptors, but none of the ATP analogues tested excited cat corneal nociceptors or caused pain when instilled into the eyes of conscious rats. The P2X3 subtype was also expressed in knee joint neurones in the dorsal root ganglia. ATP, the stable P2X1 and P2X3 selective agonist, aß- methylene ATP and the P2 agonists, ATPyS and benzoylbenzoyl ATP (BzATP), caused a rapid- onset, short- lasting increase in action potential discharge from nociceptors innervating the rat knee joint. These responses were antagonised by the P2 antagonist PPADS. ATP and ATPyS also caused a delayed- onset, long- lasting increase in firing which was probably mediated by adenosine Al receptors since adenosine, and the Al selective agonists GR79236 and CPA evoked a similar response. These slow -onset responses were antagonised by the Al selective antagonist DPCPX. Paradoxically, systemic injections of DPCPX were not analgesic in behavioural studies, while the adenosine uptake inhibitor, dipyridamole, which increases the extracellular levels of endogenous adenosine, was. GR79236 had no effect on the pain of arthritis but did possess anti -inflammatory properties. The ability of ATP to indirectly activate rat knee joint nociceptors via P2X7 receptors expressed on inflammatory cells was assessed by injecting high concentrations of BzATP, ATPyS and ATP intraiv articularly to the knee joint and monitoring their effects on spontaneous and bradykinin- evoked neural discharge. BzATP did not cause any increase the basal action potential discharge rate nor did it sensitise the nociceptors to bradykinin. The data from the other agonists was complicated by their metabolism to adenosine but, like BzATP, no evidence was found for a sensitising effect.This data supports the hypothesis that ATP and its metabolite, adenosine can directly excite nociceptors innervating the rat knee joint via P2X and Al receptor subtype(s), respectively. However, it does not support a role for P2X mediated initiation of pain from the cat or rat cornea nor does it indicate that ATP could cause pain via an indirect action on inflammatory cells. These findings have implications for the development of novel therapies for the treatment of pain