Analysis of Large-Amplitude Pulses in Short Time Intervals: Application to Neuron Interactions

This paper deals with the analysis of a nonlinear dynamical system which characterizes the axons interaction and is based on a generalization of FitzHugh-Nagumo system. The parametric domain of stability is investigated for both the linear and third-order approximation. A further generalization is studied in presence of high-amplitude (time-dependent) pulse. The corresponding numerical solution for some given values of parameters are analyzed through the wavelet coefficients, showing both the sensitivity to local jumps and some unexpected inertia of neuron's as response to the high-amplitude spike

Assessing Multi-Unit Muscle Sympathetic Nerve Activity Across Groups: A Multivariate Approach to a Multivariate Concept

Baseline muscle sympathetic nerve activity (MSNA) comparisons of age (young & old, n=40) and cardiovascular disease (CVD) state (young: Y, old: O, metabolic syndrome: MET, coronary artery disease: CAD, chronic heart failure: CHF, n= 36) groups were made to assess which MSNA variable(s) best describe group differences using a MANOVA-based approach across 10 variables. The hypothesis that action potential (AP) variables would better discriminate between groups than integrated variables was not supported. Burst frequency and incidence together significantly differentiated between groups with variability attributed to spikes/min and spikes/100hb removed. Significant group differences were noted for these variables such that

On the complex dynamics of intracellular ganglion cell light responses in the cat retina

We recorded intracellular responses from cat retinal ganglion cells to sinusoidal flickering lights and compared the response dynamics to a theoretical model based on coupled nonlinear oscillators. Flicker responses for several different spot sizes were separated in a 'smooth' generator (G) potential and eorresponding spike trains. We have previously shown that the G-potential reveals complex, stimulus dependent, oscillatory behavior in response to sinusoidally flickering lights. Such behavior could be simulated by a modified van der Pol oscillator. In this paper, we extend the model to account for spike generation as well, by including extended Hodgkin-Huxley equations describing local membrane properties. We quantified spike responses by several parameters describing the mean and standard deviation of spike burst duration, timing (phase shift) of bursts, and the number of spikes in a burst. The dependence of these response parameters on stimulus frequency and spot size could be reproduced in great detail by coupling the van der Pol oscillator, and Hodgkin-Huxley equations. The model mimics many experimentally observed response patterns, including non-phase-locked irregular oscillations. Our findings suggest that the information in the ganglion cell spike train reflects both intraretinal processing, simulated by the van der Pol oscillator) and local membrane properties described by Hodgkin-Huxley equations. The interplay between these complex processes can be simulated by changing the coupling coefficients between the two oscillators. Our simulations therefore show that irregularities in spike trains, which normally are considered to be noise, may be interpreted as complex oscillations that might earry information.Whitehall Foundation (S93-24

Sympathetic neural activation: An ordered affair

Is there an ordered pattern in the recruitment of postganglionic sympathetic neurones? Using new multi-unit action potential detection and analysis techniques we sought to determine whether the activation of sympathetic vasomotor neurones during stress is governed by the size principle of recruitment. Multi-unit postganglionic sympathetic activity (fibular nerve) was collected from five male subjects at rest and during periods of elevated sympathetic stress (end-inspiratory apnoeas; 178 ± 37 s(mean ± S.D.)). Compared to baseline (0.24 ± 0.04 V), periods of elevated stress resulted in augmented sympathetic burst size (1.34 ± 0.38 V, P \u3c 0.05). Increased burst size was directly related to both the number of action potentials within a multi-unit burst of postganglionic sympathetic activity (r= 0.88 ± 0.04, P \u3c 0.001 in all subjects), and the amplitude of detected action potentials (r= 0.88 ± 0.06, P \u3c 0.001 in all subjects). The recruitment of larger, otherwise silent, neurons accounted for approximately 74% of the increase in detected action potentials across burst sizes. Further, action potential conduction velocities (inverse of latencies) were increased as a function of action potential size (R2= 0.936, P= 0.001). As axon diameter is positively correlated with action potential size and conduction velocity, these data suggest that the principle of ordered recruitment based on neuronal size applies to postganglionic sympathetic vasomotor neurones. This information may be pertinent to our understanding of reflex-specific recruitment strategies in postganglionic sympathetic nerves, patterns of vasomotor control during stress, and the malleability of sympathetic neuronal properties and recruitment in health and disease.The sympathetic nervous system is an important controller of blood pressure and blood flow to critical tissues and organs. In other neural systems (e.g. the skeletal motor system) there is a well understood pattern of neural recruitment during activation. Alternatively, our understanding of how sympathetic neurones are coordinated during stress is limited. We demonstrate that during stress otherwise silent sympathetic neurones are activated in an order based on neuronal size (from smallest to largest). This recruitment pattern is similar to what is known in other neural systems. This information has important implications for how blood pressure and blood flow are controlled, and the malleability of sympathetic activation in health and disease. © 2010 The Authors. Journal compilation © 2010 The Physiological Society

Wavelet Signal Processing of Physiologic Waveforms

The prime objective of this piece of work is to devise novel techniques for computer based classification of Electrocardiogram (ECG) arrhythmias with a focus on less computational time and better accuracy. As an initial stride in this direction, ECG beat classification is achieved by using feature extracting techniques to make a neural network (NN) system more effective. The feature extraction technique used is Wavelet Signal Processing. Coefficients from the discrete wavelet transform were used to represent the ECG diagnostic information and features were extracted using the coefficients and were normalised. These feature sets were then used in the classifier i.e. a simple feed forward back propagation neural network (FFBNN). This paper presents a detail study of the classification accuracy of ECG signal by using these four structures for computationally efficient early diagnosis. Neural network used in this study is a well-known neural network architecture named as multi-Layered perceptron (MLP) with back propagation training algorithm. The ECG signals have been taken from MIT-BIH ECG database, and are used in training to classify 3 different Arrhythmias out of ten arrhythmias. These are normal sinus rhythm, paced beat, left bundle branch block. Before testing, the proposed structures are trained by back propagation algorithm. The results show that the wavelet decomposition method is very effective and efficient for fast computation of ECG signal analysis in conjunction with the classifier

Pharmacological assessment of the contribution of the arterial baroreflex to sympathetic discharge patterns in healthy humans

To study how changes in baroreceptor afferent activity affect patterns of sympathetic neural activation, we manipulated arterial blood pressure with intravenous nitroprusside (NTP) and phenylephrine (PE) and measured action potential (AP) patterns with wavelet-based methodology. We hypothesized that 1) baroreflex unloading (NTP) would increase firing of low-threshold axons and recruitment of latent axons and 2) baroreflex loading (PE) would decrease firing of low-threshold axons. Heart rate (HR, ECG), arterial blood pressure (BP, brachial catheter), and muscle sympathetic nerve activity (MSNA, microneurography of peroneal nerve) were measured at baseline and during steady-state systemic, intravenous NTP (0.5-1.2 µg·kg -1 ·min -1 , n = 13) or PE (0.2-1.0 µg·kg -1 ·min -1 , n = 9) infusion. BP decreased and HR and integrated MSNA increased with NTP (P \u3c 0.01). AP incidence (326 ± 66 to 579 ± 129 APs/100 heartbeats) and AP content per integrated burst (8 ± 1 to 11 ± 2 APs/burst) increased with NTP (P \u3c 0.05). The firing probability of low-threshold axons increased with NTP, and recruitment of high-threshold axons was observed (22 ± 3 to 24 ± 3 max cluster number, 9 ± 1 to 11 ± 1 clusters/burst; P \u3c 0.05). BP increased and HR and integrated MSNA decreased with PE (P \u3c 0.05). PE decreased AP incidence (406 ± 128 to 166 ± 42 APs/100 heartbeats) and resulted in fewer unique clusters (15 ± 2 to 9 ± 1 max cluster number, P \u3c 0.05); components of an integrated burst (APs or clusters per burst) were not altered (P \u3e 0.05). These data support a hierarchical pattern of sympathetic neural activation during manipulation of baroreceptor afferent activity, with rate coding of active neurons playing the predominant role and recruitment/derecruitment of higher-threshold units occurring with steady-state hypotensive stress. NEW & NOTEWORTHY To study how changes in baroreceptor afferent activity affect patterns of sympathetic neural activation, we manipulated arterial blood pressure with intravenous nitroprusside and phenylephrine and measured sympathetic outflow with wavelet-based methodology. Baroreflex unloading increased sympathetic activity by increasing firing probability of low-threshold axons (rate coding) and recruiting new populations of high-threshold axons. Baroreflex loading decreased sympathetic activity by decreasing the firing probability of larger axons (derecruitment); however, the components of an integrated burst were unaffected

Development of ECG and EMG platform with IMU to eliminate the motion artifacts found in measurements

The long term measurement and analysis of electrophysiological parameters is crucial for diagnosis of chronic diseases, and to monitor critical health parameters. It is also very important to monitor physical fitness improvement, or degradation level, of human beings where physical fitness is entirely critical for their work, or of more vulnerable members of society such as senior citizens and the sick. The state-of-the-art technological developments are leading to the use of artificial intelligence in the continuous monitoring and identification of life-threatening events in the daily life of ordinary people. However, these ambulatory measurements of electrophysiological parameters leads to drastic motion artifacts because of the test subject’s movements. Therefore, there is a dire need for the development of both hardware and software solutions to address this challenge. The scope of this thesis is to develop a hardware platform, by using off-the-shelf discrete and IC electronic components, to measure two electrophysiological parameters, electrocardiogram (ECG) and electromyogram (EMG), with an additional motion sensor inertial measurement unit (IMU) comprising nine degrees of freedom. The ECG, EMG and IMU data will be collected using the developed measurement platform from various predefined day-to-day routine activity events. A Bluetooth interface will be developed to transmit the data wirelessly, and record it on a laptop for further real-time processing. The resources of the electrical workshop and measurement lab at Aalto University will be used for the development, assembly, testing and finally for research of the measurement platform. The second aspect of the study is to prepare, process and analyze the recorded ECG and EMG data by using MATLAB. Various filtering, denoising, processing and analysis algorithms will be developed and executed to extract the features of the ECG and EMG waveform structures. Finally, graphical representations will be made for the resulting outputs of the aforementioned techniques

Recruitment Strategies In Human Sympathetic Nerve Activity

The overall objectives of the current dissertation were to 1) establish the neural coding principles employed by the sympathetic nervous system (SNS) in response to acute physiological stress; and 2) to determine the various mechanisms of control underlying these sympathetic neural recruitment strategies. This research tested the working hypothesis that efferent post-ganglionic muscle sympathetic nerve activity exhibits neural coding patterns reflecting increased firing of lower-threshold axons, recruitment of latent sub-populations of higher-threshold axons, as well as malleable synaptic delays, and further, that these strategies are governed by factors such as reflex-specificity, stress severity, perception of effort or stress, age, and cardiovascular disease. Specifically, we utilized a novel signal processing approach to study sympathetic action potential discharge patterning during periods of acute reflex-mediated sympathoexcitation. Overall, these studies support the working hypothesis and confirm that neural coding principles operate within the SNS. Specifically, in response to acute homeostatic perturbation, the SNS has options to increase the firing rate of already-active, lower-threshold axons, recruit sub-populations of previously silent (i.e., not present at baseline), larger-sized and faster conducting sympathetic axons, as well as modify acutely synaptic delays. Study 1 demonstrated that the ability to recruit latent neural sub-populations represents a fixed, reflex-independent recruitment strategy, as this pattern was observed during chemoreflex- and baroreflex-mediated sympathoexcitation. In turn, this option appears reserved for severe stress scenarios. Furthermore, study 2 suggests that central, perceptual features may play a specific role in modifying the synaptic delay aspect of efferent discharge timing, whereas peripheral-reflex mechanisms mediate the recruitment of latent axons. Study 3 demonstrates that, while the ability to acutely modify synaptic delays appears preserved, the ability to increase firing frequency of already-active axons, and importantly, the capacity of the SNS to recruit latent sub-populations of higher-threshold axons are reduced with healthy aging and perhaps lost altogether with cardiovascular disease. Finally, study 4 suggests that the lack of ventilation itself, rather than the ever-increasing chemical drive, mediates the robust sympathetic neural recruitment observed during apnea. In conclusion, the series of studies contained herein confirm the presence of neural coding patterns in human efferent post-ganglionic sympathetic nerve activity

Efficiency evaluation of external environments control using bio-signals

There are many types of bio-signals with various control application prospects. This dissertation regards possible application domain of electroencephalographic signal. The implementation of EEG signals, as a source of information used for control of external devices, became recently a growing concern in the scientific world. Application of electroencephalographic signals in Brain-Computer Interfaces (BCI) (variant of Human-Computer Interfaces (HCI)) as an implement, which enables direct and fast communication between the human brain and an external device, has become recently very popular. Currently available on the market, BCI solutions require complex signal processing methodology, which results in the need of an expensive equipment with high computing power. In this work, a study on using various types of EEG equipment in order to apply the most appropriate one was conducted. The analysis of EEG signals is very complex due to the presence of various internal and external artifacts. The signals are also sensitive to disturbances and non-stochastic, what makes the analysis a complicated task. The research was performed on customised (built by the author of this dissertation) equipment, on professional medical device and on Emotiv EPOC headset. This work concentrated on application of an inexpensive, easy to use, Emotiv EPOC headset as a tool for gaining EEG signals. The project also involved application of embedded system platform - TS-7260. That solution caused limits in choosing an appropriate signal processing method, as embedded platforms characterise with a little efficiency and low computing power. That aspect was the most challenging part of the whole work. Implementation of the embedded platform enables to extend the possible future application of the proposed BCI. It also gives more flexibility, as the platform is able to simulate various environments. The study did not involve the use of traditional statistical or complex signal processing methods. The novelty of the solution relied on implementation of the basic mathematical operations. The efficiency of this method was also presented in this dissertation. Another important aspect of the conducted study is that the research was carried out not only in a laboratory, but also in an environment reflecting real-life conditions. The results proved efficiency and suitability of the implementation of the proposed solution in real-life environments. The further study will focus on improvement of the signal-processing method and application of other bio-signals - in order to extend the possible applicability and ameliorate its effectiveness