A Wavelet-Based Method for Action Potential Detection from Extracellular Neural Signal Recording with Low Signal-to-Noise Ratio

We present a method for the detection of action potentials, an essential first step in the analysis of extracellular neural signals. The low signal-to-noise ratio (SNR) and similarity of spectral characteristic between the target signal and background noise are obstacles to solving this problem and, thus, in previous studies on experimental neurophysiology, only action potentials with sufficiently large amplitude have been detected and analyzed. In order to lower the level of SNR required for successful detection, we propose an action potential detector based on a prudent combination of wavelet coefficients of multiple scales and demonstrate its performance for neural signal recording with varying degrees of similarity between signal and noise. The experimental data include recordings from the rat somatosensory cortex, the giant medial nerve of crayfish, and the cutaneous nerve of bullfrog. The proposed method was tested for various SNR values and degrees of spectral similarity. The method was superior to the Teager energy operator and even comparable to or better than the optimal linear detector. A detection ratio higher than 80% at a false alarm ratio lower than 10% was achieved, under an SNR of 2.35 for the rat cortex data where the spectral similarity was very high.This work was supported by the Korea Science and Engineering Foundation (KOSEF) through the Nano Bioelectronics and Systems Research Center

Spike detection using the continuous wavelet transform

This paper combines wavelet transforms with basic detection theory to develop a new unsupervised method for robustly detecting and localizing spikes in noisy neural recordings. The method does not require the construction of templates, or the supervised setting of thresholds. We present extensive Monte Carlo simulations, based on actual extracellular recordings, to show that this technique surpasses other commonly used methods in a wide variety of recording conditions. We further demonstrate that falsely detected spikes corresponding to our method resemble actual spikes more than the false positives of other techniques such as amplitude thresholding. Moreover, the simplicity of the method allows for nearly real-time execution

A control algorithm for autonomous optimization of extracellular recordings

This paper develops a control algorithm that can autonomously position an electrode so as to find and then maintain an optimal extracellular recording position. The algorithm was developed and tested in a two-neuron computational model representative of the cells found in cerebral cortex. The algorithm is based on a stochastic optimization of a suitably defined signal quality metric and is shown capable of finding the optimal recording position along representative sampling directions, as well as maintaining the optimal signal quality in the face of modeled tissue movements. The application of the algorithm to acute neurophysiological recording experiments and its potential implications to chronic recording electrode arrays are discussed

Wavelet Transform for Real-Time Detection of Action Potentials in Neural Signals

We present a study on wavelet detection methods of neuronal action potentials (APs). Our final goal is to implement the selected algorithms on custom integrated electronics for on-line processing of neural signals; therefore we take real-time computing as a hard specification and silicon area as a price to pay. Using simulated neural signals including APs, we characterize an efficient wavelet method for AP extraction by evaluating its detection rate and its implementation cost. We compare software implementation for three methods: adaptive threshold, discrete wavelet transform (DWT), and stationary wavelet transform (SWT). We evaluate detection rate and implementation cost for detection functions dynamically comparing a signal with an adaptive threshold proportional to its SD, where the signal is the raw neural signal, respectively: (i) non-processed; (ii) processed by a DWT; (iii) processed by a SWT. We also use different mother wavelets and test different data formats to set an optimal compromise between accuracy and silicon cost. Detection accuracy is evaluated together with false negative and false positive detections. Simulation results show that for on-line AP detection implemented on a configurable digital integrated circuit, APs underneath the noise level can be detected using SWT with a well-selected mother wavelet, combined to an adaptive threshold

Communication channel analysis and real time compressed sensing for high density neural recording devices

Next generation neural recording and Brain- Machine Interface (BMI) devices call for high density or distributed systems with more than 1000 recording sites. As the recording site density grows, the device generates data on the scale of several hundred megabits per second (Mbps). Transmitting such large amounts of data induces significant power consumption and heat dissipation for the implanted electronics. Facing these constraints, efficient on-chip compression techniques become essential to the reduction of implanted systems power consumption. This paper analyzes the communication channel constraints for high density neural recording devices. This paper then quantifies the improvement on communication channel using efficient on-chip compression methods. Finally, This paper describes a Compressed Sensing (CS) based system that can reduce the data rate by > 10x times while using power on the order of a few hundred nW per recording channel

A New Signal Processing Approach to Study Action Potential Content in Sympathetic Neural Signals

Sympathetic nerve activity plays an essential role in the normal regulation of blood pressure in humans and in the etiology and progression of many chronic diseases. Sympathetic nerve recordings associated with blood pressure regulation can be recorded directly using microneurography. A general characteristic of this signal is spontaneous burst activity of spikes (action potentials) separated by silent periods against a background of considerable gaussian noise. During measurement with electrodes, the raw muscle sympathetic nerve activity (MSNA) signal is amplified, band-pass filtered, rectified and integrated. This integration process removes important information regarding action potential content and their discharge properties. The first objective of this thesis was to propose a new method for detecting action potentials from the raw MSNA signal to enable investigation of post-ganglionic neural discharge properties. The new method is based on the design of a mother wavelet that is matched to an actual mean action potential template extracted from a raw MSNA signal and applying it to the raw MSNA signal using a continues wavelet transform (CWT) for spike detection. The performance of the proposed method versus two previous wavelet-based approaches was evaluated using 1) MSNA recorded from seven healthy participants and, 2) simulated MSNA. The results show that the new matched wavelet performs better than the previous wavelet-based methods that use a non-matched wavelet in detecting action potentials in the MSNA signal. The second objective of this thesis was to employ the proposed action potential detection and classification technique to study the relationship between the recruitment of sympathetic action potentials (i.e., neurons) and the size of integrated sympathetic bursts in human MSNA signal. While in other neural systems (e.g. the skeletal motor system) there is a well understood pattern of neural recruitment during activation, our understanding of how sympathetic neurons are coordinated during baseline and baroreceptor unloading are very limited. We demonstrate that there exists a hierarchical pattern of recruitment of additional faster conducting neurons of larger amplitude as the sympathetic bursts become stronger. This information has important implications for how blood pressure is controlled, and the malleability of sympathetic activation in health and disease

Neuronal cell signal analysis: spike detection algorithm development for microelectrode array recordings

Neural signal acquisition and processing techniques are rising trends among wide scientific and commercial areas. Microelectrode array (MEA) technology makes it possible to access and record the electrical activity of neural cells. In this work, human pluripotent stem cell (hPSC) -derived neuronal populations were grown on MEA plates. The activity of the cells was recorded and the research about modern signal processing methods for the neural spike detection was performed. A list of approaches was selected for detailed investigation and the most efficient one was chosen as the new technique for permanent use in the research group. The performed laboratory activities involved cell culture plating, regular medium changes, spontaneous activity recordings and pharmacological manipulations. The data acquired from pharmacological experiments were used for the comparison between the old and new spike detection algorithms in terms of the numbers of the detected events. The Stationary Wavelet Transform-based Teager Energy Operator (SWTTEO) shows prominent performance in the tests with synthetic data. The use of the proposed algorithm in conjunction with the common amplitude-based thresholding enables to lower the threshold and to detect more spikes without an excessive number of false positives. This mode is applicable for real cell data. The detection method was considered superior and was further distributed for the processing of all neural data of the research group which include signals acquired from neuronal populations derived from human embryonic and induced pluripotent stem cells (hESCs and iPSCs) as well as rat cells

Heuristic Spike Sorting Tuner (HSST), a framework to determine optimal parameter selection for a generic spike sorting algorithm

Extracellular microelectrodes frequently record neural activity from more than one neuron in the vicinity of the electrode. The process of labeling each recorded spike waveform with the identity of its source neuron is called spike sorting and is often approached from an abstracted statistical perspective. However, these approaches do not consider neurophysiological realities and may ignore important features that could improve the accuracy of these methods. Further, standard algorithms typically require selection of at least one free parameter, which can have significant effects on the quality of the output. We describe a Heuristic Spike Sorting Tuner (HSST) that determines the optimal choice of the free parameters for a given spike sorting algorithm based on the neurophysiological qualification of unit isolation and signal discrimination. A set of heuristic metrics are used to score the output of a spike sorting algorithm over a range of free parameters resulting in optimal sorting quality. We demonstrate that these metrics can be used to tune parameters in several spike sorting algorithms. The HSST algorithm shows robustness to variations in signal to noise ratio, number and relative size of units per channel. Moreover, the HSST algorithm is computationally efficient, operates unsupervised, and is parallelizable for batch processing