Comparison of Different Spike Sorting Subtechniques Based on Rat Brain Basolateral Amygdala Neuronal Activity

Developing electrophysiological recordings of brain neuronal activity and their analysis provide a basis for exploring the structure of brain function and nervous system investigation. The recorded signals are typically a combination of spikes and noise. High amounts of background noise and possibility of electric signaling recording from several neurons adjacent to the recording site have led scientists to develop neuronal signal processing tools such as spike sorting to facilitate brain data analysis. Spike sorting plays a pivotal role in understanding the electrophysiological activity of neuronal networks. This process prepares recorded data for interpretations of neurons interactions and understanding the overall structure of brain functions. Spike sorting consists of three steps: spike detection, feature extraction, and spike clustering. There are several methods to implement each of spike sorting steps. This paper provides a systematic comparison of various spike sorting sub-techniques applied to real extracellularly recorded data from a rat brain basolateral amygdala. An efficient sorted data resulted from careful choice of spike sorting sub-methods leads to better interpretation of the brain structures connectivity under different conditions, which is a very sensitive concept in diagnosis and treatment of neurological disorders. Here, spike detection is performed by appropriate choice of threshold level via three different approaches. Feature extraction is done through PCA and Kernel PCA methods, which Kernel PCA outperforms. We have applied four different algorithms for spike clustering including K-means, Fuzzy C-means, Bayesian and Fuzzy maximum likelihood estimation. As one requirement of most clustering algorithms, optimal number of clusters is achieved through validity indices for each method. Finally, the sorting results are evaluated using inter-spike interval histograms.Comment: 8 pages, 12 figure

Untersuchung von Verarbeitungsalgorithmen zur automatischen Auswertung neuronaler Signale aus Multielektroden-Arrays

Mit Hilfe von Multielektroden-Arrays (MEAs) können viele Zellen gleichzeitig kontaktiert und deren elektrische Aktivität abgeleitet werden. Für die weitere Analyse müssen die abgeleiteten Signale in ihre Einzelbestandteile zerlegt werden. Dieser Vorgang wird als Spike Sorting bezeichnet. In der vorliegenden Arbeit werden Ansätze für ein vollständig automatisiertes Spike Sorting vorgestellt und untersucht. Dabei werden Verfahren aufgezeigt, die mit Hilfe von adaptiven Verfahren die abgeleiteten Zellsignale optimal filtern und automatisch in deren Einzelkomponenten zerlegen

On Improving Generalization of CNN-Based Image Classification with Delineation Maps Using the CORF Push-Pull Inhibition Operator

Deployed image classification pipelines are typically dependent on the images captured in real-world environments. This means that images might be affected by different sources of perturbations (e.g. sensor noise in low-light environments). The main challenge arises by the fact that image quality directly impacts the reliability and consistency of classification tasks. This challenge has, hence, attracted wide interest within the computer vision communities. We propose a transformation step that attempts to enhance the generalization ability of CNN models in the presence of unseen noise in the test set. Concretely, the delineation maps of given images are determined using the CORF push-pull inhibition operator. Such an operation transforms an input image into a space that is more robust to noise before being processed by a CNN. We evaluated our approach on the Fashion MNIST data set with an AlexNet model. It turned out that the proposed CORF-augmented pipeline achieved comparable results on noise-free images to those of a conventional AlexNet classification model without CORF delineation maps, but it consistently achieved significantly superior performance on test images perturbed with different levels of Gaussian and uniform noise

Sistema computacional automatizado para a identificação e contagem de eventos epileptiformes em sinais de eletroencefalografia de longa duração

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia Elétrica, Florianópolis, 2014Este trabalho teve como objetivo o desenvolvimento de um sistema automatizado para a identificação e contagem de eventos epileptiformes em sinais de eletroencefalografia (EEG) do tipo interictal e de longo termo. Como diferencial este trabalho propôs a utilização da transformada wavelet como um filtro específico para atenuar oscilações de baixa frequência que a atividade de fundo do EEG apresenta, bem como, outras interferências de alta frequência presentes nos sinais de EEG. Foram utilizadas combinações de sinais decompostos e reconstruídos, através das aproximações que a transformada wavelet disponibiliza. O filtro desenvolvido utiliza a função wavelet Db4, como função base para o filtro. Foi proposta uma faixa específica de trabalho para a localização dos eventos epileptiformes entre 5 e 25 Hz. Após o processamento pelo filtro wavelet, os picos dos eventos epileptiformes tem uma amplitude relativamente alta, em relação à atividade normal de fundo do EEG. Dessa forma, a metodologia baseia-se na localização dos picos dos eventos epileptiformes, através do pré-processamento dos sinais de EEG. Depois de processados, os canais de EEG apresentam picos de sinal marcados, e pela comparação em uma janela de amplitudes entre -40µV e -400µV, eliminando segmentos de sinal que não compreendam esta faixa. Caso não sejam eliminados, os sinais marcados são direcionados para o classificador neural, responsável por classificar os segmentos de sinal em eventos epileptiformes ou em outra categoria, entre piscadas, atividade normal de fundo do EEG ou ruídos. Através deste estudo foi possível comprovar que a função wavelet mais adequada para o processamento dos registros de EEG é a função Db4. Também foi possível comprovar a viabilidade do desenvolvimento de um sistema classificador de eventos epileptiformes utilizando apenas uma rede neural artificial como classificador, desde que ela seja bem treinada. O classificador neural obtido apresentou uma taxa de sensibilidade de 97,45%, taxa de especificidade de 97,28%, valor preditivo positivo de 98,83%, valor preditivo negativo de 94,21% e um índice de desempenho de 97,40%. A metodologia desenvolvida possibilitou alcançar uma taxa de falsos positivos por minuto de 0,064 FP/min, ficando abaixo de muitas metodologias encontradas na literatura.Abstract: In this work it was developed a system for the automatic identification and counting of epileptiform events applied in long term electroencephalography signals. This paper proposes the use of the wavelet transform as a digital filter using a combination of decomposed and reconstructed signals through the wavelet transform approximations. Thus, it was possible to create a specific digital filter to attenuate low frequency oscillations in the background activity of the EEG signals, as well as, other high-frequency interferences present in EEG signals. The filter uses the Db4 wavelet function, as the base function for the filter. A specific working range for the filter was proposed, between 5 and 25 Hz for the epileptiform events identification. After processing by wavelet filter, the peaks of epileptiform events has a higher amplitude in relation to the normal EEG background activity. Thus, the method is based on the location of the peaks of epileptiform events through the pre-processing of EEG signals. Once processed, the marked peaks of the EEG signals are compared with a window of amplitudes between -40µV and -400µV, eliminating signal segments out of this band. If not eliminated, the marked signals are sent to the neural classifier and the signal segments are classified in epileptiform events, eye blinks, EEG background activity or noise. Through this study it was possible to prove that the Db4 wavelet function is most suitable for processing EEG records and it is also possible to develop a classifier system of epileptiform events using only an artificial neural network as a classifier, since it is well trained. The used neural classifier showed a sensitivity rate of 97.45%, a specificity rate of 97.28%, positive predictive value 98.83%, negative predictive value of 94.21% and a performance index of 97.40 %. The developed methodology achieved a false positive rate of 0,064 FP/min, below of many methodologies reported in the literature

Methods for analyzing the influence of molecular dynamics on neuronal activity

Magdeburg, Univ., Fak. für Informatik, Diss., 2015von Stefan Sokol