WAVELET ANALYSIS OF HUMAN PHOTORECEPTORAL RESPONSE

Feature detection of biomedical signals is crucial for deepening our knowledge of the physiological phenomena giving rise to them. To achieve this aim, even if many analytic approaches have been suggested only few are able to deal with signals whose features are time dependent, and to provide useful clinical information. In this work we use the wavelet analysis to extract peculiarities of the early response of the photoreceptoral human system, known as a-wave ERG-component. The analysis of the a-wave features is important since this component reflects the functional integrity of the two populations of photoreceptors, rods and cones whose activation dynamics are not well known. Moreover, in incipient photoreceptoral pathologies the eventual anomalies in a-wave are not always detectable with a naked eye analysis of the traces. We here propose the possibility to discriminate the pathologic from the healthy traces throughout the differentiation of their time-frequency characteristics, revealed by the wavelet analysis. The investigated pathologies are the Achromatopsia, a cone disease and the Congenital Stationary Night Blindness, a rod trouble. The results show that the number of stable frequencies present and their times of occurrence are indicative of the status of the retinal photoreceptors. In particular, in the pathological cases, the frequency components shift toward lower values and change their times of occurrence, with respect to healthy traces

Diagnosis of multiple sclerosis using multifocal ERG data feature fusion

The purpose of this paper is to implement a computer-aided diagnosis (CAD) system for multiple sclerosis (MS) based on analysing the outer retina as assessed by multifocal electroretinograms (mfERGs). MfERG recordings taken with the RETI?port/scan 21 (Roland Consult) device from 15 eyes of patients diagnosed with incipient relapsing-remitting MS and without prior optic neuritis, and from 6 eyes of control subjects, are selected. The mfERG recordings are grouped (whole macular visual field, five rings, and four quadrants). For each group, the correlation with a normative database of adaptively filtered signals, based on empirical model decomposition (EMD) and three features from the continuous wavelet transform (CWT) domain, are obtained. Of the initial 40 features, the 4 most relevant are selected in two stages: a) using a filter method and b) using a wrapper-feature selection method. The Support Vector Machine (SVM) is used as a classifier. With the optimal CAD configuration, a Matthews correlation coefficient value of 0.89 (accuracy = 0.95, specificity = 1.0 and sensitivity = 0.93) is obtained. This study identified an outer retina dysfunction in patients with recent MS by analysing the outer retina responses in the mfERG and employing an SVM as a classifier. In conclusion, a promising new electrophysiological-biomarker method based on feature fusion for MS diagnosis was identified.Agencia Estatal de InvestigaciónInstituto de Salud Carlos II

Machine Learning based Predictive Modeling of Stochastic Systems

Title from PDF of title page, viewed June 14, 2023Dissertation advisor: Amirfarhang MehdizadehVitaIncludes bibliographical references (pages 89-121)Dissertation (Ph.D.)--Department of Civil and Mechanical Engineering, Department of Mathematics and Statistics. University of Missouri--Kansas City, 2023Complex signals are ubiquitous in our daily lives, and interpreting and modeling them is vital for scientific advancement. Traditional methods for predictive modeling of complex signals include statistical signal processing and physics-based simulations. However, statistical signal processing methods often struggle to fully utilize complex and rich datasets, while physics-based simulations can be computationally demanding. As an alternative approach, machine learning (ML) offers a more effective method for the predictive modeling of complex signals. This research explores the applicability of ML-based predictive modeling to a biomedical and a mechanical system through two case studies. The first case study focuses on developing a machine learning-based model for early-stage glaucoma detection using electroretinogram signals, which has been a challenging problem in ophthalmology. By leveraging medically relevant information contained in ERG signals, the study aims to establish a novel and reliable predictive framework for the early detection of glaucoma using a machine-learning-based algorithm. The results demonstrate that machine-learning-based models, trained using advanced wavelet-based features, can effectively detect the early stage of glaucoma from ERG stochastic signals. The second case study centers on developing a machine learning-based model for stall delay correction in wind turbines. Existing stall delay correction models rely on 2D airfoil characteristics, which can lead to inaccuracies in predicting aerodynamic loads during design and, consequently, result in structural failure due to excessive load. To address this issue, the study proposes a novel stall delay correction model based on the soft computing technique of symbolic regression. The model offers high-level precise aerodynamic performance prediction through the blade element momentum process, making it a promising alternative for accurate and efficient stall delay correction in wind turbines.Introduction -- Case study 1: Novel machine-learning based framework using electroretinography data for the detection of early-stage glaucoma -- Case study 2: Novel machine-learning-based stall delay correction model for improving blade element momentum analysis in wind turbine performance prediction -- Conclusio

A multilayered approach to the automatic analysis of the multifocal electroretinogram

The multifocal electroretinogram (mfERG) provides spatial and temporal information on the retina’s function in an objective manner, making it a valuable tool for monitoring a wide range of retinal abnormalities. Analysis of this clinical test can however be both difficult and subjective, particularly if recordings are contaminated with noise, for example muscle movement or blinking. This can sometimes result in inconsistencies in the interpretation process. An automated and objective method for analysing the mfERG would be beneficial, for example in multi-centre clinical trials when large volumes of data require quick and consistent interpretation. The aim of this thesis was therefore to develop a system capable of standardising mfERG analysis. A series of methods aimed at achieving this are presented. These include a technique for grading the quality of a recording, both during and after a test, and several approaches for stating if a waveform contains a physiological response or no significant retinal function. Different techniques are also utilised to report if a response is within normal latency and amplitude values. The integrity of a recording was assessed by viewing the raw, uncorrelated data in the frequency domain; clear differences between acceptable and unacceptable recordings were revealed. A scale ranging from excellent to unreportable was defined for the recording quality, first in terms of noise resulting from blinking and loss of fixation, and secondly, for muscle noise. 50 mfERG tests of varying recording quality were graded using this method with particular emphasis on the distinction between a test which should or should not be reported. Three experts also assessed the mfERG recordings independently; the grading provided by the experts was compared with that of the system. Three approaches were investigated to classify a mfERG waveform as ‘response’ or ‘no response’ (i.e. whether or not it contained a physiological response): artificial neural networks (ANN); analysis of the frequency domain profile; and the signal to noise ratio. These techniques were then combined using an ANN to provide a final classification for ‘response’ or ‘no response’. Two methods were studied to differentiate responses which were delayed from those within normal timing limits: ANN; and spline fitting. Again the output of each was combined to provide a latency classification for the mfERG waveform. Finally spline fitting was utilised to classify responses as ‘decreased in amplitude’ or ‘not decreased’. 1000 mfERG waveforms were subsequently analysed by an expert; these represented a wide variety of retinal function and quality. Classifications stated by the system were compared with those of the expert to assess its performance. An agreement of 94% was achieved between the experts and the system when making the distinction between tests which should or should not be reported. The final system classified 95% of the 1000 mfERG waveforms correctly as ‘response’ or ‘no response’. Of those said to represent an area of functioning retina it concurred with the expert for 93% of the responses when categorising them as normal or abnormal in terms of their P1 amplitude and latency. The majority of misclassifications were made when analysing waveforms with a P1 amplitude or latency close to the boundary between normal and abnormal. It was evident that the multilayered system has the potential to provide an objective and automated assessment of the mfERG test; this would not replace the expert but can provide an initial analysis for the expert to review

Advanced bioelectrical signal processing methods: Past, present and future approach - Part III: Other biosignals

Analysis of biomedical signals is a very challenging task involving implementation of various advanced signal processing methods. This area is rapidly developing. This paper is a Part III paper, where the most popular and efficient digital signal processing methods are presented. This paper covers the following bioelectrical signals and their processing methods: electromyography (EMG), electroneurography (ENG), electrogastrography (EGG), electrooculography (EOG), electroretinography (ERG), and electrohysterography (EHG).Web of Science2118art. no. 606

Phenotypic Characterization with Software Development for Analysis of the Visual System in Animal Models of Neurodevelopmental Diseases

A neurofibromatose tipo 1 (NF1) é uma perturbação do desenvolvimento neurológico com implicações cognitivas adultas. Provoca anomalias do sistema nervoso central e afeta 1 em 3000 indivíduos em todo o mundo. Contudo, pouco se sabe sobre os efeitos no sistema visual e como estes podem estar associados a défices cognitivos e preveem a sua progressão. Neste trabalho, avalia-se as potenciais alterações na fisiologia da retina num modelo genético de murgalho de NF1, utilizando uma técnica neurofisiológica não invasiva, o eletroretinograma (ERG), para determinar o seu potencial diagnóstico. Como um indicador fiável da função da retina em resposta à luz, o ERG tem a capacidade de ajudar a nossa interpretação da fisiopatologia das perturbações do neurodesenvolvimento e neurodegenerativas. Os principais objetivos desta tese são a caracterização fenotípica do sistema visual num modelo animal de NF1 e o desenvolvimento de ferramentas informáticas (MATLAB e Phyton) para processamento de sinais, análise de forma de onda, extração de características, e classificação. Verificou-se que os parâmetros ERG relacionados principalmente com a atividade oscilatória inibitória revelam alterações subtis dependentes do sexo. Para vários potenciais oscilatórios, machos e fêmeas exibem alterações opostas associadas ao genótipo mutante. Além disso, as características do ERG foram utilizadas para formar um classificador de aprendizagem de máquina baseado nos aglomerados significativos encontrados para algumas interações entre indivíduos, um classificador que se destina a ser capaz de receber um sinal e devolver o provável diagnóstico.Neurofibromatosis type 1 (NF1) is a neurodevelopmental disorder with adult cognitive implications. It causes central nervous system anomalies and affects 1 in 3000 individuals worldwide. However, little is known about the effects on the visual system circuitry and how these may be associated with cognitive deficits and predicts its progression. In this work, it was evaluated the potential alterations in retinal physiology in a genetic mouse model of NF1, using a non-invasive neurophysiological technique, the electroretinogram (ERG), to ascertain its diagnostic potential. As a reliable indicator of retinal function in response to light, the ERG has the ability to aid our interpretation of the pathophysiology of neurodevelopmental and neurodegenerative disorders. The main objectives of this thesis are the phenotypic characterization of the visual system in an animal model of NF1 and the development of computer tools (MATLAB and Phyton) for signal processing, waveform analysis, feature extraction, and classification. This work found that ERG parameters mainly related to inhibitory oscillatory activity reveal subtle sex-dependent alterations. For various oscillatory potentials males and females exhibit opposite changes associated with the transgenic background. Furthermore, the ERG features were used to form a machine learning classifier based on the significant clusters found for some interactions between individuals, a classifier that is meant to be able to receive a signal and return the likely diagnosis

Empirical mode decomposition-based filter applied to multifocal electroretinograms in multiple sclerosis diagnosis

As multiple sclerosis (MS) usually affects the visual pathway, visual electrophysiological tests can be used to diagnose it. The objective of this paper is to research methods for processing multifocal electroretinogram (mfERG) recordings to improve the capacity to diagnose MS. MfERG recordings from 15 early-stage MS patients without a history of optic neuritis and from 6 control subjects were examined. A normative database was built from the control subject signals. The mfERG recordings were filtered using empirical mode decomposition (EMD). The correlation with the signals in a normative database was used as the classification feature. Using EMD-based filtering and performance correlation, the mean area under the curve (AUC) value was 0.90. The greatest discriminant capacity was obtained in ring 4 and in the inferior nasal quadrant (AUC values of 0.96 and 0.94, respectively). Our results suggest that the combination of filtering mfERG recordings using EMD and calculating the correlation with a normative database would make mfERG waveform analysis applicable to assessment of multiple sclerosis in early-stage patients

Enhancement of Eeg Signal

This project is concerned with the rectification of EEG recording. EEG signal is often gets distorted due to the presence of various signals which are known as artifacts. Eye blinking is one of the major artifacts causing EEG to distort. Eye blinking distorts the EEG signal by varying the electric potential present over the scalp. To remove the artifacts, signal separation techniques are widely used in modern days. There are various methods used for removing different types of artifacts present in EEG recording and one of the techniques is Blind Source Separation which is used for separation of source signal from artifacts.This thesis also demonstrates the use of Second Order Blind Identification with Robust Orthogonalization (known as SOBI-RO) algorithm to remove the ocular artifacts and reconstruct the original EEG signal. Finally, the original signal and estimated signal is compared. To illustrate the algorithm a raw EEG data has been taken from the database. The data has been processed on MATLAB platform using the SOBI-RO algorithm. In the end it was found that the ocular artifacts are successfully removed from the raw EEG data. The performance is evaluated using signal to distortion ratio