Continuous‑wavelet‑transform analysis of the multifocal ERG waveform in glaucoma diagnosis

The vast majority of multifocal electroretinogram (mfERG) signal analyses to detect glaucoma study the signals’ amplitudes and latencies. The purpose of this paper is to investigate application of wavelet analysis of mfERG signals in diagnosis of glaucoma. This analysis method applies the continuous wavelet transform (CWT) to the signals, using the real Morlet wavelet. CWT coefficients resulting from the scale of maximum correlation are used as inputs to a neural network, which acts as a classifier. mfERG recordings are taken from the eyes of 47 subjects diagnosed with chronic open-angle glaucoma and from those of 24 healthy subjects. The high sensitivity in the classification (0.894) provides reliable detection of glaucomatous sectors, while the specificity achieved (0.844) reflects accurate detection of healthy sectors. The results obtained in this paper improve on the previous findings reported by the authors using the same visual stimuli and database.Ministerio de Ciencia e Innovació

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

Discrete Wavelet Transform Analysis of the Electroretinogram in Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder

Background: To evaluate the electroretinogram waveform in autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) using a discrete wavelet transform (DWT) approach. Methods: A total of 55 ASD, 15 ADHD and 156 control individuals took part in this study. Full field light-adapted electroretinograms (ERGs) were recorded using a Troland protocol, accounting for pupil size, with five flash strengths ranging from -0.12 to 1.20 log photopic cd.s.m-2. A DWT analysis was performed using the Haar wavelet on the waveforms to examine the energy within the time windows of the a- and b-waves and the oscillatory potentials (OPs) which yielded six DWT coefficients related to these parameters. The central frequency bands were from 20-160 Hz relating to the a-wave, b-wave and OPs represented by the coefficients: a20, a40, b20, b40, op80, and op160, respectively. In addition, the b-wave amplitude and percentage energy contribution of the OPs (%OPs) in the total ERG broadband energy was evaluated. Results: There were significant group differences (p < 0.001) in the coefficients corresponding to energies in the b-wave (b20, b40) and OPs (op80 and op160) as well as the b-wave amplitude. Notable differences between the ADHD and control groups were found in the b20 and b40 coefficients. In contrast, the greatest differences between the ASD and control group were found in the op80 and op160 coefficients. The b-wave amplitude showed both ASD and ADHD significant group differences from the control participants, for flash strengths greater than 0.4 log photopic cd.s.m-2 (p < 0.001). Conclusion: This methodological approach may provide insights about neuronal activity in studies investigating group differences where retinal signaling may be altered through neurodevelopment or neurodegenerative conditions. However, further work will be required to determine if retinal signal analysis can offer a classification model for neurodevelopmental conditions in which there is a co-occurrence such as ASD and ADHD

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

The multifocal visual evoked cortical potential in visual field mapping: a methodological study.

The application of multifocal techniques to the visual evoked cortical potential permits objective electrophysiological mapping of the visual field. The multifocal visual evoked cortical potential (mfVECP) presents several technical challenges. Signals are small, are influenced by a number of sources of noise and waveforms vary both across the visual field and between subjects due to the complex geometry of the visual cortex. Together these factors hamper the ability to distinguish between a mfVECP response from the healthy visual pathway, and a response that is reduced or absent and is therefore representative of pathology. This thesis presents a series of methodological investigations with the aim of maximising the information available in the recorded electrophysiological response, thereby improving the performance of the mfVECP. A novel method of calculating the signal to noise ratio (SNR) of mfVECP waveform responses is introduced. A noise estimate unrelated to the response of the visual cortex to the visual stimulus is created. This is achieved by cross-correlating m-sequences which are created when the orthogonal set of m-sequences are created but are not used to control a stimulus region, with the physiological record. This metric is compared to the approach of defining noise within a delayed time window and shows good correlation. ROC analysis indicates a small improvement in the ability to distinguish between physiological waveform responses and noise. Defining the signal window as 45-250ms is recommended. Signal quality is improved by post-acquisition bandwidth filtering. A wide range of bandwidths are compared and the greatest gains are seen with a bandpass of 3 to 20Hz applied after cross-correlation. Responses evoked when stimulation is delivered using a cathode ray tube (CRT) and a liquid crystal display (LCD) projector system are compared. The mode of stimulus delivery affects the waveshape of responses. A significantly higher SNR is seen in waveforms is shown in waveforms evoked by an m=16 bit m-sequence delivered by a CRT monitor. Differences for shorter m-sequences were not statistically significant. The area of the visual field which can usefully be tested is investigated by increasing the field of view of stimulation from 20° to 40° of radius in 10° increments. A field of view of 30° of radius is shown to provide stimulation of as much of the visual field as possible without losing signal quality. Stimulation rates of 12.5 to 75Hz are compared. Slowing the stimulation rate produced increases waveform amplitudes, latencies and SNR values. The best performance was achieved with 25Hz stimulation. It is shown that a six-minute recording stimulated at 25Hz is superior to an eight-minute, 75Hz acquisition. An electrophysiology system capable of providing multifocal stimulation, synchronising with the acquisition of data from a large number of electrodes and performing cross-correlation has been created. This is a powerful system which permits the interrogation of the dipoles evoked within the complex geometry of the visual cortex from a very large number of orientations, which will improve detection ability. The system has been used to compare the performance of 16 monopolar recording channels in detecting responses to stimulation throughout the visual field. A selection of four electrodes which maximise the available information throughout the visual field has been made. It is shown that a several combinations of four electrodes provide good responses throughout the visual field, but that it is important to have them distributed on either hemisphere and above and below Oz. A series of investigations have indicated methods of maximising the available information in mfVECP recordings and progress the technique towards becoming a robust clinical tool. A powerful multichannel multifocal electrophysiology system has been created, with the ability to simultaneously acquire data from a very large number of bipolar recording channels and thereby detect many small dipole responses to stimulation of many small areas of the visual field. This will be an invaluable tool in future investigations. Performance has been shown to improve when the presence or absence of a waveform is determined by a novel SNR metric, when data is filtered post-acquisition through a 3-20Hz bandpass after cross-correlation and when a CRT is used to deliver the stimulus. The field of view of stimulation can usefully be extended to a radius of 30° when a 60-region dartboard pattern is employed. Performance can be enhanced at the same time as acquisition time is reduced by 25%, by the use of a 25Hz rate of stimulation instead of the frequently employed rate of 75Hz

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

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

Contribución al análisis de registros de electrorretinografía multifocal para detección precoz de glaucoma

Premio Extraordinario de Doctorado de la UAH en 2015La neuropatía óptica glaucomatosa es una de las enfermedades oftalmológicas crónicas que más prevalecen, afectando al 5% de la población mundial entre 40 y 60 años, llegando a causar ceguera irreversible en el paciente. Este es el motivo por el que se prioriza la detección precoz de la enfermedad mediante métodos de diagnosis sensibles y objetivos. Las técnicas actuales utilizadas en la clínica, muestran el estado retiniano del paciente de forma global, subjetiva, y estudiada visualmente por el facultativo (tonometría, perimetría automatizada, etc.), por lo que no muestran indicios glaucomatosos sobre la retina en fases iniciales de la enfermedad. Algunas de estas técnicas, como la campimetría, proporcionan resultados subjetivos que dependen de la percepción del propio paciente. Las tres características que tradicionalmente definían la presencia de glaucoma en un sujeto eran: una presión intraocular aumentada, cambios en la papila óptica y defectos en el campo visual. Más recientemente, se ha demostrado que puede haber una pérdida importante de células ganglionares de la retina y fibras nerviosas ópticas, antes de que aparezcan signos de pérdida funcional en la prueba de campo visual convencional. Otros estudios han revelado que aunque una presión intraocular elevada es uno de los principales factores de riesgo para el glaucoma, no todos los ojos glaucomatosos muestran tener una presión elevada. Algunos sujetos también pueden tolerar una presión intraocular mayor antes de que se produzca la pérdida de fibras nerviosas por glaucoma. Por lo tanto, la presencia de una presión intraocular elevada por sí sola no es suficiente para el diagnóstico de glaucoma. El electrorretinograma multifocal (mfERG) es una técnica novedosa que puede proporcionar información objetiva de la retina y un mapa de sensibilidades de la misma, con una resolución topográfica elevada. Sin embargo, mediante la utilización de los parámetros que actualmente se estudian con la técnica multifocal, amplitudes y latencias de la respuesta retiniana, no es posible discriminar la respuesta de las capas internas de la retina, especialmente involucradas en el proceso del glaucoma. En esta tesis se estudian las señales procedentes de registros mfERG, utilizando diversas técnicas matemáticas hasta ahora no aplicadas en este campo clínico. Estas técnicas son el análisis morfológico avanzado de las señales y la transformada wavelet, incluyendo su versión continua, discreta y de paquetes wavelet. Mediante el análisis morfológico es posible extraer un conjunto numeroso de parámetros sobre las señales mfERG, que permita una mejor caracterización de la respuesta retiniana, para luego clasificarlas como sanas o glaucomatosas con ayuda de una red neuronal. Por otra parte, la aplicación de la transformada wavelet a estos registros mfERG nos permite obtener una serie de marcadores de presencia de glaucoma, ante cambios frecuenciales sutiles en estadios precoces. En todos los casos, los valores de sensibilidad y especificidad que se obtienen son superiores a los conseguidos con las técnicas tradicionales utilizadas en el entorno clínico

Contribución al análisis de registros de electrorretinografía multifocal para detección precoz de glaucoma

Premio Extraordinario de Doctorado de la UAH en 2015La neuropatía óptica glaucomatosa es una de las enfermedades oftalmológicas crónicas que más prevalecen, afectando al 5% de la población mundial entre 40 y 60 años, llegando a causar ceguera irreversible en el paciente. Este es el motivo por el que se prioriza la detección precoz de la enfermedad mediante métodos de diagnosis sensibles y objetivos. Las técnicas actuales utilizadas en la clínica, muestran el estado retiniano del paciente de forma global, subjetiva, y estudiada visualmente por el facultativo (tonometría, perimetría automatizada, etc.), por lo que no muestran indicios glaucomatosos sobre la retina en fases iniciales de la enfermedad. Algunas de estas técnicas, como la campimetría, proporcionan resultados subjetivos que dependen de la percepción del propio paciente. Las tres características que tradicionalmente definían la presencia de glaucoma en un sujeto eran: una presión intraocular aumentada, cambios en la papila óptica y defectos en el campo visual. Más recientemente, se ha demostrado que puede haber una pérdida importante de células ganglionares de la retina y fibras nerviosas ópticas, antes de que aparezcan signos de pérdida funcional en la prueba de campo visual convencional. Otros estudios han revelado que aunque una presión intraocular elevada es uno de los principales factores de riesgo para el glaucoma, no todos los ojos glaucomatosos muestran tener una presión elevada. Algunos sujetos también pueden tolerar una presión intraocular mayor antes de que se produzca la pérdida de fibras nerviosas por glaucoma. Por lo tanto, la presencia de una presión intraocular elevada por sí sola no es suficiente para el diagnóstico de glaucoma. El electrorretinograma multifocal (mfERG) es una técnica novedosa que puede proporcionar información objetiva de la retina y un mapa de sensibilidades de la misma, con una resolución topográfica elevada. Sin embargo, mediante la utilización de los parámetros que actualmente se estudian con la técnica multifocal, amplitudes y latencias de la respuesta retiniana, no es posible discriminar la respuesta de las capas internas de la retina, especialmente involucradas en el proceso del glaucoma. En esta tesis se estudian las señales procedentes de registros mfERG, utilizando diversas técnicas matemáticas hasta ahora no aplicadas en este campo clínico. Estas técnicas son el análisis morfológico avanzado de las señales y la transformada wavelet, incluyendo su versión continua, discreta y de paquetes wavelet. Mediante el análisis morfológico es posible extraer un conjunto numeroso de parámetros sobre las señales mfERG, que permita una mejor caracterización de la respuesta retiniana, para luego clasificarlas como sanas o glaucomatosas con ayuda de una red neuronal. Por otra parte, la aplicación de la transformada wavelet a estos registros mfERG nos permite obtener una serie de marcadores de presencia de glaucoma, ante cambios frecuenciales sutiles en estadios precoces. En todos los casos, los valores de sensibilidad y especificidad que se obtienen son superiores a los conseguidos con las técnicas tradicionales utilizadas en el entorno clínico