Actividad gamma del electroencefalograma. Métodos de análisis con objetivos clínicos

Objetivos: El objetivo general de la presente tesis doctoral ha sido investigar la identificación de la actividad gamma [30-90 Hz] del electroencefalograma (EEG) utilizando un sistema monocanal, para posibles aplicaciones clínicas. Métodos: Se ha obtenido la actividad gamma del EEG de las áreas motoras de la corteza cerebral en 25 sujetos sanos, con movimiento real e imaginario. Se analiza la señal mediante un método no lineal que utiliza transformaciones tiempo-frecuencia con wavelets. Posteriormente se añade un filtro, basado en la función matemática de descomposición en modo empírico (EMD: Empirical Mode Decomposition) y se comparan los resultados. Resultados: Se identifica la actividad de la banda gamma de las áreas motoras cerebrales durante el movimiento voluntario real e imaginario. Se obtiene un método para cuantificar la plasticidad cerebral analizando la actividad gamma motora. Se demuestra que filtrando las señales EEG mediante EMD, se obtienen mejores resultados que con el análisis original de la señal. Conclusiones: En esta tesis se demuestra que es posible obtener la actividad gamma del EEG de una forma simplificada, en relación a la adquisición y a la utilización de métodos relativamente sencillos de análisis de las señales. Los resultados experimentales de la tesis, principalmente las variaciones de la actividad gamma de las áreas motoras cerebrales, se pueden utilizar desde un punto de vista práctico para proponer aplicaciones clínicas. Palabras clave: Electroencefalografía, actividad gamma, áreas motoras, densidad espectral de potencia, aplicaciones clínicas, descomposición en modo empírico, plasticidad cerebral, tareas motoras reales, tareas motoras imaginarias

Analysis of gamma-band activity from human EEG using empirical mode decomposition

The purpose of this paper is to determine whether gamma-band activity detection is improved when a filter, based on empirical mode decomposition (EMD), is added to the pre-processing block of single-channel electroencephalography (EEG) signals. EMD decomposes the original signal into a finite number of intrinsic mode functions (IMFs). EEGs from 25 control subjects were registered in basal and motor activity (hand movements) using only one EEG channel. Over the basic signal, IMF signals are computed. Gamma-band activity is computed using power spectrum density in the 30–60 Hz range. Event-related synchronization (ERS) was defined as the ratio of motor and basal activity. To evaluate the performance of the new EMD based method, ERS was computed from the basic and IMF signals. The ERS obtained using IMFs improves, from 31.00% to 73.86%, on the original ERS for the right hand, and from 22.17% to 47.69% for the left hand. As EEG processing is improved, the clinical applications of gamma-band activity will expand.Universidad de AlcaláInstituto de Salud Carlos II

A computer-aided diagnosis of multiple sclerosis based on mfVEP recordings.

Introduction: The aim of this study is to develop a computer-aided diagnosis system to identify subjects at differing stages of development of multiple sclerosis (MS) using multifocal visual-evoked potentials (mfVEPs). Using an automatic classifier, diagnosis is performed first on the eyes and then on the subjects. Patients: MfVEP signals were obtained from patients with Radiologically Isolated Syndrome (RIS) (n = 30 eyes), patients with Clinically Isolated Syndrome (CIS) (n = 62 eyes), patients with definite MS (n = 56 eyes) and 22 control subjects (n = 44 eyes). The CIS and MS groups were divided into two subgroups: those with eyes affected by optic neuritis (ON) and those without (non-ON). Methods: For individual eye diagnosis, a feature vector was formed with information about the intensity, latency and singular values of the mfVEP signals. A flat multiclass classifier (FMC) and a hierarchical classifier (HC) were tested and both were implemented using the k-Nearest Neighbour (k-NN) algorithm. The output of the best eye classifier was used to classify the subjects. In the event of divergence, the eye with the best mfVEP recording was selected. Results: In the eye classifier, the HC performed better than the FMC (accuracy = 0.74 and extended Matthew Correlation Coefficient (MCC) = 0.68). In the subject classification, accuracy = 0.95 and MCC = 0.93, confirming that it may be a promising tool for MS diagnosis. Chirped-pulse φOTDR provides distributed strain measurement via a time-delay estimation process. We propose a lower bound for performance, after reducing sampling error and compensating phase-noise. We attempt to reach the limit, attaining unprecedented pε/√Hz sensitivities. Conclusion: In addition to amplitude (axonal loss) and latency (demyelination), it has shown that the singular values of the mfVEP signals provide discriminatory information that may be used to identify subjects with differing degrees of the disease.Secretaría de Estado de Investigación, Desarrollo e InnovaciónInstituto de Salud Carlos II

A computer-aided diagnosis of multiple sclerosis based on mfVEP recordings.

IntroductionThe aim of this study is to develop a computer-aided diagnosis system to identify subjects at differing stages of development of multiple sclerosis (MS) using multifocal visual-evoked potentials (mfVEPs). Using an automatic classifier, diagnosis is performed first on the eyes and then on the subjects.PatientsMfVEP signals were obtained from patients with Radiologically Isolated Syndrome (RIS) (n = 30 eyes), patients with Clinically Isolated Syndrome (CIS) (n = 62 eyes), patients with definite MS (n = 56 eyes) and 22 control subjects (n = 44 eyes). The CIS and MS groups were divided into two subgroups: those with eyes affected by optic neuritis (ON) and those without (non-ON).MethodsFor individual eye diagnosis, a feature vector was formed with information about the intensity, latency and singular values of the mfVEP signals. A flat multiclass classifier (FMC) and a hierarchical classifier (HC) were tested and both were implemented using the k-Nearest Neighbour (k-NN) algorithm. The output of the best eye classifier was used to classify the subjects. In the event of divergence, the eye with the best mfVEP recording was selected.ResultsIn the eye classifier, the HC performed better than the FMC (accuracy = 0.74 and extended Matthew Correlation Coefficient (MCC) = 0.68). In the subject classification, accuracy = 0.95 and MCC = 0.93, confirming that it may be a promising tool for MS diagnosis.ConclusionIn addition to amplitude (axonal loss) and latency (demyelination), it has shown that the singular values of the mfVEP signals provide discriminatory information that may be used to identify subjects with differing degrees of the disease