Sigvard, Claudio S. (2020) Aplicación de técnicas de análisis de señales en la identificación y caracterización de crisis epilépticas / Application of signal analysis techniques in the indentification and characterization of epileptic seizures. Proyecto Integrador Ingeniería en Telecomunicaciones, Universidad Nacional de Cuyo, Instituto Balseiro.
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Resumen en español
La epilepsia es una enfermedad del sistema nervioso, la cual produce en la persona que la padece eventos comúnmente llamados crisis epilépticas. Solamente el 70% de los casos son tratables con medicina química convencional, el resto se deben someter a otros procedimientos (por ejemplo una cirugía ), o aprender a convivir con la enfermedad. En aquellos casos que se sometan a un procedimiento quirúrgico, es habitual previo a la cirugía curativa, realizar un estudio utilizando un electroencefalograma (EEG) con electrodos intracraneales. Con el fin de determinar la zona inicial de la crisis y sus caminos de propagación con precisión, se registra la actividad eléctrica del cerebro por períodos de tiempo extensos, del orden de 1 semana. Por este motivo y con el n de facilitar el trabajo de los neurólogos, se diseñan algoritmos de detección automáticas de crisis. La cantidad de potencia en diferentes bandas de frecuencias de un EEG intracranial porta información acerca del estado del paciente. Por otro lado, los neurólogos están entrenados para analizar estas señales en las bandas de frecuencia delta, theta, alpha, beta y gamma. En este trabajo realizamos un análisis de componentes principales sobre una ventana deslizante a través de la señal de cada electrodo del EEG filtrada en las 5 bandas mencionadas, el cual nos permitió desarrollar un método no supervisado que detecta anomalías transitorias en la cantidad de potencia sobre alguna banda en específica, o una combinación de ellas. Las crisis son detectadas automáticamente una vez que el valor del componente principal cruza un umbral previamente establecido. Una vez diseñado y optimizado el algoritmo de detección, utilizamos etapas intermedias del procesamiento para caracterizar las crisis presentes en la base de datos, con el n de encontrar correlaciones entre las señales electrosiológicas procesadas, y el nivel de pérdida de conciencia del paciente, aspectos cognitivos del evento y el área de donde provienen dichas señales. Teniendo en cuenta el criterio de los médicos como verdad absoluta, y corriendo el algoritmo sobre 30 crisis de 5 pacientes diferentes con un promedio de 10 puntos de registros involucrados por paciente, obtenemos 83% de verdaderos positivos con 17% de falsos positivos para detección de crisis, y 81% de verdaderos positivos con 19% de falsos positivos para la detección de electrodos involucrados. Para identificar la correlación entre el comportamiento y las anomalías electrosiológicas, identificamos cambios transitorios en la varianza de cada banda de frecuencia correlacionados con el nivel de pérdida de conciencia del paciente, mensurado con el índice Consciousness Seizure Scale (CSS), el cual promedia el desempeño del paciente en 8 pruebas comportamentales llevadas a cabo por personal médico durante la crisis. Las crisis con mayor compromiso de conciencia tienden a exhibir un incremento en la varianza aproximadamente 40 segundos después del inicio de la crisis. En las mismas, el autovector asociado al autovalor principal contiene un significativo aumento de potencia en las bandas theta y alpha, y una disminución en la delta y beta. Luego analizamos las correlaciones electrosiológicas de las diferentes funciones cognitivas que componen el CSS. Encontramos que las pruebas que se relacionan con un trastorno de la memoria están positivamente correlacionadas con la duración total de la crisis, con una correlación máxima entre las anomalías eléctricas y la prueba comportamental aproximadamente 60 segundos luego de iniciada la crisis. En cambio, las pruebas relacionadas con la habilidades de interacción del paciente, están correlacionadas positivamente con la velocidad de propagación de la crisis sobre las áreas reclutadas, con una correlación máxima entre las anomalías eléctricas y la prueba comportamental aproximadamente 30 segundos después del inicio. Finalmente, analizamos la dependencia de estas correlaciones con la posición espacial de los electrodos implantados. La alteración de las funciones mnemónicas se vio correlacionado con las crisis provenientes del lóbulo temporal, mientras que las afecciones a las funcionalidades de interacción y comunicación se vieron correlacionadas con crisis focalizadas en el lóbulo frontal. Con esto concluimos que, en los pacientes analizados, la señal registrada con electrodos intracraneales revela perfiles temporales específicos en crisis con mayor disrupción de la conciencia. Aún más, las diferentes capacidades que sostienen el procesamiento consciente siguen perles espacio-temporales discernibles. Por lo tanto, creemos que el método propuesto en este trabajo tiene el potencial de caracterizar las crisis de forma espacial y temporal además de poder caracterizar aspectos cognitivos del episodio.
Resumen en inglés
Epilepsy is a disease of the nervous system, which produces in the person who suffers events commonly called epileptic seizures. Only 70 % of the cases are treatable with conventional chemical medicine, the rest must undergo other procedures (for example, surgery), or learn to live with the disease. In those cases that undergo a surgical procedure, it is usual prior to curative surgery, to perform a study using an electroencephalogram (EEG) with intracranial electrodes. In order to determine the initial seizure zone and its propagation paths accurately, the electrical activity of the brain is recorded for long periods of time, of around 1 week. For this reason and in order to facilitate the work of neurologists, automatic seizure detection algorithms are designed. The amount of power in different frequencies bands of intracranial EEG signals carries information about the state of the subject. Neurologists are well trained to analyze these signals in the delta, theta, alpha, beta and gamma frequency bands. Performing a Principal Component Analysis (PCA) over a window that slides throughout the ltered recording, we here develop an unsupervised method to detect transient anomalies in the amount of power along specic frequency bands, or combinations of bands. Good sampling of the non-ictal periods is required, whereas no demands are imposed on the amount of data during ictal activity. Seizures are detected automatically, as segments in which the eigenvalue corresponding to the most signicant principal component crosses a pre-set threshold. Once the detection algorithm has been designed and optimized, we use it to characterize the crises present in the database, looking for correlations between the processed electrophysiological signals and the level of the patient's loss of consciousness, cognitive aspects of the event and the area from which it comes said signals. Taking the opinion of trained experts as the grand truth, and running the algorithm over 30 seizures from 5 different patients with an average of 10 recording recruited sites per patient, we get 83 % true positives with 17 % false positives for crisis detection, and 81 % true positives with 19 % false positives for the detection of recruited electrodes. To identify the behavioral correlates of the physiological anomalies, we identied transient changes in the variance of each band that were correlated with the degree of loss of consciousness, the latter assessed by the so-called Consciousness Seizure Scale, summarizing the performance of the subject in 8 behavioral tests requested by a qualied member of our clinical staff during seizures. Seizures with maximal impairment of consciousness tended to exhibit an increase of variance approximately 40 seconds after onset. Whenever consciousness was severely impaired, the eigenvector corresponding to the most significant eigenvalue contained a significant increase of power in the theta and alpha bands, and a significant decrease in the delta and beta bands. Then we analyzed the electrophysiological correlates of the different cognitive functions that compose the CSS. We found that memory impairment was positively correlated with the total duration of the seizure, and with an electrical anomaly approximately 60 seconds after seizure onset. The ability to interact with the practitioner, instead, were positively correlated with the velocity with which the seizure propagated throughout the recruited areas, and with an electrical anomaly approximately 30 seconds after seizure onset. Finally, we analyzed the dependence of these correlations with the special position of the implanted electrodes. The impairment of mnemonic functions was correlated with temporal lobe seizures, whereas interaction or communicative disorders tended to involve the frontal lobes. We conclude that, in the sampled patients, the signals recorded with intracranial electrodes reveal specic temporal proles in seizures with major impairment of consciousness. Moreover, the different capacities that sustain conscious processing follow discernable spatio-temporal proles. Therefore, we believe that the method proposed in this work has the potential to characterize the seizures spatially and temporally, as well as being able to characterize cognitive aspects of the episode.
| Tipo de objeto: | Tesis (Proyecto Integrador Ingeniería en Telecomunicaciones) |
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| Palabras Clave: | Epilepsy; Epilepsia; [Epileptic seizures; Crisis epilépticas; Electroencephalogram; Electroencefalograma; Consciousness; Consciencia] |
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| Materias: | Ingeniería en telecomunicaciones > Análisis de señales |
| Divisiones: | Gerencia de Area Medicina Nuclear |
| Código ID: | 913 |
| Depositado Por: | Marisa G. Velazco Aldao |
| Depositado En: | 30 Abr 2021 10:05 |
| Última Modificación: | 16 Jun 2021 08:24 |
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