Detecció automàtica i robusta de Bursts en EEG de nounats amb HIE. Enfocament tensorial

[ANGLÈS] Hypoxic-Ischemic Encephalopathy (HIE) is an important cause of brain injury in the newborn, and can result in long-term devastating consequences. Burst-suppression pattern is one of several indicators of severe pathology in the EEG signal that may occur after brain damage caused by e.g. asphyxia around the time of birth. The goal of this thesis is to design a robust method to detect burst patterns automatically regardless of the physiologic and extra-physiologic artifacts that may occur at any time. At first, a pre-detector has been designed to obtain potential burst candidates from different patients. Then, a post-classification has been implemented, applying high dimensional feature extraction methods, to get the real burst patterns from these patients with a high sensitivity.[CASTELLÀ] La Hipoxia-Isquemia Encefálica (HIE) es una causa importante de lesión cerebral en los recién nacidos, pudiendo acarrear devastadoras consecuencias a largo plazo. El patrón Burst-Suppression es uno de los indicadores dados en patologías severas en señales EEG los cuales ocurren después de una lesión cerebral causada, por ejemplo, por una asfixia poco después del nacimiento. El objetivo de esta tésis es diseñar un método robusto que detecte automáticamente patrones Burst, prescindiendo de los artefactos fisiológicos y extra-fisiológicos que puedan aparecer en cualquier momento. Primeramente, se ha diseñado un pre-detector para obtener los candidatos potenciales a Burst provenientes de diferentes pacientes. Seguidamente, se ha implementado una post-clasificación, aplicando métodos de extracción de características para altas dimensiones, para obtener patrones reales de Burst con una alta sensitividad.[CATALÀ] La Hipòxia-Isquèmia Encefàlica (HIE) és una causa important de lesió cerebral en nounats, que poden comportar devastadores conseqüències a llarg termini. El patró Burst-Suppression és un dels indicadors donats en patologies severes en els senyals EEG els quals ocorren després d'una lesió cerebral causada, per exemple, per una asfixia poc després del naixement. L'objectiu d'aquesta tesis és dissenyar un mètode robust que detecti automàticament patrons Burst, prescindint dels artefactes fisiològics i extra-fisiològics que poden aparèixer en qualsevol moment. Primerament, s'ha dissenyat un pre-detector per obtenir els candidats potencials a Burst provinents de diferents pacients. Seguidament, s'ha implementat una post-classificació, aplicant mètodes d'extracció de característiques per a altes dimensions, per tal d'obtenir patrons reals de Burst amb una alta sensitivitat

A knowledge-based approach to automatic detection of equipment alarm sounds in a neonatal intensive care unit environment

A large number of alarm sounds triggered by biomedical equipment occur frequently in the noisy environment of a neonatal intensive care unit (NICU) and play a key role in providing healthcare. In this paper, our work on the development of an automatic system for detection of acoustic alarms in that difficult environment is presented. Such automatic detection system is needed for the investigation of how a preterm infant reacts to auditory stimuli of the NICU environment and for an improved real-time patient monitoring. The approach presented in this paper consists of using the available knowledge about each alarm class in the design of the detection system. The information about the frequency structure is used in the feature extraction stage and the time structure knowledge is incorporated at the post-processing stage. Several alternative methods are compared for feature extraction, modelling and post-processing. The detection performance is evaluated with real data recorded in the NICU of the hospital, and by using both frame-level and period-level metrics. The experimental results show that the inclusion of both spectral and temporal information allows to improve the baseline detection performance by more than 60%Peer ReviewedPostprint (published version

Automatic detection of equipment alarms in a neonatal intensive care unit environment: a knowledge-based approach

Alarm sounds triggered by biomedical equipment play a key role in providing healthcare in a neonatal intensive care unit (NICU). This paper presents our work on automatic detection of acoustic alarms in a noisy NICU environment, where knowledge about the particular characteristics of each alarm class is integrated at different stages of the detection system. The feature extraction is based on applying, around alarm-specific frequencies, a method for detection of sinusoidal signals, which employs the normalised short-term magnitude and phase spectrum. Also, the ratios of magnitudes at those frequencies are taken as features. The system consists of a set of GMM-based detectors, each designed to deal with a specific alarm. Temporal structure of alarms, in terms of duration of signal and silence intervals in every alarm period, is incorporated by aggregating the frame-level posterior probabilities. The experimental evaluations are performed with a database recorded in a real-world hospital environment. The performance of the detection system is assessed both at the frame level and at the alarm period level.Peer Reviewe

Automatic detection of equipment alarms in a neonatal intensive care unit environment: a knowledge-based approach

Alarm sounds triggered by biomedical equipment play a key role in providing healthcare in a neonatal intensive care unit (NICU). This paper presents our work on automatic detection of acoustic alarms in a noisy NICU environment, where knowledge about the particular characteristics of each alarm class is integrated at different stages of the detection system. The feature extraction is based on applying, around alarm-specific frequencies, a method for detection of sinusoidal signals, which employs the normalised short-term magnitude and phase spectrum. Also, the ratios of magnitudes at those frequencies are taken as features. The system consists of a set of GMM-based detectors, each designed to deal with a specific alarm. Temporal structure of alarms, in terms of duration of signal and silence intervals in every alarm period, is incorporated by aggregating the frame-level posterior probabilities. The experimental evaluations are performed with a database recorded in a real-world hospital environment. The performance of the detection system is assessed both at the frame level and at the alarm period level.Peer ReviewedPostprint (published version

Automatic detection of equipment alarms in a neonatal intensive care unit environment: a knowledge-based approach

Alarm sounds triggered by biomedical equipment play a key role in providing healthcare in a neonatal intensive care unit (NICU). This paper presents our work on automatic detection of acoustic alarms in a noisy NICU environment, where knowledge about the particular characteristics of each alarm class is integrated at different stages of the detection system. The feature extraction is based on applying, around alarm-specific frequencies, a method for detection of sinusoidal signals, which employs the normalised short-term magnitude and phase spectrum. Also, the ratios of magnitudes at those frequencies are taken as features. The system consists of a set of GMM-based detectors, each designed to deal with a specific alarm. Temporal structure of alarms, in terms of duration of signal and silence intervals in every alarm period, is incorporated by aggregating the frame-level posterior probabilities. The experimental evaluations are performed with a database recorded in a real-world hospital environment. The performance of the detection system is assessed both at the frame level and at the alarm period level.Peer Reviewe

A knowledge-based approach to automatic detection of equipment alarm sounds in a neonatal intensive care unit environment

A large number of alarm sounds triggered by biomedical equipment occur frequently in the noisy environment of a neonatal intensive care unit (NICU) and play a key role in providing healthcare. In this paper, our work on the development of an automatic system for detection of acoustic alarms in that difficult environment is presented. Such automatic detection system is needed for the investigation of how a preterm infant reacts to auditory stimuli of the NICU environment and for an improved real-time patient monitoring. The approach presented in this paper consists of using the available knowledge about each alarm class in the design of the detection system. The information about the frequency structure is used in the feature extraction stage and the time structure knowledge is incorporated at the post-processing stage. Several alternative methods are compared for feature extraction, modelling and post-processing. The detection performance is evaluated with real data recorded in the NICU of the hospital, and by using both frame-level and period-level metrics. The experimental results show that the inclusion of both spectral and temporal information allows to improve the baseline detection performance by more than 60%Peer Reviewe