Neonatal Seizure Detection using Convolutional Neural Networks

This study presents a novel end-to-end architecture that learns hierarchical representations from raw EEG data using fully convolutional deep neural networks for the task of neonatal seizure detection. The deep neural network acts as both feature extractor and classifier, allowing for end-to-end optimization of the seizure detector. The designed system is evaluated on a large dataset of continuous unedited multi-channel neonatal EEG totaling 835 hours and comprising of 1389 seizures. The proposed deep architecture, with sample-level filters, achieves an accuracy that is comparable to the state-of-the-art SVM-based neonatal seizure detector, which operates on a set of carefully designed hand-crafted features. The fully convolutional architecture allows for the localization of EEG waveforms and patterns that result in high seizure probabilities for further clinical examination.Comment: IEEE International Workshop on Machine Learning for Signal Processin

Neonatal Seizure Detection Using Deep Convolutional Neural Networks

Identifying a core set of features is one of the most important steps in the development of an automated seizure detector. In most of the published studies describing features and seizure classifiers, the features were hand-engineered, which may not be optimal. The main goal of the present paper is using deep convolutional neural networks (CNNs) and random forest to automatically optimize feature selection and classification. The input of the proposed classifier is raw multi-channel EEG and the output is the class label: seizure/nonseizure. By training this network, the required features are optimized, while fitting a nonlinear classifier on the features. After training the network with EEG recordings of 26 neonates, five end layers performing the classification were replaced with a random forest classifier in order to improve the performance. This resulted in a false alarm rate of 0.9 per hour and seizure detection rate of 77% using a test set of EEG recordings of 22 neonates that also included dubious seizures. The newly proposed CNN classifier outperformed three data-driven feature-based approaches and performed similar to a previously developed heuristic method

Android Implementation of a Visualisation, Sonification and AI-Assisted Interpretation of Neonatal EEG

Development of deep neural network models for detection of neonatal seizures. Implementation of the detection system as an Android application.The aim of this project is the implementation of an Android App to help healthcare professionals to check newborn health status by observing neonatal EEG signals, without having extensive training in EEG interpretation. To satisfy that aim, this project is divided in three blocks: AI-assisted neonatal EEG interpretation, EEG sonification and graphical user interface. The AI-assisted block has the function to detect neonatal seizures using a fully- convolutional deep neural network using the offline-trained existing model. The sonification work consisted of the adaptation of a previously developed algorithm, based on the phase vocoder, which was already implemented by another UPC student in the Android environment. The developed application core provides both sonification and AI detection functionalities, which are integrated in a user friendly graphical user interface.El objetivo de este proyecto era la implementación de una aplicación Android para ayudar a profesionales del ámbito médico a comprobar el estado de salud de neonatos en base a la observación del electroencefalograma (EEG), sin necesidad de tener mucha experiencia en el campo de la neonatología. Para cumplir dicho objetivo, el proyecto se ha dividido en tres bloques: interpretación asistida por IA, sonificación y interfaz de usuario gráfica. El bloque de IA se encarga de la detección de epilepsias en recién nacidos utilizando una red neuronal totalmente convolucional implementada en Android llevando a cabo la adaptación de un modelo ya existente en Python. El trabajo de sonificación del EEG ha consistido en la adaptación de un algoritmo basado en Phase Vocoder realizado por otro estudiante de la UPC La finalidad de la interfaz gráfica es mostrar de forma integrada la información recibida de la sonificación y la red neuronal para que el usuario pueda interpretarlas con facilidad, de forma que la aplicación resulte útil a un gran número de usuarios.L'objectiu d'aquest projecte era la implementació d'una aplicació Android per ajudar a professionals de l'àmbit mèdic a comprovar l'estat de salut de nounats en base a l'observació de l'electroencefalograma (EEG), sense necessitat de tenir molta experiència en neonatologia. Per tal d'acomplir aquest objectiu, el projecte s'ha dividit en tres blocs: interpretació assistida per IA, sonificació i interfície d'usuari gràfica. El bloc d'IA s'encarrega de la detecció d'epilèpsies en nadons utilitzant una xarxa neuronal totalment convolucional implementada en Android duent a terme l'adaptació d'un model ja existent programat en Python. El treball de sonificació de l'EEG ha consistit en l'adaptació d'un algoritme basat en Phase Vocoder realitzat per un altre estudiant de la UPC La finalitat de la interfície gràfica és mostrar de forma integrada la informació rebuda de la sonificació i la xarxa neuronal perquè l'usuari pugui interpretar-les amb facilitat, de manera que l'aplicació resulti útil a un gran nombre d'usuaris

Ensemble Learning Using Individual Neonatal Data for Seizure Detection

Objective: Sharing medical data between institutions is difficult in practice due to data protection laws and official procedures within institutions. Therefore, most existing algorithms are trained on relatively small electroencephalogram (EEG) data sets which is likely to be detrimental to prediction accuracy. In this work, we simulate a case when the data can not be shared by splitting the publicly available data set into disjoint sets representing data in individual institutions. Methods and procedures: We propose to train a (local) detector in each institution and aggregate their individual predictions into one final prediction. Four aggregation schemes are compared, namely, the majority vote, the mean, the weighted mean and the Dawid-Skene method. The method was validated on an independent data set using only a subset of EEG channels. Results: The ensemble reaches accuracy comparable to a single detector trained on all the data when sufficient amount of data is available in each institution. Conclusion: The weighted mean aggregation scheme showed best performance, it was only marginally outperformed by the Dawid-Skene method when local detectors approach performance of a single detector trained on all available data. Clinical impact: Ensemble learning allows training of reliable algorithms for neonatal EEG analysis without a need to share the potentially sensitive EEG data between institutions.Peer reviewe