A convolutional neural network-based decision support system for neonatal quiet sleep detection

Sleep plays an important role in neonatal brain and physical development, making its detection and characterization important for assessing early-stage development. In this study, we propose an automatic and computationally efficient algorithm to detect neonatal quiet sleep (QS) using a convolutional neural network (CNN). Our study used 38-hours of electroencephalography (EEG) recordings, collected from 19 neonates at Fudan Children's Hospital in Shanghai, China (Approval No. (2020) 22). To train and test the CNN, we extracted 12 prominent time and frequency domain features from 9 bipolar EEG channels. The CNN architecture comprised two convolutional layers with pooling and rectified linear unit (ReLU) activation. Additionally, a smoothing filter was applied to hold the sleep stage for 3 minutes. Through performance testing, our proposed method achieved impressive results, with 94.07% accuracy, 89.70% sensitivity, 94.40% specificity, 79.82% F1-score and a 0.74 kappa coefficient when compared to human expert annotations. A notable advantage of our approach is its computational efficiency, with the entire training and testing process requiring only 7.97 seconds. The proposed algorithm has been validated using leave one subject out (LOSO) validation, which demonstrates its consistent performance across a diverse range of neonates. Our findings highlight the potential of our algorithm for real-time neonatal sleep stage classification, offering a fast and cost-effective solution. This research opens avenues for further investigations in early-stage development monitoring and the assessment of neonatal health

Análisis de electroencefalogramas para la detección automática de las fases del sueño

Las enfermedades del sueño son cada vez más comunes debido al estresante estilo de vida de la sociedad actual. Un paso fundamental en su estudio y diagnóstico es detectar correctamente las diferentes fases del sueño. Avances en áreas como el Deep learning han permitido desarrollar métodos que automatizan esta detección, presentando una alternativa a la clasificación mediante inspección visual realizada hasta la fecha. En este trabajo se ha indagado en el uso de redes neuronales convolucionales (CNN) como clasificadores de fases del sueño, usando para ello la señal de electroencefalograma (EEG). El comportamiento de esta señal difiere entre niños y adultos. Sin embargo, los estudios publicados hasta ahora se han centrado únicamente en pacientes adultos, lo que provoca que los modelos de clasificación no sean fácilmente generalizables. Conseguir un método de clasificación basado en CNN que permita una detección precisa de las fases del sueño en niños, y comprobar si se puede entrenar un modelo que alcance resultados óptimos al evaluar sujetos de diferentes edades, son los objetivos principales de este trabajo. Para ello, se han usado dos amplias bases de datos públicas procedentes de los estudios Sleep Heart Health Study (SHHS) y Childhood Adenotonsillectomy Trial (CHAT), que contienen 5793 registros de adultos y 453 registros de niños, respectivamente. El proceso de entrenamiento y optimización de la red CNN se ha probado modificando el número de capas y su parámetro de regularización, este último buscando asegurar que no haya sobreentrenamiento. Tras conseguir un modelo con alto rendimiento al clasificar la población adulta, se ha evaluado dicho modelo en los registros pediátricos. El mismo procedimiento se ha realizado de manera inversa, probando en la población adulta un modelo entrenado únicamente con niños. Además, se ha obtenido un modelo conjunto usando registros de ambas bases de datos en los grupos de entrenamiento/validación/test. Para homogeneizar las señales de las dos bases, se ha implementado re-muestreo a la misma frecuencia, re-referenciado a la media de los canales utilizados en cada caso, y estandarización para igualar los límites de amplitud. Los resultados muestran que los modelos entrenados con registros de una única base de datos clasifican con alta precisión siempre que se apliquen sobre sujetos en los mismos rangos de edad, consiguiéndose una precisión del 0.815 y un kappa de Cohen de 0.738 en el caso de sujetos adultos y precisión de 0.84 y kappa de 0.77 en el caso de niños, lo que es coherente con estudios previos. No obstante, al clasificar un grupo de edad diferente, estos valores disminuyen. Sin embargo, el modelo entrenado con registros de diferentes edades sí que consigue detectar de manera precisa registros de ambas bases, llegando a una precisión de 0.81 y a un kappa de 0.75 al evaluarlo en un grupo de test conjunto. Estos resultados sugieren la necesidad de incluir sujetos de diferentes edades en el entrenamiento para conseguir modelos más generalizables.Sleep disorders are very common nowadays due to the stressful lifestyle of the current society. A fundamental step in the study and diagnosis of these disorders is to successfully detect the different sleep stages. Recent investigation in fields like Deep learning has led to the development of methods that automatize this detection, becoming an alternative to the visual classification mostly used up to the date. This project explores the application of convolutional neural networks (CNN) as methods for sleep staging classification, using the brain signal of the electroencephalogram (EEG). The behavior of this signal changes between children and adults. However, studies published up to the date mostly focus on grown up patients, issue that causes a poor generalization of the classification models when applied to other age ranges. Finding a classification method based on CNN that shows an accurate detection of children´s sleep stages, and training a model that reaches high performance when evaluated with subjects of different ages, are the two main goals of this work. In order to achieve these goals, two large public data bases have been used, coming from the Sleep Heart Health Study (SHHS) and the Childhood Adenotonsillectomy Trial (CHAT), and containing 5793 adults´ recordings and 453 children´s recordings, respectively. The process of training and optimizing the neural network has been conducted by varying the number of convolutional layers and the dropout percentage, the latter being used to minimize the risk of model overfitting. Once an effective model for the classification of adults´ recordings is found, it gets tested with the pediatric recordings. The same procedure is followed the other way around, testing with the recordings of adults a model trained only using kids´ signals. Furthermore, a mixed model is obtained by including subjects from both data bases in the training/validation/test groups. With the aim of homogenize the signals of the two data bases, three different actions have been taken: re-sampling the recordings to the same frequency, applying an average reference, and standardizing the signals to keep them with in the same amplitude limits. The results show that the models trained with just one of the data bases only classify accurately recordings from subjects of that data base, obtaining a Kappa coefficient of 0.74 and an accuracy of 0.82 when just using grown up subjects and a Kappa of 0.77 and accuracy of 0.84 with only children. However, when testing these models on subjects of different age from the ones in the training set the level of performance decreased significantly. On the contrary, the mixed model does succeed when classifying recordings from both age ranges, obtaining an accuracy of 0.81 and a Kappa of 0.75 in the classification of a test group formed by the same number of adults and children. These results support the need to consider subjects of different ages when developing methods for the automatic detection of sleep stages, so the models obtained can adapt to a wider range of patients.Grado en Ingeniería de Tecnologías de Telecomunicació