Search results for "class imbalance problem"

showing 2 items of 2 documents

Alleviating Class Imbalance Problem in Automatic Sleep Stage Classification

2022

For real-world automatic sleep-stage classification tasks, various existing deep learning-based models are biased toward the majority with a high proportion. Because of the unique sleep structure, most of the current polysomnography (PSG) datasets suffer an inherent class imbalance problem (CIP), in which the number of each sleep stage is severely unequal. In this study, we first define the class imbalance factor (CIF) to describe the level of CIP quantitatively. Afterward, we propose two balancing methods to alleviate this problem from the dataset quantity and the relationship between the class distribution and the applied model, respectively. The first one is to employ the data augmentati…

sleep-stage classificationunitutkimusdeep neural networksignaalianalyysisyväoppiminenneuroverkotdata augmentation (DA)uni (lepotila)koneoppiminenClass imbalance problem (CIP)network connectionEEGElectrical and Electronic Engineeringgenerative adversarial network (GAN)InstrumentationIEEE Transactions on Instrumentation and Measurement
researchProduct

Convolutional Neural Network Based Sleep Stage Classification with Class Imbalance

2022

Accurate sleep stage classification is vital to assess sleep quality and diagnose sleep disorders. Numerous deep learning based models have been designed for accomplishing this labor automatically. However, the class imbalance problem existing in polysomnography (PSG) datasets has been barely investigated in previous studies, which is one of the most challenging obstacles for the real-world sleep staging application. To address this issue, this paper proposes novel methods with signal-driven and image-driven ways of noise addition to balance the imbalanced relationship in the training dataset samples. We evaluate the effectiveness of the proposed methods which are integrated into a convolut…

mallintaminenluokitus (toiminta)trainingdatabasessleep stage classificationtime-frequency imagedeep learningsyväoppiminenneuroverkotneural networksuni (lepotila)convolutional neural networksclass imbalance problemtietokannatwhite noiseunihäiriötdata augmentation2022 International Joint Conference on Neural Networks (IJCNN)
researchProduct