Alleviating Class Imbalance Problem in Automatic Sleep Stage Classification
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…
LightSleepNet: A Lightweight Deep Model for Rapid Sleep Stage Classification with Spectrograms.
Deep learning has achieved unprecedented success in sleep stage classification tasks, which starts to pave the way for potential real-world applications. However, due to its enormous size, deployment of deep neural networks is hindered by high cost at various aspects, such as computation power, storage, network bandwidth, power consumption, and hardware complexity. For further practical applications (e.g., wearable sleep monitoring devices), there is a need for simple and compact models. In this paper, we propose a lightweight model, namely LightSleepNet, for rapid sleep stage classification based on spectrograms. Our model is assembled by a much fewer number of model parameters compared to…
A Deep Learning Model for Automatic Sleep Scoring using Multimodality Time Series
Sleep scoring is a fundamental but time-consuming process in any sleep laboratory. Automatic sleep scoring is crucial and urgent to help address the increasing unmet need for sleep research. Therefore, this paper aims to develop an end-to-end deep learning architecture using raw polysomnographic recordings to automate sleep scoring. The proposed model adopts two-dimensional convolutional neural networks (2D-CNN) to automatically learn features from multi-modality signals, together with a "squeeze and excitation" block for recalibrating channel-wise feature responses. The learnt representations are finally fed to a softmax classifier to generate predictions for each sleep stage. The model pe…
SingleChannelNet : A model for automatic sleep stage classification with raw single-channel EEG
In diagnosing sleep disorders, sleep stage classification is a very essential yet time-consuming process. Various existing state-of-the-art approaches rely on hand-crafted features and multi-modality polysomnography (PSG) data, where prior knowledge is compulsory and high computation cost can be expected. Besides, it is a big challenge to handle the task with raw single-channel electroencephalogram (EEG). To overcome these shortcomings, this paper proposes an end-to-end framework with a deep neural network, namely SingleChannelNet, for automatic sleep stage classification based on raw single-channel EEG. The proposed model utilizes a 90s epoch as the textual input and employs two multi-conv…
Automatic sleep scoring: A deep learning architecture for multi-modality time series
Background: Sleep scoring is an essential but time-consuming process, and therefore automatic sleep scoring is crucial and urgent to help address the growing unmet needs for sleep research. This paper aims to develop a versatile deep-learning architecture to automate sleep scoring using raw polysomnography recordings. Method: The model adopts a linear function to address different numbers of inputs, thereby extending model applications. Two-dimensional convolution neural networks are used to learn features from multi-modality polysomnographic signals, a “squeeze and excitation” block to recalibrate channel-wise features, together with a long short-term memory module to exploit long-range co…
Convolutional Neural Network Based Sleep Stage Classification with Class Imbalance
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…
Automatic sleep stage classification based on single-channel EEG
Uniongelmat lisääntyvät ja niillä on kielteinen vaikutus maailman väestön terveyteen, kuten COVID-19-pandemia osoitti. Uniongelmien analysoimisessa tärkein vaihe on arvioida oikein unen laatua ja diagnosoida unihäiriöt luokittelemalla unen vaiheet (kutsutaan myös unipisteytykseksi). Yleisin unen pisteytyksen työkalu on polysomnografiatallennus. Tämä toimenpide on kuitenkin aikaa vievä ja on tehtävä asiantuntevalla klinikalla. Tästä syystä tarvitaan automaattisia univaiheen luokittelumenetelmiä, jotka täyttävät unitutkimuksen kasvavat vaatimukset. Tässä väitöskirjassa keskitymme kehittämään syväoppimiseen perustuvia menetelmiä ja etsimään ratkaisuja luokkaepätasapaino-ongelmaan ja mallin tul…