1210 Development Of An Auditory Neurofeedback During Sleep Onset Process

Abstract Introduction Biofeedback is proposed as an alternative method to help patients with insomnia reducing their anxiety. Some studies have shown that auditory neurofeedback can be effective at reducing sleep-onset latency. However, the AASM sleep stage classification only describes the sleep-onset as a binary state (i.e. wake or N1) which makes it not adapted for neurofeedback. We introduced a simple 4-stages classification for sleep-onset, on 10 seconds EEG epoch. The aim of this study was to develop an automatic method to detect these stages, and an online algorithm embedded in the Dreem headband (DH) that adapted the auditory feedback based on the current stage. Methods Fourteen subjects underwent an overnight PSG monitoring, from which the first sleep-onset period was extracted. We defined the simple 4-stages classification for sleep-onset on 10 seconds EEG epoch as following: SO1) > 75% of the epoch covered by alpha frequencies SO2) between 25% and 50% of the window covered with alpha frequencies, SO3) Alpha frequencies covered less than 25% and theta frequencies covered less than 30% of the epoch, and SO4) Theta frequency covered more than 30% of the epoch. For the manual scoring, 4 sleep scorers have been given the instructions and a Q&A session after scoring the first two records. For the algorithm, a sound triggering algorithm was linked to a neural network trained on the scored data, to dynamically adapt the sound to the sleep-onset stage. Results The scorers reached an average agreement of 68 + 15% over all the records. The neural network reached an accuracy of 68%. Per state the accuracy was: 71 ± 32% (S1), 52 ± 22% (S2), 54 ± 23% (S3), 79 ± 21% (S4). The automatic neurofeedback was able to adapt sound stimulations in real-time based on stages and was well perceived among first testers. Conclusion The results of this preliminary work show that we can reach a higher agreement by reducing the epoch duration and use this classification to produce automatic biofeedback during the sleep onset period. Further studies using a data-driven method should be conducted. Support This study supported by Dreem sas.

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Transfer Learning Convolutional Neural Network for Sleep Stage Classification Using Two-Stage Data Fusion Framework

IEEE Access ◽

10.1109/access.2020.3027289 ◽

2020 ◽

Vol 8 ◽

pp. 180618-180632

Author(s):

Mehdi Abdollahpour ◽

Tohid Yousefi Rezaii ◽

Ali Farzamnia ◽

Ismail Saad

Keyword(s):

Neural Network ◽

Data Fusion ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Sleep Stage ◽

Two Stage ◽

Stage Classification ◽

Sleep Stage Classification ◽

Fusion Framework

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Competition convolutional neural network for sleep stage classification

Biomedical Signal Processing and Control ◽

10.1016/j.bspc.2020.102318 ◽

2021 ◽

Vol 64 ◽

pp. 102318

Author(s):

Junming Zhang ◽

Yan Wu

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Sleep Stage ◽

Stage Classification ◽

Sleep Stage Classification

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Utility of Activity Monitors and Thermometry in Assessing Sleep Stages and Sleep Quality

Journal for the Measurement of Physical Behaviour ◽

10.1123/jmpb.2018-0012 ◽

2018 ◽

Vol 1 (3) ◽

pp. 108-121

Author(s):

Natashia Swalve ◽

Brianna Harfmann ◽

John Mitrzyk ◽

Alexander H. K. Montoye

Keyword(s):

Sleep Quality ◽

Skin Temperature ◽

Sleep Stage ◽

Sleep Onset ◽

Sleep Efficiency ◽

Sleep Stages ◽

Sleep Onset Latency ◽

Onset Latency ◽

Activity Monitors ◽

Research Grade

Activity monitors provide an inexpensive and convenient way to measure sleep, yet relatively few studies have been conducted to validate the use of these devices in examining measures of sleep quality or sleep stages and if other measures, such as thermometry, could inform their accuracy. The purpose of this study was to compare one research-grade and four consumer-grade activity monitors on measures of sleep quality (sleep efficiency, sleep onset latency, and wake after sleep onset) and sleep stages (awake, sleep, light, deep, REM) against an electroencephalography criterion. The use of a skin temperature device was also explored to ascertain whether skin temperature monitoring may provide additional data to increase the accuracy of sleep determination. Twenty adults stayed overnight in a sleep laboratory during which sleep was assessed using electroencephalography and compared to data concurrently collected by five activity monitors (research-grade: ActiGraph GT9X Link; consumer-grade: Fitbit Charge HR, Fitbit Flex, Jawbone UP4, Misfit Flash) and a skin temperature sensor (iButton). The majority of the consumer-grade devices overestimated total sleep time and sleep efficiency while underestimating sleep onset latency, wake after sleep onset, and number of awakenings during the night, with similar results being seen in the research-grade device. The Jawbone UP4 performed better than both the consumer- and research-grade devices, having high levels of agreement overall and in epoch-by-epoch sleep stage data. Changes in temperature were moderately correlated with sleep stages, suggesting that addition of skin temperature could increase the validity of activity monitors in sleep measurement.

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Convolution- and Attention-Based Neural Network for Automated Sleep Stage Classification

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17114152 ◽

2020 ◽

Vol 17 (11) ◽

pp. 4152 ◽

Cited By ~ 3

Author(s):

Tianqi Zhu ◽

Wei Luo ◽

Feng Yu

Keyword(s):

Neural Network ◽

Sleep Stage ◽

Attention Mechanism ◽

Set Partitioning ◽

Sleep Health ◽

Convolutional Network ◽

The Public ◽

Signal Characteristics ◽

Sleep Stage Classification ◽

Deep Learning Model

Analyzing polysomnography (PSG) is an effective method for evaluating sleep health; however, the sleep stage scoring required for PSG analysis is a time-consuming effort for an experienced medical expert. When scoring sleep epochs, experts pay attention to find specific signal characteristics (e.g., K-complexes and spindles), and sometimes need to integrate information from preceding and subsequent epochs in order to make a decision. To imitate this process and to build a more interpretable deep learning model, we propose a neural network based on a convolutional network (CNN) and attention mechanism to perform automatic sleep staging. The CNN learns local signal characteristics, and the attention mechanism excels in learning inter- and intra-epoch features. In experiments on the public sleep-edf and sleep-edfx databases with different training and testing set partitioning methods, our model achieved overall accuracies of 93.7% and 82.8%, and macro-average F1-scores of 84.5 and 77.8, respectively, outperforming recently reported machine learning-based methods.

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Automatic sleep stage classification of single-channel EEG by using complex-valued convolutional neural network

Biomedical Engineering / Biomedizinische Technik ◽

10.1515/bmt-2016-0156 ◽

2018 ◽

Vol 63 (2) ◽

pp. 177-190 ◽

Cited By ~ 18

Author(s):

Junming Zhang ◽

Yan Wu

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Single Channel ◽

Sleep Stage ◽

Experimental Results ◽

Sleep Stages ◽

Stage Classification ◽

Sleep Stage Classification ◽

Complex Valued ◽

Learned Features

AbstractMany systems are developed for automatic sleep stage classification. However, nearly all models are based on handcrafted features. Because of the large feature space, there are so many features that feature selection should be used. Meanwhile, designing handcrafted features is a difficult and time-consuming task because the feature designing needs domain knowledge of experienced experts. Results vary when different sets of features are chosen to identify sleep stages. Additionally, many features that we may be unaware of exist. However, these features may be important for sleep stage classification. Therefore, a new sleep stage classification system, which is based on the complex-valued convolutional neural network (CCNN), is proposed in this study. Unlike the existing sleep stage methods, our method can automatically extract features from raw electroencephalography data and then classify sleep stage based on the learned features. Additionally, we also prove that the decision boundaries for the real and imaginary parts of a complex-valued convolutional neuron intersect orthogonally. The classification performances of handcrafted features are compared with those of learned features via CCNN. Experimental results show that the proposed method is comparable to the existing methods. CCNN obtains a better classification performance and considerably faster convergence speed than convolutional neural network. Experimental results also show that the proposed method is a useful decision-support tool for automatic sleep stage classification.

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Automatic Sleep Stage Classification Using 1D Convolutional Neural Network

Frontiers in Biomedical Technologies ◽

10.18502/fbt.v7i3.4616 ◽

2020 ◽

Author(s):

Asma Salamatian ◽

Ali Khadem

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Deep Learning ◽

Convolutional Neural Network ◽

Single Channel ◽

Sleep Stage ◽

Sleep Stages ◽

Eeg Signal ◽

Stage Classification ◽

Sleep Stage Classification

Purpose: Sleep is one of the necessities of the body, such as eating, drinking, etc., that affects different aspects of human life. Sleep monitoring and sleep stage classification play an important role in the diagnosis of sleeprelated diseases and neurological disorders. Empirically, classification of sleep stages is a time-consuming, tedious, and complex task, which heavily depends on the experience of the experts. As a result, there is a crucial need for an automatic efficient sleep staging system. Materials and Methods: This study develops a 13-layer 1D Convolutional Neural Network (CNN) using singlechannel Electroencephalogram (EEG) signal for extracting features automatically and classifying the sleep stages. To overcome the negative effect of an imbalance dataset, we have used the Synthetic Minority Oversampling Technique (SMOTE). In our study, the single-channel EEG signal is given to a 1D CNN, without any feature extraction/selection processes. This deep network can self-learn the discriminative features from the EEG signal. Results: Applying the proposed method to sleep-EDF dataset resulted in overall accuracy, sensitivity, specificity, and Precision of 94.09%, 74.73%, 96.43%, and 71.02%, respectively, for classifying five sleep stages. Using single-channel EEG and providing a network with fewer trainable parameters than most of the available deep learning-based methods are the main advantages of the proposed method. Conclusion: In this study, a 13-layer 1D CNN model was proposed for sleep stage classification. This model has an end-to-end complete architecture and does not require any separate feature extraction/selection and classification stages. Having a low number of network parameters and layers while still having high classification accuracy, is the main advantage of the proposed method over most of the previous deep learning-based approaches.

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