scholarly journals Automating sleep stage classification using wireless, wearable sensors

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Alexander J. Boe ◽  
Lori L. McGee Koch ◽  
Megan K. O’Brien ◽  
Nicholas Shawen ◽  
John A. Rogers ◽  
...  
Keyword(s):  
High Resolution ◽  
Sleep Stage ◽  
Low Cost ◽  
Wearable Sensors ◽  
Sleep Stages ◽  
Sleep Monitoring ◽  
Promising Alternative ◽  
High Resolution Data ◽  
Sleep Stage Classification

AbstractPolysomnography (PSG) is the current gold standard in high-resolution sleep monitoring; however, this method is obtrusive, expensive, and time-consuming. Conversely, commercially available wrist monitors such as ActiWatch can monitor sleep for multiple days and at low cost, but often overestimate sleep and cannot differentiate between sleep stages, such as rapid eye movement (REM) and non-REM. Wireless wearable sensors are a promising alternative for their portability and access to high-resolution data for customizable analytics. We present a multimodal sensor system measuring hand acceleration, electrocardiography, and distal skin temperature that outperforms the ActiWatch, detecting wake and sleep with a recall of 74.4% and 90.0%, respectively, as well as wake, non-REM, and REM with recall of 73.3%, 59.0%, and 56.0%, respectively. This approach will enable clinicians and researchers to more easily, accurately, and inexpensively assess long-term sleep patterns, diagnose sleep disorders, and monitor risk factors for disease in both laboratory and home settings.

scholarly journals EOGNET: A Novel Deep Learning Model for Sleep Stage Classification Based on Single-Channel EOG Signal

2021 ◽  
Vol 15 ◽  
Author(s):  
Jiahao Fan ◽  
Chenglu Sun ◽  
Meng Long ◽  
Chen Chen ◽  
Wei Chen
Keyword(s):  
Neural Network ◽  
Single Channel ◽  
Sleep Stage ◽  
Sleep Staging ◽  
Eeg Signals ◽  
Sleep Monitoring ◽  
Sleep Stage Classification ◽  
Sequential Information ◽  
Deep Learning Model

In recent years, automatic sleep staging methods have achieved competitive performance using electroencephalography (EEG) signals. However, the acquisition of EEG signals is cumbersome and inconvenient. Therefore, we propose a novel sleep staging approach using electrooculogram (EOG) signals, which are more convenient to acquire than the EEG. A two-scale convolutional neural network first extracts epoch-wise temporary-equivalent features from raw EOG signals. A recurrent neural network then captures the long-term sequential information. The proposed method was validated on 101 full-night sleep data from two open-access databases, the montreal archive of sleep studies and Sleep-EDF, achieving an overall accuracy of 81.2 and 76.3%, respectively. The results are comparable to those models trained with EEG signals. In addition, comparisons with six state-of-the-art methods further demonstrate the effectiveness of the proposed approach. Overall, this study provides a new avenue for sleep monitoring.

scholarly journals Classification of Brainwaves for Sleep Stages by High-Dimensional FFT Features from EEG Signals

2020 ◽  
Vol 10 (5) ◽  
pp. 1797 ◽  
Author(s):  
Mera Kartika Delimayanti ◽  
Bedy Purnama ◽  
Ngoc Giang Nguyen ◽  
Mohammad Reza Faisal ◽  
Kunti Robiatul Mahmudah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sleep Stage ◽  
Machine Learning Algorithms ◽  
High Dimensional ◽  
Sleep Stages ◽  
Eeg Signals ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
Low Dimensional

Manual classification of sleep stage is a time-consuming but necessary step in the diagnosis and treatment of sleep disorders, and its automation has been an area of active study. The previous works have shown that low dimensional fast Fourier transform (FFT) features and many machine learning algorithms have been applied. In this paper, we demonstrate utilization of features extracted from EEG signals via FFT to improve the performance of automated sleep stage classification through machine learning methods. Unlike previous works using FFT, we incorporated thousands of FFT features in order to classify the sleep stages into 2–6 classes. Using the expanded version of Sleep-EDF dataset with 61 recordings, our method outperformed other state-of-the art methods. This result indicates that high dimensional FFT features in combination with a simple feature selection is effective for the improvement of automated sleep stage classification.

scholarly journals Capturing the Development of Internal Representations in a High-Performing Deep Network for Sleep Stage Classification

2021 ◽  
Vol 2 (4) ◽  
Author(s):  
Sarun Paisarnsrisomsuk ◽  
Carolina Ruiz ◽  
Sergio A. Alvarez
Keyword(s):  
Input Data ◽  
Single Channel ◽  
Sleep Stage ◽  
Quantitative Measure ◽  
Classification Performance ◽  
Sleep Stages ◽  
Layer Depth ◽  
Human Sleep ◽  
Stage Classification ◽  
Sleep Stage Classification

AbstractDeep neural networks can provide accurate automated classification of human sleep signals into sleep stages that enables more effective diagnosis and treatment of sleep disorders. We develop a deep convolutional neural network (CNN) that attains state-of-the-art sleep stage classification performance on input data consisting of human sleep EEG and EOG signals. Nested cross-validation is used for optimal model selection and reliable estimation of out-of-sample classification performance. The resulting network attains a classification accuracy of $$84.50 \pm 0.13\%$$ 84.50 ± 0.13 % ; its performance exceeds human expert inter-scorer agreement, even on single-channel EEG input data, therefore providing more objective and consistent labeling than human experts demonstrate as a group. We focus on analyzing the learned internal data representations of our network, with the aim of understanding the development of class differentiation ability across the layers of processing units, as a function of layer depth. We approach this problem visually, using t-Stochastic Neighbor Embedding (t-SNE), and propose a pooling variant of Centered Kernel Alignment (CKA) that provides an objective quantitative measure of the development of sleep stage specialization and differentiation with layer depth. The results reveal a monotonic progression of both of these sleep stage modeling abilities as layer depth increases.

scholarly journals Three distinct Holocene intervals of stalagmite deposition and nondeposition revealed in NW Madagascar, and their paleoclimate implications

2017 ◽  
Vol 13 (12) ◽  
pp. 1771-1790 ◽  
Author(s):  
Ny Riavo Gilbertinie Voarintsoa ◽  
Loren Bruce Railsback ◽  
George Albert Brook ◽  
Lixin Wang ◽  
Gayatri Kathayat ◽  
...  
Keyword(s):  
Stable Isotopes ◽  
High Resolution ◽  
Late Holocene ◽  
Intertropical Convergence Zone ◽  
Convergence Zone ◽  
High Resolution Data ◽  
The Holocene ◽  
Middle Holocene ◽  
Resolution Data

Abstract. Petrographic features, mineralogy, and stable isotopes from two stalagmites, ANJB-2 and MAJ-5, respectively from Anjohibe and Anjokipoty caves, allow distinction of three intervals of the Holocene in NW Madagascar. The Malagasy early Holocene (between ca. 9.8 and 7.8 ka) and late Holocene (after ca. 1.6 ka) intervals (MEHI and MLHI, respectively) record evidence of stalagmite deposition. The Malagasy middle Holocene interval (MMHI, between ca. 7.8 and 1.6 ka) is marked by a depositional hiatus of ca. 6500 years. Deposition of these stalagmites indicates that the two caves were sufficiently supplied with water to allow stalagmite formation. This suggests that the MEHI and MLHI intervals may have been comparatively wet in NW Madagascar. In contrast, the long-term depositional hiatus during the MMHI implies it was relatively drier than the MEHI and the MLHI. The alternating wet–dry–wet conditions during the Holocene may have been linked to the long-term migrations of the Intertropical Convergence Zone (ITCZ). When the ITCZ's mean position is farther south, NW Madagascar experiences wetter conditions, such as during the MEHI and MLHI, and when it moves north, NW Madagascar climate becomes drier, such as during the MMHI. A similar wet–dry–wet succession during the Holocene has been reported in neighboring locations, such as southeastern Africa. Beyond these three subdivisions, the records also suggest wet conditions around the cold 8.2 ka event, suggesting a causal relationship. However, additional Southern Hemisphere high-resolution data will be needed to confirm this.

scholarly journals Light Sleep Detection based on Surface Electromyography Signals for Nap Monitoring

2022 ◽  
Vol 16 ◽  
pp. 140-145
Author(s):  
Wachiraporn Aiamklin ◽  
Yutana Jewajinda ◽  
Yunyong Punsawad
Keyword(s):  
Surface Electromyography ◽  
Sleep Stage ◽  
Low Cost ◽  
Trapezius Muscle ◽  
Sleep Stages ◽  
Upper Trapezius ◽  
Emg Signal ◽  
Power Frequency ◽  
Upper Trapezius Muscle ◽  
Waking Up

This paper proposes the development of automatic sleep stage detection by using physiological signals. We aim to develop an application to assist drivers after drowsiness or fatigue detection by a commercial driver vigilance system. The proposed method used a low-cost surface electromyography (EMG) device for sleep stage detection. We investigate skeletal muscle location and EMG features from sleep stage 2 to provide an EMG-based nap monitoring system. The results showed that using only one channel of a bipolar EMG signal from an upper trapezius muscle with median power frequency can achieve 84% accuracy. We implement a MyoWare muscle sensor into the proposed nap monitoring device. The results showed that the proposed system is feasible for detecting sleep stages and waking up the napper. A combination of EMG and electroencephalogram (EEG) signals might be yield a high system performance for nap monitoring and alarm system. We will prototype a portable device to connect the application to a smartphone and test with a target group, such as truck drivers and physicians.

Adopting Citizen Observations in Operational Weather Prediction

2019 ◽  
Vol 101 (1) ◽  
pp. E43-E57 ◽  
Author(s):  
Thomas N. Nipen ◽  
Ivar A. Seierstad ◽  
Cristian Lussana ◽  
Jørn Kristiansen ◽  
Øystein Hov
Keyword(s):  
High Resolution ◽  
Low Cost ◽  
Weather Prediction ◽  
Urban Heat Islands ◽  
Production Chain ◽  
Near Surface ◽  
High Resolution Data ◽  
Baltic Countries ◽  
Operational Systems ◽  
Weather Stations

Abstract Citizen weather stations are rapidly increasing in prevalence and are becoming an emerging source of weather information. These low-cost consumer-grade devices provide observations in real time and form parts of dense networks that capture high-resolution meteorological information. Despite these benefits, their adoption into operational weather prediction systems has been slow. However, MET Norway recently introduced observations from Netatmo’s network of weather stations in the postprocessing of near-surface temperature forecasts for Scandinavia, Finland, and the Baltic countries. The observations are used to continually correct errors in the weather model output caused by unresolved features such as cold pools, inversions, urban heat islands, and an intricate coastline. Corrected forecasts are issued every hour. Integrating citizen observations into operational systems comes with a number of challenges. First, operational systems must be robust and therefore rely on strict quality control procedures to filter out unreliable measurements. Second, postprocessing methods must be selected and tuned to make use of the high-resolution data that at times can contain conflicting information. Central to resolving these challenges is the need to use the massive redundancy of citizen observations, with up to dozens of observations per square kilometer, and treating the data source as a network rather than a collection of individual stations. We present our experiences with introducing citizen observations into the operational production chain of automated public weather forecasts. Their inclusion shows a clear improvement to the accuracy of short-term temperature forecasts, especially in areas where existing professional stations are sparse.

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

2018 ◽  
Vol 63 (2) ◽  
pp. 177-190 ◽  
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.

Incorporating emergency management needs in the development of weather radar networks

2009 ◽  
Vol 7 (1) ◽  
pp. 45 ◽  
Author(s):  
Ellen J. Bass, PhD ◽  
Leigh Baumgart, MS ◽  
Brenda Philips, MBA ◽  
Kevin Kloesel, PhD ◽  
Kathleen Dougherty, MA ◽  
...  
Keyword(s):  
High Resolution ◽  
Emergency Management ◽  
Low Cost ◽  
High Resolution Data ◽  
Product Usage ◽  
Adaptive Sensing ◽  
Emergency Managers ◽  
Radar Networks ◽  
Network Concept ◽  
Resolution Data

The Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) is developing networks of lowpower, low-cost radars that adaptively collect, process, and visualize high-resolution data in the lowest portion of the atmosphere. CASA researchers are working with emergency managers (EM) to ensure that the network concept is designed with EMs’ needs in mind. Interviews, surveys, analysis of product usage logs, and simulated scenarios are being used to solicit EM input. Results indicate the need for products for both high- and low-bandwidth end users, visualizations for velocity products that are more easily interpreted, and enhanced training. CASA researchers are developing interventions to address these needs.

Design and Validation of a Low-Cost Microscope for Diagnostics in the Developing World

2009 ◽  
Vol 17 (6) ◽  
pp. 16-19 ◽  
Author(s):  
B. Cline ◽  
R. Luo ◽  
K. Kuhlmann
Keyword(s):  
High Resolution ◽  
Infectious Diseases ◽  
Developing World ◽  
Low Cost ◽  
High Resolution Imaging ◽  
Microscope Examination ◽  
Resource Limited Settings ◽  
Resource Limited ◽  
Resolution Imaging

Many infectious diseases prevalent in the developing world, including malaria and tuberculosis, are difficult to diagnose on the basis of symptoms alone but can be accurately detected using microscope examination. Currently the expense, size, and fragility of optical microscopes impede their widespread use in resource-limited settings. Addressing these obstacles facing microscopy in the developing world is a pressing need; over 800,000 people, primarily children in Africa, die annually of malaria, and more than 1,500,000 people die annually of tuberculosis [1][2]. The aim of this study is to design and validate a microscope for use in the developing world that combines high-resolution imaging, extreme affordability, and long-term durability.

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