scholarly journals A Novel Sleep Staging Network Based on Data Adaptation and Multimodal Fusion

2021 ◽  
Vol 15 ◽  
Author(s):  
Lijuan Duan ◽  
Mengying Li ◽  
Changming Wang ◽  
Yuanhua Qiao ◽  
Zeyu Wang ◽  
...  
Keyword(s):  
Single Channel ◽  
Feature Fusion ◽  
Multimodal Fusion ◽  
Computer Assisted ◽  
Sleep Staging ◽  
Time Frequency ◽  
Probabilistic Network ◽  
Sleep Analysis ◽  
High Layer ◽  
3D Cnn

Sleep staging is one of the important methods to diagnosis and treatment of sleep diseases. However, it is laborious and time-consuming, therefore, computer assisted sleep staging is necessary. Most of the existing sleep staging researches using hand-engineered features rely on prior knowledges of sleep analysis, and usually single channel electroencephalogram (EEG) is used for sleep staging task. Prior knowledge is not always available, and single channel EEG signal cannot fully represent the patient’s sleeping physiological states. To tackle the above two problems, we propose an automatic sleep staging network model based on data adaptation and multimodal feature fusion using EEG and electrooculogram (EOG) signals. 3D-CNN is used to extract the time-frequency features of EEG at different time scales, and LSTM is used to learn the frequency evolution of EOG. The nonlinear relationship between the High-layer features of EEG and EOG is fitted by deep probabilistic network. Experiments on SLEEP-EDF and a private dataset show that the proposed model achieves state-of-the-art performance. Moreover, the prediction result is in accordance with that from the expert diagnosis.

scholarly journals A Multi-Class Automatic Sleep Staging Method Based on Photoplethysmography Signals

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 116
Author(s):  
Xiangfa Zhao ◽  
Guobing Sun
Keyword(s):  
Time Domain ◽  
Single Channel ◽  
Kappa Statistic ◽  
Gradient Boosting ◽  
Sleep Staging ◽  
Challenging Problem ◽  
Sleep State ◽  
Light Gradient ◽  
Gradient Boosting Machine ◽  
The Time Domain

Automatic sleep staging with only one channel is a challenging problem in sleep-related research. In this paper, a simple and efficient method named PPG-based multi-class automatic sleep staging (PMSS) is proposed using only a photoplethysmography (PPG) signal. Single-channel PPG data were obtained from four categories of subjects in the CAP sleep database. After the preprocessing of PPG data, feature extraction was performed from the time domain, frequency domain, and nonlinear domain, and a total of 21 features were extracted. Finally, the Light Gradient Boosting Machine (LightGBM) classifier was used for multi-class sleep staging. The accuracy of the multi-class automatic sleep staging was over 70%, and the Cohen’s kappa statistic k was over 0.6. This also showed that the PMSS method can also be applied to stage the sleep state for patients with sleep disorders.

Analysis of Vibration Signals in Monitoring Titanium End Milling Process Using Triaxial Accelerometer

2021 ◽  
Author(s):  
John Henry Navarro-Devia ◽  
Dzung Viet Dao ◽  
Yun Chen ◽  
Huaizhong Li
Keyword(s):  
Titanium Alloys ◽  
Frequency Domain ◽  
Single Channel ◽  
End Milling ◽  
Cutting Parameters ◽  
Frequency Component ◽  
Spindle Speed ◽  
Triaxial Accelerometer ◽  
Vibration Signals ◽  
Time Frequency

Abstract Vibrations during milling of hard-to-cut materials can cause low productivity, inferior quality and short tool life. It is one of the common issues in the machining of hard-to-cut materials employed in aerospace applications, such as titanium alloys. This paper presents an analysis of the vibration signals in the 3 axes of movement during titanium end milling, under diverse cutting parameters, manipulating spindle speed and feed rate. Signals were obtained using a triaxial accelerometer and processed in MATLAB. The analysis was conducted in the frequency-domain and the time-frequency domain. The results show that high-frequency vibration could occur in any direction with different amplitudes. Response on each axis depends on spindle speed, feed, and type of milling. A frequency component continually appeared in each axis regardless of cutting conditions and is located near the natural frequencies. Finally, the triaxial accelerations were compared for the milling cases with a new and a worn tool. Results highlight the importance and need for continuous monitoring of vibration in the 3 axes, instead of only using a single-channel signal, providing experimental data which could expand knowledge relating to the milling of titanium alloys.

Computer-assisted sleep staging

2001 ◽  
Vol 48 (12) ◽  
pp. 1412-1423 ◽  
Author(s):  
R. Agarwal ◽  
J. Gotman
Keyword(s):  
Computer Assisted ◽  
Sleep Staging

scholarly journals Detection of Solitary Pulmonary Nodules Based on Brain-Computer Interface

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Shi Qiu ◽  
Junjun Li ◽  
Mengdi Cong ◽  
Chun Wu ◽  
Yan Qin ◽  
...  
Keyword(s):  
Single Channel ◽  
Brain Computer Interface ◽  
Pulmonary Nodules ◽  
Electrical Signal ◽  
Computer Interface ◽  
Solitary Pulmonary Nodules ◽  
Brain Response ◽  
Time Frequency ◽  
Image Presentation ◽  
The Brain

Solitary pulmonary nodules are the main manifestation of pulmonary lesions. Doctors often make diagnosis by observing the lung CT images. In order to further study the brain response structure and construct a brain-computer interface, we propose an isolated pulmonary nodule detection model based on a brain-computer interface. First, a single channel time-frequency feature extraction model is constructed based on the analysis of EEG data. Second, a multilayer fusion model is proposed to establish the brain-computer interface by connecting the brain electrical signal with a computer. Finally, according to image presentation, a three-frame image presentation method with different window widths and window positions is proposed to effectively detect the solitary pulmonary nodules.

scholarly journals Small-Scale Face Detection Based on Improved R-FCN

2020 ◽  
Vol 10 (12) ◽  
pp. 4177
Author(s):  
Chaowei Tang ◽  
Shiyu Chen ◽  
Xu Zhou ◽  
Shuai Ruan ◽  
Haotian Wen
Keyword(s):  
Receptive Field ◽  
Face Detection ◽  
Feature Fusion ◽  
Selection Method ◽  
Local Information ◽  
Small Scale ◽  
Average Precision ◽  
Convolutional Network ◽  
High Layer ◽  
Novel Method

Face detection is an important basic technique for face-related applications, such as face analysis, recognition, and reconstruction. Images in unconstrained scenes may contain many small-scale faces. The features that the detector can extract from small-scale faces are limited, which will cause missed detection and greatly reduce the precision of face detection. Therefore, this study proposes a novel method to detect small-scale faces based on region-based fully convolutional network (R-FCN). First, we propose a novel R-FCN framework with the ability of feature fusion and receptive field adaptation. Second, a bottom-up feature fusion branch is established to enrich the local information of high-layer features. Third, a receptive field adaptation block (RFAB) is proposed to ensure that the receptive field can be adaptively selected to strengthen the expression ability of features. Finally, we improve the anchor setting method and adopt soft non-maximum suppression (SoftNMS) as the selection method of candidate boxes. Experimental results show that average precision for small-scale face detection of R-FCN with feature fusion branch and RFAB (RFAB-f-R-FCN) is improved by 0.8%, 2.9%, and 11% on three subsets of Wider Face compared with that of R-FCN.

scholarly journals Unsupervised Learning for Monaural Source Separation Using Maximization–Minimization Algorithm with Time–Frequency Deconvolution

Sensors ◽  
2018 ◽  
Vol 18 (5) ◽  
pp. 1371 ◽  
Author(s):  
Wai Lok Woo ◽  
Bin Gao ◽  
Ahmed Bouridane ◽  
Bingo Wing-Kuen Ling ◽  
Cheng Siong Chin
Keyword(s):  
Unsupervised Learning ◽  
Single Channel ◽  
Learning Algorithm ◽  
Source Separation ◽  
Nonnegative Matrix ◽  
Least Square ◽  
Separation Performance ◽  
Time Frequency ◽  
Special Cases ◽  
Leibler Divergence

This paper presents an unsupervised learning algorithm for sparse nonnegative matrix factor time–frequency deconvolution with optimized fractional β-divergence. The β-divergence is a group of cost functions parametrized by a single parameter β. The Itakura–Saito divergence, Kullback–Leibler divergence and Least Square distance are special cases that correspond to β=0, 1, 2, respectively. This paper presents a generalized algorithm that uses a flexible range of β that includes fractional values. It describes a maximization–minimization (MM) algorithm leading to the development of a fast convergence multiplicative update algorithm with guaranteed convergence. The proposed model operates in the time–frequency domain and decomposes an information-bearing matrix into two-dimensional deconvolution of factor matrices that represent the spectral dictionary and temporal codes. The deconvolution process has been optimized to yield sparse temporal codes through maximizing the likelihood of the observations. The paper also presents a method to estimate the fractional β value. The method is demonstrated on separating audio mixtures recorded from a single channel. The paper shows that the extraction of the spectral dictionary and temporal codes is significantly more efficient by using the proposed algorithm and subsequently leads to better source separation performance. Experimental tests and comparisons with other factorization methods have been conducted to verify its efficacy.

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