An Intelligent EEG Classification Methodology Based on Sparse Representation Enhanced Deep Learning Networks

Huang, Jingshan; Li, Yang; Chen, Bin-Qiang; Chen, Lin; Yao, Bin

doi:10.3389/fnins.2020.00808

Cited by 20 publications

(7 citation statements)

References 33 publications

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“…Huang et al (2020) employed a classifier by the use of sparse representation (SR) and fast compression residual CNN (FCRes‐CNN). At last, the samples of the EEG types are imported into the FCRes‐CNN model with a fast down‐sampling module and residual block structural modules to be detected and classified.…”

Section: Existing Work On Ecg Bio‐signal Classificationmentioning

confidence: 99%

Internet of things and deep learning enabled healthcare disease diagnosis using biomedical electrocardiogram signals

et al. 2021

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With recent advancements in the internet of things (IoT), wearables, and sensing technologies, the quality of healthcare services gets improved and it caused a shift from conventional clinical‐based healthcare to real‐time monitoring. The sensors are commonly integrated into several medical gadgets to save the bio‐signals produced by the physiological activities of the human body. At the same time, a biomedical electrocardiogram (ECG) signal is employed as a familiar way to examine and diagnose cardiovascular diseases (CVDs), which is rapid and non‐invasive. Since the increasing number of patients degrades the classification performance due to high differences in the ECG signal patterns among several patients, computer‐assisted automated diagnostic tools are essential for ECG signal classification. With this motivation, this paper introduces a new IoT and deep learning (DL) enabled healthcare disease diagnosis (IoTDL‐HDD) model using biomedical ECG signals. The proposed IoTDL‐HDD model aims to detect the presence of CVDs by the use of DL models in biomedical ECG signals. In addition, the proposed IoTDL‐HDD model utilizes a BiLSTM feature extraction technique to extract useful feature vectors from the ECG signals. For improving the efficiency of the BiLSTM technique, the artificial flora optimization (AFO) algorithm is employed as a hyperparameter optimizer. Besides, a fuzzy deep neural network (FDNN) classifier is employed for assigning proper class labels to the ECG signals. The performance of the IoTDL‐HDD model is examined on biomedical ECG signals and the outcomes are inspected in distinct features. The resultant experimental outcomes pointed out the supremacy of the IoTDL‐HDD model with the maximum accuracy of 93.452%.

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Section: Existing Work On Ecg Bio‐signal Classificationmentioning

confidence: 99%

Internet of things and deep learning enabled healthcare disease diagnosis using biomedical electrocardiogram signals

et al. 2021

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“…Although EEG signals have good temporal resolution, they suffer from low spatial resolution [29][30][31] , which makes decoding these types of brain signals a challenging issue. So, processing collected EEG signals to identify brain commands is an important step in BCI systems 32 . Motor Imagery signal is a type of EEG signal that records when the subject is imagining the movement of a limb.…”

Section: Introductionmentioning

confidence: 99%

EEG Signal Processing to Control a Finger Rehabilitation System

FallahTaherpazir,

Menhaj,

Sajedin

2023

Preprint

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This study aims to provide a comprehensive comparison for classification of Electroencephalography (EEG) signal based motor imagery, in time domain and time-frequency domain with different classifiers. We used EEG signals recorded while the subjects were imagining the movement of individual fingers, and analyzed the signals in time domain as well as using wavelet transform and Wigner transform. Our main goal is to compare different methods of feature extraction and classification as the important steps in the process of EEG signals for the Brain-Computer Interface (BCI) system. The experimental results indicate that the Support Vector Machine (SVM) method provides a better classification performance compared with other classification methods. Also, Linear Discriminative Analysis (LDA) performs as well as the SVM, after applying PCA for dimension reduction. The proposed scheme can be applied successfully to BCI systems where the amount of large data.

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“…Artificial intelligence (AI) has recently made remarkable progress. It is widely used in medical data analysis, such as detection of diabetic retinopathy [ 6 ], classification of neonates cry [ 7 ], and analysis of electroencephalogram (EEG) signals [ 8 ]. The detection of rib fracture can be regarded as a computer vision task.…”

Section: Introductionmentioning

confidence: 99%

Rib Fracture Detection with Dual-Attention Enhanced U-Net

Zhou

Jia

et al. 2022

Computational and Mathematical Methods in Medicine

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Rib fractures are common injuries caused by chest trauma, which may cause serious consequences. It is essential to diagnose rib fractures accurately. Low-dose thoracic computed tomography (CT) is commonly used for rib fracture diagnosis, and convolutional neural network- (CNN-) based methods have assisted doctors in rib fracture diagnosis in recent years. However, due to the lack of rib fracture data and the irregular, various shape of rib fractures, it is difficult for CNN-based methods to extract rib fracture features. As a result, they cannot achieve satisfying results in terms of accuracy and sensitivity in detecting rib fractures. Inspired by the attention mechanism, we proposed the CFSG U-Net for rib fracture detection. The CSFG U-Net uses the U-Net architecture and is enhanced by a dual-attention module, including a channel-wise fusion attention module (CFAM) and a spatial-wise group attention module (SGAM). CFAM uses the channel attention mechanism to reweight the feature map along the channel dimension and refine the U-Net’s skip connections. SGAM uses the group technique to generate spatial attention to adjust feature maps in the spatial dimension, which allows the spatial attention module to capture more fine-grained semantic information. To evaluate the effectiveness of our proposed methods, we established a rib fracture dataset in our research. The experimental results on our dataset show that the maximum sensitivity of our proposed method is 89.58%, and the average FROC score is 81.28%, which outperforms the existing rib fracture detection methods and attention modules.

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An Intelligent EEG Classification Methodology Based on Sparse Representation Enhanced Deep Learning Networks

Cited by 20 publications

References 33 publications

Internet of things and deep learning enabled healthcare disease diagnosis using biomedical electrocardiogram signals

Internet of things and deep learning enabled healthcare disease diagnosis using biomedical electrocardiogram signals

EEG Signal Processing to Control a Finger Rehabilitation System

Rib Fracture Detection with Dual-Attention Enhanced U-Net

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