Deep back propagation–long short-term memory network based upper-limb sEMG signal classification for automated rehabilitation

Wang, Ying; Wu, Qun; Dey, Nilanjan; Fong, Simon; Ashour, Amira S.

doi:10.1016/j.bbe.2020.05.003

Cited by 45 publications

(10 citation statements)

References 46 publications

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“…In fully connected networks, the layers are fully connected and the nodes between layers are connectionless and process only one input. In the case of LSTM, the nodes are connected from a directed graph along a temporal sequence that is considered an input with a specific order [ 21 ]. Hence, the 2-D CNN and LSTM layout feature combination improves classification greatly.…”

Section: Introductionmentioning

confidence: 99%

A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images

Islam

Asraf

2020

Informatics in Medicine Unlocked

527

278

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Nowadays, automatic disease detection has become a crucial issue in medical science due to rapid population growth. An automatic disease detection framework assists doctors in the diagnosis of disease and provides exact, consistent, and fast results and reduces the death rate. Coronavirus (COVID-19) has become one of the most severe and acute diseases in recent times and has spread globally. Therefore, an automated detection system, as the fastest diagnostic option, should be implemented to impede COVID-19 from spreading. This paper aims to introduce a deep learning technique based on the combination of a convolutional neural network (CNN) and long short-term memory (LSTM) to diagnose COVID-19 automatically from X-ray images. In this system, CNN is used for deep feature extraction and LSTM is used for detection using the extracted feature. A collection of 4575 X-ray images, including 1525 images of COVID-19, were used as a dataset in this system. The experimental results show that our proposed system achieved an accuracy of 99.4%, AUC of 99.9%, specificity of 99.2%, sensitivity of 99.3%, and F1-score of 98.9%. The system achieved desired results on the currently available dataset, which can be further improved when more COVID-19 images become available. The proposed system can help doctors to diagnose and treat COVID-19 patients easily.

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Section: Introductionmentioning

confidence: 99%

A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images

Islam

Asraf

2020

Informatics in Medicine Unlocked

527

278

View full text Add to dashboard Cite

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“…Some of them transformed the 1D time series into spectrograms using wavelet transforms, Fourier transforms, etc. [ 19 , 35 ], whereas others obtained time–domain characteristics by plotting the waveforms directly onto a canvas [ 36 , 37 ]. The former strategy aimed to highlight the time–frequency characteristics, whereas the latter focused on the original time–domain information.…”

Section: Discussionmentioning

confidence: 99%

Identifying Patient–Ventilator Asynchrony on a Small Dataset Using Image-Based Transfer Learning

Pan

Jia

Liu

et al. 2021

Sensors

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Mechanical ventilation is an essential life-support treatment for patients who cannot breathe independently. Patient–ventilator asynchrony (PVA) occurs when ventilatory support does not match the needs of the patient and is associated with a series of adverse clinical outcomes. Deep learning methods have shown a strong discriminative ability for PVA detection, but they require a large number of annotated data for model training, which hampers their application to this task. We developed a transfer learning architecture based on pretrained convolutional neural networks (CNN) and used it for PVA recognition based on small datasets. The one-dimensional signal was converted to a two-dimensional image, and features were extracted by the CNN using pretrained weights for classification. A partial dropping cross-validation technique was developed to evaluate model performance on small datasets. When using large datasets, the performance of the proposed method was similar to that of non-transfer learning methods. However, when the amount of data was reduced to 1%, the accuracy of transfer learning was approximately 90%, whereas the accuracy of the non-transfer learning was less than 80%. The findings suggest that the proposed transfer learning method can obtain satisfactory accuracies for PVA detection when using small datasets. Such a method can promote the application of deep learning to detect more types of PVA under various ventilation modes.

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“…Various properties were obtained from each subband; these features were then classified using a radial basis function support vector machine (SVM), with an accuracy of 97.74%. Wang et al [26] proposed an approach that could be used in rehabilitation devices for individuals with disabilities in their upper-limbs. They used a deep model to achieve high classification rates, reaching 92% accuracy using their recurrent deep model for six class classification.…”

Section: Introductionmentioning

confidence: 99%

A Hand-Modeled Feature Extraction-Based Learning Network to Detect Grasps Using sEMG Signal

Bayğın

Barua

Doğan

et al. 2022

Sensors

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Recently, deep models have been very popular because they achieve excellent performance with many classification problems. Deep networks have high computational complexities and require specific hardware. To overcome this problem (without decreasing classification ability), a hand-modeled feature selection method is proposed in this paper. A new shape-based local feature extractor is presented which uses the geometric shape of the frustum. By using a frustum pattern, textural features are generated. Moreover, statistical features have been extracted in this model. Textures and statistics features are fused, and a hybrid feature extraction phase is obtained; these features are low-level. To generate high level features, tunable Q factor wavelet transform (TQWT) is used. The presented hybrid feature generator creates 154 feature vectors; hence, it is named Frustum154. In the multilevel feature creation phase, this model can select the appropriate feature vectors automatically and create the final feature vector by merging the appropriate feature vectors. Iterative neighborhood component analysis (INCA) chooses the best feature vector, and shallow classifiers are then used. Frustum154 has been tested on three basic hand-movement sEMG datasets. Hand-movement sEMG datasets are commonly used in biomedical engineering, but there are some problems in this area. The presented models generally required one dataset to achieve high classification ability. In this work, three sEMG datasets have been used to test the performance of Frustum154. The presented model is self-organized and selects the most informative subbands and features automatically. It achieved 98.89%, 94.94%, and 95.30% classification accuracies using shallow classifiers, indicating that Frustum154 can improve classification accuracy.

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Deep back propagation–long short-term memory network based upper-limb sEMG signal classification for automated rehabilitation

Cited by 45 publications

References 46 publications

A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images

A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images

Identifying Patient–Ventilator Asynchrony on a Small Dataset Using Image-Based Transfer Learning

A Hand-Modeled Feature Extraction-Based Learning Network to Detect Grasps Using sEMG Signal

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