Low-Dimensional Denoising Embedding Transformer for ECG Classification

Guan, Jian; Wang, Wenbo; Feng, Pengming; Wang, Xinxin; Wang, Wenwu

doi:10.1109/icassp39728.2021.9413766

Cited by 16 publications

(4 citation statements)

References 17 publications

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“…Alternatively, hybrid architectures such as CNN-RNN have shown promising results in classifying hand movements [6], [25], as they benefit from advantages of different modules in extracting temporal and spatial features. Meanwhile, with the advent of the attention mechanism [10], transformers are being considered as a new ML technique for sequential data modeling [37], [38]. Capitalizing on the recent success of transformers in various fields such as machine translation [11], [39], speech recognition [40] and computer vision [12], we aim to examine its applicability and potentials for sEMGbased gesture recognition.…”

Section: Related Workmentioning

confidence: 99%

TraHGR: Transformer for Hand Gesture Recognition via Electromyography

Zabihi,

Rahimian,

Asif

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Deep learning-based Hand Gesture Recognition (HGR) via surface Electromyogram (sEMG) signals have recently shown considerable potential for development of advanced myoelectric-controlled prosthesis. Although deep learning techniques can improve HGR accuracy compared to their classical counterparts, classifying hand movements based on sparse multichannel sEMG signals is still a challenging task. Furthermore, existing deep learning approaches, typically, include only one model as such can hardly extract representative features.In this paper, we aim to address this challenge by capitalizing on the recent advances in hybrid models and transformers. In other words, we propose a hybrid framework based on the transformer architecture, which is a relatively new and revolutionizing deep learning model. The proposed hybrid architecture, referred to as the Transformer for Hand Gesture Recognition (TraHGR), consists of two parallel paths followed by a linear layer that acts as a fusion center to integrate the advantage of each module. We evaluated the proposed architecture TraHGR based on the commonly used second Ninapro dataset, referred to as the DB2. The sEMG signals in the DB2 dataset are measured in real-life conditions from 40 healthy users, each performing 49 gestures. We have conducted an extensive set of experiments to test and validate the proposed TraHGR architecture, and compare its achievable accuracy with several recently proposed HGR classification algorithms over the same dataset. We have also compared the results of the proposed TraHGR architecture with each individual path and demonstrated the distinguishing power of the proposed hybrid architecture. The recognition accuracies of the proposed TraHGR architecture for the window of size 200ms and step size of 100ms are 86.00%, 88.72%, 81.27%, and 93.74%, which are 2.30%, 4.93%, 8.65%, and 4.20% higher than the state-ofthe-art performance for DB2 (49 gestures), DB2-B (17 gestures), DB2-C (23 gestures), and DB2-D (9 gestures), respectively.

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Section: Related Workmentioning

confidence: 99%

TraHGR: Transformer for Hand Gesture Recognition via Electromyography

Zabihi,

Rahimian,

Asif

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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show abstract

“…Alternatively, hybrid architectures such as CNN-RNN have shown promising results in classifying hand movements [6], [16], as they benefit from advantages of different modules in extracting temporal and spatial features. Meanwhile, with the advent of the attention mechanism [22], transformers are being considered as a new ML technique for sequential data modeling [36], [37]. Capitalizing on the recent success of transformers in various fields such as machine translation [23], [38], speech recognition [39] and computer vision [24], we aim to examine its applicability and potentials for sEMGbased gesture recognition.…”

Section: Related Workmentioning

confidence: 99%

TraHGR: Transformer for Hand Gesture Recognition via ElectroMyography

Zabihi¹,

Rahimian²,

Asif³

et al. 2022

Preprint

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Deep learning-based Hand Gesture Recognition (HGR) via surface Electromyogram (sEMG) signals has recently shown considerable potentials for development of advanced myoelectric-controlled prosthesis. Although deep learning techniques can improve HGR accuracy compared to their classical counterparts, classifying hand movements based on sparse multichannel sEMG signals is still a challenging task. Furthermore, existing deep learning approaches, typically, include only one model as such can hardly maintain acceptable generalized performance in changing scenarios. In this paper, we aim to address this challenge by capitalizing on the recent advances of hybrid models and transformers. In other words, we propose a hybrid framework based on the transformer architecture, which is a relatively new and revolutionizing deep learning model. The proposed hybrid architecture, referred to as the Transformer forHand Gesture Recognition (TraHGR), consists of two parallel paths followed by a linear layer that acts as a fusion center to integrate the advantage of each module and provide robustness over different scenarios. We evaluated the proposed architecture TraHGR based on the commonly used second Ninapro dataset, referred to as the DB2. The sEMG signals in the DB2 dataset are measured in the real-life conditions from 40 healthy users, each performing 49 gestures. We have conducted extensive set of experiments to test and validate the proposed TraHGR architecture, and compare its achievable accuracy with more than five recently proposed HGR classification algorithms over the same dataset. W have also compared the results of the proposed TraHGR architecture with each individual path and demonstrated the distinguishing power of the proposed hybrid architecture. The recognition accuracies of the proposed TraHGR architecture are 86.18%, 88.91%, 81.44%, and 93.84%, which are 2.48%, 5.12%, 8.82%, and 4.30% higher than the state-ofthe-art performance for DB2 (49 gestures), DB2-B (17 gestures), DB2-C (23 gestures), and DB2-D (9 gestures), respectively.

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“…Transformer, the first sequence transduction model entirely based on attention, replacing the recurrent layers most commonly used in encoder-decoder architectures with multi-headed self-attention [23]. Existing studies have shown that the transformer can not only handle the problem in the field of translation, but can deal with the classification of temporal sequence [23], such as ECG sequence [33,34]. For the first time, we propose a DI-Transformer model to deal with the problem of ECG SQA, and its overall structure is shown in Fig.…”

Section: Proposed Dual-input Transformer Modelmentioning

confidence: 99%

Fusing depth local dual-view features and dual-input transformer framework for improving the recognition ability of motion artifact-contaminated electrocardiogram

Yuan

Zhao

et al. 2022

Complex Intell. Syst.

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Heart health monitoring based on wearable devices is often contaminated by various noises to varying degrees. Using signal quality indicators (SQIs) to achieve signal quality assessment (SQA) is among the most promising ways to solve this problem, but the performance of SQIs in expressing ECG quality features contaminated by motion artifact (MA) noise remains disappointing. Here, we present a novel SQA method that fuses the proposed depth local dual-view (DLDV) features and the dual-input transformer (DI-Transformer) framework to improve the recognition ability of MA-contaminated ECGs. The proposed DLDV features are to identify subtle differences between MA and ECG through depth local amplitude and phase angle features. When it fuses with the temporal relationship features extracted by DI-Transformer, its accuracy is significantly improved compared to the SQIs-based methods. In addition, we also verify the robustness and the accuracy of DLDV features on four traditional classifiers. Finally, we conduct our experiments on the two datasets. On the PhysioNet/Computing in Cardiology Challenge dataset, the DLDV features (Acc = 95.49%) outperform the combination of six SQIs features (Acc = 91.26%). When combined with our DI-Transformer, it delivered an accuracy of 99.62%, outperforming the state-of-the-art SQA methods. On the artificial testset constructed by MA noise, our DI-Transformer outperforms four traditional methods and also delivered an accuracy of 97.69%.

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Low-Dimensional Denoising Embedding Transformer for ECG Classification

Cited by 16 publications

References 17 publications

TraHGR: Transformer for Hand Gesture Recognition via Electromyography

TraHGR: Transformer for Hand Gesture Recognition via Electromyography

TraHGR: Transformer for Hand Gesture Recognition via ElectroMyography

Fusing depth local dual-view features and dual-input transformer framework for improving the recognition ability of motion artifact-contaminated electrocardiogram

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