A Deep-Learning Approach to ECG Classification Based on Adversarial Domain Adaptation

Niu, Lisha; Chen, Chao; Liu, Hui; Zhou, Shuwang; Shu, Minglei

doi:10.3390/healthcare8040437

Cited by 38 publications

(23 citation statements)

References 24 publications

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“…Wang et al proposed a method combining CNN with multilayer perceptron (MLP), in which CNN was used to extract features, and the extracted features were fused with the RR interval and input into MLP, with an Acc rate of 96.27% [26]. Wang et al used [28]. Our model exhibited excellent performance, particularly in terms of Sen. Our model…”

Section: Discussionmentioning

confidence: 93%

Automated Detection of Arrhythmia for Hybrid Neural Network of LSTM-Residual with Multi-Information Fusion

Tao

Liu

Liang

2021

Mathematical Problems in Engineering

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Arrhythmia is a common cardiovascular disease; the electrocardiogram (ECG) is widely used as an effective tool for detecting arrhythmia. However, real-time arrhythmia detection monitoring is difficult, so this study proposes a long short-term memory-residual model. Individual beats provide morphological features and combined with adjacent segments provide temporal features. Our proposed model captures the time-domain and morphological ECG signal information simultaneously and fuses the two information types. At the same time, the attention block is applied to the network to further strengthen the useful information, capture the hidden information in the ECG signal, and improve the model classification performance. Our model was finally trained and tested on the MIT-BIH arrhythmia database, and the entire dataset was divided into intrapatient and interpatient modes. Accuracies of 99.11% and 85.65%, respectively, were obtained under the two modes. Experimental results demonstrate that our proposed method is an efficient automated detection method.

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Section: Discussionmentioning

confidence: 93%

Automated Detection of Arrhythmia for Hybrid Neural Network of LSTM-Residual with Multi-Information Fusion

Tao

Liu

Liang

2021

Mathematical Problems in Engineering

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“…(2) Heartbeat relocating: Due to the interception according to the temporal causes, the start position and the end position of the heartbeat in different samples were inconsistent. In this work, the start position and end position of the ECG signal were relocated based on Niu's work (Niu et al, 2020). It takes the middle temporal position between the first R wave in the sample and its preceding R wave in the collected ECG data as the start position.…”

Section: Electrocardiogram Preprocessingmentioning

confidence: 99%

Real-time mental stress detection using multimodality expressions with a deep learning framework

et al. 2022

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Mental stress is becoming increasingly widespread and gradually severe in modern society, threatening people’s physical and mental health. To avoid the adverse effects of stress on people, it is imperative to detect stress in time. Many studies have demonstrated the effectiveness of using objective indicators to detect stress. Over the past few years, a growing number of researchers have been trying to use deep learning technology to detect stress. However, these works usually use single-modality for stress detection and rarely combine stress-related information from multimodality. In this paper, a real-time deep learning framework is proposed to fuse ECG, voice, and facial expressions for acute stress detection. The framework extracts the stress-related information of the corresponding input through ResNet50 and I3D with the temporal attention module (TAM), where TAM can highlight the distinguishing temporal representation for facial expressions about stress. The matrix eigenvector-based approach is then used to fuse the multimodality information about stress. To validate the effectiveness of the framework, a well-established psychological experiment, the Montreal imaging stress task (MIST), was applied in this work. We collected multimodality data from 20 participants during MIST. The results demonstrate that the framework can combine stress-related information from multimodality to achieve 85.1% accuracy in distinguishing acute stress. It can serve as a tool for computer-aided stress detection.

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“…ECG [50] is the heart activity which is recorded by electrocardiography for determining heart rhythm, multifaceted arrhythmia, and the myocardial ischemia [51]. LPWAN devices can be used to capture these data for obtaining maximum information, which can be used for extensive analysis [52].…”

Section: Electrocardiogram (Ecg)mentioning

confidence: 99%

Machine Learning and LPWAN Based Internet of Things Applications in Healthcare Sector during COVID-19 Pandemic

2021

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Low power wide area networks (LPWAN) are comprised of small devices having restricted processing resources and limited energy budget. These devices are connected with each other using communication protocols. Considering their available resources, these devices can be used in a number of different Internet of Things (IoT) applications. Another interesting paradigm is machine learning, which can also be integrated with LPWAN technology to embed intelligence into these IoT applications. These machine learning-based applications combine intelligence with LPWAN and prove to be a useful tool. One such IoT application is in the medical field, where they can be used to provide multiple services. In the scenario of the COVID-19 pandemic, the importance of LPWAN-based medical services has gained particular attention. This article describes various COVID-19-related healthcare services, using the the applications of machine learning and LPWAN in improving the medical domain during the current COVID-19 pandemic. We validate our idea with the help of a case study that describes a way to reduce the spread of any pandemic using LPWAN technology and machine learning. The case study compares k-Nearest Neighbors (KNN) and trust-based algorithms for mitigating the flow of virus spread. The simulation results show the effectiveness of KNN for curtailing the COVID-19 spread.

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A Deep-Learning Approach to ECG Classification Based on Adversarial Domain Adaptation

Cited by 38 publications

References 24 publications

Automated Detection of Arrhythmia for Hybrid Neural Network of LSTM-Residual with Multi-Information Fusion

Automated Detection of Arrhythmia for Hybrid Neural Network of LSTM-Residual with Multi-Information Fusion

Real-time mental stress detection using multimodality expressions with a deep learning framework

Machine Learning and LPWAN Based Internet of Things Applications in Healthcare Sector during COVID-19 Pandemic

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