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Qi M, Shao H, Shi N, Wang G, Lv Y. Arrhythmia classification detection based on multiple electrocardiograms databases. PLoS One 2023; 18:e0290995. [PMID: 37756278 PMCID: PMC10529562 DOI: 10.1371/journal.pone.0290995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 08/20/2023] [Indexed: 09/29/2023] Open
Abstract
According to the World Health Organization, cardiovascular diseases are the leading cause of deaths globally. Electrocardiogram (ECG) is a non-invasive approach for detecting heart diseases and reducing the risk of heart disease-related death. However, there are limited numbers of ECG samples and imbalance distribution for existing ECG databases. It is difficult to train practical and efficient neural networks. Based on the analysis and research of many existing ECG databases, this paper conduct an in-depth study on three fine-labeled ECG databases, to extract heartbeats, unify the sampling frequency, and propose a self-processing method of heartbeats, and finally form a unified ECG arrhythmia classification database, noted as Hercules-3. It is separated into training sets (80%) and testing sets (the remaining 20%). In order to verify its capabilities, we have trained a 16-classification fully connected neural network based on Hercules-3 and it achieves an accuracy rate of up to 98.67%. Compared with other data processing, our proposed method improves classification recall by at least 6%, classification accuracy by at least 4%, and F1-score by at least 7%.
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Affiliation(s)
- Meng Qi
- Computer and Information Engineering Department, Luoyang Institute of Science and Technology, Luoyang, China
- Henan Province Engineering Research Center of Industrial Intelligent Vision, Luoyang, China
| | - Hongxiang Shao
- Computer and Information Engineering Department, Luoyang Institute of Science and Technology, Luoyang, China
- Henan Province Engineering Research Center of Industrial Intelligent Vision, Luoyang, China
| | - Nianfeng Shi
- Computer and Information Engineering Department, Luoyang Institute of Science and Technology, Luoyang, China
- Henan Province Engineering Research Center of Industrial Intelligent Vision, Luoyang, China
| | - Guoqiang Wang
- Computer and Information Engineering Department, Luoyang Institute of Science and Technology, Luoyang, China
- Henan Province Engineering Research Center of Industrial Intelligent Vision, Luoyang, China
| | - Yifei Lv
- School of Computer Science and Engineering Department, Tianjin University of Technology, Tianjin, China
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Xiao D, Liu P, Wang J, Gu Z, Yu H. Mining belt foreign body detection method based on YOLOv4_GECA model. Sci Rep 2023; 13:8881. [PMID: 37264072 DOI: 10.1038/s41598-023-35962-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 05/26/2023] [Indexed: 06/03/2023] Open
Abstract
In the process of mining belt transportation, various foreign objects may appear, which will have a great impact on the crusher and belt, thus affecting production progress and causing serious safety accidents. Therefore, it is important to detect foreign objects in the early stages of intrusion in mining belt conveyor systems. To solve this problem, the YOLOv4_GECA method is proposed in this paper. Firstly, the GECA attention module is added to establish the YOLOv4_GECA foreign object detection model in the mineral belt to enhance the foreign object feature extraction capability. Secondly, based on this model, the learning rate decay of restart cosine annealing is used to improve the foreign object image detection performance of the model. Finally, we collected belt transport image information from the Pai Shan Lou gold mine site in Shenyang and established a belt foreign body detection dataset. The experimental results show that the average detection accuracy of the YOLOv4_GECA method proposed in this paper is 90.1%, the recall rate is 90.7%, and the average detection time is 30 ms, which meets the requirements for detection accuracy and real-time performance at the mine belt transportation site.
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Affiliation(s)
- Dong Xiao
- Information Science and Engineering School, Northeastern University, Shenyang, 110004, China.
- Liaoning Key Laboratory of Intelligent Diagnosis and Safety for Metallurgical Industry, Northeastern University, Shenyang, 110819, China.
| | - Panpan Liu
- Information Science and Engineering School, Northeastern University, Shenyang, 110004, China
- Liaoning Key Laboratory of Intelligent Diagnosis and Safety for Metallurgical Industry, Northeastern University, Shenyang, 110819, China
| | - Jichun Wang
- Shenyang Institute of Computing Technology Co. Ltd., CAS, Shenyang, 110168, Liaoning, China
- College of Science, Shenyang Jianzhu University, Shenyang, 110168, Liaoning, China
| | - Zhengmin Gu
- The First Hospital of China Medical University, Shenyang, China
| | - Hang Yu
- Information Science and Engineering School, Northeastern University, Shenyang, 110004, China
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Fan T, Qiu S, Wang Z, Zhao H, Jiang J, Wang Y, Xu J, Sun T, Jiang N. A new deep convolutional neural network incorporating attentional mechanisms for ECG emotion recognition. Comput Biol Med 2023; 159:106938. [PMID: 37119553 DOI: 10.1016/j.compbiomed.2023.106938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Revised: 03/28/2023] [Accepted: 04/14/2023] [Indexed: 05/01/2023]
Abstract
Using ECG signals captured by wearable devices for emotion recognition is a feasible solution. We propose a deep convolutional neural network incorporating attentional mechanisms for ECG emotion recognition. In order to address the problem of individuality differences in emotion recognition tasks, we incorporate an improved Convolutional Block Attention Module (CBAM) into the proposed deep convolutional neural network. The deep convolutional neural network is responsible for capturing ECG features. Channel attention in CBAM is responsible for adding weight information to ECG features of different channels and spatial attention is responsible for the weighted representation of ECG features of different regions inside the channel. We used three publicly available datasets, WESAD, DREAMER, and ASCERTAIN, for the ECG emotion recognition task. The new state-of-the-art results are set in three datasets for multi-class classification results, WESAD for tri-class results, and ASCERTAIN for two-category results, respectively. A large number of experiments are performed, providing an interesting analysis of the design of the convolutional structure parameters and the role of the attention mechanism used. We propose to use large convolutional kernels to improve the effective perceptual field of the model and thus fully capture the ECG signal features, which achieves better performance compared to the commonly used small kernels. In addition, channel attention and spatial attention were added to the deep convolutional model separately to explore their contribution levels. We found that in most cases, channel attention contributed to the model at a higher level than spatial attention.
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Affiliation(s)
- Tianqi Fan
- Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian, China.
| | - Sen Qiu
- Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian, China.
| | - Zhelong Wang
- Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian, China.
| | - Hongyu Zhao
- Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian, China.
| | - Junhan Jiang
- First Affiliated Hospital of China Medical University, Shenyang, China.
| | | | - Junnan Xu
- Department of Medical Oncology, Cancer Hospital of Dalian University of Technology, Shenyang, China.
| | - Tao Sun
- Department of Medical Oncology, Cancer Hospital of Dalian University of Technology, Shenyang, China.
| | - Nan Jiang
- College of Information Engineering, East China Jiaotong University, Nanchang, China.
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Moradi H, Al-Hourani A, Concilia G, Khoshmanesh F, Nezami FR, Needham S, Baratchi S, Khoshmanesh K. Recent developments in modeling, imaging, and monitoring of cardiovascular diseases using machine learning. Biophys Rev 2023; 15:19-33. [PMID: 36909958 PMCID: PMC9995635 DOI: 10.1007/s12551-022-01040-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 12/21/2022] [Indexed: 01/12/2023] Open
Abstract
Cardiovascular diseases are the leading cause of mortality, morbidity, and hospitalization around the world. Recent technological advances have facilitated analyzing, visualizing, and monitoring cardiovascular diseases using emerging computational fluid dynamics, blood flow imaging, and wearable sensing technologies. Yet, computational cost, limited spatiotemporal resolution, and obstacles for thorough data analysis have hindered the utility of such techniques to curb cardiovascular diseases. We herein discuss how leveraging machine learning techniques, and in particular deep learning methods, could overcome these limitations and offer promise for translation. We discuss the remarkable capacity of recently developed machine learning techniques to accelerate flow modeling, enhance the resolution while reduce the noise and scanning time of current blood flow imaging techniques, and accurate detection of cardiovascular diseases using a plethora of data collected by wearable sensors.
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Affiliation(s)
- Hamed Moradi
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Akram Al-Hourani
- School of Engineering, RMIT University, Melbourne, Victoria Australia
| | | | - Farnaz Khoshmanesh
- School of Allied Health, Human Services & Sport, La Trobe University, Melbourne, Victoria Australia
| | - Farhad R. Nezami
- Division of Thoracic and Cardiac Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA USA
| | - Scott Needham
- Leading Technology Group, Melbourne, Victoria Australia
| | - Sara Baratchi
- School of Health and Biomedical Sciences, RMIT University, Melbourne, Victoria Australia
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Wang J, Wu X. A deep learning refinement strategy based on efficient channel attention for atrial fibrillation and atrial flutter signals identification. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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X-CTRSNet: 3D cervical vertebra CT reconstruction and segmentation directly from 2D X-ray images. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2021.107680] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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