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Li R, Zhao G, Muir DR, Ling Y, Burelo K, Khoe M, Wang D, Xing Y, Qiao N. Real-time sub-milliwatt epilepsy detection implemented on a spiking neural network edge inference processor. Comput Biol Med 2024; 183:109225. [PMID: 39413626 DOI: 10.1016/j.compbiomed.2024.109225] [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: 11/16/2023] [Revised: 06/05/2024] [Accepted: 09/26/2024] [Indexed: 10/18/2024]
Abstract
Analyzing electroencephalogram (EEG) signals to detect the epileptic seizure status of a subject presents a challenge to existing technologies aimed at providing timely and efficient diagnosis. In this study, we aimed to detect interictal and ictal periods of epileptic seizures using a spiking neural network (SNN). Our proposed approach provides an online and real-time preliminary diagnosis of epileptic seizures and helps to detect possible pathological conditions. To validate our approach, we conducted experiments using multiple datasets. We utilized a trained SNN to identify the presence of epileptic seizures and compared our results with those of related studies. The SNN model was deployed on Xylo, a digital SNN neuromorphic processor designed to process temporal signals. Xylo efficiently simulates spiking leaky integrate-and-fire neurons with exponential input synapses. Xylo has much lower energy requirements than traditional approaches to signal processing, making it an ideal platform for developing low-power seizure detection systems. Our proposed method has a high test accuracy of 93.3% and 92.9% when classifying ictal and interictal periods. At the same time, the application has an average power consumption of 87.4 μW (IO power) + 287.9 μW (compute power) when deployed to Xylo. Our method demonstrates excellent low-latency performance when tested on multiple datasets. Our work provides a new solution for seizure detection, and it is expected to be widely used in portable and wearable devices in the future.
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Affiliation(s)
- Ruixin Li
- State Key Laboratory of Digital Medical Engineering, Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Sanya, 572025, China; Chengdu SynSense Tech. Co. Ltd., 1577 Tianfu Road, Chengdu, 610041, Sichuan, China
| | - Guoxu Zhao
- State Key Laboratory of Digital Medical Engineering, Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Sanya, 572025, China
| | | | - Yuya Ling
- Chengdu SynSense Tech. Co. Ltd., 1577 Tianfu Road, Chengdu, 610041, Sichuan, China
| | - Karla Burelo
- Synsense, Thurgauerstrasse 60, Zürich, 8050, Switzerland
| | - Mina Khoe
- Synsense, Thurgauerstrasse 60, Zürich, 8050, Switzerland
| | - Dong Wang
- State Key Laboratory of Digital Medical Engineering, Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Sanya, 572025, China.
| | - Yannan Xing
- Chengdu SynSense Tech. Co. Ltd., 1577 Tianfu Road, Chengdu, 610041, Sichuan, China.
| | - Ning Qiao
- Chengdu SynSense Tech. Co. Ltd., 1577 Tianfu Road, Chengdu, 610041, Sichuan, China; Synsense, Thurgauerstrasse 60, Zürich, 8050, Switzerland
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2
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Zhang Y, Lai J, Zhao C, Wang J, Yan Y, Chen M, Ji L, Guo J, Han B, Shi Y, Chen Y, Yang W, Feng Q. Abnormal recognition-assisted and onset-offset aware network for pathological wearable ECG delineation. Artif Intell Med 2024; 157:102992. [PMID: 39369633 DOI: 10.1016/j.artmed.2024.102992] [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: 03/06/2024] [Revised: 08/19/2024] [Accepted: 09/26/2024] [Indexed: 10/08/2024]
Abstract
Electrocardiogram (ECG) delineation is essential to the identification of abnormal cardiac status, especially when ECG signals are remotely monitored with wearable devices. The complexity and diversity of cardiac conditions generate numerous pathological ECG patterns, not only requiring the recognition of normal ECG but also addressing an extensive range of abnormal ECG patterns, posing a challenging task. Therefore, we propose an abnormal recognition-assisted network to integrate supplementary information on diverse ECG patterns. Simultaneously, we design an onset-offset aware loss to enhance precise waveform localization. Specifically, we establish a two-branch framework where ECG delineation serves as the target task, producing the final segmentation results. Additionally, the abnormal recognition-assisted network serves as an auxiliary task, extracting multi-label pathological information from ECGs. This joint learning approach establishes crucial correlations between ECG delineation and associated ECG abnormalities. The correlations enable the model to demonstrate sufficient generalization in the presence of diverse abnormal ECG patterns. Besides, onset-offset aware loss focuses intensively on wave onsets and offsets by applying biased weights to various waveform positions. This approach ensures a focus on precise localization, facilitating seamless integration into cross-entropy loss function. A large-scale wearable 12-lead dataset containing 4,913 signals is collected, offering an extensive range of ECG data for model training. Results demonstrate that our method achieves outstanding performance on two test datasets, attaining sensitivity of 94.97% and 94.27% and an error tolerance lower than 20 ms. Furthermore, our method is effective for various aberrant ECG signals, including ST-segment changes, atrial premature beats, and right and left bundle branch blocks.
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Affiliation(s)
- Yue Zhang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Guangzhou, China
| | - Jiewei Lai
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Guangzhou, China
| | - Chenyu Zhao
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Guangzhou, China
| | - Jinliang Wang
- CardioCloud Medical Technology (Beijing) Co. Ltd., Beijing, China
| | - Yong Yan
- CardioCloud Medical Technology (Beijing) Co. Ltd., Beijing, China
| | - Mingyang Chen
- CardioCloud Medical Technology (Beijing) Co. Ltd., Beijing, China
| | - Lei Ji
- IT Department, Chinese PLA General Hospital, Beijing, China
| | - Jun Guo
- Department of Cardiology, Chinese PLA General Hospital, Beijing, China
| | - Baoshi Han
- Department of Cardiology, Chinese PLA General Hospital, Beijing, China
| | - Yajun Shi
- Department of Cardiology, Chinese PLA General Hospital, Beijing, China
| | - Yundai Chen
- Department of Cardiology, Chinese PLA General Hospital, Beijing, China.
| | - Wei Yang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Guangzhou, China.
| | - Qianjin Feng
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Guangzhou, China.
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Chen Y, Liu Z, Wang Z, Yi Y, Yan C, Xu W, Zhou F, Gao Y, Zhou Q, Zhang C, Deng H. Bioinspired Robust Gas-Permeable On-Skin Electronics: Armor-Designed Nanoporous Flash Graphene Assembly Enhancing Mechanical Resilience. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2402759. [PMID: 38704681 PMCID: PMC11234450 DOI: 10.1002/advs.202402759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 04/13/2024] [Indexed: 05/07/2024]
Abstract
Soft on-skin electrodes play an important role in wearable technologies, requiring attributes such as wearing comfort, high conductivity, and gas permeability. However, conventional fabrication methods often compromise simplicity, cost-effectiveness, or mechanical resilience. In this study, a mechanically robust and gas-permeable on-skin electrode is presented that incorporates Flash Graphene (FG) integrated with a bioinspired armor design. FG, synthesized through Flash Joule Heating process, offers a small-sized and turbostratic arrangement that is ideal for the assembly of a conductive network with nanopore structures. Screen-printing is used to embed the FG assembly into the framework of polypropylene melt-blown nonwoven fabrics (PPMF), forming a soft on-skin electrode with low sheet resistance (125.2 ± 4.7 Ω/□) and high gas permeability (≈10.08 mg cm⁻2 h⁻¹). The "armor" framework ensures enduring mechanical stability through adhesion, washability, and 10,000 cycles of mechanical contact friction tests. Demonstrating capabilities in electrocardiogram (ECG) and electromyogram (EMG) monitoring, along with serving as a self-powered triboelectric sensor, the FG/PPMF electrode holds promise for scalable, high-performance flexible sensing applications, thereby enriching the landscape of integrated wearable technologies.
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Affiliation(s)
- Yang Chen
- Faculty of Materials Science and ChemistryChina University of GeosciencesWuhan430074P. R. China
| | - Zixuan Liu
- College of EngineeringNanjing Agricultural UniversityNanjing210031P. R. China
| | - Zhigang Wang
- Faculty of Materials Science and ChemistryChina University of GeosciencesWuhan430074P. R. China
| | - Ying Yi
- School of Mechanical Engineering and Electronic InformationChina University of GeosciencesWuhan430074P. R. China
| | - Chunjie Yan
- Faculty of Materials Science and ChemistryChina University of GeosciencesWuhan430074P. R. China
| | - Wenxia Xu
- Faculty of Materials Science and ChemistryChina University of GeosciencesWuhan430074P. R. China
| | - Feng Zhou
- Faculty of Materials Science and ChemistryChina University of GeosciencesWuhan430074P. R. China
| | - Yuting Gao
- Faculty of Materials Science and ChemistryChina University of GeosciencesWuhan430074P. R. China
| | - Qitao Zhou
- Faculty of Materials Science and ChemistryChina University of GeosciencesWuhan430074P. R. China
| | - Cheng Zhang
- College of EngineeringNanjing Agricultural UniversityNanjing210031P. R. China
| | - Heng Deng
- Faculty of Materials Science and ChemistryChina University of GeosciencesWuhan430074P. R. China
- Shenzhen Research InstituteChina University of GeosciencesShenzhen518000P. R. China
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4
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Butler L, Karabayir I, Kitzman DW, Alonso A, Tison GH, Chen LY, Chang PP, Clifford G, Soliman EZ, Akbilgic O. A generalizable electrocardiogram-based artificial intelligence model for 10-year heart failure risk prediction. CARDIOVASCULAR DIGITAL HEALTH JOURNAL 2023; 4:183-190. [PMID: 38222101 PMCID: PMC10787146 DOI: 10.1016/j.cvdhj.2023.11.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2024] Open
Abstract
Background Heart failure (HF) is a progressive condition with high global incidence. HF has two main subtypes: HF with preserved ejection fraction (HFpEF) and HF with reduced ejection fraction (HFrEF). There is an inherent need for simple yet effective electrocardiogram (ECG)-based artificial intelligence (AI; ECG-AI) models that can predict HF risk early to allow for risk modification. Objective The main objectives were to validate HF risk prediction models using Multi-Ethnic Study of Atherosclerosis (MESA) data and assess performance on HFpEF and HFrEF classification. Methods There were six models in comparision derived using ARIC data. 1) The ECG-AI model predicting HF risk was developed using raw 12-lead ECGs with a convolutional neural network. The clinical models from 2) ARIC (ARIC-HF) and 3) Framingham Heart Study (FHS-HF) used 9 and 8 variables, respectively. 4) Cox proportional hazards (CPH) model developed using the clinical risk factors in ARIC-HF or FHS-HF. 5) CPH model using the outcome of ECG-AI and the clinical risk factors used in CPH model (ECG-AI-Cox) and 6) A Light Gradient Boosting Machine model using 288 ECG Characteristics (ECG-Chars). All the models were validated on MESA. The performances of these models were evaluated using the area under the receiver operating characteristic curve (AUC) and compared using the DeLong test. Results ECG-AI, ECG-Chars, and ECG-AI-Cox resulted in validation AUCs of 0.77, 0.73, and 0.84, respectively. ARIC-HF and FHS-HF yielded AUCs of 0.76 and 0.74, respectively, and CPH resulted in AUC = 0.78. ECG-AI-Cox outperformed all other models. ECG-AI-Cox provided an AUC of 0.85 for HFrEF and 0.83 for HFpEF. Conclusion ECG-AI using ECGs provides better-validated predictions when compared to HF risk calculators, and the ECG feature model and also works well with HFpEF and HFrEF classification.
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Affiliation(s)
- Liam Butler
- Epidemiological Cardiology Research Center, Section on Cardiovascular Medicine, Department of Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Ibrahim Karabayir
- Epidemiological Cardiology Research Center, Section on Cardiovascular Medicine, Department of Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Dalane W. Kitzman
- Epidemiological Cardiology Research Center, Section on Cardiovascular Medicine, Department of Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Alvaro Alonso
- Rollins School of Public Health, Emory University, Atlanta, Georgia
| | - Geoffrey H. Tison
- Division of Cardiology, University of California, San Francisco, California
| | - Lin Yee Chen
- Lillehei Heart Institute and the Department of Medicine (Cardiovascular Division), University of Minnesota Medical School, Minneapolis, Minnesota
| | - Patricia P. Chang
- Department of Medicine (Division of Cardiology), University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Gari Clifford
- Department of Biomedical Informatics, Emory School of Medicine, Emory University, Atlanta, Georgia
- Wallace H. Coulter Department of Biomedical Engineering, College of Engineering, Georgia Institute of Technology, Atlanta, Georgia
| | - Elsayed Z. Soliman
- Epidemiological Cardiology Research Center, Section on Cardiovascular Medicine, Department of Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Oguz Akbilgic
- Epidemiological Cardiology Research Center, Section on Cardiovascular Medicine, Department of Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina
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Panjaitan F, Nurmaini S, Partan RU. Accurate Prediction of Sudden Cardiac Death Based on Heart Rate Variability Analysis Using Convolutional Neural Network. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:1394. [PMID: 37629684 PMCID: PMC10456609 DOI: 10.3390/medicina59081394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 07/17/2023] [Accepted: 07/27/2023] [Indexed: 08/27/2023]
Abstract
Sudden cardiac death (SCD) is a significant global health issue that affects individuals with and without a history of heart disease. Early identification of SCD risk factors is crucial in reducing mortality rates. This study aims to utilize electrocardiogram (ECG) tools, specifically focusing on heart rate variability (HRV), to detect early SCD risk factors. In this study, we expand the comparison group dataset to include five groups: Normal Sinus Rhythm (NSR), coronary artery disease (CAD), Congestive Heart Failure (CHF), Ventricular Tachycardia (VT), and SCD. ECG signals were recorded for 30 min and segmented into 5 min intervals, following the recommended HRV feature analysis guidelines. We introduce an innovative approach to HRV signal analysis by utilizing Convolutional Neural Networks (CNN). The CNN model was optimized by tuning hyperparameters such as the number of layers, learning rate, and batch size, significantly impacting the prediction accuracy. The findings demonstrate that the HRV approach, in conjunction with linear features and the DL method, achieved a higher accuracy rate, averaging 99.30%, reaching 97% sensitivity, 99.60% specificity, and 97.87% precision. Future research should focus on further exploring and refining DL methods in the context of HRV analysis to improve SCD prediction.
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Affiliation(s)
- Febriyanti Panjaitan
- Doctoral Program of Engineering Science, Faculty of Engineering, Universitas Sriwijaya, Palembang 30128, Indonesia;
- Faculty of Science and Technology, Universitas Bina Darma, Palembang 30264, Indonesia
| | - Siti Nurmaini
- Intelligent System Research Group, Universitas Sriwijaya, Palembang 30128, Indonesia
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6
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Darmawahyuni A, Nurmaini S, Rachmatullah MN, Avi PP, Teguh SBP, Sapitri AI, Tutuko B, Firdaus F. Improved delineation model of a standard 12-lead electrocardiogram based on a deep learning algorithm. BMC Med Inform Decis Mak 2023; 23:139. [PMID: 37507698 PMCID: PMC10375607 DOI: 10.1186/s12911-023-02233-0] [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: 10/24/2022] [Accepted: 07/12/2023] [Indexed: 07/30/2023] Open
Abstract
BACKGROUND Signal delineation of a standard 12-lead electrocardiogram (ECG) is a decisive step for retrieving complete information and extracting signal characteristics for each lead in cardiology clinical practice. However, it is arduous to manually assess the leads, as a variety of signal morphological variations in each lead have potential defects in recording, noise, or irregular heart rhythm/beat. METHOD A computer-aided deep-learning algorithm is considered a state-of-the-art delineation model to classify ECG waveform and boundary in terms of the P-wave, QRS-complex, and T-wave and indicated the satisfactory result. This study implemented convolution layers as a part of convolutional neural networks for automated feature extraction and bidirectional long short-term memory as a classifier. For beat segmentation, we have experimented beat-based and patient-based approach. RESULTS The empirical results using both beat segmentation approaches, with a total of 14,588 beats were showed that our proposed model performed excellently well. All performance metrics above 95% and 93%, for beat-based and patient-based segmentation, respectively. CONCLUSIONS This is a significant step towards the clinical pertinency of automated 12-lead ECG delineation using deep learning.
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Affiliation(s)
- Annisa Darmawahyuni
- Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang, 30139, Indonesia
| | - Siti Nurmaini
- Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang, 30139, Indonesia.
| | - Muhammad Naufal Rachmatullah
- Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang, 30139, Indonesia
| | - Prazna Paramitha Avi
- Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang, 30139, Indonesia
| | - Samuel Benedict Putra Teguh
- Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang, 30139, Indonesia
| | - Ade Iriani Sapitri
- Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang, 30139, Indonesia
| | - Bambang Tutuko
- Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang, 30139, Indonesia
| | - Firdaus Firdaus
- Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang, 30139, Indonesia
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7
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Yang X, Ji Z. Automatic Classification Method of Arrhythmias Based on 12-Lead Electrocardiogram. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23094372. [PMID: 37177575 PMCID: PMC10181542 DOI: 10.3390/s23094372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Revised: 04/17/2023] [Accepted: 04/26/2023] [Indexed: 05/15/2023]
Abstract
Cardiovascular disease is one of the main causes of death worldwide. Arrhythmias are an important group of cardiovascular diseases. The standard 12-lead electrocardiogram signals are an important tool for diagnosing arrhythmias. Although 12-lead electrocardiogram signals provide more comprehensive arrhythmia information than single-lead electrocardiogram signals, it is difficult to effectively fuse information between different leads. In addition, most of the current researches working on automatic diagnosis of cardiac arrhythmias are based on modeling and analysis of single-mode features extracted from one-dimensional electrocardiogram sequences, ignoring the frequency domain features of electrocardiogram signals. Therefore, developing an automatic arrhythmia detection algorithm based on 12-lead electrocardiogram with high accuracy and strong generalization ability is still challenging. In this paper, a multimodal feature fusion model based on the mechanism is developed. This model utilizes a dual channel deep neural network to extract different dimensional features from one-dimensional and two-dimensional electrocardiogram time-frequency maps, and combines attention mechanism to effectively fuse the important features of 12-lead, thereby obtaining richer arrhythmia information and ultimately achieving accurate classification of nine types of arrhythmia signals. This study used electrocardiogram signals from a mixed dataset to train, validate, and evaluate the model, with an average of F1 score and average accuracy reached 0.85 and 0.97, respectively. Experimental results show that our algorithm has stable and reliable performance, so it is expected to have good practical application potential.
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Affiliation(s)
- Xiao Yang
- College of Bioengineering, Chongqing University, Chongqing 400030, China
| | - Zhong Ji
- College of Bioengineering, Chongqing University, Chongqing 400030, China
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Majeed RR, Alkhafaji SKD. ECG classification system based on multi-domain features approach coupled with least square support vector machine (LS-SVM). Comput Methods Biomech Biomed Engin 2023; 26:540-547. [PMID: 35549774 DOI: 10.1080/10255842.2022.2072684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
Developing a robust authentication and identification method becomes an urgent demand to protect the integrity of devices data. Although the use of passwords provides an acceptable control and authentication, it has shown much weakness in terms of speed and integrity, which make biometrics the ideal authentication solution. As a result, electrocardiogram (ECG) signals have received a great attention in most authentication systems due to the individualized nature of the ECG signals that make them difficult to counterfeit and ubiquitous. In this paper, we propose a new model for ECG verification using multi-domain features coupled with a least square support vector machine (LS-SVM). Two types of features are investigated to find the best set of features to individual from ECG signals. Time domain and frequency domain features based on optimized Triple Band filter bank are extracted from ECG signals. The extracted features are investigated to figure out the best relevant features and remove the redundant ones. The selected features are fed to three classifiers, including Least Square Support Vector Machine (LS-SVM), K-means, and K-nearest. The obtained results have shown that our ECG biometric authentication system outperforms existing methods. The proposed model obtained an average of accuracy of 88%, 95% with time and frequency features, respectively, while it recorded 99% when a combination of time and frequency features are used to classify ECG signals. A public dataset is used to assess the proposed model.
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Affiliation(s)
- Russel R Majeed
- College of Education for Pure Sciences, University of Thi-Qar, Nasiriyah, Iraq
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9
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Denysyuk HV, Pinto RJ, Silva PM, Duarte RP, Marinho FA, Pimenta L, Gouveia AJ, Gonçalves NJ, Coelho PJ, Zdravevski E, Lameski P, Leithardt V, Garcia NM, Pires IM. Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review. Heliyon 2023; 9:e13601. [PMID: 36852052 PMCID: PMC9958295 DOI: 10.1016/j.heliyon.2023.e13601] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Revised: 01/31/2023] [Accepted: 02/05/2023] [Indexed: 02/12/2023] Open
Abstract
The prevalence of cardiovascular diseases is increasing around the world. However, the technology is evolving and can be monitored with low-cost sensors anywhere at any time. This subject is being researched, and different methods can automatically identify these diseases, helping patients and healthcare professionals with the treatments. This paper presents a systematic review of disease identification, classification, and recognition with ECG sensors. The review was focused on studies published between 2017 and 2022 in different scientific databases, including PubMed Central, Springer, Elsevier, Multidisciplinary Digital Publishing Institute (MDPI), IEEE Xplore, and Frontiers. It results in the quantitative and qualitative analysis of 103 scientific papers. The study demonstrated that different datasets are available online with data related to various diseases. Several ML/DP-based models were identified in the research, where Convolutional Neural Network and Support Vector Machine were the most applied algorithms. This review can allow us to identify the techniques that can be used in a system that promotes the patient's autonomy.
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Key Words
- AI, Artificial Intelligence
- BNN, Binarized Neural Network
- CNN, Concolutional Neural Networks
- Cardiovascular diseases
- DL, Deep Learning
- DNN, Deep Neural Networks
- Diagnosis
- ECG sensors
- ECG, Electrocardiography
- GAN, Generative Adversarial Networks
- GMM, Gaussian Mixture Model
- GNB, Gaussian Naive bayes
- GRU, Gated Recurrent Unit
- LASSO, Least Absolute Shrinkage and Selection Operator
- LDA, Linear Discriminant Analysis
- LR, Linear Regression
- LSTM, Long Short-Term Memory
- ML, Machine Learning
- MLP, Multiplayer Perceptron
- MLR, Multiple Linear Regression
- NLP, Natural Language Processing
- POAF, Postoperative Atrial Fibrillation
- RF, Random Forest
- RNN, Recurrent Neural Network
- SHAP, SHapley Additive exPlanations
- SVM, Support Vector Machine
- Systematic review
- WHO, World Health Organization
- kNN, k-nearest neighbors
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Affiliation(s)
| | - Rui João Pinto
- Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
| | - Pedro Miguel Silva
- Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
| | - Rui Pedro Duarte
- Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
| | - Francisco Alexandre Marinho
- Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
| | - Luís Pimenta
- Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
| | - António Jorge Gouveia
- Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
| | - Norberto Jorge Gonçalves
- Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
| | - Paulo Jorge Coelho
- Polytechnic of Leiria, Leiria, Portugal
- Institute for Systems Engineering and Computers at Coimbra (INESC Coimbra), Coimbra, Portugal
| | - Eftim Zdravevski
- Faculty of Computer Science and Engineering, University Ss Cyril and Methodius, 1000 Skopje, Macedonia
| | - Petre Lameski
- Faculty of Computer Science and Engineering, University Ss Cyril and Methodius, 1000 Skopje, Macedonia
| | - Valderi Leithardt
- VALORIZA, Research Center for Endogenous Resources Valorization, Instituto Politécnico de Portalegre, 7300-555 Portalegre, Portugal
- COPELABS, Universidade Lusófona de Humanidades e Tecnologias, Lisboa, Portugal
| | - Nuno M. Garcia
- Instituto de Telecomunicações, Universidade da Beira Interior, 6200-001 Covilhã, Portugal
| | - Ivan Miguel Pires
- Instituto de Telecomunicações, Universidade da Beira Interior, 6200-001 Covilhã, Portugal
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10
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Constantinou M, Exarchos T, Vrahatis AG, Vlamos P. COVID-19 Classification on Chest X-ray Images Using Deep Learning Methods. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:ijerph20032035. [PMID: 36767399 PMCID: PMC9915705 DOI: 10.3390/ijerph20032035] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 01/17/2023] [Accepted: 01/20/2023] [Indexed: 05/27/2023]
Abstract
Since December 2019, the coronavirus disease has significantly affected millions of people. Given the effect this disease has on the pulmonary systems of humans, there is a need for chest radiographic imaging (CXR) for monitoring the disease and preventing further deaths. Several studies have been shown that Deep Learning models can achieve promising results for COVID-19 diagnosis towards the CXR perspective. In this study, five deep learning models were analyzed and evaluated with the aim of identifying COVID-19 from chest X-ray images. The scope of this study is to highlight the significance and potential of individual deep learning models in COVID-19 CXR images. More specifically, we utilized the ResNet50, ResNet101, DenseNet121, DenseNet169 and InceptionV3 using Transfer Learning. All models were trained and validated on the largest publicly available repository for COVID-19 CXR images. Furthermore, they were evaluated on unknown data that was not used for training or validation, authenticating their performance and clarifying their usage in a medical scenario. All models achieved satisfactory performance where ResNet101 was the superior model achieving 96% in Precision, Recall and Accuracy, respectively. Our outcomes show the potential of deep learning models on COVID-19 medical offering a promising way for the deeper understanding of COVID-19.
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11
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Al-Daraghmeh MY, Stone RT. A review of medical wearables: materials, power sources, sensors, and manufacturing aspects of human wearable technologies. J Med Eng Technol 2023; 47:67-81. [PMID: 35856912 DOI: 10.1080/03091902.2022.2097743] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Wearable technology is a promising and revolutionary technology that is changing some aspects of our standard of living to a great extent, including health monitoring, sport and fitness, performance tracking, education, and entertainment. This article presents a comprehensive literature review of over 160 articles related to state-of-the-art human wearable technologies. We provide a thorough understanding of the materials, power sources, sensors, and manufacturing processes, and the relationships between these to capture opportunities for enhancement and challenges to overcome in wearables. As a result of our review, we have determined the need for the development of a comprehensive, robust manufacturing system alongside specific standards and regulations that take into account wearables' unique characteristics. Seeing the whole picture will provide a frame reference and road map for researchers and industries through the design, manufacturing, and commercialisation of effective, portable, self-powered, multi-sensing ultimate future wearable devices and create opportunities for new innovations and applications.
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Affiliation(s)
- Mohammad Y Al-Daraghmeh
- Department of Industrial and Manufacturing Systems Engineering, Iowa State University, Ames, IA, USA.,Department of Industrial Engineering, Yarmouk University, Irbid, Jordan
| | - Richard T Stone
- Department of Industrial and Manufacturing Systems Engineering, Iowa State University, Ames, IA, USA
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12
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Irin Sherly S, Mathivanan G. An efficient honey badger based Faster region CNN for chronc heart Failure prediction. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104165] [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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13
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Ayano YM, Schwenker F, Dufera BD, Debelee TG. Interpretable Machine Learning Techniques in ECG-Based Heart Disease Classification: A Systematic Review. Diagnostics (Basel) 2022; 13:111. [PMID: 36611403 PMCID: PMC9818170 DOI: 10.3390/diagnostics13010111] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 12/22/2022] [Accepted: 12/23/2022] [Indexed: 12/31/2022] Open
Abstract
Heart disease is one of the leading causes of mortality throughout the world. Among the different heart diagnosis techniques, an electrocardiogram (ECG) is the least expensive non-invasive procedure. However, the following are challenges: the scarcity of medical experts, the complexity of ECG interpretations, the manifestation similarities of heart disease in ECG signals, and heart disease comorbidity. Machine learning algorithms are viable alternatives to the traditional diagnoses of heart disease from ECG signals. However, the black box nature of complex machine learning algorithms and the difficulty in explaining a model's outcomes are obstacles for medical practitioners in having confidence in machine learning models. This observation paves the way for interpretable machine learning (IML) models as diagnostic tools that can build a physician's trust and provide evidence-based diagnoses. Therefore, in this systematic literature review, we studied and analyzed the research landscape in interpretable machine learning techniques by focusing on heart disease diagnosis from an ECG signal. In this regard, the contribution of our work is manifold; first, we present an elaborate discussion on interpretable machine learning techniques. In addition, we identify and characterize ECG signal recording datasets that are readily available for machine learning-based tasks. Furthermore, we identify the progress that has been achieved in ECG signal interpretation using IML techniques. Finally, we discuss the limitations and challenges of IML techniques in interpreting ECG signals.
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Affiliation(s)
| | | | - Bisrat Derebssa Dufera
- Addis Ababa Institute of Technology, Addis Ababa University, Addis Ababa 11760, Ethiopia
| | - Taye Girma Debelee
- Ethiopian Artificial Intelligence Institute, Addis Ababa 40782, Ethiopia
- College of Electrical and Computer Engineering, Addis Ababa Science and Technology University, Addis Ababa 16417, Ethiopia
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14
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Sonawane R, Patil H. A design and implementation of heart disease prediction model using data and ECG signal through hybrid clustering. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING: IMAGING & VISUALIZATION 2022. [DOI: 10.1080/21681163.2022.2156927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Affiliation(s)
- Ritesh Sonawane
- Computer Engineering, S.S.V.P.S B.S.Deore College of Engineering, Dhule, Maharashtra, India
| | - Hitendra Patil
- Computer Engineering, S.S.V.P.S B.S.Deore College of Engineering, Dhule, Maharashtra, India
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15
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Hammad M, Meshoul S, Dziwiński P, Pławiak P, Elgendy IA. Efficient Lightweight Multimodel Deep Fusion Based on ECG for Arrhythmia Classification. SENSORS (BASEL, SWITZERLAND) 2022; 22:9347. [PMID: 36502049 PMCID: PMC9736761 DOI: 10.3390/s22239347] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 11/29/2022] [Accepted: 11/29/2022] [Indexed: 06/17/2023]
Abstract
An arrhythmia happens when the electrical signals that organize the heartbeat do not work accurately. Most cases of arrhythmias may increase the risk of stroke or cardiac arrest. As a result, early detection of arrhythmia reduces fatality rates. This research aims to provide a lightweight multimodel based on convolutional neural networks (CNNs) that can transfer knowledge from many lightweight deep learning models and decant it into one model to aid in the diagnosis of arrhythmia by using electrocardiogram (ECG) signals. Thus, we gained a multimodel able to classify arrhythmia from ECG signals. Our system's effectiveness is examined by using a publicly accessible database and a comparison to the current methodologies for arrhythmia classification. The results we achieved by using our multimodel are better than those obtained by using a single model and better than most of the previous detection methods. It is worth mentioning that this model produced accurate classification results on small collection of data. Experts in this field can use this model as a guide to help them make decisions and save time.
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Affiliation(s)
- Mohamed Hammad
- Department of Information Technology, Faculty of Computers and Information, Menoufia University, Shibin El Kom 32511, Egypt
| | - Souham Meshoul
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Piotr Dziwiński
- Department of Intelligent Computer Systems, Czestochowa University of Technology, Armii Krajowej 36, 42-218 Czestochowa, Poland
| | - Paweł Pławiak
- Department of Computer Science, Faculty of Computer Science and Telecommunications, Cracow University of Technology, Warszawska 24, 31-155 Krakow, Poland
- Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Baltycka 5, 44-100 Gliwice, Poland
| | - Ibrahim A. Elgendy
- Department of Computer Science, Faculty of Computers and Information, Menoufia University, Shibin El Kom 32511, Egypt
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16
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Sakr AS, Pławiak P, Tadeusiewicz R, Pławiak J, Sakr M, Hammad M. ECG-COVID: An End-to-End Deep Model Based on Electrocardiogram for COVID-19 Detection. Inf Sci (N Y) 2022; 619:324-339. [PMCID: PMC9673093 DOI: 10.1016/j.ins.2022.11.069] [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: 02/23/2022] [Revised: 10/05/2022] [Accepted: 11/14/2022] [Indexed: 11/19/2022]
Abstract
The early and accurate detection of COVID-19 is vital nowadays to avoid the vast and rapid spread of this virus and ease lockdown restrictions. As a result, researchers developed methods to diagnose COVID-19. However, these methods have several limitations. Therefore, presenting new methods is essential to improve the diagnosis of COVID-19. Recently, investigation of the electrocardiogram (ECG) signals becoming an easy way to detect COVID-19 since the ECG process is non-invasive and easy to use. Therefore, we proposed in this paper a novel end-to-end deep learning model (ECG-COVID) based on ECG for COVID-19 detection. We employed several deep Convolutional Neural Networks (CNNs) on a dataset of 1109 ECG images, which is built for screening the perception of COVID-19 and cardiac patients. After that, we selected the most efficient model as our model for evaluation. The proposed model is end-to-end where the input ECG images are fed directly to the model for the final decision without using any additional stages. The proposed method achieved an average accuracy of 98.81%, Precision of 98.8%, Sensitivity of 98.8% and, F1-score of 98.81% for COVID-19 detection. As cases of corona continue to rise and hospitalizations continue again, hospitals may find our study helpful when dealing with these patients who did not get significantly worse.
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Affiliation(s)
- Ahmed S. Sakr
- Department of Information System, Faculty of Computers and Information, Menoufia University, Egypt
| | - Paweł Pławiak
- Department of Computer Science, Faculty of Computer Science and Telecommunications, Cracow University of Technology, Warszawska 24, 31-155 Krakow, Poland,Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Bałtycka 5, 44-100 Gliwice, Poland,Corresponding authors
| | - Ryszard Tadeusiewicz
- AGH University of Science and Technology, Department of Biocybernetics and Biomedical Engineering, Krakow, Poland
| | - Joanna Pławiak
- Faculty of Electrical and Computer Engineering, Cracow University of Technology, Warsaw 24, 31-155 Krakow, Poland
| | - Mohamed Sakr
- Computer Science Department, Faculty of Computers and Information, Menoufia University, Egypt
| | - Mohamed Hammad
- Department of Information Technology, Faculty of Computers and Information, Menoufia University, Egypt,Corresponding authors
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17
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Rai HM, Chatterjee K, Dashkevych S. The prediction of cardiac abnormality and enhancement in minority class accuracy from imbalanced ECG signals using modified deep neural network models. Comput Biol Med 2022; 150:106142. [PMID: 36182760 DOI: 10.1016/j.compbiomed.2022.106142] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 09/04/2022] [Accepted: 09/18/2022] [Indexed: 11/03/2022]
Abstract
Cardiovascular disease (CVD) is the most fatal disease in the world, so its accurate and automated detection in the early stages will certainly support the medical expert in timely diagnosis and treatment, which can save many lives. Many types of research have been carried out in this regard, but due to the problem of data imbalance in the medical and health care sector, it may not provide the desired results in all aspects. To overcome this problem, a sequential ensemble technique has been proposed that detects 6 types of cardiac arrhythmias on large ECG imbalanced datasets, and the data imbalanced issue of the ECG dataset has been addressed by using a hybrid data resampling technique called "Synthetically Minority Oversampling Technique and Tomek Link (SMOTE + Tomek)". The sequential ensemble technique employs two distinct deep learning models: Convolutional Neural Network (CNN) and a hybrid model, CNN with Long Short-Term Memory Network (CNN-LSTM). The two standard datasets "MIT-BIH arrhythmias database" (MITDB) and "PTB diagnostic database" (PTBDB) are combined and extracted 23, 998 ECG beats for the model validation. In this work, the three models CNN, CNN-LSTM, and ensemble approach were tested on four kinds of ECG datasets: the original data (imbalanced), the data sampled using a random oversampled technique, data sampled using SMOTE, and the dataset resampled using SMOTE + Tomek algorithm. The overall highest accuracy was obtained of 99.02% on the SMOTE + Tomek sampled dataset by ensemble technique and the minority class accuracy result (Recall) is improved by 20% as compared to the imbalanced data.
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Affiliation(s)
- Hari Mohan Rai
- Department of Electrical Engineering, Indian Institute of Technology(ISM), Dhanbad, India; Department of Electronics and Communication Engineering, Dronacharya Group of Institutions, Greater Noida, India.
| | - Kalyan Chatterjee
- Department of Electrical Engineering, Indian Institute of Technology(ISM), Dhanbad, India.
| | - Serhii Dashkevych
- Data Scientist, Polsko-Japońska Akademia Technik Komputerowych, Koszykowa, Warszawa, Poland.
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18
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El-Rahiem BA, El-Samie FEA, Amin M. Multimodal biometric authentication based on deep fusion of electrocardiogram (ECG) and finger vein. MULTIMEDIA SYSTEMS 2022; 28:1325-1337. [DOI: 10.1007/s00530-021-00810-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 05/13/2021] [Indexed: 09/01/2023]
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19
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Hammad M, Alkinani MH, Gupta BB, Abd El-Latif AA. Myocardial infarction detection based on deep neural network on imbalanced data. MULTIMEDIA SYSTEMS 2022; 28:1373-1385. [DOI: 10.1007/s00530-020-00728-8] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2020] [Accepted: 12/01/2020] [Indexed: 09/02/2023]
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20
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Chen SW, Wang SL, Qi XZ, Samuri SM, Yang C. Review of ECG detection and classification based on deep learning: Coherent taxonomy, motivation, open challenges and recommendations. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103493] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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21
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Hammad M, Tawalbeh L, Iliyasu AM, Sedik A, Abd El-Samie FE, Alkinani MH, Abd El-Latif AA. Efficient multimodal deep-learning-based COVID-19 diagnostic system for noisy and corrupted images. JOURNAL OF KING SAUD UNIVERSITY. SCIENCE 2022; 34:101898. [PMID: 35185304 PMCID: PMC8832871 DOI: 10.1016/j.jksus.2022.101898] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 02/02/2022] [Accepted: 02/04/2022] [Indexed: 05/28/2023]
Abstract
INTRODUCTION In humanity's ongoing fight against its common enemy of COVID-19, researchers have been relentless in finding efficient technologies to support mitigation, diagnosis, management, contact tracing, and ultimately vaccination. OBJECTIVES Engineers and computer scientists have deployed the potent properties of deep learning models (DLMs) in COVID-19 detection and diagnosis. However, publicly available datasets are often adulterated during collation, transmission, or storage. Meanwhile, inadequate, and corrupted data are known to impact the learnability and efficiency of DLMs. METHODS This study focuses on enhancing previous efforts via two multimodal diagnostic systems to extract required features for COVID-19 detection using adulterated chest X-ray images. Our proposed DLM consists of a hierarchy of convolutional and pooling layers that are combined to support efficient COVID-19 detection using chest X-ray images. Additionally, a batch normalization layer is used to curtail overfitting that usually arises from the convolution and pooling (CP) layers. RESULTS In addition to matching the performance of standard techniques reported in the literature, our proposed diagnostic systems attain an average accuracy of 98% in the detection of normal, COVID-19, and viral pneumonia cases using corrupted and noisy images. CONCLUSIONS Such robustness is crucial for real-world applications where data is usually unavailable, corrupted, or adulterated.
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Affiliation(s)
- Mohamed Hammad
- Information Technology Department, Faculty of Computers and Information, Menoufia University, Shebin El-koom 32511, Egypt
| | - Lo'ai Tawalbeh
- Director of Cyber Security Center, Department of Computing and Cybersecurity, Texas A&M University-San Antonio, San Antonio, TX, USA
| | - Abdullah M Iliyasu
- School of Computing, Tokyo Institute of Technology, Yokohama 226-8502, Japan
| | - Ahmed Sedik
- Department of the Robotics and Intelligent Machines, Kafrelsheikh University, Kafrelsheikh 33511, Egypt
| | - Fathi E Abd El-Samie
- Department of Electronics and Electrical Communications Menoufa University, Menouf 32952, Egypt
| | - Monagi H Alkinani
- College of Computer Sciences and Engineering, Department of Computer Science and Artificial Intelligence, University of Jeddah, Saudi Arabia
| | - Ahmed A Abd El-Latif
- EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
- Department of Mathematics and Computer Science, Faculty of Science, Menoufia University, Shebin El-koom 32511, Egypt
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22
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Sakr AS, Pławiak P, Tadeusiewicz R, Hammad M. Cancelable ECG biometric based on combination of deep transfer learning with DNA and amino acid approaches for human authentication. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2021.11.066] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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23
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Guess M, Zavanelli N, Yeo WH. Recent Advances in Materials and Flexible Sensors for Arrhythmia Detection. MATERIALS 2022; 15:ma15030724. [PMID: 35160670 PMCID: PMC8836661 DOI: 10.3390/ma15030724] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 01/06/2022] [Accepted: 01/16/2022] [Indexed: 12/24/2022]
Abstract
Arrhythmias are one of the leading causes of death in the United States, and their early detection is essential for patient wellness. However, traditional arrhythmia diagnosis by expert evaluation from intermittent clinical examinations is time-consuming and often lacks quantitative data. Modern wearable sensors and machine learning algorithms have attempted to alleviate this problem by providing continuous monitoring and real-time arrhythmia detection. However, current devices are still largely limited by the fundamental mismatch between skin and sensor, giving way to motion artifacts. Additionally, the desirable qualities of flexibility, robustness, breathability, adhesiveness, stretchability, and durability cannot all be met at once. Flexible sensors have improved upon the current clinical arrhythmia detection methods by following the topography of skin and reducing the natural interface mismatch between cardiac monitoring sensors and human skin. Flexible bioelectric, optoelectronic, ultrasonic, and mechanoelectrical sensors have been demonstrated to provide essential information about heart-rate variability, which is crucial in detecting and classifying arrhythmias. In this review, we analyze the current trends in flexible wearable sensors for cardiac monitoring and the efficacy of these devices for arrhythmia detection.
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Affiliation(s)
- Matthew Guess
- George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA; (M.G.); (N.Z.)
- Center for Human-Centric Interfaces and Engineering, Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Nathan Zavanelli
- George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA; (M.G.); (N.Z.)
- Center for Human-Centric Interfaces and Engineering, Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Woon-Hong Yeo
- George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA; (M.G.); (N.Z.)
- Center for Human-Centric Interfaces and Engineering, Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332, USA
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
- Parker H. Petit Institute for Bioengineering and Biosciences, Neural Engineering Center, Institute for Materials, Institute for Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, GA 30332, USA
- Correspondence: ; Tel.: +1-404-385-5710
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24
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Sedik A, Hammad M, Abd El-Samie FE, Gupta BB, Abd El-Latif AA. Efficient deep learning approach for augmented detection of Coronavirus disease. Neural Comput Appl 2022. [PMID: 33487885 DOI: 10.1016/j.compeleceng.2022.108011] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/29/2023]
Abstract
The new Coronavirus disease 2019 (COVID-19) is rapidly affecting the world population with statistics quickly falling out of date. Due to the limited availability of annotated Coronavirus X-ray and CT images, the detection of COVID-19 remains the biggest challenge in diagnosing this disease. This paper provides a promising solution by proposing a COVID-19 detection system based on deep learning. The proposed deep learning modalities are based on convolutional neural network (CNN) and convolutional long short-term memory (ConvLSTM). Two different datasets are adopted for the simulation of the proposed modalities. The first dataset includes a set of CT images, while the second dataset includes a set of X-ray images. Both of these datasets consist of two categories: COVID-19 and normal. In addition, COVID-19 and pneumonia image categories are classified in order to validate the proposed modalities. The proposed deep learning modalities are tested on both X-ray and CT images as well as a combined dataset that includes both types of images. They achieved an accuracy of 100% and an F1 score of 100% in some cases. The simulation results reveal that the proposed deep learning modalities can be considered and adopted for quick COVID-19 screening.
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Affiliation(s)
- Ahmed Sedik
- Department of the Robotics and Intelligent Machines, Kafrelsheikh University, Kafrelsheikh, Egypt
| | - Mohamed Hammad
- Information Technology Department, Faculty of Computers and Information, Menoufia University, Shebeen El-Kom, Egypt
| | - Fathi E Abd El-Samie
- Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufa University, Menouf, 32952 Egypt
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, 84428 Saudi Arabia
| | - Brij B Gupta
- National Institute of Technology, Kurukshetra, India
- Department of Computer Science and Information Engineering, Asia University, Taichung City, Taiwan
| | - Ahmed A Abd El-Latif
- Department of Mathematics and Computer Science, Faculty of Science, Menoufia University, Shebeen El-Kom, 32511 Egypt
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25
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Akbilgic O, Butler L, Karabayir I, Chang PP, Kitzman DW, Alonso A, Chen LY, Soliman EZ. ECG-AI: electrocardiographic artificial intelligence model for prediction of heart failure. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2021; 2:626-634. [PMID: 34993487 PMCID: PMC8715759 DOI: 10.1093/ehjdh/ztab080] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 08/19/2021] [Accepted: 09/01/2021] [Indexed: 01/30/2023]
Abstract
AIMS Heart failure (HF) is a leading cause of death. Early intervention is the key to reduce HF-related morbidity and mortality. This study assesses the utility of electrocardiograms (ECGs) in HF risk prediction. METHODS AND RESULTS Data from the baseline visits (1987-89) of the Atherosclerosis Risk in Communities (ARIC) study was used. Incident hospitalized HF events were ascertained by ICD codes. Participants with good quality baseline ECGs were included. Participants with prevalent HF were excluded. ECG-artificial intelligence (AI) model to predict HF was created as a deep residual convolutional neural network (CNN) utilizing standard 12-lead ECG. The area under the receiver operating characteristic curve (AUC) was used to evaluate prediction models including (CNN), light gradient boosting machines (LGBM), and Cox proportional hazards regression. A total of 14 613 (45% male, 73% of white, mean age ± standard deviation of 54 ± 5) participants were eligible. A total of 803 (5.5%) participants developed HF within 10 years from baseline. Convolutional neural network utilizing solely ECG achieved an AUC of 0.756 (0.717-0.795) on the hold-out test data. ARIC and Framingham Heart Study (FHS) HF risk calculators yielded AUC of 0.802 (0.750-0.850) and 0.780 (0.740-0.830). The highest AUC of 0.818 (0.778-0.859) was obtained when ECG-AI model output, age, gender, race, body mass index, smoking status, prevalent coronary heart disease, diabetes mellitus, systolic blood pressure, and heart rate were used as predictors of HF within LGBM. The ECG-AI model output was the most important predictor of HF. CONCLUSIONS ECG-AI model based solely on information extracted from ECG independently predicts HF with accuracy comparable to existing FHS and ARIC risk calculators.
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Affiliation(s)
- Oguz Akbilgic
- Department of Health Informatics and Data Science, Parkinson School of Health Sciences and Public Health, Loyola University Chicago, 2160 S 1st Street, Maywood, IL 60153, USA
- Sections on Cardiovascular Medicine and Geriatrics, Department of Internal Medicine, Wake Forest School of Medicine, 475 Vine Street, Winston-Salem, NC 27101, USA
| | - Liam Butler
- Department of Health Informatics and Data Science, Parkinson School of Health Sciences and Public Health, Loyola University Chicago, 2160 S 1st Street, Maywood, IL 60153, USA
| | - Ibrahim Karabayir
- Department of Health Informatics and Data Science, Parkinson School of Health Sciences and Public Health, Loyola University Chicago, 2160 S 1st Street, Maywood, IL 60153, USA
- Departmet of Econometrics, Kirklareli University, 3 Kayalı Kampüsü Kofçaz, Kirklareli, Turkey, Department of Medicine, Division of Cardiology, University of North Carolina at Chapel Hill, 160 Dental Circle, Chapel Hill, NC 27599, USA
| | - Patricia P Chang
- Sections on Cardiovascular Medicine and Geriatrics, Department of Internal Medicine, Wake Forest School of Medicine, 475 Vine Street, Winston-Salem, NC 27101, USA
| | - Dalane W Kitzman
- Sections on Cardiovascular Medicine and Geriatrics, Department of Internal Medicine, Wake Forest School of Medicine, 475 Vine Street, Winston-Salem, NC 27101, USA
| | - Alvaro Alonso
- Department of Epidemiology, Rollins School of Public Health, Emory University, 1518 Clifton Rd. NE Atlanta, GA, 30322, USA
| | - Lin Y Chen
- Cardiovascular Division, Department of Medicine, University of Minnesota Medical School, 401 East River Parkway, Minneapolis, MN 55455, USA
| | - Elsayed Z Soliman
- Sections on Cardiovascular Medicine and Geriatrics, Department of Internal Medicine, Wake Forest School of Medicine, 475 Vine Street, Winston-Salem, NC 27101, USA
- Internal Medicine, Epidemiological Cardiology Research Center, Sections on Cardiovascular Medicine, Wake Forest School of Medicine, 525 Vine Street, Winston-Salem, NC 27101, USA
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26
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Hybrid feature fusion for classification optimization of short ECG segment in IoT based intelligent healthcare system. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-06693-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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Naeem H, Bin-Salem AA. A CNN-LSTM network with multi-level feature extraction-based approach for automated detection of coronavirus from CT scan and X-ray images. Appl Soft Comput 2021; 113:107918. [PMID: 34608379 PMCID: PMC8482540 DOI: 10.1016/j.asoc.2021.107918] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 08/17/2021] [Accepted: 09/14/2021] [Indexed: 12/18/2022]
Abstract
Auto-detection of diseases has become a prime issue in medical sciences as population density is fast growing. An intelligent framework for disease detection helps physicians identify illnesses, give reliable and consistent results, and reduce death rates. Coronavirus (Covid-19) has recently been one of the most severe and acute diseases in the world. An automatic detection framework should therefore be introduced as the fastest diagnostic alternative to avoid Covid-19 spread. In this paper, an automatic Covid-19 identification in the CT scan and chest X-ray is obtained with the help of a combined deep learning and multi-level feature extraction methodology. In this method, the multi-level feature extraction approach comprises GIST, Scale Invariant Feature Transform (SIFT), and Convolutional Neural Network (CNN) extract features from CT scans and chest X-rays. The objective of multi-level feature extraction is to reduce the training complexity of CNN network, which significantly assists in accurate and robust Covid-19 identification. Finally, Long Short-Term Memory (LSTM) along the CNN network is used to detect the extracted Covid-19 features. The Kaggle SARS-CoV-2 CT scan dataset and the Italian SIRM Covid-19 CT scan and chest X-ray dataset were employed for testing purposes. Experimental outcomes show that proposed approach obtained 98.94% accuracy with the SARS-CoV-2 CT scan dataset and 83.03% accuracy with the SIRM Covid-19 CT scan and chest X-ray dataset. The proposed approach helps radiologists and practitioners to detect and treat Covid-19 cases effectively over the pandemic.
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Affiliation(s)
- Hamad Naeem
- School of Computer Science and Technology, Zhoukou Normal University, Zhoukou 466001, Henan, China
| | - Ali Abdulqader Bin-Salem
- School of Computer Science and Technology, Zhoukou Normal University, Zhoukou 466001, Henan, China
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Wang J. Automated detection of premature ventricular contraction based on the improved gated recurrent unit network. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 208:106284. [PMID: 34304005 DOI: 10.1016/j.cmpb.2021.106284] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2021] [Accepted: 07/11/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE Premature ventricular contraction (PVC) is the common arrhythmia disease, affecting thousands of individuals worldwide. However, the traditional PVC detection is cumbersome by visually inspecting electrocardiogram (ECG) signals. METHODS In this work, we specially propose an improved gated recurrent unit (IGRU) by setting a scale parameter into existing bidirectional GRU (BGRU) model for PVC signals recognition, which is used to alleviate the problem of information redundancy in BGRU. To verify the effectiveness, IGRU model will be embedded into a convolutional network frame and existing GRU and BGRU models are employed as control groups for a fair comparison. RESULTS The results exhibit that the model attains better model performance than control groups and several state-of-the-art algorithms with the accuracy of 98.3% and 97.9% with the MIT-BIH arrhythmia database and China Physiological Signal Challenge 2018 database. Besides, motivated from the waveform characteristics of ECG signals in PVC, the proposed model can provide certain physiological interpretability for physicians and researchers. CONCLUSIONS To our knowledge, this is the first attempt to re-design the existing GRU network for ECG signals classification, thus exhibiting great application potentials especially in lightweight equipment such as mobile phone and camera.
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Affiliation(s)
- Jibin Wang
- School of Mathematics, Tianjin University, Tianjin 300354, China.
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Hammad M, Pławiak P, Wang K, Acharya UR. ResNet‐Attention model for human authentication using ECG signals. EXPERT SYSTEMS 2021; 38. [DOI: 10.1111/exsy.12547] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Accepted: 02/17/2020] [Indexed: 07/23/2024]
Abstract
AbstractAuthentication is the process of verifying the claimed identity of the user. Recently, traditional authentication methods such as passwords, tokens, and so on are no longer used for authentication as they are more prone to theft and different types of violations. Therefore, new authentication approaches based on biometric modalities such as heartbeat pattern obtained from electrocardiogram (ECG) signals are considered. Unlike other biometrics, ECG provides the assurance that the person is alive, and is considered as one of the most accurate recent methods for authentication. In this article, two end‐to‐end deep neural network models for ECG‐based authentication are proposed. In the first model, a convolutional neural network (CNN) is developed and in the second model, a residual convolutional neural network (ResNet) with attention mechanism called ResNet‐Attention is designed for human authentication. We have used 2‐s duration ECG signals obtained from two ECG databases (Physikalisch‐Technische Bundesanstalt [PTB] and Check Your Bio‐signals Here initiative [CYBHi]) for authentication. Our proposed ResNet‐Attention algorithm achieved an accuracy of 98.85 and 99.27% using PTB and CYBHi, respectively. The results obtained by our developed model show that the performance is better than existing algorithms and can be used in real‐time authentication systems after the validation with more diverse ECG data.
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Affiliation(s)
- Mohamed Hammad
- School of Computer Science and Technology Harbin Institute of Technology Harbin China
- Faculty of Computers and Information Menoufia University Menoufia Egypt
| | - Paweł Pławiak
- Department of Information and Communications Technology, Faculty of Computer Science and Telecommunications Cracow University of Technology Krakow Poland
- Institute of Theoretical and Applied Informatics Polish Academy of Sciences Gliwice Poland
| | - Kuanquan Wang
- School of Computer Science and Technology Harbin Institute of Technology Harbin China
| | - Udyavara Rajendra Acharya
- Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore Singapore
- Department of Biomedical Engineering, School of Science and Technology Singapore School of Social Sciences Singapore Singapore
- Department of Bioinformatics and Medical Engineering Asia University Taichung Taiwan
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Chen S, Qi J, Fan S, Qiao Z, Yeo JC, Lim CT. Flexible Wearable Sensors for Cardiovascular Health Monitoring. Adv Healthc Mater 2021; 10:e2100116. [PMID: 33960133 DOI: 10.1002/adhm.202100116] [Citation(s) in RCA: 82] [Impact Index Per Article: 27.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 03/15/2021] [Indexed: 12/26/2022]
Abstract
Cardiovascular diseases account for the highest mortality globally, but recent advances in wearable technologies may potentially change how these illnesses are diagnosed and managed. In particular, continuous monitoring of cardiovascular vital signs for early intervention is highly desired. To this end, flexible wearable sensors that can be comfortably worn over long durations are gaining significant attention. In this review, advanced flexible wearable sensors for monitoring cardiovascular vital signals are outlined and discussed. Specifically, the functional materials, configurations, mechanisms, and recent advances of these flexible sensors for heart rate, blood pressure, blood oxygen saturation, and blood glucose monitoring are highlighted. Different mechanisms in bioelectric, mechano-electric, optoelectric, and ultrasonic wearable sensors are presented to monitor cardiovascular vital signs from different body locations. Present challenges, possible strategies, and future directions of these wearable sensors are also discussed. With rapid development, these flexible wearable sensors will potentially be applicable for both medical diagnosis and daily healthcare use in tackling cardiovascular diseases.
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Affiliation(s)
- Shuwen Chen
- Institute for Health Innovation and Technology (iHealthtech) National University of Singapore Singapore 117599 Singapore
| | - Jiaming Qi
- Department of Biomedical Engineering National University of Singapore Singapore 117583 Singapore
| | - Shicheng Fan
- Department of Biomedical Engineering National University of Singapore Singapore 117583 Singapore
| | - Zheng Qiao
- Department of Biomedical Engineering National University of Singapore Singapore 117583 Singapore
| | - Joo Chuan Yeo
- Institute for Health Innovation and Technology (iHealthtech) National University of Singapore Singapore 117599 Singapore
| | - Chwee Teck Lim
- Institute for Health Innovation and Technology (iHealthtech) National University of Singapore Singapore 117599 Singapore
- Department of Biomedical Engineering National University of Singapore Singapore 117583 Singapore
- Mechanobiology Institute National University of Singapore Singapore 117411 Singapore
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31
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Zhang WZ, Elgendy IA, Hammad M, Iliyasu AM, Du X, Guizani M, El-Latif AAA. Secure and Optimized Load Balancing for Multitier IoT and Edge-Cloud Computing Systems. IEEE INTERNET OF THINGS JOURNAL 2021; 8:8119-8132. [DOI: 10.1109/jiot.2020.3042433] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/23/2024]
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32
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Saini SK, Gupta R. Artificial intelligence methods for analysis of electrocardiogram signals for cardiac abnormalities: state-of-the-art and future challenges. Artif Intell Rev 2021. [DOI: 10.1007/s10462-021-09999-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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33
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Abstract
This paper puts forward a 1-D convolutional neural network (CNN) that exploits a novel analysis of the correlation between the two leads of the noisy electrocardiogram (ECG) to classify heartbeats. The proposed method is one-dimensional, enabling complex structures while maintaining a reasonable computational complexity. It is based on the combination of elementary handcrafted time domain features, frequency domain features through spectrograms and the use of autoregressive modeling. On the MIT-BIH database, a 95.52% overall accuracy is obtained by classifying 15 types, whereas a 95.70% overall accuracy is reached when classifying 7 types from the INCART database.
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MANDAL SAURAV, SINHA NABANITA. ARRHYTHMIA DIAGNOSIS FROM ECG SIGNAL ANALYSIS USING STATISTICAL FEATURES AND NOVEL CLASSIFICATION METHOD. J MECH MED BIOL 2021. [DOI: 10.1142/s0219519421500251] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
This study aims to present an efficient model for autodetection of cardiac arrhythmia by the diagnosis of self-affinity and identification of governing processes of a number of Electrocardiogram (ECG) signals taken from MIT-BIH database. In this work, the proposed model includes statistical methods to find the diagnosis pattern for detecting cardiac abnormalities which is useful for the computer aided system for arrhythmia detection. First, the Rescale Range (R/S) analysis has been employed for ECG signals to understand the scaling property of ECG signals. The value of Hurst exponent identifies the presence of abnormality in ECG signals taken for consideration with 92.58% accuracy. In this study, Higuchi method which deals with unifractality or monofractality of signals has been applied and it is found that unifractality is sufficient to detect arrhythmia with 91.61% accuracy. The Multifractal Detrended Fluctuation Analysis (MFDFA) has been used over the present signals to identify and confirm the multifractality. The nature of multifractality is different for arrhythmia patients and normal heart condition. The multifractal analysis is useful to detect abnormalities with 93.75% accuracy. Finally, the autocorrelation analysis has been used to identify the prevalent governing process in the present arrhythmic ECG signals and study confirms that all the signals are governed by stationary autoregressive methods of certain orders. In order to increase the overall efficiency, this present model deals with analyzing all the statistical features extracted from different statistical techniques for a large number of ECG signals of normal and abnormal heart condition. Finally, the result of present analysis altogether possibly indicates that the proposed model is efficient to detect cardiac arrhythmia with 99.3% accuracy.
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Affiliation(s)
- SAURAV MANDAL
- Department of Radio Physics and Electronics, University of Calcutta, 92, Acharya Prafulla Chandra Road, Kolkata 700009, India
| | - NABANITA SINHA
- Department of Radio Physics and Electronics, University of Calcutta, 92, Acharya Prafulla Chandra Road, Kolkata 700009, India
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35
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Efficiently Updating ECG-Based Biometric Authentication Based on Incremental Learning. SENSORS 2021; 21:s21051568. [PMID: 33668148 PMCID: PMC7956719 DOI: 10.3390/s21051568] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 02/05/2021] [Accepted: 02/12/2021] [Indexed: 11/17/2022]
Abstract
Recently, the interest in biometric authentication based on electrocardiograms (ECGs) has increased. Nevertheless, the ECG signal of a person may vary according to factors such as the emotional or physical state, thus hindering authentication. We propose an adaptive ECG-based authentication method that performs incremental learning to identify ECG signals from a subject under a variety of measurement conditions. An incremental support vector machine (SVM) is adopted for authentication implementing incremental learning. We collected ECG signals from 11 subjects during 10 min over six days and used the data from days 1 to 5 for incremental learning, and those from day 6 for testing. The authentication results show that the proposed system consistently reduces the false acceptance rate from 6.49% to 4.39% and increases the true acceptance rate from 61.32% to 87.61% per single ECG wave after incremental learning using data from the five days. In addition, the authentication results tested using data obtained a day after the latest training show the false acceptance rate being within reliable range (3.5–5.33%) and improvement of the true acceptance rate (70.05–87.61%) over five days.
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36
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Automatic diagnosis of cardiovascular disorders by sub images of the ECG signal using multi-feature extraction methods and randomized neural network. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102260] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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37
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Hammad M, Iliyasu AM, Subasi A, Ho ESL, El-Latif AAA. A Multitier Deep Learning Model for Arrhythmia Detection. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2021; 70:1-9. [PMID: 0 DOI: 10.1109/tim.2020.3033072] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
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38
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Villarreal-González R, Acosta-Hoyos AJ, Garzon-Ochoa JA, Galán-Freyle NJ, Amar-Sepúlveda P, Pacheco-Londoño LC. Anomaly Identification during Polymerase Chain Reaction for Detecting SARS-CoV-2 Using Artificial Intelligence Trained from Simulated Data. Molecules 2020; 26:molecules26010020. [PMID: 33374492 PMCID: PMC7793083 DOI: 10.3390/molecules26010020] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 12/02/2020] [Accepted: 12/10/2020] [Indexed: 11/16/2022] Open
Abstract
Real-time reverse transcription (RT) PCR is the gold standard for detecting Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), owing to its sensitivity and specificity, thereby meeting the demand for the rising number of cases. The scarcity of trained molecular biologists for analyzing PCR results makes data verification a challenge. Artificial intelligence (AI) was designed to ease verification, by detecting atypical profiles in PCR curves caused by contamination or artifacts. Four classes of simulated real-time RT-PCR curves were generated, namely, positive, early, no, and abnormal amplifications. Machine learning (ML) models were generated and tested using small amounts of data from each class. The best model was used for classifying the big data obtained by the Virology Laboratory of Simon Bolivar University from real-time RT-PCR curves for SARS-CoV-2, and the model was retrained and implemented in a software that correlated patient data with test and AI diagnoses. The best strategy for AI included a binary classification model, which was generated from simulated data, where data analyzed by the first model were classified as either positive or negative and abnormal. To differentiate between negative and abnormal, the data were reevaluated using the second model. In the first model, the data required preanalysis through a combination of prepossessing. The early amplification class was eliminated from the models because the numbers of cases in big data was negligible. ML models can be created from simulated data using minimum available information. During analysis, changes or variations can be incorporated by generating simulated data, avoiding the incorporation of large amounts of experimental data encompassing all possible changes. For diagnosing SARS-CoV-2, this type of AI is critical for optimizing PCR tests because it enables rapid diagnosis and reduces false positives. Our method can also be used for other types of molecular analyses.
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Affiliation(s)
- Reynaldo Villarreal-González
- MacondoLab, Universidad Simón Bolívar, Barranquilla 080002, Colombia; (R.V.-G.); (J.A.G.-O.); (N.J.G.-F.); (P.A.-S.)
| | - Antonio J. Acosta-Hoyos
- School of Basic and Biomedical Science, Universidad Simón Bolívar, Barranquilla 080002, Colombia
- Correspondence: (A.J.A.-H.); (L.C.P.-L.); Tel.: +57-304-648-9549 (L.C.P.-L.)
| | - Jaime A. Garzon-Ochoa
- MacondoLab, Universidad Simón Bolívar, Barranquilla 080002, Colombia; (R.V.-G.); (J.A.G.-O.); (N.J.G.-F.); (P.A.-S.)
| | - Nataly J. Galán-Freyle
- MacondoLab, Universidad Simón Bolívar, Barranquilla 080002, Colombia; (R.V.-G.); (J.A.G.-O.); (N.J.G.-F.); (P.A.-S.)
- School of Basic and Biomedical Science, Universidad Simón Bolívar, Barranquilla 080002, Colombia
| | - Paola Amar-Sepúlveda
- MacondoLab, Universidad Simón Bolívar, Barranquilla 080002, Colombia; (R.V.-G.); (J.A.G.-O.); (N.J.G.-F.); (P.A.-S.)
| | - Leonardo C. Pacheco-Londoño
- MacondoLab, Universidad Simón Bolívar, Barranquilla 080002, Colombia; (R.V.-G.); (J.A.G.-O.); (N.J.G.-F.); (P.A.-S.)
- School of Basic and Biomedical Science, Universidad Simón Bolívar, Barranquilla 080002, Colombia
- Correspondence: (A.J.A.-H.); (L.C.P.-L.); Tel.: +57-304-648-9549 (L.C.P.-L.)
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39
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A New Multichannel Parallel Network Framework for the Special Structure of Multilead ECG. JOURNAL OF HEALTHCARE ENGINEERING 2020; 2020:8889483. [PMID: 33343853 PMCID: PMC7728482 DOI: 10.1155/2020/8889483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 10/26/2020] [Accepted: 11/24/2020] [Indexed: 11/18/2022]
Abstract
Electrocardiogram (ECG) contains the rhythmic features of continuous heartbeat and morphological features of ECG waveforms and varies among different diseases. Based on ECG signal features, we propose a combination of multiple neural networks, the multichannel parallel neural network (MLCNN-BiLSTM), to explore feature information contained in ECG. The MLCNN channel is used in extracting the morphological features of ECG waveforms. Compared with traditional convolutional neural network (CNN), the MLCNN can accurately extract strong relevant information on multilead ECG while ignoring irrelevant information. It is suitable for the special structures of multilead ECG. The Bidirectional Long Short-Term Memory (BiLSTM) channel is used in extracting the rhythmic features of ECG continuous heartbeat. Finally, by initializing the core threshold parameters and using the backpropagation algorithm to update automatically, the weighted fusion of the temporal-spatial features extracted from multiple channels in parallel is used in exploring the sensitivity of different cardiovascular diseases to morphological and rhythmic features. Experimental results show that the accuracy rate of multiple cardiovascular diseases is 87.81%, sensitivity is 86.00%, and specificity is 87.76%. We proposed the MLCNN-BiLSTM neural network that can be used as the first-round screening tool for clinical diagnosis of ECG.
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40
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Rai HM, Chatterjee K, Mukherjee C. Hybrid CNN-LSTM model for automatic prediction of cardiac arrhythmias from ECG big data. 2020 IEEE 7TH UTTAR PRADESH SECTION INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONICS AND COMPUTER ENGINEERING (UPCON) 2020. [DOI: 10.1109/upcon50219.2020.9376450] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/09/2023]
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41
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Książek W, Hammad M, Pławiak P, Acharya UR, Tadeusiewicz R. Development of novel ensemble model using stacking learning and evolutionary computation techniques for automated hepatocellular carcinoma detection. Biocybern Biomed Eng 2020. [DOI: 10.1016/j.bbe.2020.08.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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42
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Electrocardiogram heartbeat classification based on a deep convolutional neural network and focal loss. Comput Biol Med 2020; 123:103866. [DOI: 10.1016/j.compbiomed.2020.103866] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Revised: 06/14/2020] [Accepted: 06/14/2020] [Indexed: 01/23/2023]
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43
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Jafarian K, Vahdat V, Salehi S, Mobin M. Automating detection and localization of myocardial infarction using shallow and end-to-end deep neural networks. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2020.106383] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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44
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Sedik A, Iliyasu AM, Abd El-Rahiem B, Abdel Samea ME, Abdel-Raheem A, Hammad M, Peng J, Abd El-Samie FE, Abd El-Latif AA. Deploying Machine and Deep Learning Models for Efficient Data-Augmented Detection of COVID-19 Infections. Viruses 2020; 12:E769. [PMID: 32708803 PMCID: PMC7411959 DOI: 10.3390/v12070769] [Citation(s) in RCA: 89] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Revised: 07/01/2020] [Accepted: 07/14/2020] [Indexed: 12/16/2022] Open
Abstract
This generation faces existential threats because of the global assault of the novel Corona virus 2019 (i.e., COVID-19). With more than thirteen million infected and nearly 600000 fatalities in 188 countries/regions, COVID-19 is the worst calamity since the World War II. These misfortunes are traced to various reasons, including late detection of latent or asymptomatic carriers, migration, and inadequate isolation of infected people. This makes detection, containment, and mitigation global priorities to contain exposure via quarantine, lockdowns, work/stay at home, and social distancing that are focused on "flattening the curve". While medical and healthcare givers are at the frontline in the battle against COVID-19, it is a crusade for all of humanity. Meanwhile, machine and deep learning models have been revolutionary across numerous domains and applications whose potency have been exploited to birth numerous state-of-the-art technologies utilised in disease detection, diagnoses, and treatment. Despite these potentials, machine and, particularly, deep learning models are data sensitive, because their effectiveness depends on availability and reliability of data. The unavailability of such data hinders efforts of engineers and computer scientists to fully contribute to the ongoing assault against COVID-19. Faced with a calamity on one side and absence of reliable data on the other, this study presents two data-augmentation models to enhance learnability of the Convolutional Neural Network (CNN) and the Convolutional Long Short-Term Memory (ConvLSTM)-based deep learning models (DADLMs) and, by doing so, boost the accuracy of COVID-19 detection. Experimental results reveal improvement in terms of accuracy of detection, logarithmic loss, and testing time relative to DLMs devoid of such data augmentation. Furthermore, average increases of 4% to 11% in COVID-19 detection accuracy are reported in favour of the proposed data-augmented deep learning models relative to the machine learning techniques. Therefore, the proposed algorithm is effective in performing a rapid and consistent Corona virus diagnosis that is primarily aimed at assisting clinicians in making accurate identification of the virus.
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Affiliation(s)
- Ahmed Sedik
- Department of the Robotics and Intelligent Machines, Kafrelsheikh University, Kafrelsheikh 33511, Egypt;
| | - Abdullah M Iliyasu
- Electrical Engineering Department, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
- School of Computing, Tokyo Institute of Technology, Yokohama 226-8502, Japan
- School of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China
| | - Basma Abd El-Rahiem
- Department of Mathematics and Computer Science, Faculty of Science, Menoufia University, Shebin El-Koom 32511, Egypt;
- Centre for Excellence in Cybersecurity, Quantum Information Processing, and Artificial Intelligence, Menoufia University, Shebin El-Koom 32511, Egypt
| | - Mohammed E. Abdel Samea
- Medical Imaging and Interventional Radiology Departement, National Liver Institute, Menoufia university, Shebin El-Koom 32511, Egypt;
| | - Asmaa Abdel-Raheem
- Public Health and Community Medicine Department, Faculty of Medicine Menoufia University, Shebin El-Koom 32511, Egypt;
| | - Mohamed Hammad
- Information Technology Department, Faculty of Computers and Information, Menoufia University, Shebin El-Koom 32511, Egypt;
| | - Jialiang Peng
- School of Data Science and Technology, Heilongjiang University, Harbin 150080, China;
| | - Fathi E. Abd El-Samie
- Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufa University, Menouf 32952, Egypt;
| | - Ahmed A. Abd El-Latif
- Department of Mathematics and Computer Science, Faculty of Science, Menoufia University, Shebin El-Koom 32511, Egypt;
- Centre for Excellence in Cybersecurity, Quantum Information Processing, and Artificial Intelligence, Menoufia University, Shebin El-Koom 32511, Egypt
- School of Information Technology and Computer Science, Nile University, Giza 12588, Egypt
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On-line anxiety level detection from biosignals: Machine learning based on a randomized controlled trial with spider-fearful individuals. PLoS One 2020; 15:e0231517. [PMID: 32574167 PMCID: PMC7310735 DOI: 10.1371/journal.pone.0231517] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Accepted: 03/24/2020] [Indexed: 11/19/2022] Open
Abstract
We present performance results concerning the validation for anxiety level detection based on trained mathematical models using supervised machine learning techniques. The model training is based on biosignals acquired in a randomized controlled trial. Wearable sensors were used to collect electrocardiogram, electrodermal activity, and respiration from spider-fearful individuals. We designed and applied ten approaches for data labeling considering individual biosignals as well as subjective ratings. Performance results revealed a selection of trained models adapted for two-level (low and high) and three-level (low, medium and high) classification of anxiety using a minimal set of six features. We obtained a remarkable accuracy of 89.8% for the two-level classification and of 74.4% for the three-level classification using a short time window length of ten seconds when applying the approach that uses subjective ratings for data labeling. Bagged Trees proved to be the most suitable classifier type among the classification models studied. The trained models will have a practical impact on the feasibility study of an augmented reality exposure therapy based on a therapeutic game for the treatment of arachnophobia.
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46
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ECG signal classification with binarized convolutional neural network. Comput Biol Med 2020; 121:103800. [DOI: 10.1016/j.compbiomed.2020.103800] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Revised: 04/27/2020] [Accepted: 04/30/2020] [Indexed: 12/21/2022]
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47
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A review of fabrication polymer scaffolds for biomedical applications using additive manufacturing techniques. Biocybern Biomed Eng 2020. [DOI: 10.1016/j.bbe.2020.01.015] [Citation(s) in RCA: 80] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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48
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Alghamdi A, Hammad M, Ugail H, Abdel-Raheem A, Muhammad K, Khalifa HS, Abd El-Latif AA. Detection of myocardial infarction based on novel deep transfer learning methods for urban healthcare in smart cities. MULTIMEDIA TOOLS AND APPLICATIONS 2020. [DOI: 10.1007/s11042-020-08769-x] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Revised: 11/29/2019] [Accepted: 02/17/2020] [Indexed: 09/02/2023]
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49
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Wang Z, Qian L, Han C, Shi L. Application of multi-feature fusion and random forests to the automated detection of myocardial infarction. COGN SYST RES 2020. [DOI: 10.1016/j.cogsys.2019.09.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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50
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Hammad M, Zhang S, Wang K. A novel two-dimensional ECG feature extraction and classification algorithm based on convolution neural network for human authentication. FUTURE GENERATION COMPUTER SYSTEMS 2019; 101:180-196. [DOI: 10.1016/j.future.2019.06.008] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/23/2024]
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