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Tekin H, Kaya Y. A new approach for heart disease detection using Motif transform-based CWT's time-frequency images with DenseNet deep transfer learning methods. BIOMED ENG-BIOMED TE 2024; 69:407-417. [PMID: 38425179 DOI: 10.1515/bmt-2023-0580] [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] [Received: 09/08/2023] [Accepted: 02/15/2024] [Indexed: 03/02/2024]
Abstract
OBJECTIVES Electrocardiogram (ECG) signals are extensively utilized in the identification and assessment of diverse cardiac conditions, including congestive heart failure (CHF) and cardiac arrhythmias (ARR), which present potential hazards to human health. With the aim of facilitating disease diagnosis and assessment, advanced computer-aided systems are being developed to analyze ECG signals. METHODS This study proposes a state-of-the-art ECG data pattern recognition algorithm based on Continuous Wavelet Transform (CWT) as a novel signal preprocessing model. The Motif Transformation (MT) method was devised to diminish the drawbacks and limitations inherent in the CWT, such as the issue of boundary effects, limited localization in time and frequency, and overfitting conditions. This transformation technique facilitates the formation of diverse patterns (motifs) within the signals. The patterns (motifs) are constructed by comparing the amplitudes of each individual sample value in the ECG signals in terms of their largeness and smallness. In the subsequent stage, the obtained one-dimensional signals from the MT transformation were subjected to CWT to obtain scalogram images. In the last stage, the obtained scalogram images were subjected to classification using DenseNET deep transfer learning techniques. RESULTS AND CONCLUSIONS The combined approach of MT + CWT + DenseNET yielded an impressive success rate of 99.31 %.
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Affiliation(s)
- Hazret Tekin
- Electrical Department, Sirnak University, Sirnak, Türkiye
| | - Yılmaz Kaya
- Computer Engineering, Batman University, Batman, Türkiye
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2
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Berrahou N, El Alami A, Mesbah A, El Alami R, Berrahou A. Arrhythmia detection in inter-patient ECG signals using entropy rate features and RR intervals with CNN architecture. Comput Methods Biomech Biomed Engin 2024:1-20. [PMID: 39021157 DOI: 10.1080/10255842.2024.2378105] [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: 02/05/2024] [Accepted: 06/27/2024] [Indexed: 07/20/2024]
Abstract
The classification of inter-patient ECG data for arrhythmia detection using electrocardiogram (ECG) signals presents a significant challenge. Despite the recent surge in deep learning approaches, there remains a noticeable gap in the performance of inter-patient ECG classification. In this study, we introduce an innovative approach for ECG classification in arrhythmia detection by employing a 1D convolutional neural network (CNN) to leverage both morphological and temporal characteristics of cardiac cycles. Through the utilization of 1D-CNN layers, we automatically capture the morphological attributes of ECG data, allowing us to represent the shape of the ECG waveform around the R peaks. Additionally, we incorporate four RR interval features to provide temporal context, and we explore the potential application of entropy rate as a feature extraction technique for ECG signal classification. Consequently, the classification layers benefit from the combination of both temporal and learned features, leading to the achievement of the final arrhythmia classification. We validate our approach using the MIT-BIH arrhythmia dataset, employing both intra-patient and inter-patient paradigms for model training and testing. The model's generalization ability is assessed by evaluating it on the INCART dataset. The model attains average accuracy rates of 99.13% and 99.17% for 2-fold and 5-fold cross-validation, respectively, in intra-patient classification with five classes. In inter-patient classification with three and five classes, the model achieves average accuracies of 98.73% and 97.91%, respectively. For the INCART dataset, the model achieves an average accuracy of 98.20% for three classes. The experimental outcomes demonstrate the superiority of the proposed model compared to state-of-the-art models in recognizing arrhythmias. Thus, the proposed model exhibits enhanced generalization and the potential to serve as an effective solution for recognizing arrhythmias in real-world datasets characterized by class imbalances in practical applications.
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Affiliation(s)
- Nadia Berrahou
- Faculty of sciences dhar el mahraz, Sidi Mohamed Ben Abdellah University, Fez, Morocco
| | - Abdelmajid El Alami
- Faculty of sciences dhar el mahraz, Sidi Mohamed Ben Abdellah University, Fez, Morocco
| | | | - Rachid El Alami
- Faculty of sciences dhar el mahraz, Sidi Mohamed Ben Abdellah University, Fez, Morocco
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3
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Quigley KS, Gianaros PJ, Norman GJ, Jennings JR, Berntson GG, de Geus EJC. Publication guidelines for human heart rate and heart rate variability studies in psychophysiology-Part 1: Physiological underpinnings and foundations of measurement. Psychophysiology 2024:e14604. [PMID: 38873876 DOI: 10.1111/psyp.14604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 12/22/2023] [Accepted: 04/04/2024] [Indexed: 06/15/2024]
Abstract
This Committee Report provides methodological, interpretive, and reporting guidance for researchers who use measures of heart rate (HR) and heart rate variability (HRV) in psychophysiological research. We provide brief summaries of best practices in measuring HR and HRV via electrocardiographic and photoplethysmographic signals in laboratory, field (ambulatory), and brain-imaging contexts to address research questions incorporating measures of HR and HRV. The Report emphasizes evidence for the strengths and weaknesses of different recording and derivation methods for measures of HR and HRV. Along with this guidance, the Report reviews what is known about the origin of the heartbeat and its neural control, including factors that produce and influence HRV metrics. The Report concludes with checklists to guide authors in study design and analysis considerations, as well as guidance on the reporting of key methodological details and characteristics of the samples under study. It is expected that rigorous and transparent recording and reporting of HR and HRV measures will strengthen inferences across the many applications of these metrics in psychophysiology. The prior Committee Reports on HR and HRV are several decades old. Since their appearance, technologies for human cardiac and vascular monitoring in laboratory and daily life (i.e., ambulatory) contexts have greatly expanded. This Committee Report was prepared for the Society for Psychophysiological Research to provide updated methodological and interpretive guidance, as well as to summarize best practices for reporting HR and HRV studies in humans.
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Affiliation(s)
- Karen S Quigley
- Department of Psychology, Northeastern University, Boston, Massachusetts, USA
| | - Peter J Gianaros
- Department of Psychology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Greg J Norman
- Department of Psychology, The University of Chicago, Chicago, Illinois, USA
| | - J Richard Jennings
- Department of Psychiatry & Psychology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Gary G Berntson
- Department of Psychology & Psychiatry, The Ohio State University, Columbus, Ohio, USA
| | - Eco J C de Geus
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
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4
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Zacarias H, Marques JAL, Felizardo V, Pourvahab M, Garcia NM. ECG Forecasting System Based on Long Short-Term Memory. Bioengineering (Basel) 2024; 11:89. [PMID: 38247966 PMCID: PMC10813352 DOI: 10.3390/bioengineering11010089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 12/31/2023] [Accepted: 01/08/2024] [Indexed: 01/23/2024] Open
Abstract
Worldwide, cardiovascular diseases are some of the primary causes of death; yet the early detection and diagnosis of such diseases have the potential to save many lives. Technological means of detection are becoming increasingly essential and numerous techniques have been created for this purpose, such as forecasting. Of these techniques, the time series forecasting technique seeks to predict future events. The long-term time series forecasting of physiological data could assist medical professionals in predicting and treating patients based on very early diagnosis. This article presents a model that utilizes a deep learning technique to predict long-term ECG signals. The forecasting model can learn signals' nonlinearity, nonstationarity, and complexity based on a long short-term memory architecture. However, this is not a trivial task as the correct forecasting of a signal that closely resembles the original complex signal's structure and behavior while minimizing any differences in amplitude continues to pose challenges. To achieve this goal, we used a dataset available on the Physio net database, called MIT-BIH, with 48 ECG recordings of 30 min each. The developed model starts with pre-processing to reduce interference in the original signals, then applies a deep learning algorithm, based on a long short-term memory (LTSM) neural network with two hidden layers. Next, we applied the root mean square error (RMSE) and mean absolute error (MAE) metrics to evaluate the performance of the model and obtained an average RMSE of 0.0070±0.0028 and an average MAE of 0.0522±0.0098 across all simulations. The results indicate that the proposed LSTM model is a promising technique for ECG forecasting, considering the trends of the changes in the original data series, most notably in R-peak amplitude. Given the model's accuracy and the features of the physiological signals, the system could be used to improve existing predictive healthcare systems for cardiovascular monitoring.
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Affiliation(s)
- Henriques Zacarias
- Faculdade de Ciências de Saúde, Universidade da Beira Interior, 6201-001 Covilha, Portugal
- Instituto de Telecomunicacoes, 6201-001 Lisboa, Portugal; (V.F.); (N.M.G.)
- Instituto Politécnico da Huíla, Universidade Mandume Ya Ndemufayo, Lubango 1049-001, Angola
| | | | - Virginie Felizardo
- Instituto de Telecomunicacoes, 6201-001 Lisboa, Portugal; (V.F.); (N.M.G.)
- Departamento de Informática, Universidade da Beira Interior, 6201-001 Covilha, Portugal;
| | - Mehran Pourvahab
- Departamento de Informática, Universidade da Beira Interior, 6201-001 Covilha, Portugal;
| | - Nuno M. Garcia
- Instituto de Telecomunicacoes, 6201-001 Lisboa, Portugal; (V.F.); (N.M.G.)
- Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal
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5
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Witczyńska A, Alaburda A, Grześk G, Nowaczyk J, Nowaczyk A. Unveiling the Multifaceted Problems Associated with Dysrhythmia. Int J Mol Sci 2023; 25:263. [PMID: 38203440 PMCID: PMC10778936 DOI: 10.3390/ijms25010263] [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] [Received: 11/27/2023] [Revised: 12/15/2023] [Accepted: 12/21/2023] [Indexed: 01/12/2024] Open
Abstract
Dysrhythmia is a term referring to the occurrence of spontaneous and repetitive changes in potentials with parameters deviating from those considered normal. The term refers to heart anomalies but has a broader meaning. Dysrhythmias may concern the heart, neurological system, digestive system, and sensory organs. Ion currents conducted through ion channels are a universal phenomenon. The occurrence of channel abnormalities will therefore result in disorders with clinical manifestations depending on the affected tissue, but phenomena from other tissues and organs may also manifest themselves. A similar problem concerns the implementation of pharmacotherapy, the mechanism of which is related to the impact on various ion currents. Treatment in this case may cause unfavorable effects on other tissues and organs. Drugs acting through the modulation of ion currents are characterized by relatively low tissue specificity. To assess a therapy's efficacy and safety, the risk of occurrences in other tissues with similar mechanisms of action must be considered. In the present review, the focus is shifted prominently onto a comparison of abnormal electrical activity within different tissues and organs. This review includes an overview of the types of dysrhythmias and the basic techniques of clinical examination of electrophysiological disorders. It also presents a concise overview of the available pharmacotherapy in particular diseases. In addition, the authors review the relevant ion channels and their research technique based on patch clumping.
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Affiliation(s)
- Adrianna Witczyńska
- Department of Organic Chemistry, Faculty of Pharmacy, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University, 87-100 Toruń, Poland;
| | - Aidas Alaburda
- Department of Neurobiology and Biophysics, Institute of Bioscience, Vilnius University Saulėtekio Ave. 7, LT-10257 Vilnius, Lithuania;
| | - Grzegorz Grześk
- Department of Cardiology and Clinical Pharmacology, Faculty of Health Sciences, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University, 87-100 Toruń, Poland;
| | - Jacek Nowaczyk
- Department of Physical Chemistry and Physicochemistry of Polymers, Faculty of Chemistry, Nicolaus Copernicus University, 7 Gagarina St., 87-100 Toruń, Poland;
| | - Alicja Nowaczyk
- Department of Organic Chemistry, Faculty of Pharmacy, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University, 87-100 Toruń, Poland;
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6
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Ansari MY, Qaraqe M, Charafeddine F, Serpedin E, Righetti R, Qaraqe K. Estimating age and gender from electrocardiogram signals: A comprehensive review of the past decade. Artif Intell Med 2023; 146:102690. [PMID: 38042607 DOI: 10.1016/j.artmed.2023.102690] [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] [Received: 05/05/2023] [Revised: 10/13/2023] [Accepted: 10/18/2023] [Indexed: 12/04/2023]
Abstract
Twelve lead electrocardiogram signals capture unique fingerprints about the body's biological processes and electrical activity of heart muscles. Machine learning and deep learning-based models can learn the embedded patterns in the electrocardiogram to estimate complex metrics such as age and gender that depend on multiple aspects of human physiology. ECG estimated age with respect to the chronological age reflects the overall well-being of the cardiovascular system, with significant positive deviations indicating an aged cardiovascular system and a higher likelihood of cardiovascular mortality. Several conventional, machine learning, and deep learning-based methods have been proposed to estimate age from electronic health records, health surveys, and ECG data. This manuscript comprehensively reviews the methodologies proposed for ECG-based age and gender estimation over the last decade. Specifically, the review highlights that elevated ECG age is associated with atherosclerotic cardiovascular disease, abnormal peripheral endothelial dysfunction, and high mortality, among many other cardiovascular disorders. Furthermore, the survey presents overarching observations and insights across methods for age and gender estimation. This paper also presents several essential methodological improvements and clinical applications of ECG-estimated age and gender to encourage further improvements of the state-of-the-art methodologies.
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Affiliation(s)
- Mohammed Yusuf Ansari
- Texas A&M University, College Station, TX, USA; Texas A&M University at Qatar, Doha, Qatar.
| | - Marwa Qaraqe
- Division of Information and Computing Technology, Hamad Bin Khalifa University, Doha, Qatar; Texas A&M University at Qatar, Doha, Qatar
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7
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Aldughayfiq B, Ashfaq F, Jhanjhi NZ, Humayun M. A Deep Learning Approach for Atrial Fibrillation Classification Using Multi-Feature Time Series Data from ECG and PPG. Diagnostics (Basel) 2023; 13:2442. [PMID: 37510187 PMCID: PMC10377944 DOI: 10.3390/diagnostics13142442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 07/08/2023] [Accepted: 07/13/2023] [Indexed: 07/30/2023] Open
Abstract
Atrial fibrillation is a prevalent cardiac arrhythmia that poses significant health risks to patients. The use of non-invasive methods for AF detection, such as Electrocardiogram and Photoplethysmogram, has gained attention due to their accessibility and ease of use. However, there are challenges associated with ECG-based AF detection, and the significance of PPG signals in this context has been increasingly recognized. The limitations of ECG and the untapped potential of PPG are taken into account as this work attempts to classify AF and non-AF using PPG time series data and deep learning. In this work, we emploted a hybrid deep neural network comprising of 1D CNN and BiLSTM for the task of AF classification. We addressed the under-researched area of applying deep learning methods to transmissive PPG signals by proposing a novel approach. Our approach involved integrating ECG and PPG signals as multi-featured time series data and training deep learning models for AF classification. Our hybrid 1D CNN and BiLSTM model achieved an accuracy of 95% on test data in identifying atrial fibrillation, showcasing its strong performance and reliable predictive capabilities. Furthermore, we evaluated the performance of our model using additional metrics. The precision of our classification model was measured at 0.88, indicating its ability to accurately identify true positive cases of AF. The recall, or sensitivity, was measured at 0.85, illustrating the model's capacity to detect a high proportion of actual AF cases. Additionally, the F1 score, which combines both precision and recall, was calculated at 0.84, highlighting the overall effectiveness of our model in classifying AF and non-AF cases.
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Affiliation(s)
- Bader Aldughayfiq
- Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia
| | - Farzeen Ashfaq
- School of Computer Science, SCS, Taylor's University, Subang Jaya 47500, Malaysia
| | - N Z Jhanjhi
- School of Computer Science, SCS, Taylor's University, Subang Jaya 47500, Malaysia
| | - Mamoona Humayun
- Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia
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8
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Tang L, Yang J, Wang Y, Deng R. Recent Advances in Cardiovascular Disease Biosensors and Monitoring Technologies. ACS Sens 2023; 8:956-973. [PMID: 36892106 DOI: 10.1021/acssensors.2c02311] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/10/2023]
Abstract
Cardiovascular disease (CVD) causes significant mortality and remains the leading cause of death globally. Thus, to reduce mortality, early diagnosis by measurement of cardiac biomarkers and heartbeat signals presents fundamental importance. Traditional CVD examination requires bulky hospital instruments to conduct electrocardiography recording and immunoassay analysis, which are both time-consuming and inconvenient. Recently, development of biosensing technologies for rapid CVD marker screening attracted great attention. Thanks to the advancement in nanotechnology and bioelectronics, novel biosensor platforms are developed to achieve rapid detection, accurate quantification, and continuous monitoring throughout disease progression. A variety of sensing methodologies using chemical, electrochemical, optical, and electromechanical means are explored. This review first discusses the prevalence and common categories of CVD. Then, heartbeat signals and cardiac blood-based biomarkers that are widely employed in clinic, as well as their utilizations for disease prognosis, are summarized. Emerging CVD wearable and implantable biosensors and monitoring bioelectronics, allowing these cardiac markers to be continuously measured are introduced. Finally, comparisons of the pros and cons of these biosensing devices along with perspectives on future CVD biosensor research are presented.
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Affiliation(s)
- Lichao Tang
- Targeted Tracer Research and Development Laboratory, Institute of Respiratory Health, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
- Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, 60208, Illinois, United States
| | - Jiyuan Yang
- Targeted Tracer Research and Development Laboratory, Institute of Respiratory Health, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, 47906, Indiana, United States
| | - Yuxi Wang
- Targeted Tracer Research and Development Laboratory, Institute of Respiratory Health, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
- Precision Medicine Key Laboratory of Sichuan Province & Precision Medicine Research Center, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Ruijie Deng
- College of Biomass Science and Engineering, Healthy Food Evaluation Research Center, Sichuan University, Chengdu, 610064, Sichuan, China
- Precision Medicine Key Laboratory of Sichuan Province & Precision Medicine Research Center, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
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9
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Min Hyun C, Jun Jang T, Nam J, Kwon H, Jeon K, Lee K. Machine learning-based signal quality assessment for cardiac volume monitoring in electrical impedance tomography. MACHINE LEARNING: SCIENCE AND TECHNOLOGY 2023. [DOI: 10.1088/2632-2153/acc637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/01/2023] Open
Abstract
Abstract
Owing to recent advances in thoracic electrical impedance tomography (EIT), a patient’s hemodynamic function can be noninvasively and continuously estimated in real-time by surveilling a cardiac volume signal (CVS) associated with stroke volume and cardiac output. In clinical applications, however, a CVS is often of low quality, mainly because of the patient’s deliberate movements or inevitable motions during clinical interventions. This study aims to develop a signal quality indexing method that assesses the influence of motion artifacts on transient CVSs. The assessment is performed on each cardiac cycle to take advantage of the periodicity and regularity in cardiac volume changes. Time intervals are identified using the synchronized electrocardiography system. We apply divergent machine-learning methods, which can be sorted into discriminative-model and manifold-learning approaches. The use of machine-learning could be suitable for our real-time monitoring application that requires fast inference and automation as well as high accuracy. In the clinical environment, the proposed method can be utilized to provide immediate warnings so that clinicians can minimize confusion regarding patients’ conditions, reduce clinical resource utilization, and improve the confidence level of the monitoring system. Numerous experiments using actual EIT data validate the capability of CVSs degraded by motion artifacts to be accurately and automatically assessed in real-time by machine learning. The best model achieved an accuracy of 0.95, positive and negative predictive values of 0.96 and 0.86, sensitivity of 0.98, specificity of 0.77, and AUC of 0.96.
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10
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Farag MM. A Tiny Matched Filter-Based CNN for Inter-Patient ECG Classification and Arrhythmia Detection at the Edge. SENSORS (BASEL, SWITZERLAND) 2023; 23:1365. [PMID: 36772404 PMCID: PMC9919183 DOI: 10.3390/s23031365] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 01/12/2023] [Accepted: 01/17/2023] [Indexed: 06/18/2023]
Abstract
Automated electrocardiogram (ECG) classification using machine learning (ML) is extensively utilized for arrhythmia detection. Contemporary ML algorithms are typically deployed on the cloud, which may not always meet the availability and privacy requirements of ECG monitoring. Edge inference is an emerging alternative that overcomes the concerns of cloud inference; however, it poses new challenges due to the demanding computational requirements of modern ML algorithms and the tight constraints of edge devices. In this work, we propose a tiny convolutional neural network (CNN) classifier for real-time monitoring of ECG at the edge with the aid of the matched filter (MF) theory. The MIT-BIH dataset with inter-patient division is used for model training and testing. The model generalization capability is validated on the INCART, QT, and PTB diagnostic databases, and the model performance in the presence of noise is experimentally analyzed. The proposed classifier can achieve average accuracy, sensitivity, and F1 scores of 98.18%, 91.90%, and 92.17%, respectively. The sensitivity of detecting supraventricular and ventricular ectopic beats (SVEB and VEB) is 85.3% and 96.34%, respectively. The model is 15 KB in size, with an average inference time of less than 1 ms. The proposed model achieves superior classification and real-time performance results compared to the state-of-the-art ECG classifiers while minimizing the model complexity. The proposed classifier can be readily deployed on a wide range of resource-constrained edge devices for arrhythmia monitoring, which can save millions of cardiovascular disease patients.
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Affiliation(s)
- Mohammed M. Farag
- Electrical Engineering Department, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi Arabia;
- Engineering Department, Faculty of Engineering, Alexandria University, Alexandria 5424041, Egypt;
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11
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Liu X, Long Z, Li Z, Huang S, Wang Z. An improved adaptive periodical segment matrix algorithm for ECG denoising based on singular value decomposition. Technol Health Care 2023; 31:269-281. [PMID: 36031921 DOI: 10.3233/thc-220316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
BACKGROUND Wearable devices that monitor heart health of cardiac disease patients in real time are in great demand. OBJECTIVE We propose an algorithm of improved segment periodical matrix construction for irregular electrocardiogram (ECG) signal denoising. METHOD While splitting the heartbeat based on each RR interval for periodical segments matrix construction, the as-filtered ECG signal is reconstructed by the maximum singular value after a singular value decomposition. RESULTS The results demonstrate a higher noise reduction effect with lower signal distortions of our methods compared to several singular value decomposition counterpart approaches. CONCLUSION Our method has great potential to enhance wearable devices diagnosis accuracy by denoising the complex noises such as electromyography artifacts in real-time ECG sensing.
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Affiliation(s)
- Xinggu Liu
- Med+X Center for Manufacturing, West China Hospital, Sichuan University, Chengdu, Sichuan, China.,Mechanic Engineering Department, University of Sichuan, Chengdu, Sichuan, China
| | - Zhiming Long
- Med+X Center for Manufacturing, West China Hospital, Sichuan University, Chengdu, Sichuan, China.,Mechanic Engineering Department, University of Sichuan, Chengdu, Sichuan, China
| | - Zongyuan Li
- Mechanic Engineering Department, University of Sichuan, Chengdu, Sichuan, China
| | - Shudong Huang
- College of Computer Science, University of Sichuan, Chengdu, Sichuan, China
| | - Zhuqing Wang
- Med+X Center for Manufacturing, West China Hospital, Sichuan University, Chengdu, Sichuan, China.,Mechanic Engineering Department, University of Sichuan, Chengdu, Sichuan, China
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12
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Zhu Y, Wang M, Yin X, Zhang J, Meijering E, Hu J. Deep Learning in Diverse Intelligent Sensor Based Systems. SENSORS (BASEL, SWITZERLAND) 2022; 23:s23010062. [PMID: 36616657 PMCID: PMC9823653 DOI: 10.3390/s23010062] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 12/06/2022] [Accepted: 12/14/2022] [Indexed: 05/27/2023]
Abstract
Deep learning has become a predominant method for solving data analysis problems in virtually all fields of science and engineering. The increasing complexity and the large volume of data collected by diverse sensor systems have spurred the development of deep learning methods and have fundamentally transformed the way the data are acquired, processed, analyzed, and interpreted. With the rapid development of deep learning technology and its ever-increasing range of successful applications across diverse sensor systems, there is an urgent need to provide a comprehensive investigation of deep learning in this domain from a holistic view. This survey paper aims to contribute to this by systematically investigating deep learning models/methods and their applications across diverse sensor systems. It also provides a comprehensive summary of deep learning implementation tips and links to tutorials, open-source codes, and pretrained models, which can serve as an excellent self-contained reference for deep learning practitioners and those seeking to innovate deep learning in this space. In addition, this paper provides insights into research topics in diverse sensor systems where deep learning has not yet been well-developed, and highlights challenges and future opportunities. This survey serves as a catalyst to accelerate the application and transformation of deep learning in diverse sensor systems.
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Affiliation(s)
- Yanming Zhu
- School of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2052, Australia
| | - Min Wang
- School of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2612, Australia
| | - Xuefei Yin
- School of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2612, Australia
| | - Jue Zhang
- School of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2612, Australia
| | - Erik Meijering
- School of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2052, Australia
| | - Jiankun Hu
- School of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2612, Australia
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13
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Implementation of Predictive Algorithms for the Study of the Endarterectomy LOS. Bioengineering (Basel) 2022; 9:bioengineering9100546. [PMID: 36290514 PMCID: PMC9598220 DOI: 10.3390/bioengineering9100546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 10/04/2022] [Accepted: 10/07/2022] [Indexed: 11/26/2022] Open
Abstract
Background: In recent years, the length of hospital stay (LOS) following endarterectomy has decreased significantly from 4 days to 1 day. LOS is influenced by several common complications and factors that can adversely affect the patient’s health and may vary from one healthcare facility to another. The aim of this work is to develop a forecasting model of the LOS value to investigate the main factors affecting LOS in order to save healthcare cost and improve management. Methods: We used different regression and machine learning models to predict the LOS value based on the clinical and organizational data of patients undergoing endarterectomy. Data were obtained from the discharge forms of the “San Giovanni di Dio e Ruggi d’Aragona” University Hospital (Salerno, Italy). R2 goodness of fit and the results in terms of accuracy, precision, recall and F1-score were used to compare the performance of various algorithms. Results: Before implementing the models, the preliminary correlation study showed that LOS was more dependent on the type of endarterectomy performed. Among the regression algorithms, the best was the multiple linear regression model with an R2 value of 0.854, while among the classification algorithms for LOS divided into classes, the best was decision tree, with an accuracy of 80%. The best performance was obtained in the third class, which identifies patients with prolonged LOS, with a precision of 95%. Among the independent variables, the most influential on LOS was type of endarterectomy, followed by diabetes and kidney disorders. Conclusion: The resulting forecast model demonstrates its effectiveness in predicting the value of LOS that could be used to improve the endarterectomy surgery planning.
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Marzog HA, Abd HJ. Machine Learning ECG Classification Using Wavelet Scattering of Feature Extraction. APPLIED COMPUTATIONAL INTELLIGENCE AND SOFT COMPUTING 2022; 2022:1-8. [DOI: 10.1155/2022/9884076] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/02/2023] Open
Abstract
The heart’s electrical activity is registered by an electrocardiogram (ECG), which consists of a wealth of pathological data on heart diseases such as arrhythmia. However, with increasing complexity and nonlinearity, direct observation of ECG signals and analysis is very tough. The highest accuracy of classification performance for machine learning approaches are 99.7 for neural network with wavelet scattering features extraction and 99.92 for SVM also with wavelet scattering features extraction. Through wavelet cascades with a neural network, the wavelet scattering transform can yield a translation invariant and deflection depictions of ECG signals. We suggested a new wavelet scattering transform-based method for automatically classifying three types of ECG heart diseases as follows: arrhythmia (ARR), congestive heart failure (CHF), and normal sinus rhythm (NSR). The bandwidth of the scaling function is used to critically downsample the wavelet scattering transform in time. As a result, each of the scattering paths has 16-time windows. Beat classification performance is classified by utilizing the MIT-BIH arrhythmia dataset. The suggested method is able to conduct high accuracy arrhythmia classification, with a 99.7% and 99.92% accuracy rate of the neural network (NN) and support vector machine (SVM), respectively, and will aid physicians in ECG explanation.
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Affiliation(s)
- Heyam A. Marzog
- Electrical Engineering Department, College of Engineering, University of Babylon, Hilla, Babil, Iraq
- Engineering Technical College/Najaf, Al-Furat Al-Awsat Technical University, Al Najaf 31001, Iraq
| | - Haider. J. Abd
- Electrical Engineering Department, College of Engineering, University of Babylon, Hilla, Babil, Iraq
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15
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Liu J, Li Z, Jin Y, Liu Y, Liu C, Zhao L, Chen X. A review of arrhythmia detection based on electrocardiogram with artificial intelligence. Expert Rev Med Devices 2022; 19:549-560. [DOI: 10.1080/17434440.2022.2115887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Affiliation(s)
- Jinlei Liu
- School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
| | - Zhiyuan Li
- School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
| | - Yanrui Jin
- School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
| | - Yunqing Liu
- School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
| | - Chengliang Liu
- School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
- MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, China
| | - Liqun Zhao
- Department of Cardiology, Shanghai First People’s Hospital Affiliated to Shanghai Jiao Tong University, 100 Haining Road, Shanghai 200080, China
| | - Xiaojun Chen
- School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
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16
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DE-PNN: Differential Evolution-Based Feature Optimization with Probabilistic Neural Network for Imbalanced Arrhythmia Classification. SENSORS 2022; 22:s22124450. [PMID: 35746232 PMCID: PMC9227752 DOI: 10.3390/s22124450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 05/31/2022] [Accepted: 06/06/2022] [Indexed: 11/17/2022]
Abstract
In this research, a heartbeat classification method is presented based on evolutionary feature optimization using differential evolution (DE) and classification using a probabilistic neural network (PNN) to discriminate between normal and arrhythmic heartbeats. The proposed method follows four steps: (1) preprocessing, (2) heartbeat segmentation, (3) DE feature optimization, and (4) PNN classification. In this method, we have employed direct signal amplitude points constituting the heartbeat acquired from the ECG holter device with no secondary feature extraction step usually used in case of hand-crafted, frequency transformation or other features. The heartbeat types include normal, left bundle branch block, right bundle branch block, premature ventricular contraction, atrial premature, ventricular escape, ventricular flutter and paced beat. Using ECG records from the MIT-BIH, heartbeats are identified to start at 250 ms before and end at 450 ms after the respective R-peak positions. In the next step, the DE method is applied to reduce and optimize the direct heartbeat features. Although complex and highly computational ECG heartbeat classification algorithms have been proposed in the literature, they failed to achieve high performance in detecting some minority heartbeat categories, especially for imbalanced datasets. To overcome this challenge, we propose an optimization step for the deep CNN model using a novel classification metric called the Matthews correlation coefficient (MCC). This function focuses on arrhythmia (minority) heartbeat classes by increasing their importance. Maximum MCC is used as a fitness function to identify the optimum combination of features for the uncorrelated and non-uniformly distributed eight beat class samples. The proposed DE-PNN scheme can provide better classification accuracy considering 8 classes with only 36 features optimized from a 253 element feature set implying an 85.77% reduction in direct amplitude features. Our proposed method achieved overall 99.33% accuracy, 94.56% F1, 93.84% sensitivity, and 99.21% specificity.
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17
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A Decision-making System with Reject Option for Atrial Fibrillation Prediction without ECG Signals. Ing Rech Biomed 2022. [DOI: 10.1016/j.irbm.2022.04.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
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18
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Savithri CN, Priya E, Rajasekar K. A machine learning approach to identify hand actions from single-channel sEMG signals. BIOMED ENG-BIOMED TE 2022; 67:89-103. [PMID: 35191277 DOI: 10.1515/bmt-2021-0072] [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: 03/23/2021] [Accepted: 01/24/2022] [Indexed: 11/15/2022]
Abstract
Surface Electromyographic (sEMG) signal is a prime source of information to activate prosthetic hand such that it is able to restore a few basic hand actions of amputee, making it suitable for rehabilitation. In this work, a non-invasive single channel sEMG amplifier is developed that captures sEMG signal for three typical hand actions from the lower elbow muscles of able bodied subjects and amputees. The recorded sEMG signal detrends and has frequencies other than active frequencies. The Empirical Mode Decomposition Detrending Fluctuation Analysis (EMD-DFA) is attempted to de-noise the sEMG signal. A feature vector is formed by extracting eight features in time domain, seven features each in spectral and wavelet domain. Prominent features are selected by Fuzzy Entropy Measure (FEM) to ease the computational complexity and reduce the recognition time of classification. Classification of different hand actions is attempted based on multi-class approach namely Partial Least Squares Discriminant Analysis (PLS-DA) to control the prosthetic hand. It is inferred that an accuracy of 89.72% & 84% is observed for the pointing action whereas the accuracy for closed fist is 81.2% & 79.54% while for spherical grasp it is 80.6% & 76% respectively for normal subjects and amputees. The performance of the classifier is compared with Linear Discriminant Analysis (LDA) and an improvement of 5% in mean accuracy is observed for both normal subjects and amputees. The mean accuracy for all the three different hand actions is significantly high (83.84% & 80.18%) when compared with LDA. The proposed work frame provides a fair mean accuracy in classifying the hand actions of amputees. This methodology thus appears to be useful in actuating the prosthetic hand.
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Affiliation(s)
- Chanda Nagarajan Savithri
- Department of Electronics and Communication Engineering, Sri Sai Ram Engineering College, West Tambaram, Chennai, India
| | - Ebenezer Priya
- Department of Electronics and Communication Engineering, Sri Sai Ram Engineering College, West Tambaram, Chennai, India
| | - Kevin Rajasekar
- Rosenheim Technical University of Applied Sciences, Rosenheim, Germany
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19
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Machine Algorithm for Heartbeat Monitoring and Arrhythmia Detection Based on ECG Systems. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2021:7677568. [PMID: 35003247 PMCID: PMC8739908 DOI: 10.1155/2021/7677568] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Accepted: 12/17/2021] [Indexed: 11/17/2022]
Abstract
Cardiac arrhythmia is an illness in which a heartbeat is erratic, either too slow or too rapid. It happens as a result of faulty electrical impulses that coordinate the heartbeats. Sudden cardiac death can occur as a result of certain serious arrhythmia disorders. As a result, the primary goal of electrocardiogram (ECG) investigation is to reliably perceive arrhythmias as life-threatening to provide a suitable therapy and save lives. ECG signals are waveforms that denote the electrical movement of the human heart (P, QRS, and T). The duration, structure, and distances between various peaks of each waveform are utilized to identify heart problems. The signals' autoregressive (AR) analysis is then used to obtain a specific selection of signal features, the parameters of the AR signal model. Groups of retrieved AR characteristics for three various ECG kinds are cleanly separated in the training dataset, providing high connection classification and heart problem diagnosis to each ECG signal within the training dataset. A new technique based on two-event-related moving averages (TERMAs) and fractional Fourier transform (FFT) algorithms is suggested to better evaluate ECG signals. This study could help researchers examine the current state-of-the-art approaches employed in the detection of arrhythmia situations. The characteristic of our suggested machine learning approach is cross-database training and testing with improved characteristics.
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Diao T, Kushzad F, Patel MD, Bindiganavale MP, Wasi M, Kochenderfer MJ, Moss HE. Comparison of Machine Learning Approaches to Improve Diagnosis of Optic Neuropathy Using Photopic Negative Response Measured Using a Handheld Device. Front Med (Lausanne) 2021; 8:771713. [PMID: 34926514 PMCID: PMC8677942 DOI: 10.3389/fmed.2021.771713] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 11/05/2021] [Indexed: 11/20/2022] Open
Abstract
The photopic negative response of the full-field electroretinogram (ERG) is reduced in optic neuropathies. However, technical requirements for measurement and poor classification performance have limited widespread clinical application. Recent advances in hardware facilitate efficient clinic-based recording of the full-field ERG. Time series classification, a machine learning approach, may improve classification by using the entire ERG waveform as the input. In this study, full-field ERGs were recorded in 217 eyes (109 optic neuropathy and 108 controls) of 155 subjects. User-defined ERG features including photopic negative response were reduced in optic neuropathy eyes (p < 0.0005, generalized estimating equation models accounting for age). However, classification of optic neuropathy based on user-defined features was only fair with receiver operating characteristic area under the curve ranging between 0.62 and 0.68 and F1 score at the optimal cutoff ranging between 0.30 and 0.33. In comparison, machine learning classifiers using a variety of time series analysis approaches had F1 scores of 0.58–0.76 on a test data set. Time series classifications are promising for improving optic neuropathy diagnosis using ERG waveforms. Larger sample sizes will be important to refine the models.
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Affiliation(s)
- Tina Diao
- Department of Management Science & Engineering, Stanford University, Stanford, CA, United States
| | - Fareshta Kushzad
- Department of Ophthalmology, Stanford University, Palo Alto, CA, United States
| | - Megh D Patel
- Department of Ophthalmology, Stanford University, Palo Alto, CA, United States
| | | | - Munam Wasi
- Department of Ophthalmology, Stanford University, Palo Alto, CA, United States
| | - Mykel J Kochenderfer
- Department of Ophthalmology, Stanford University, Palo Alto, CA, United States.,Department of Aeronautics and Astronautics, Stanford University, Stanford, CA, United States
| | - Heather E Moss
- Department of Ophthalmology, Stanford University, Palo Alto, CA, United States.,Department of Neurology and Neurological Sciences, Stanford University, Palo Alto, CA, United States
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21
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Alamgir A, Mousa O, Shah Z. Artificial Intelligence in Predicting Cardiac Arrest: Scoping Review. JMIR Med Inform 2021; 9:e30798. [PMID: 34927595 PMCID: PMC8726033 DOI: 10.2196/30798] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 10/07/2021] [Accepted: 10/10/2021] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND Cardiac arrest is a life-threatening cessation of activity in the heart. Early prediction of cardiac arrest is important, as it allows for the necessary measures to be taken to prevent or intervene during the onset. Artificial intelligence (AI) technologies and big data have been increasingly used to enhance the ability to predict and prepare for the patients at risk. OBJECTIVE This study aims to explore the use of AI technology in predicting cardiac arrest as reported in the literature. METHODS A scoping review was conducted in line with the guidelines of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extension for scoping reviews. Scopus, ScienceDirect, Embase, the Institute of Electrical and Electronics Engineers, and Google Scholar were searched to identify relevant studies. Backward reference list checks of the included studies were also conducted. Study selection and data extraction were independently conducted by 2 reviewers. Data extracted from the included studies were synthesized narratively. RESULTS Out of 697 citations retrieved, 41 studies were included in the review, and 6 were added after backward citation checking. The included studies reported the use of AI in the prediction of cardiac arrest. Of the 47 studies, we were able to classify the approaches taken by the studies into 3 different categories: 26 (55%) studies predicted cardiac arrest by analyzing specific parameters or variables of the patients, whereas 16 (34%) studies developed an AI-based warning system. The remaining 11% (5/47) of studies focused on distinguishing patients at high risk of cardiac arrest from patients who were not at risk. Two studies focused on the pediatric population, and the rest focused on adults (45/47, 96%). Most of the studies used data sets with a size of <10,000 samples (32/47, 68%). Machine learning models were the most prominent branch of AI used in the prediction of cardiac arrest in the studies (38/47, 81%), and the most used algorithm was the neural network (23/47, 49%). K-fold cross-validation was the most used algorithm evaluation tool reported in the studies (24/47, 51%). CONCLUSIONS AI is extensively used to predict cardiac arrest in different patient settings. Technology is expected to play an integral role in improving cardiac medicine. There is a need for more reviews to learn the obstacles to the implementation of AI technologies in clinical settings. Moreover, research focusing on how to best provide clinicians with support to understand, adapt, and implement this technology in their practice is also necessary.
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Affiliation(s)
- Asma Alamgir
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Osama Mousa
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Zubair Shah
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia
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22
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Tao R, Zhang S, Wang Y, Mi X, Ma J, Shen C, Zheng G. MCG-Net: End-to-end Fine-grained Delineation and Diagnostic Classification of Cardiac Events from Magnetocardiographs. IEEE J Biomed Health Inform 2021; 26:1057-1067. [PMID: 34780340 DOI: 10.1109/jbhi.2021.3128169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
In this paper, we propose an end-to-end deep learning architecture, referred as MCG-Net, integrating convolutional neural network (CNN) with transformer-based global context block for fine-grained delineation and diagnostic classification of four cardiac events from magnetocardiogram (MCG) data, namely Q-, R-, S- and T-waves. MCG-Net} takes advantage of a multi-resolution CNN backbone as well as the state-of-the-art (SOTA) transformer encoders that facilitate global temporal feature aggregation. Besides the novel network architecture, we introduce a multi-task learning scheme to achieve simultaneous delineation and classification. Specifically, the problem of MCG delineation is formulated as multi-class heatmap regression. Meanwhile, a binary diagnostic classification label as well as a duration are jointly estimated for each cardiac event using features that are temporally aligned by event heatmaps. The framework is evaluated on a clinical MCG dataset, containing data collected from 270 subjects with cardiac anomalies and 108 control subjects. We designed and conducted a two-fold cross-validation study to validate the proposed method and to compare its performance with the SOTA methods. Experimental results demonstrated that our method outperformed counterparts on both event delineation and diagnostic classification tasks, achieving respectively an average ECG-F1 of 0.987 and an average Event-F1 of 0.975 for MCG delineation, and an average accuracy of 0.870, an average sensitivity of 0.732, an average specificity of 0.914 and an average AUC of 0.903 for diagnostic classification. Comprehensive ablation experiments are additionally performed to investigate effectiveness of different network components.
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23
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Cardiac Arrhythmia Classification Based on One-Dimensional Morphological Features. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11209460] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The electrocardiogram (ECG) is the most commonly used tool for diagnosing cardiovascular diseases. Recently, there have been a number of attempts to classify cardiac arrhythmias using machine learning and deep learning techniques. In this study, we propose a novel method to generate the gray-level co-occurrence matrix (GLCM) and gray-level run-length matrix (GLRLM) from one-dimensional signals. From the GLCM and GLRLM, we extracted morphological features for automatic ECG signal classification. The extracted features were combined with six machine learning algorithms (decision tree, k-nearest neighbor, naïve Bayes, logistic regression, random forest, and XGBoost) to classify cardiac arrhythmias. Experiments were conducted on a 12-lead ECG database collected from Chapman University and Shaoxing People’s Hospital. Of the six machine learning algorithms, combining XGBoost with the proposed features yielded an accuracy of 90.46%, an AUC of 0.982, a sensitivity of 0.892, a precision of 0.900, and an F1 score of 0.895 and presented better results than wavelet features with XGBoost. The experimental results show the effectiveness of the proposed feature extraction algorithm.
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24
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Sarvan Ç, Özkurt N. Multi-objective advisory system for arrhytmia classification. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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25
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Lee H, Shin M. Learning Explainable Time-Morphology Patterns for Automatic Arrhythmia Classification from Short Single-Lead ECGs. SENSORS 2021; 21:s21134331. [PMID: 34202805 PMCID: PMC8272104 DOI: 10.3390/s21134331] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 06/17/2021] [Accepted: 06/21/2021] [Indexed: 11/16/2022]
Abstract
Automatic detection of abnormal heart rhythms, including atrial fibrillation (AF), using signals obtained from a single-lead wearable electrocardiogram (ECG) device, is useful for daily cardiac health monitoring. In this study, we propose a novel image-based deep learning framework to classify single-lead ECG recordings of short variable length into several different rhythms associated with arrhythmias. By transforming variable-length 1D ECG signals into fixed-size 2D time-morphology representations and feeding them to the beat-interval-texture convolutional neural network (BIT-CNN) model, we aimed to learn the comprehensible characteristics of beat shape and inter-beat patterns over time for arrhythmia classification. The proposed approach allows feature embedding vectors to provide interpretable time-morphology patterns focused at each step of the learning process. In addition, this method reduces the number of model parameters needed to be trained and aids visual interpretation, while maintaining similar performance to other CNN-based approaches to arrhythmia classification. For experiments, we used the PhysioNet/CinC Challenge 2017 dataset and achieved an overall F1_NAO of 81.75% and F1_NAOP of 76.87%, which are comparable to those of the state-of-the-art methods for variable-length ECGs.
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26
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Development of a Simple In Vitro Artery Model and an Evaluation of the Impact of Pulsed Flow on High-Intensity Focused Ultrasound Ablation. Ing Rech Biomed 2021. [DOI: 10.1016/j.irbm.2020.11.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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27
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El-Kenawy ESM, Mirjalili S, Ibrahim A, Alrahmawy M, El-Said M, Zaki RM, Eid MM. Advanced Meta-Heuristics, Convolutional Neural Networks, and Feature Selectors for Efficient COVID-19 X-Ray Chest Image Classification. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:36019-36037. [PMID: 34812381 PMCID: PMC8545230 DOI: 10.1109/access.2021.3061058] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 02/16/2021] [Indexed: 05/09/2023]
Abstract
The chest X-ray is considered a significant clinical utility for basic examination and diagnosis. The human lung area can be affected by various infections, such as bacteria and viruses, leading to pneumonia. Efficient and reliable classification method facilities the diagnosis of such infections. Deep transfer learning has been introduced for pneumonia detection from chest X-rays in different models. However, there is still a need for further improvements in the feature extraction and advanced classification stages. This paper proposes a classification method with two stages to classify different cases from the chest X-ray images based on a proposed Advanced Squirrel Search Optimization Algorithm (ASSOA). The first stage is the feature learning and extraction processes based on a Convolutional Neural Network (CNN) model named ResNet-50 with image augmentation and dropout processes. The ASSOA algorithm is then applied to the extracted features for the feature selection process. Finally, the Multi-layer Perceptron (MLP) Neural Network's connection weights are optimized by the proposed ASSOA algorithm (using the selected features) to classify input cases. A Kaggle chest X-ray images (Pneumonia) dataset consists of 5,863 X-rays is employed in the experiments. The proposed ASSOA algorithm is compared with the basic Squirrel Search (SS) optimization algorithm, Grey Wolf Optimizer (GWO), and Genetic Algorithm (GA) for feature selection to validate its efficiency. The proposed (ASSOA + MLP) is also compared with other classifiers, based on (SS + MLP), (GWO + MLP), and (GA + MLP), in performance metrics. The proposed (ASSOA + MLP) algorithm achieved a classification mean accuracy of (99.26%). The ASSOA + MLP algorithm also achieved a classification mean accuracy of (99.7%) for a chest X-ray COVID-19 dataset tested from GitHub. The results and statistical tests demonstrate the high effectiveness of the proposed method in determining the infected cases.
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Affiliation(s)
- El-Sayed M. El-Kenawy
- Department of Communications and ElectronicsDelta Higher Institute of Engineering and Technology (DHIET)Mansoura35111Egypt
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and OptimizationTorrens University AustraliaFortitude ValleyQLD4006Australia
- Yonsei Frontier LabYonsei UniversitySeoul03722South Korea
| | - Abdelhameed Ibrahim
- Computer Engineering and Control Systems DepartmentFaculty of EngineeringMansoura UniversityMansoura35516Egypt
| | - Mohammed Alrahmawy
- Department of Computer ScienceFaculty of Computers and InformationMansoura UniversityMansoura35516Egypt
| | - M. El-Said
- Electrical Engineering DepartmentFaculty of EngineeringMansoura UniversityMansoura35516Egypt
- Delta Higher Institute of Engineering and Technology (DHIET)Mansoura35111Egypt
| | - Rokaia M. Zaki
- Department of Communications and ElectronicsDelta Higher Institute of Engineering and Technology (DHIET)Mansoura35111Egypt
- Department of Electrical EngineeringShoubra Faculty of EngineeringBenha UniversityBenha11629Egypt
| | - Marwa Metwally Eid
- Department of Communications and ElectronicsDelta Higher Institute of Engineering and Technology (DHIET)Mansoura35111Egypt
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Over-fitting suppression training strategies for deep learning-based atrial fibrillation detection. Med Biol Eng Comput 2021; 59:165-173. [PMID: 33387183 DOI: 10.1007/s11517-020-02292-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Accepted: 11/22/2020] [Indexed: 10/22/2022]
Abstract
Nowadays, deep learning-based models have been widely developed for atrial fibrillation (AF) detection in electrocardiogram (ECG) signals. However, owing to the inevitable over-fitting problem, classification accuracy of the developed models severely differed when applying on the independent test datasets. This situation is more significant for AF detection from dynamic ECGs. In this study, we explored two potential training strategies to address the over-fitting problem in AF detection. The first one is to use the Fast Fourier transform (FFT) and Hanning-window-based filter to suppress the influence from individual difference. Another is to train the model on the wearable ECG data to improve the robustness of model. Wearable ECG data from 29 patients with arrhythmia were collected for at least 24 h. To verify the effectiveness of the training strategies, a Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN)-based model was proposed and tested. We tested the model on the independent wearable ECG data set, as well as the MIT-BIH Atrial Fibrillation database and PhysioNet/Computing in Cardiology Challenge 2017 database. The model achieved 96.23%, 95.44%, and 95.28% accuracy rates on the three databases, respectively. Pertaining to the comparison of the accuracy rates on each training set, the accuracy of the model trained in conjunction with the proposed training strategies only reduced by 2%, while the accuracy of the model trained without the training strategies decreased by approximately 15%. Therefore, the proposed training strategies serve as effective mechanisms for devising a robust AF detector and significantly enhanced the detection accuracy rates of the resulting deep networks.
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29
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Physiological and Behavior Monitoring Systems for Smart Healthcare Environments: A Review. SENSORS 2020; 20:s20082186. [PMID: 32290639 PMCID: PMC7218909 DOI: 10.3390/s20082186] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/14/2020] [Revised: 04/01/2020] [Accepted: 04/10/2020] [Indexed: 02/04/2023]
Abstract
Healthcare optimization has become increasingly important in the current era, where numerous challenges are posed by population ageing phenomena and the demand for higher quality of the healthcare services. The implementation of Internet of Things (IoT) in the healthcare ecosystem has been one of the best solutions to address these challenges and therefore to prevent and diagnose possible health impairments in people. The remote monitoring of environmental parameters and how they can cause or mediate any disease, and the monitoring of human daily activities and physiological parameters are among the vast applications of IoT in healthcare, which has brought extensive attention of academia and industry. Assisted and smart tailored environments are possible with the implementation of such technologies that bring personal healthcare to any individual, while living in their preferred environments. In this paper we address several requirements for the development of such environments, namely the deployment of physiological signs monitoring systems, daily activity recognition techniques, as well as indoor air quality monitoring solutions. The machine learning methods that are most used in the literature for activity recognition and body motion analysis are also referred. Furthermore, the importance of physical and cognitive training of the elderly population through the implementation of exergames and immersive environments is also addressed.
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