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Jaya Mabel Rani A, Srivenkateswaran C, Vishnupriya G, Subramanian N, Ilango P, Jacintha VK. A big data scheme for heart disease classification in map reduce using jellyfish search flow regime optimization enabled Spinalnet. Pacing Clin Electrophysiol 2024; 47:953-965. [PMID: 38751036 DOI: 10.1111/pace.14975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 02/22/2024] [Accepted: 03/16/2024] [Indexed: 07/06/2024]
Abstract
BACKGROUND The disease related to the heart is serious and can lead to death. Precise heart disease prediction is imperative for the effective treatment of cardiac patients. This can be attained by machine learning (ML) techniques using healthcare data. Several models on the basis of ML predict and identify disease in the heart, but this model cannot manage a huge database because of the deficiency of the smart model. This paper provides an optimized SpinalNet with a MapReduce model to categorize heart disease. OBJECTIVE The objective is to design a big data approach for heart disease classification using the proposed Jellyfish Search Flow Regime Optimization (JSFRO)-based SpinalNet. METHOD The binary image conversion is applied on Electrocardiogram (ECG) images for converting the image to binary image. MapReduce model is adapted, in which the mappers execute feature extraction and the reducer performs heart disease classification. In the mapper phase, the features like statistical features, shape features and temporal features are extracted and in reducer, the SpinalNet with JSFRO is considered. Here, the training of SpinalNet is done with JSFRO, which is produced by the unification of Jellyfish Search Optimization (JSO) and Flow Regime Optimization (FRO). METHOD The JSFRO-based SpinalNet offered effectual performance with the finest accuracy of 90.8%, sensitivity of 95.2% and specificity of 93.6%.
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Affiliation(s)
- Antony Jaya Mabel Rani
- Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India
| | - Chinnapillai Srivenkateswaran
- Department of Artificial Intelligence and Data Science, Rajalakshmi Institute of Technology, Chennai, Tamil Nadu, India
| | - Gurunathan Vishnupriya
- Department of Computer Science and Engineering, Easwari Engineering College, Chennai, Tamil Nadu, India
| | - Nalini Subramanian
- Department of Information Technology, Rajalakshmi Engineering College, Chennai, Tamil Nadu, India
| | - Poonguzhali Ilango
- Department of ECE, Panimalar Engineering College, Chennai, Tamil Nadu, India
| | - Vijaya Kumar Jacintha
- Department of ECE, SRM Institute of Science & Technology, Chennai, Tamil Nadu, India
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Chen S, Wang H, Zhang H, Peng C, Li Y, Wang B. A novel method of swin transformer with time-frequency characteristics for ECG-based arrhythmia detection. Front Cardiovasc Med 2024; 11:1401143. [PMID: 38911517 PMCID: PMC11193364 DOI: 10.3389/fcvm.2024.1401143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Accepted: 05/20/2024] [Indexed: 06/25/2024] Open
Abstract
Introduction Arrhythmia is an important indication of underlying cardiovascular diseases (CVD) and is prevalent worldwide. Accurate diagnosis of arrhythmia is crucial for timely and effective treatment. Electrocardiogram (ECG) plays a key role in the diagnosis of arrhythmia. With the continuous development of deep learning and machine learning processes in the clinical field, ECG processing algorithms have significantly advanced the field with timely and accurate diagnosis of arrhythmia. Methods In this study, we combined the wavelet time-frequency maps with the novel Swin Transformer deep learning model for the automatic detection of cardiac arrhythmias. In specific practice, we used the MIT-BIH arrhythmia dataset, and to improve the signal quality, we removed the high-frequency noise, artifacts, electromyographic noise and respiratory motion effects in the ECG signals by the wavelet thresholding method; we used the complex Morlet wavelet for the feature extraction, and plotted wavelet time-frequency maps to visualise the time-frequency information of the ECG; we introduced the Swin Transformer model for classification and achieve high classification accuracy of ECG signals through hierarchical construction and self attention mechanism, and combines windowed multi-head self-attention (W-MSA) and shifted window-based multi-head self-attention (SW-MSA) to comprehensively utilise the local and global information. Results To enhance the confidence of the experimental results, we evaluated the performance using intra-patient and inter-patient paradigm analyses, and the model classification accuracies reached 99.34% and 98.37%, respectively, which are better than the currently available detection methods. Discussion The results reveal that our proposed method is superior to currently available methods for detecting arrhythmia ECG. This provides a new idea for ECG based arrhythmia diagnosis.
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Affiliation(s)
- Siyuan Chen
- Heilongjiang University of Chinese Medicine, Harbin, China
| | - Hao Wang
- College of Computer Science and Technology, Harbin Engineering University, Harbin, China
| | - Huijie Zhang
- First Affiliated Hospital, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Cailiang Peng
- First Affiliated Hospital, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Yang Li
- Heilongjiang University of Chinese Medicine, Harbin, China
- First Affiliated Hospital, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Bing Wang
- Heilongjiang University of Chinese Medicine, Harbin, China
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Jha CK. Automated cardiac arrhythmia detection techniques: a comprehensive review for prospective approach. Comput Methods Biomech Biomed Engin 2024:1-16. [PMID: 38566498 DOI: 10.1080/10255842.2024.2332942] [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: 04/20/2023] [Accepted: 03/13/2024] [Indexed: 04/04/2024]
Abstract
Abnormal cardiac functionality produces irregular heart rhythms which are commonly known as arrhythmias. In some conditions, arrhythmias are treated as very dangerous which may lead to sudden cardiac arrest. The incidence and prevalence of cardiac anomalies seeks early detection of arrhythmias using automated classification techniques. In the past, numerous automated arrhythmia detection techniques have been developed that are based on electrocardiogram (ECG) signal analysis. Focusing on the prospective research in this field, this article reports a comprehensive review of existing techniques that are obtained using search engines such as IEEE explore, Google scholar and science direct. Based on the review, the existing techniques are broadly categorized into two types: machine-learning and deep-learning-based techniques. In this study, it is noticed that the performance of the machine-learning-based arrhythmia detection techniques depend on pre-processing of ECG signal, R-peaks detection, features extraction and classification tools while the deep-learning-based techniques do not require the features extraction step. Generally, the existing techniques utilize Massachusetts Institute of Technology-Beth Israel Hospital arrhythmia database to evaluate the classification performance. The classification performance of automated techniques also depends on ECG data used for training and testing of the classifier. It is expected that the performance should be evaluated using a variety of ECG signals including the cases of inter-patient and intra-patient paradigm. The existing techniques also require to deal with the class-imbalance problem. In addition to this, a specific partition-ratio between training and testing datasets should be maintained for fair comparison of performance of different techniques.
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Affiliation(s)
- Chandan Kumar Jha
- Department of Electronics & Communication Engineering, Indian Institute of Information Technology Bhagalpur, India
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Safdar MF, Nowak RM, Pałka P. Pre-Processing techniques and artificial intelligence algorithms for electrocardiogram (ECG) signals analysis: A comprehensive review. Comput Biol Med 2024; 170:107908. [PMID: 38217973 DOI: 10.1016/j.compbiomed.2023.107908] [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: 10/10/2023] [Revised: 12/19/2023] [Accepted: 12/24/2023] [Indexed: 01/15/2024]
Abstract
Electrocardiogram (ECG) are the physiological signals and a standard test to measure the heart's electrical activity that depicts the movement of cardiac muscles. A review study has been conducted on ECG signals analysis with the help of artificial intelligence (AI) methods over the last ten years i.e., 2012-22. Primarily, the method of ECG analysis by software systems was divided into classical signal processing (e.g. spectrograms or filters), machine learning (ML) and deep learning (DL), including recursive models, transformers and hybrid. Secondly, the data sources and benchmark datasets were depicted. Authors grouped resources by ECG acquisition methods into hospital-based portable machines and wearable devices. Authors also included new trends like advanced pre-processing, data augmentation, simulations and agent-based modeling. The study found improvement in ECG examination perfection made each year through ML, DL, hybrid models, and transformers. Convolutional neural networks and hybrid models were more targeted and proved efficient. The transformer model extended the accuracy from 90% to 98%. The Physio-Net library helps acquire ECG signals, including the popular benchmark databases such as MIT-BIH, PTB, and challenging datasets. Similarly, wearable devices have been established as a appropriate option for monitoring patient health without the time and place limitations and are also helpful for AI model calibration with so far accuracy of 82%-83% on Samsung smartwatch. In the pre-processing signals, spectrogram generation through Fourier and wavelet transformations are erected leading approaches promoting on average accuracy of 90%-95%. Likewise, data enhancement using geometrical techniques is well-considered; however, extraction and concatenation-based methods need attention. As the what-if analysis in healthcare or cardiac issues can be performed using a complex simulation, the study reviews agent-based modeling and simulation approaches for cardiovascular risk event assessment.
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Affiliation(s)
- Muhammad Farhan Safdar
- Institute of Computer Science, Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-665 Warsaw, Poland.
| | - Robert Marek Nowak
- Institute of Computer Science, Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-665 Warsaw, Poland
| | - Piotr Pałka
- Institute of Control and Computation Engineering, Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-665 Warsaw, Poland
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Stephen O, Nguyen M. A Unified Efficient Deep Learning Architecture for Rapid Safety Objects Classification Using Normalized Quantization-Aware Learning. SENSORS (BASEL, SWITZERLAND) 2023; 23:8982. [PMID: 37960681 PMCID: PMC10650581 DOI: 10.3390/s23218982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 10/23/2023] [Accepted: 11/03/2023] [Indexed: 11/15/2023]
Abstract
The efficient recognition and classification of personal protective equipment are essential for ensuring the safety of personnel in complex industrial settings. Using the existing methods, manually performing macro-level classification and identification of personnel in intricate spheres is tedious, time-consuming, and inefficient. The availability of several artificial intelligence models in recent times presents a new paradigm shift in object classification and tracking in complex settings. In this study, several compact and efficient deep learning model architectures are explored, and a new efficient model is constructed by fusing the learning capabilities of the individual, efficient models for better object feature learning and optimal inferencing. The proposed model ensures rapid identification of personnel in complex working environments for appropriate safety measures. The new model construct follows the contributory learning theory whereby each fussed model brings its learned features that are then combined to obtain a more accurate and rapid model using normalized quantization-aware learning. The major contribution of the work is the introduction of a normalized quantization-aware learning strategy to fuse the features learned by each of the contributing models. During the investigation, a separable convolutional driven model was constructed as a base model, and then the various efficient architectures were combined for the rapid identification and classification of the various hardhat classes used in complex industrial settings. A remarkable rapid classification and accuracy were recorded with the new resultant model.
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Affiliation(s)
- Okeke Stephen
- Computer Science & Software Engineering, Auckland University of Technology, Auckland 1010, New Zealand;
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Lai J, Tan H, Wang J, Ji L, Guo J, Han B, Shi Y, Feng Q, Yang W. Practical intelligent diagnostic algorithm for wearable 12-lead ECG via self-supervised learning on large-scale dataset. Nat Commun 2023; 14:3741. [PMID: 37353501 PMCID: PMC10290151 DOI: 10.1038/s41467-023-39472-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Accepted: 06/15/2023] [Indexed: 06/25/2023] Open
Abstract
Cardiovascular disease is a major global public health problem, and intelligent diagnostic approaches play an increasingly important role in the analysis of electrocardiograms (ECGs). Convenient wearable ECG devices enable the detection of transient arrhythmias and improve patient health by making it possible to seek intervention during continuous monitoring. We collected 658,486 wearable 12-lead ECGs, among which 164,538 were annotated, and the remaining 493,948 were without diagnostic. We present four data augmentation operations and a self-supervised learning classification framework that can recognize 60 ECG diagnostic terms. Our model achieves an average area under the receiver-operating characteristic curve (AUROC) and average F1 score on the offline test of 0.975 and 0.575. The average sensitivity, specificity and F1-score during the 2-month online test are 0.736, 0.954 and 0.468, respectively. This approach offers real-time intelligent diagnosis, and detects abnormal segments in long-term ECG monitoring in the clinical setting for further diagnosis by cardiologists.
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Affiliation(s)
- Jiewei Lai
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Guangzhou, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Guangzhou, China
| | - Huixin Tan
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Guangzhou, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Guangzhou, China
| | - Jinliang Wang
- 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
| | - Qianjin Feng
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
- Guangdong Provincial Key Laboratory of Medical Image Processing, Guangzhou, China.
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Guangzhou, China.
| | - Wei Yang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
- Guangdong Provincial Key Laboratory of Medical Image Processing, Guangzhou, China.
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Guangzhou, China.
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7
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Fan L, Shi X, Wang Z, Zhang R, Zhang J. Disease identification method based on graph features between pulse cycles. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
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Nagaraj J, Leema A. Light weight multi-branch network-based extraction and classification of myocardial infarction from 12 lead electrocardiogram images. THE IMAGING SCIENCE JOURNAL 2023. [DOI: 10.1080/13682199.2023.2178608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Affiliation(s)
- Jothiaruna Nagaraj
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India
| | - Anny Leema
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India
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9
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Sun L, Wu J, Xu Y, Zhang Y. A federated learning and blockchain framework for physiological signal classification based on continual learning. Inf Sci (N Y) 2023. [DOI: 10.1016/j.ins.2023.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
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10
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Wang Y, Yang G, Li S, Li Y, He L, Liu D. Arrhythmia classification algorithm based on multi-head self-attention mechanism. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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11
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Ullah H, Heyat MBB, Akhtar F, Muaad AY, Ukwuoma CC, Bilal M, Miraz MH, Bhuiyan MAS, Wu K, Damaševičius R, Pan T, Gao M, Lin Y, Lai D. An Automatic Premature Ventricular Contraction Recognition System Based on Imbalanced Dataset and Pre-Trained Residual Network Using Transfer Learning on ECG Signal. Diagnostics (Basel) 2022; 13:diagnostics13010087. [PMID: 36611379 PMCID: PMC9818233 DOI: 10.3390/diagnostics13010087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Revised: 12/05/2022] [Accepted: 12/23/2022] [Indexed: 12/29/2022] Open
Abstract
The development of automatic monitoring and diagnosis systems for cardiac patients over the internet has been facilitated by recent advancements in wearable sensor devices from electrocardiographs (ECGs), which need the use of patient-specific approaches. Premature ventricular contraction (PVC) is a common chronic cardiovascular disease that can cause conditions that are potentially fatal. Therefore, for the diagnosis of likely heart failure, precise PVC detection from ECGs is crucial. In the clinical settings, cardiologists typically employ long-term ECGs as a tool to identify PVCs, where a cardiologist must put in a lot of time and effort to appropriately assess the long-term ECGs which is time consuming and cumbersome. By addressing these issues, we have investigated a deep learning method with a pre-trained deep residual network, ResNet-18, to identify PVCs automatically using transfer learning mechanism. Herein, features are extracted by the inner layers of the network automatically compared to hand-crafted feature extraction methods. Transfer learning mechanism handles the difficulties of required large volume of training data for a deep model. The pre-trained model is evaluated on the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia and Institute of Cardiological Technics (INCART) datasets. First, we used the Pan-Tompkins algorithm to segment 44,103 normal and 6423 PVC beats, as well as 106,239 normal and 9987 PVC beats from the MIT-BIH Arrhythmia and IN-CART datasets, respectively. The pre-trained model employed the segmented beats as input after being converted into 2D (two-dimensional) images. The method is optimized with the using of weighted random samples, on-the-fly augmentation, Adam optimizer, and call back feature. The results from the proposed method demonstrate the satisfactory findings without the using of any complex pre-processing and feature extraction technique as well as design complexity of model. Using LOSOCV (leave one subject out cross-validation), the received accuracies on MIT-BIH and INCART are 99.93% and 99.77%, respectively, suppressing the state-of-the-art methods for PVC recognition on unseen data. This demonstrates the efficacy and generalizability of the proposed method on the imbalanced datasets. Due to the absence of device-specific (patient-specific) information at the evaluating stage on the target datasets in this study, the method might be used as a general approach to handle the situations in which ECG signals are obtained from different patients utilizing a variety of smart sensor devices.
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Affiliation(s)
- Hadaate Ullah
- State Key Laboratory of Electronic Thin Films and Integrated Devices, School of Materials and Energy, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Md Belal Bin Heyat
- IoT Research Center, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
- Correspondence: (M.B.B.H.); (R.D.); (Y.L.); (D.L.)
| | - Faijan Akhtar
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | | | - Chiagoziem C. Ukwuoma
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Muhammad Bilal
- College of Pharmacy, Liaquat University of Medical and Health Sciences, Jamshoro 76090, Pakistan
| | - Mahdi H. Miraz
- School of Computing and Data Science, Xiamen University Malaysia, Bandar Sunsuria, Sepang 43900, Malaysia
- School of Computing, Glyndŵr University, Wrexham LL11 2AW, UK
| | | | - Kaishun Wu
- IoT Research Center, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
| | - Robertas Damaševičius
- Department of Software Engineering, Kaunas University of Technology, 44249 Kaunas, Lithuania
- Correspondence: (M.B.B.H.); (R.D.); (Y.L.); (D.L.)
| | - Taisong Pan
- State Key Laboratory of Electronic Thin Films and Integrated Devices, School of Materials and Energy, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Min Gao
- State Key Laboratory of Electronic Thin Films and Integrated Devices, School of Materials and Energy, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Yuan Lin
- State Key Laboratory of Electronic Thin Films and Integrated Devices, School of Materials and Energy, University of Electronic Science and Technology of China, Chengdu 610054, China
- Medico-Engineering Corporation on Applied Medicine Research Center, University of Electronic Science and Technology of China, Chengdu 610054, China
- Correspondence: (M.B.B.H.); (R.D.); (Y.L.); (D.L.)
| | - Dakun Lai
- Biomedical Imaging and Electrophysiology Laboratory, School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
- Correspondence: (M.B.B.H.); (R.D.); (Y.L.); (D.L.)
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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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An End-to-End Cardiac Arrhythmia Recognition Method with an Effective DenseNet Model on Imbalanced Datasets Using ECG Signal. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:9475162. [PMID: 36210977 PMCID: PMC9536938 DOI: 10.1155/2022/9475162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 07/25/2022] [Accepted: 08/17/2022] [Indexed: 01/10/2023]
Abstract
Electrocardiography (ECG) is a well-known noninvasive technique in medical science that provides information about the heart's rhythm and current conditions. Automatic ECG arrhythmia diagnosis relieves doctors' workload and improves diagnosis effectiveness and efficiency. This study proposes an automatic end-to-end 2D CNN (two-dimensional convolution neural networks) deep learning method with an effective DenseNet model for addressing arrhythmias recognition. To begin, the proposed model is trained and evaluated on the 97720 and 141404 beat images extracted from the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia and St. Petersburg Institute of Cardiological Technics (INCART) datasets (both are imbalanced class datasets) using a stratified 5-fold evaluation strategy. The data is classified into four groups: N (normal), V (ventricular ectopic), S (supraventricular ectopic), and F (fusion), based on the Association for the Advancement of Medical Instrumentation® (AAMI). The experimental results show that the proposed model outperforms state-of-the-art models for recognizing arrhythmias, with the accuracy of 99.80% and 99.63%, precision of 98.34% and 98.94%, and F1-score of 98.91% and 98.91% on the MIT-BIH arrhythmia and INCART datasets, respectively. Using a transfer learning mechanism, the proposed model is also evaluated with only five individuals of supraventricular MIT-BIH arrhythmia and five individuals of European ST-T datasets (both of which are also class imbalanced) and achieved satisfactory results. So, the proposed model is more generalized and could be a prosperous solution for arrhythmias recognition from class imbalance datasets in real-life applications.
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An efficient neural network-based method for patient-specific information involved arrhythmia detection. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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15
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Javid I, Ghazali R, Zulqarnain M, Hassan N. Data pre-processing for cardiovascular disease classification: A systematic literature review. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-220061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
The important task in the medical field is the early detection of disease. Heart disease is one of the greatest challenging diseases in all other diseases subsequently 17.3 million people died once a year due to heart disease. A minute error in heart disease diagnosis is a risk for an individual lifespan. Precise heart disease diagnosis is consequently critical. Different approaches including data mining have been used for the prediction of heart disease. However, there are some solemn concerns related to the data quality for example inconsistencies, missing values, noise, high dimensionality, and imbalanced statistics. In order to improve the accuracy of Data Mining based prediction systems, techniques for data preparation were applied to increase the quality of the data. The foremost objective of this paper is to highlight and summarize the research work about (i) data preparation techniques mostly used, (ii) the impact of pre-processing procedures on the accuracy of a heart disease prediction system, (iii) classifier enactment with data pre-processing techniques, (4) comparison in terms of accuracy of the different pre-processing model. A systematic literature review on the use of data pre-processing in heart disease diagnosis is carried out from January 2001 to July 2021 by studying the published material. Almost 30 studies were designated and examined related to the above-mentioned benchmarks. The literature review concludes that data reduction and data cleaning pre-processing techniques are mostly used in heart disease prediction systems. Overall this study concludes that data pre-processing has improved the accuracy of models used for heart disease prediction. Some hybrid models including (ANN+CHI), (ANN+PCA), (DNN+CHI) and (SVM+PCA) have shown improved accuracy level. However, due to the lack of clarification, there is a number of limitations and challenges in order to implementing these models in the real world.
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Affiliation(s)
- Irfan Javid
- Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn, Malaysia
- Department of Computer Science & IT, University of Poonch Rawalakot, AJK, Pakistan
| | - Rozaida Ghazali
- Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn, Malaysia
| | - Muhammad Zulqarnain
- Riphah College of Computing, Riphah International University Faisalabad Campus, Pakistan
| | - Norlida Hassan
- Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn, Malaysia
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Class-specific weighted broad learning system for imbalanced heartbeat classification. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.07.074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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17
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Li R, Han T, Song X. Stock price index forecasting using a multiscale modelling strategy based on frequency components analysis and intelligent optimization. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109089] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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18
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Yakut Ö, Bolat ED. A high-performance arrhythmic heartbeat classification using ensemble learning method and PSD based feature extraction approach. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2022.05.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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19
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Mohamed Suhail M, Abdul Razak T. Cardiac disease detection from ECG signal using discrete wavelet transform with machine learning method. Diabetes Res Clin Pract 2022; 187:109852. [PMID: 35341778 DOI: 10.1016/j.diabres.2022.109852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 03/22/2022] [Indexed: 11/18/2022]
Abstract
OBJECTIVES Cardiac disease is the leading cause of death worldwide. If a proper diagnosis is made early, cardiovascular problems can be prevented. The ECG test is a diagnostic method used on the screen for heart disease. Based on a combination of multi-field extraction and nonlinear analysis of ECG data, this paper presents a framework for automated detection of heart disease. The main aim of this study is to develop a model for future diagnosis of cardiac vascular disease using ECG analysis and symptom-based detection. METHODS Discrete wavelet transform and Nonlinear Vector Decomposed Neural Network methods are used to predict Cardiac disease. Here is the discrete wavelet transform used for preprocessing to remove unwanted noise or artifacts. The neural network was fed with thirteen clinical features as input which was then trained using a non-linear vector decomposition of the presence or absence of heart disease. RESULTS The modules were implemented, trained, and tested using UCI and Physio net data repositories. The sensitivity, specificity and accuracy of this research work are 92.0%, 89.33% and 90.67% CONCLUSIONS: The proposed approach can discover complex non-linear correlations between dependent and independent variables without requiring traditional statistical training. The suggested approach improves ECG classification accuracy, allowing for more accurate cardiac disease diagnosis. The accuracy of ECG categorization in identifying cardiac illness is far greater than these other approaches.
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Affiliation(s)
- M Mohamed Suhail
- Department of Computer Science, Jamal Mohamed College, Tiruchirappalli, India.
| | - T Abdul Razak
- Department of Computer Science, Jamal Mohamed College, Tiruchirappalli, India
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20
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Petmezas G, Stefanopoulos L, Kilintzis V, Tzavelis A, Rogers JA, Katsaggelos AK, Maglaveras N. State-of-the-art Deep Learning Methods on Electrocardiogram Data: A Systematic Review (Preprint). JMIR Med Inform 2022; 10:e38454. [PMID: 35969441 PMCID: PMC9425174 DOI: 10.2196/38454] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 06/03/2022] [Accepted: 07/03/2022] [Indexed: 11/13/2022] Open
Abstract
Background Electrocardiogram (ECG) is one of the most common noninvasive diagnostic tools that can provide useful information regarding a patient’s health status. Deep learning (DL) is an area of intense exploration that leads the way in most attempts to create powerful diagnostic models based on physiological signals. Objective This study aimed to provide a systematic review of DL methods applied to ECG data for various clinical applications. Methods The PubMed search engine was systematically searched by combining “deep learning” and keywords such as “ecg,” “ekg,” “electrocardiogram,” “electrocardiography,” and “electrocardiology.” Irrelevant articles were excluded from the study after screening titles and abstracts, and the remaining articles were further reviewed. The reasons for article exclusion were manuscripts written in any language other than English, absence of ECG data or DL methods involved in the study, and absence of a quantitative evaluation of the proposed approaches. Results We identified 230 relevant articles published between January 2020 and December 2021 and grouped them into 6 distinct medical applications, namely, blood pressure estimation, cardiovascular disease diagnosis, ECG analysis, biometric recognition, sleep analysis, and other clinical analyses. We provide a complete account of the state-of-the-art DL strategies per the field of application, as well as major ECG data sources. We also present open research problems, such as the lack of attempts to address the issue of blood pressure variability in training data sets, and point out potential gaps in the design and implementation of DL models. Conclusions We expect that this review will provide insights into state-of-the-art DL methods applied to ECG data and point to future directions for research on DL to create robust models that can assist medical experts in clinical decision-making.
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Affiliation(s)
- Georgios Petmezas
- Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Leandros Stefanopoulos
- Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Vassilis Kilintzis
- Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Andreas Tzavelis
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, United States
| | - John A Rogers
- Department of Material Science, Northwestern University, Evanston, IL, United States
| | - Aggelos K Katsaggelos
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, United States
| | - Nicos Maglaveras
- Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
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21
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Shahimi NH, Lim R, Mat S, Goh CH, Tan MP, Lim E. Association between mental illness and blood pressure variability: a systematic review. Biomed Eng Online 2022; 21:19. [PMID: 35313918 PMCID: PMC8935841 DOI: 10.1186/s12938-022-00985-w] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 02/15/2022] [Indexed: 02/04/2023] Open
Abstract
Background Mental illness represents a major global burden of disease worldwide. It has been hypothesised that individuals with mental illness have greater blood pressure fluctuations that lead to increased cardiovascular risk and target organ damage. This systematic review aims to (i) investigate the association between mental illness and blood pressure variability (BPV) and (ii) describe methods of BPV measurements and analysis which may affect pattern and degree of variability. Methods Four electronic databases were searched from inception until 2020. The quality assessment was performed using STROBE criteria. Studies were included if they investigated BPV (including either frequency or time domain analysis) in individuals with mental illness (particularly anxiety/generalised anxiety disorder, depression/major depressive disorder, panic disorder and hostility) and without hypertension. Two authors independently screened titles, abstracts and full texts. A third author resolved any disagreements. Results Twelve studies met the inclusion criteria. Three studies measured short-term BPV, two measured long-term BPV and seven measured ultra-short-term BPV. All studies related to short-term BPV using ambulatory and home blood pressure monitoring found a higher BPV in individuals with depression or panic disorder. The two studies measuring long-term BPV were limited to the older population and found mixed results. Mental illness is significantly associated with an increased BPV in younger and middle-aged adults. All studies of ultra-short-term BPV using standard cardiac autonomic assessment; non-invasive continuous finger blood pressure and heart rate signals found significant association between BPV and mental illness. A mixed result related to degree of tilt during tilt assessment and between controlled and spontaneous breathing were observed in patients with psychological state. Conclusions Current review found that people with mental illness is significantly associated with an increased BPV regardless of age. Since mental illness can contribute to the deterioration of autonomic function (HRV, BPV), early therapeutic intervention in mental illness may prevent diseases associated with autonomic dysregulation and reduce the likelihood of negative cardiac outcomes. Therefore, these findings may have important implications for patients' future physical health and well-being, highlighting the need for comprehensive cardiovascular risk reduction.
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Affiliation(s)
- Nur Husna Shahimi
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia.,Ageing and Age-Associated Disorders Research Group, Faculty of Medicine, University of Malaya, 50603, Kuala Lumpur, Malaysia
| | - Renly Lim
- UniSA Clinical and Health Sciences, University of South Australia, Adelaide, 5000, Australia
| | - Sumaiyah Mat
- Ageing and Age-Associated Disorders Research Group, Faculty of Medicine, University of Malaya, 50603, Kuala Lumpur, Malaysia.,Centre for Healthy Aging and Wellness, Faculty of Health Sciences, Universiti Kebangsaan Malaysia, 50300, Kuala Lumpur, Malaysia
| | - Choon-Hian Goh
- Department of Mechatronics and BioMedical Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Bandar Sungai Long, 43200, Kajang, Selangor, Malaysia
| | - Maw Pin Tan
- Ageing and Age-Associated Disorders Research Group, Faculty of Medicine, University of Malaya, 50603, Kuala Lumpur, Malaysia.,Centre for Healthy Aging and Wellness, Faculty of Health Sciences, Universiti Kebangsaan Malaysia, 50300, Kuala Lumpur, Malaysia
| | - Einly Lim
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia.
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22
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Imbalanced heartbeat classification using EasyEnsemble technique and global heartbeat information. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103105] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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23
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Impact of the learners diversity and combination method on the generation of heterogeneous classifier ensembles. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107689] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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24
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Liang Y, Hussain A, Abbott D, Menon C, Ward R, Elgendi M. Impact of Data Transformation: An ECG Heartbeat Classification Approach. Front Digit Health 2021; 2:610956. [PMID: 34713072 PMCID: PMC8521829 DOI: 10.3389/fdgth.2020.610956] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 12/03/2020] [Indexed: 11/13/2022] Open
Abstract
Cardiovascular diseases continue to be a significant global health threat. The electrocardiogram (ECG) signal is a physiological signal that plays a major role in preventing severe and even fatal heart diseases. The purpose of this research is to explore a simple mathematical feature transformation that could be applied to ECG signal segments in order to improve the detection accuracy of heartbeats, which could facilitate automated heart disease diagnosis. Six different mathematical transformation methods were examined and analyzed using 10s-length ECG segments, which showed that a reciprocal transformation results in consistently better classification performance for normal vs. atrial fibrillation beats and normal vs. atrial premature beats, when compared to untransformed features. The second best data transformation in terms of heartbeat detection accuracy was the cubic transformation. Results showed that applying the logarithmic transformation, which is considered the go-to data transformation, was not optimal among the six data transformations. Using the optimal data transformation, the reciprocal, can lead to a 35.6% accuracy improvement. According to the overall comparison tested by different feature engineering methods, classifiers, and different dataset sizes, performance improvement also reached 4.7%. Therefore, adding a simple data transformation step, such as the reciprocal or cubic, to the extracted features can improve current automated heartbeat classification in a timely manner.
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Affiliation(s)
- Yongbo Liang
- School of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Ahmed Hussain
- Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Derek Abbott
- School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, SA, Australia.,Centre for Biomedical Engineering, The University of Adelaide, Adelaide, SA, Australia
| | - Carlo Menon
- Menrva Research Group, School of Mechatronic Systems Engineering and Engineering Science, Simon Fraser University, Surrey, BC, Canada
| | - Rabab Ward
- School of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Mohamed Elgendi
- School of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada.,Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada.,Menrva Research Group, School of Mechatronic Systems Engineering and Engineering Science, Simon Fraser University, Surrey, BC, Canada.,British Columbia Children's and Women's Hospital, Vancouver, BC, Canada
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25
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ECG-based machine-learning algorithms for heartbeat classification. Sci Rep 2021; 11:18738. [PMID: 34548508 PMCID: PMC8455569 DOI: 10.1038/s41598-021-97118-5] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 08/19/2021] [Indexed: 11/16/2022] Open
Abstract
Electrocardiogram (ECG) signals represent the electrical activity of the human hearts and consist of several waveforms (P, QRS, and T). The duration and shape of each waveform and the distances between different peaks are used to diagnose heart diseases. In this work, to better analyze ECG signals, a new algorithm that exploits two-event related moving-averages (TERMA) and fractional-Fourier-transform (FrFT) algorithms is proposed. The TERMA algorithm specifies certain areas of interest to locate desired peak, while the FrFT rotates ECG signals in the time-frequency plane to manifest the locations of various peaks. The proposed algorithm’s performance outperforms state-of-the-art algorithms. Moreover, to automatically classify heart disease, estimated peaks, durations between different peaks, and other ECG signal features were used to train a machine-learning model. Most of the available studies uses the MIT-BIH database (only 48 patients). However, in this work, the recently reported Shaoxing People’s Hospital (SPH) database, which consists of more than 10,000 patients, was used to train the proposed machine-learning model, which is more realistic for classification. The cross-database training and testing with promising results is the uniqueness of our proposed machine-learning model.
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26
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Interpretation of Electrocardiogram Heartbeat by CNN and GRU. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:6534942. [PMID: 34497664 PMCID: PMC8421156 DOI: 10.1155/2021/6534942] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 07/18/2021] [Accepted: 08/17/2021] [Indexed: 11/17/2022]
Abstract
The diagnosis of electrocardiogram (ECG) is extremely onerous and inefficient, so it is necessary to use a computer-aided diagnosis of ECG signals. However, it is still a challenging problem to design high-accuracy ECG algorithms suitable for the medical field. In this paper, a classification method is proposed to classify ECG signals. Firstly, wavelet transform is used to denoise the original data, and data enhancement technology is used to overcome the problem of an unbalanced dataset. Secondly, an integrated convolutional neural network (CNN) and gated recurrent unit (GRU) classifier is proposed. The proposed network consists of a convolution layer, followed by 6 local feature extraction modules (LFEM), a GRU, and a Dense layer and a Softmax layer. Finally, the processed data were input into the CNN-GRU network into five categories: nonectopic beats, supraventricular ectopic beats, ventricular ectopic beats, fusion beats, and unknown beats. The MIT-BIH arrhythmia database was used to evaluate the approach, and the average sensitivity, accuracy, and F1-score of the network for 5 types of ECG were 99.33%, 99.61%, and 99.42%. The evaluation criteria of the proposed method are superior to other state-of-the-art methods, and this model can be applied to wearable devices to achieve high-precision monitoring of ECG.
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27
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Rahman M, Ghasemi Y, Suley E, Zhou Y, Wang S, Rogers J. Machine Learning Based Computer Aided Diagnosis of Breast Cancer Utilizing Anthropometric and Clinical Features. Ing Rech Biomed 2021. [DOI: 10.1016/j.irbm.2020.05.005] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/09/2022]
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28
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An Intelligent Heartbeat Classification System Based on Attributable Features with AdaBoost+Random Forest Algorithm. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:9913127. [PMID: 34336169 PMCID: PMC8289583 DOI: 10.1155/2021/9913127] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Revised: 05/20/2021] [Accepted: 05/27/2021] [Indexed: 12/02/2022]
Abstract
Arrhythmia is a common cardiovascular disease that can threaten human life. In order to assist doctors in accurately diagnosing arrhythmia, an intelligent heartbeat classification system based on the selected optimal feature sets and AdaBoost + Random Forest model is developed. This system can acquire ECG signals through the Holter and transmit them to the cloud platform for preprocessing and feature extraction, and the features are input into AdaBoost + Random Forest for heartbeat classification. The analysis results are output in the form of reports. In this system, by comparing and analyzing the classification accuracy of different feature sets and classifiers, the optimal classification algorithm is obtained and applied to the system. The algorithm accuracy of the system is tested based on the MIT-BIH data set. The result shows that AdaBoost + Random Forest achieved 99.11% accuracy with optimal feature sets. The intelligent heartbeat classification system based on this algorithm has also achieved good results on clinical data.
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29
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Zhou H, Kan C. Tensor-Based ECG Anomaly Detection toward Cardiac Monitoring in the Internet of Health Things. SENSORS (BASEL, SWITZERLAND) 2021; 21:4173. [PMID: 34204575 PMCID: PMC8234952 DOI: 10.3390/s21124173] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 06/08/2021] [Accepted: 06/11/2021] [Indexed: 12/02/2022]
Abstract
Advanced heart monitors, especially those enabled by the Internet of Health Things (IoHT), provide a great opportunity for continuous collection of the electrocardiogram (ECG), which contains rich information about underlying cardiac conditions. Realizing the full potential of IoHT-enabled cardiac monitoring hinges, to a great extent, on the detection of disease-induced anomalies from collected ECGs. However, challenges exist in the current literature for IoHT-based cardiac monitoring: (1) Most existing methods are based on supervised learning, which requires both normal and abnormal samples for training. This is impractical as it is generally unknown when and what kind of anomalies will occur during cardiac monitoring. (2) Furthermore, it is difficult to leverage advanced machine learning approaches for information processing of 1D ECG signals, as most of them are designed for 2D images and higher-dimensional data. To address these challenges, a new sensor-based unsupervised framework is developed for IoHT-based cardiac monitoring. First, a high-dimensional tensor is generated from the multi-channel ECG signals through the Gramian Angular Difference Field (GADF). Then, multi-linear principal component analysis (MPCA) is employed to unfold the ECG tensor and delineate the disease-altered patterns. Obtained principal components are used as features for anomaly detection using machine learning models (e.g., deep support vector data description (deep SVDD)) as well as statistical control charts (e.g., Hotelling T2 chart). The developed framework is evaluated and validated using real-world ECG datasets. Comparing to the state-of-the-art approaches, the developed framework with deep SVDD achieves superior performances in detecting abnormal ECG patterns induced by various types of cardiac disease, e.g., an F-score of 0.9771 is achieved for detecting atrial fibrillation, 0.9986 for detecting right bundle branch block, and 0.9550 for detecting ST-depression. Additionally, the developed framework with the T2 control chart facilitates personalized cycle-to-cycle monitoring with timely detected abnormal ECG patterns. The developed framework has a great potential to be implemented in IoHT-enabled cardiac monitoring and smart management of cardiac health.
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Affiliation(s)
| | - Chen Kan
- Department of Industrial, Manufacturing, and Systems Engineering, The University of Texas at Arlington, Arlington, TX 76019, USA;
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30
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Multiscale Residual Network Based on Channel Spatial Attention Mechanism for Multilabel ECG Classification. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:6630643. [PMID: 34055274 PMCID: PMC8112932 DOI: 10.1155/2021/6630643] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 03/01/2021] [Accepted: 04/01/2021] [Indexed: 11/18/2022]
Abstract
Automatic classification of ECG is very important for early prevention and auxiliary diagnosis of cardiovascular disease patients. In recent years, many studies based on ECG have achieved good results, most of which are based on single-label problems; one record corresponds to one label. However, in actual clinical applications, an ECG record may contain multiple diseases at the same time. Therefore, it is very important to study the multilabel ECG classification. In this paper, a multiscale residual deep neural network CSA-MResNet model based on the channel spatial attention mechanism is proposed. Firstly, the residual network is integrated into a multiscale manner to obtain the characteristics of ECG data at different scales and then increase the channel spatial attention mechanism to better focus on more important channels and more important ECG data fragments. Finally, the model is used to classify multilabel in large databases. The experimental results on the multilabel CCDD show that the CSA-MResNet model has an average F1 score of 88.2% when the multilabel classification of 9 ECGs is performed. Compared with the benchmark model, the F1 score of CSA-MResNet in the multilabel ECG classification increased by up to 1.7%. And, in the model verification on another database HF-challenge, the final average F1 score is 85.8%. Compared with the state-of-the-art methods, CSA-MResNet can help cardiologists perform early-stage rapid screening of ECG and has a certain generalization performance, providing a feasible analysis method for multilabel ECG classification.
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31
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Bidias à Mougoufan JB, Eyebe Fouda JSA, Tchuente M, Koepf W. Three-class ECG beat classification by ordinal entropies. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102506] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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32
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Augustyniak P. Diagnostic Interpretation of Non-Uniformly Sampled Electrocardiogram. SENSORS (BASEL, SWITZERLAND) 2021; 21:2969. [PMID: 33922870 PMCID: PMC8123013 DOI: 10.3390/s21092969] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 04/20/2021] [Accepted: 04/21/2021] [Indexed: 11/16/2022]
Abstract
We present a set of three fundamental methods for electrocardiogram (ECG) diagnostic interpretation adapted to process non-uniformly sampled signal. The growing volume of ECGs recorded daily all over the world (roughly estimated to be 600 TB) and the expectance of long persistence of these data (on the order of 40 years) motivated us to challenge the feasibility of medical-grade diagnostics directly based on arbitrary non-uniform (i.e., storage-efficient) ECG representation. We used a refined time-independent QRS detection method based on a moving shape matching technique. We applied a graph data representation to quantify the similarity of asynchronously sampled heartbeats. Finally, we applied a correlation-based non-uniform to time-scale transform to get a multiresolution ECG representation on a regular dyadic grid and to find precise P, QRS and T wave delimitation points. The whole processing chain was implemented and tested with MIT-BIH Database (probably the most referenced cardiac database) and CSE Multilead Database (used for conformance testing of medical instruments) signals arbitrarily sampled accordingly to a perceptual model (set for variable sampling frequency of 100-500 Hz, compression ratio 3.1). The QRS detection shows an accuracy of 99.93% with false detection ratio of only 0.18%. The classification shows an accuracy of 99.27% for 14 most frequent MIT-BIH beat types and 99.37% according to AAMI beat labels. The wave delineation shows cumulative (i.e., sampling model and non-uniform processing) errors of: 9.7 ms for P wave duration, 3.4 ms for QRS, 6.7 ms for P-Q segment and 17.7 ms for Q-T segment, all the values being acceptable for medical-grade interpretive software.
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Ronca V, Giorgi A, Rossi D, Di Florio A, Di Flumeri G, Aricò P, Sciaraffa N, Vozzi A, Tamborra L, Simonetti I, Borghini G. A Video-Based Technique for Heart Rate and Eye Blinks Rate Estimation: A Potential Solution for Telemonitoring and Remote Healthcare. SENSORS 2021; 21:s21051607. [PMID: 33668921 PMCID: PMC7956514 DOI: 10.3390/s21051607] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 02/12/2021] [Accepted: 02/20/2021] [Indexed: 11/16/2022]
Abstract
Current telemedicine and remote healthcare applications foresee different interactions between the doctor and the patient relying on the use of commercial and medical wearable sensors and internet-based video conferencing platforms. Nevertheless, the existing applications necessarily require a contact between the patient and sensors for an objective evaluation of the patient’s state. The proposed study explored an innovative video-based solution for monitoring neurophysiological parameters of potential patients and assessing their mental state. In particular, we investigated the possibility to estimate the heart rate (HR) and eye blinks rate (EBR) of participants while performing laboratory tasks by mean of facial—video analysis. The objectives of the study were focused on: (i) assessing the effectiveness of the proposed technique in estimating the HR and EBR by comparing them with laboratory sensor-based measures and (ii) assessing the capability of the video—based technique in discriminating between the participant’s resting state (Nominal condition) and their active state (Non-nominal condition). The results demonstrated that the HR and EBR estimated through the facial—video technique or the laboratory equipment did not statistically differ (p > 0.1), and that these neurophysiological parameters allowed to discriminate between the Nominal and Non-nominal states (p < 0.02).
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Affiliation(s)
- Vincenzo Ronca
- Department of Anatomical, Histological, Forensic and Orthopaedic Sciences, Sapienza University, 00185 Rome, Italy; (A.V.); (L.T.); (I.S.)
- BrainSigns srl, 00185 Rome, Italy; (A.G.); (A.D.F.); (G.D.F.); (P.A.); (N.S.)
- Correspondence: (V.R.); (G.B.); Tel.: +39-06-49910941 (V.R. & G.B.)
| | - Andrea Giorgi
- BrainSigns srl, 00185 Rome, Italy; (A.G.); (A.D.F.); (G.D.F.); (P.A.); (N.S.)
| | - Dario Rossi
- Department of Business and Management, LUISS University, 00197 Rome, Italy;
| | - Antonello Di Florio
- BrainSigns srl, 00185 Rome, Italy; (A.G.); (A.D.F.); (G.D.F.); (P.A.); (N.S.)
| | - Gianluca Di Flumeri
- BrainSigns srl, 00185 Rome, Italy; (A.G.); (A.D.F.); (G.D.F.); (P.A.); (N.S.)
- Department of Molecular Medicine, Sapienza University of Rome, 00185 Rome, Italy
- IRCCS Fondazione Santa Lucia, 00179 Rome, Italy
| | - Pietro Aricò
- BrainSigns srl, 00185 Rome, Italy; (A.G.); (A.D.F.); (G.D.F.); (P.A.); (N.S.)
- Department of Molecular Medicine, Sapienza University of Rome, 00185 Rome, Italy
- IRCCS Fondazione Santa Lucia, 00179 Rome, Italy
| | - Nicolina Sciaraffa
- BrainSigns srl, 00185 Rome, Italy; (A.G.); (A.D.F.); (G.D.F.); (P.A.); (N.S.)
- Department of Molecular Medicine, Sapienza University of Rome, 00185 Rome, Italy
| | - Alessia Vozzi
- Department of Anatomical, Histological, Forensic and Orthopaedic Sciences, Sapienza University, 00185 Rome, Italy; (A.V.); (L.T.); (I.S.)
- BrainSigns srl, 00185 Rome, Italy; (A.G.); (A.D.F.); (G.D.F.); (P.A.); (N.S.)
| | - Luca Tamborra
- Department of Anatomical, Histological, Forensic and Orthopaedic Sciences, Sapienza University, 00185 Rome, Italy; (A.V.); (L.T.); (I.S.)
- People Advisory Services Department, Ernst & Young, 00187 Rome, Italy
| | - Ilaria Simonetti
- Department of Anatomical, Histological, Forensic and Orthopaedic Sciences, Sapienza University, 00185 Rome, Italy; (A.V.); (L.T.); (I.S.)
- People Advisory Services Department, Ernst & Young, 00187 Rome, Italy
| | - Gianluca Borghini
- Department of Molecular Medicine, Sapienza University of Rome, 00185 Rome, Italy
- IRCCS Fondazione Santa Lucia, 00179 Rome, Italy
- Correspondence: (V.R.); (G.B.); Tel.: +39-06-49910941 (V.R. & G.B.)
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Aghdam ZN, Rahmani AM, Hosseinzadeh M. The Role of the Internet of Things in Healthcare: Future Trends and Challenges. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 199:105903. [PMID: 33348073 DOI: 10.1016/j.cmpb.2020.105903] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Accepted: 12/05/2020] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVE With the recent advances in the Internet of Things (IoT), the field has become more and more developed in healthcare. The Internet of things will help physicians and hospital staff perform their duties comfortably and intelligently. With the latest advanced technologies, most of the challenges of using IoT have been resolved, and this technology can be a great revolution and has many benefits in the future of digital. Healthcare is one of the most useful areas for IoT use. The most important application of IoT is to monitor and make quick decisions in critical situations. Thanks to this technology-based treatment approach, there is an unprecedented opportunity to better the quality and productivity of treatments and better the patient's well-being and better government funding. METHODS In this paper, we provide a comprehensive overview of the primary uses of IoT in healthcare. We used the Systematic Literature Review (SLR) method to analyze and comparison articles published in this field between 2015 and March 2020. RESULTS A comprehensive taxonomy is presented based on the contents of the articles under study. In this article, a brief overview of selected articles based on research questions is given and highlights the most critical challenges and case studies for the future use of IoT in healthcare. CONCLUSIONS According to a detailed study of the 89 articles and a glimpse into about 208 articles, challenges and future trends in healthcare have been identified.
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Affiliation(s)
- Zahra Nasiri Aghdam
- Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Amir Masoud Rahmani
- Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Mehdi Hosseinzadeh
- Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam; Mental Health Research Center, Psychosocial Health Research Institute, Iran University of Medical Sciences, Tehran, Iran.
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Heartbeat Classification Based on Multifeature Combination and Stacking-DWKNN Algorithm. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:8811837. [PMID: 33575022 PMCID: PMC7861929 DOI: 10.1155/2021/8811837] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Revised: 12/09/2020] [Accepted: 01/15/2021] [Indexed: 11/18/2022]
Abstract
Arrhythmia is one of the most common abnormal symptoms that can threaten human life. In order to distinguish arrhythmia more accurately, the classification strategy of the multifeature combination and Stacking-DWKNN algorithm is proposed in this paper. The method consists of four modules. In the preprocessing module, the signal is denoised and segmented. Then, multiple different features are extracted based on single heartbeat morphology, P length, QRS length, T length, PR interval, ST segment, QT interval, RR interval, R amplitude, and T amplitude. Subsequently, the features are combined and normalized, and the effect of different feature combinations on heartbeat classification is analyzed to select the optimal feature combination. Finally, the four types of normal and abnormal heartbeats were identified using the Stacking-DWKNN algorithm. This method is performed on the MIT-BIH arrhythmia database. The result shows a sensitivity of 89.42% and a positive predictive value of 94.90% of S-type beats and a sensitivity of 97.21% and a positive predictive value of 97.07% of V-type beats. The obtained average accuracy is 99.01%. Compared to other models with the same features, this method can improve accuracy and has a higher positive predictive value and sensitivity, which is important for clinical decision-making.
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Ensemble of kernel extreme learning machine based random forest classifiers for automatic heartbeat classification. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102138] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Automated Atrial Fibrillation Detection using a Hybrid CNN-LSTM Network on Imbalanced ECG Datasets. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102194] [Citation(s) in RCA: 70] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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Benhar H, Idri A, Fernández-Alemán JL. Data preprocessing for heart disease classification: A systematic literature review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 195:105635. [PMID: 32652383 DOI: 10.1016/j.cmpb.2020.105635] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2019] [Accepted: 06/24/2020] [Indexed: 06/11/2023]
Abstract
CONTEXT Early detection of heart disease is an important challenge since 17.3 million people yearly lose their lives due to heart diseases. Besides, any error in diagnosis of cardiac disease can be dangerous and risks an individual's life. Accurate diagnosis is therefore critical in cardiology. Data Mining (DM) classification techniques have been used to diagnosis heart diseases but still limited by some challenges of data quality such as inconsistencies, noise, missing data, outliers, high dimensionality and imbalanced data. Data preprocessing (DP) techniques were therefore used to prepare data with the goal of improving the performance of heart disease DM based prediction systems. OBJECTIVE The purpose of this study is to review and summarize the current evidence on the use of preprocessing techniques in heart disease classification as regards: (1) the DP tasks and techniques most frequently used, (2) the impact of DP tasks and techniques on the performance of classification in cardiology, (3) the overall performance of classifiers when using DP techniques, and (4) comparisons of different combinations classifier-preprocessing in terms of accuracy rate. METHOD A systematic literature review is carried out, by identifying and analyzing empirical studies on the application of data preprocessing in heart disease classification published in the period between January 2000 and June 2019. A total of 49 studies were therefore selected and analyzed according to the aforementioned criteria. RESULTS The review results show that data reduction is the most used preprocessing task in cardiology, followed by data cleaning. In general, preprocessing either maintained or improved the performance of heart disease classifiers. Some combinations such as (ANN + PCA), (ANN + CHI) and (SVM + PCA) are promising terms of accuracy. However the deployment of these models in real-world diagnosis decision support systems is subject to several risks and limitations due to the lack of interpretation.
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Affiliation(s)
- H Benhar
- Software Project Management Research Team, ENSIAS, University Mohammed V in Rabat, Morocco.
| | - A Idri
- Software Project Management Research Team, ENSIAS, University Mohammed V in Rabat, Morocco; CSEHS-MSDA, Mohammed VI Polytechnic University, Benguerir, Morocco.
| | - J L Fernández-Alemán
- Department of Informatics and Systems, Faculty of Computer Science, University of Murcia, Spain.
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Pradhan BK, Pal K. Statistical and entropy-based features can efficiently detect the short-term effect of caffeinated coffee on the cardiac physiology. Med Hypotheses 2020; 145:110323. [PMID: 33032176 DOI: 10.1016/j.mehy.2020.110323] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2020] [Revised: 09/19/2020] [Accepted: 09/26/2020] [Indexed: 11/18/2022]
Abstract
An electrocardiograph (ECG) is the most effective way to find the changes in cardiac physiology. It is the representation of the electrical activities of the heart and can be understood using different waves, peaks, and intervals. Several factors affect the functionality of the heart that includes lifestyle, stress, daily diet, etc. Coffee, the most widely consumed beverage in the world, is an integral part of everyday life. Caffeine, the prime constituent of coffee, is believed to affect the heart physiology. However, the effect of consumption of caffeinated coffee on the cardiac electrophysiological changes, estimated from the morphological features (e.g., peaks, waves, intervals), is controversial. This has led to the exploration of other feature extraction methods to detect the changes accurately. In recent years, the statistical and entropy-based features have emerged as an efficient method to extract hidden patterns from the ECG signal. These features have been successfully explored in arrhythmia detection, noise removal, biometric identification, etc. Hence, we hypothesized that the statistical and entropy-based features could be efficiently used in detecting the changes in the ECG signal after coffee consumption. For the evaluation of our hypothesis, 5-sec ECG segments were extracted from the recorded ECG signals from 14 volunteers in pre- and post-coffee consumption conditions. From each segment, the statistical and entropy-based features were computed. Then, the statistically significant features were extracted using Wilcoxon's signed-rank test. The results showed a significant difference in the statistical parameters post-consumption of coffee. Further, to validate our findings, several machine learning models were used for the automatic detection of these changes, and the results show the highest classification accuracy of 75%. The results support our hypothesis that the statistical and entropy-based features can efficiently detect the changes in the ECG signals, which is induced by coffee consumption. The findings of the proposed hypothesis may open up a new research arena of detecting the presence of different drugs and alcohol in the human body by analyzing the ECG signals.
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Affiliation(s)
- Bikash K Pradhan
- Department of Biotechnology and Medical Engineering, National Institute of Technology, Rourkela 769008, India
| | - Kunal Pal
- Department of Biotechnology and Medical Engineering, National Institute of Technology, Rourkela 769008, India.
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Rolling Bearings Fault Diagnosis Based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, Nonlinear Entropy, and Ensemble SVM. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10165542] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Rolling bearings are fundamental elements that play a crucial role in the functioning of rotating machines; thus, fault diagnosis of rolling bearings is of great significance to reduce catastrophic failures and heavy economic loss. However, the vibration signals of rolling bearings are often nonlinear and nonstationary, resulting in difficulty for feature extraction and fault recognition. In this paper, a hybrid method for multiple fault diagnosis of rolling bearings is presented. The bearing vibration signals are decomposed with the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) to denoise and extract nonlinear entropy features. The nonlinear entropy features are further processed to select the more discriminative fault features and to reduce feature dimension. Then a multi-class intelligent recognition model based on ensemble support vector machine (ESVM) is constructed to diagnose different bearing fault modes as well as fault severities. The effectiveness of the proposed method is assessed via experimental case studies of rolling bearings under multiple operational conditions (i.e., speeds and loads). The results show that our method gives better diagnosis results as compared to some existing approaches.
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Carrillo-Alarcón JC, Morales-Rosales LA, Rodríguez-Rángel H, Lobato-Báez M, Muñoz A, Algredo-Badillo I. A Metaheuristic Optimization Approach for Parameter Estimation in Arrhythmia Classification from Unbalanced Data. SENSORS (BASEL, SWITZERLAND) 2020; 20:s20113139. [PMID: 32498271 PMCID: PMC7308921 DOI: 10.3390/s20113139] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 05/22/2020] [Accepted: 05/29/2020] [Indexed: 06/11/2023]
Abstract
The electrocardiogram records the heart's electrical activity and generates a significant amount of data. The analysis of these data helps us to detect diseases and disorders via heart bio-signal abnormality classification. In unbalanced-data contexts, where the classes are not equally represented, the optimization and configuration of the classification models are highly complex, reflecting on the use of computational resources. Moreover, the performance of electrocardiogram classification depends on the approach and parameter estimation to generate the model with high accuracy, sensitivity, and precision. Previous works have proposed hybrid approaches and only a few implemented parameter optimization. Instead, they generally applied an empirical tuning of parameters at a data level or an algorithm level. Hence, a scheme, including metrics of sensitivity in a higher precision and accuracy scale, deserves special attention. In this article, a metaheuristic optimization approach for parameter estimations in arrhythmia classification from unbalanced data is presented. We selected an unbalanced subset of those databases to classify eight types of arrhythmia. It is important to highlight that we combined undersampling based on the clustering method (data level) and feature selection method (algorithmic level) to tackle the unbalanced class problem. To explore parameter estimation and improve the classification for our model, we compared two metaheuristic approaches based on differential evolution and particle swarm optimization. The final results showed an accuracy of 99.95%, a F1 score of 99.88%, a sensitivity of 99.87%, a precision of 99.89%, and a specificity of 99.99%, which are high, even in the presence of unbalanced data.
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Affiliation(s)
- Juan Carlos Carrillo-Alarcón
- Department of Computer Science, Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE), Tonantzintla, Puebla 72840, Mexico;
| | - Luis Alberto Morales-Rosales
- Faculty of Civil Engineering, Conacyt-Universidad Michoacana de San Nicolás de Hidalgo, Morelia 58030, Michoacán, Mexico;
| | | | | | - Antonio Muñoz
- Engineering Department, University of Guadalajara, Av. Independencia Nacional 151, Autlán, Jalisco 48900, Mexico;
| | - Ignacio Algredo-Badillo
- Department of Computer Science, Conacyt-Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE), Tonantzintla, Puebla 72840, Mexico
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Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico. INFORMATION 2020. [DOI: 10.3390/info11040207] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
Heart diseases are highly ranked among the leading causes of mortality in the world. They have various types including vascular, ischemic, and hypertensive heart disease. A large number of medical features are reported for patients in the Electronic Health Records (EHR) that allow physicians to diagnose and monitor heart disease. We collected a dataset from Medica Norte Hospital in Mexico that includes 800 records and 141 indicators such as age, weight, glucose, blood pressure rate, and clinical symptoms. Distribution of the collected records is very unbalanced on the different types of heart disease, where 17% of records have hypertensive heart disease, 16% of records have ischemic heart disease, 7% of records have mixed heart disease, and 8% of records have valvular heart disease. Herein, we propose an ensemble-learning framework of different neural network models, and a method of aggregating random under-sampling. To improve the performance of the classification algorithms, we implement a data preprocessing step with features selection. Experiments were conducted with unidirectional and bidirectional neural network models and results showed that an ensemble classifier with a BiLSTM or BiGRU model with a CNN model had the best classification performance with accuracy and F1-score between 91% and 96% for the different types of heart disease. These results are competitive and promising for heart disease dataset. We showed that ensemble-learning framework based on deep models could overcome the problem of classifying an unbalanced heart disease dataset. Our proposed framework can lead to highly accurate models that are adapted for clinical real data and diagnosis use.
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M RK, Vaegae NK. Walsh code based numerical mapping method for the identification of protein coding regions in eukaryotes. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.101859] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Asgharzadeh-Bonab A, Amirani MC, Mehri A. Spectral entropy and deep convolutional neural network for ECG beat classification. Biocybern Biomed Eng 2020. [DOI: 10.1016/j.bbe.2020.02.004] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Sanivarapu PV, Rajesh KNVPS, Reddy NVR, Reddy NCS. Patient data hiding into ECG signal using watermarking in transform domain. Phys Eng Sci Med 2020. [DOI: 10.1007/s13246-019-00838-2] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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48
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Novel Methodology for Cardiac Arrhythmias Classification Based on Long-Duration ECG Signal Fragments Analysis. SERIES IN BIOENGINEERING 2020. [DOI: 10.1007/978-981-13-9097-5_11] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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Towards Real-Time Heartbeat Classification: Evaluation of Nonlinear Morphological Features and Voting Method. SENSORS 2019; 19:s19235079. [PMID: 31766323 PMCID: PMC6928852 DOI: 10.3390/s19235079] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Revised: 11/10/2019] [Accepted: 11/15/2019] [Indexed: 11/16/2022]
Abstract
Abnormal heart rhythms are one of the significant health concerns worldwide. The current state-of-the-art to recognize and classify abnormal heartbeats is manually performed by visual inspection by an expert practitioner. This is not just a tedious task; it is also error prone and, because it is performed, post-recordings may add unnecessary delay to the care. The real key to the fight to cardiac diseases is real-time detection that triggers prompt action. The biggest hurdle to real-time detection is represented by the rare occurrences of abnormal heartbeats and even more are some rare typologies that are not fully represented in signal datasets; the latter is what makes it difficult for doctors and algorithms to recognize them. This work presents an automated heartbeat classification based on nonlinear morphological features and a voting scheme suitable for rare heartbeat morphologies. Although the algorithm is designed and tested on a computer, it is intended ultimately to run on a portable i.e., field-programmable gate array (FPGA) devices. Our algorithm tested on Massachusetts Institute of Technology- Beth Israel Hospital(MIT-BIH) database as per Association for the Advancement of Medical Instrumentation(AAMI) recommendations. The simulation results show the superiority of the proposed method, especially in predicting minority groups: the fusion and unknown classes with 90.4% and 100%.
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Gao J, Zhang H, Lu P, Wang Z. An Effective LSTM Recurrent Network to Detect Arrhythmia on Imbalanced ECG Dataset. JOURNAL OF HEALTHCARE ENGINEERING 2019; 2019:6320651. [PMID: 31737240 PMCID: PMC6815557 DOI: 10.1155/2019/6320651] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/19/2019] [Revised: 06/04/2019] [Accepted: 06/19/2019] [Indexed: 11/17/2022]
Abstract
To reduce the high mortality rate from cardiovascular disease (CVD), the electrocardiogram (ECG) beat plays a significant role in computer-aided arrhythmia diagnosis systems. However, the complex variations and imbalance of ECG beats make this a challenging issue. Since ECG beat data exist in heavily imbalanced category, an effective long short-term memory (LSTM) recurrence network model with focal loss (FL) is proposed. For this purpose, the LSTM network can disentangle the timing features in complex ECG signals, while the FL is used to resolve the category imbalance by downweighting easily identified normal ECG examples. The advantages of the proposed network have been verified in the MIT-BIH arrhythmia database. Experimental results show that the LSTM network with FL achieved a reliable solution to the problem of imbalanced datasets in ECG beat classification and was not sensitive to quality of ECG signals. The proposed method can be deployed in telemedicine scenarios to assist cardiologists into more accurately and objectively diagnosing ECG signals.
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Affiliation(s)
- Junli Gao
- Cooperative Innovation Center of Internet Healthcare, Zhengzhou University, Zhengzhou 450000, China
| | - Hongpo Zhang
- Cooperative Innovation Center of Internet Healthcare, Zhengzhou University, Zhengzhou 450000, China
- State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou 450001, China
| | - Peng Lu
- Department of Automation, School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
| | - Zongmin Wang
- Cooperative Innovation Center of Internet Healthcare, Zhengzhou University, Zhengzhou 450000, China
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