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Mastoi QUA, Alqahtani A, Almakdi S, Sulaiman A, Rajab A, Shaikh A, Alqhtani SM. Heart patient health monitoring system using invasive and non-invasive measurement. Sci Rep 2024; 14:9614. [PMID: 38671304 PMCID: PMC11053009 DOI: 10.1038/s41598-024-60500-0] [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: 01/21/2024] [Accepted: 04/23/2024] [Indexed: 04/28/2024] Open
Abstract
The abnormal heart conduction, known as arrhythmia, can contribute to cardiac diseases that carry the risk of fatal consequences. Healthcare professionals typically use electrocardiogram (ECG) signals and certain preliminary tests to identify abnormal patterns in a patient's cardiac activity. To assess the overall cardiac health condition, cardiac specialists monitor these activities separately. This procedure may be arduous and time-intensive, potentially impacting the patient's well-being. This study automates and introduces a novel solution for predicting the cardiac health conditions, specifically identifying cardiac morbidity and arrhythmia in patients by using invasive and non-invasive measurements. The experimental analyses conducted in medical studies entail extremely sensitive data and any partial or biased diagnoses in this field are deemed unacceptable. Therefore, this research aims to introduce a new concept of determining the uncertainty level of machine learning algorithms using information entropy. To assess the effectiveness of machine learning algorithms information entropy can be considered as a unique performance evaluator of the machine learning algorithm which is not selected previously any studies within the realm of bio-computational research. This experiment was conducted on arrhythmia and heart disease datasets collected from Massachusetts Institute of Technology-Berth Israel Hospital-arrhythmia (DB-1) and Cleveland Heart Disease (DB-2), respectively. Our framework consists of four significant steps: 1) Data acquisition, 2) Feature preprocessing approach, 3) Implementation of learning algorithms, and 4) Information Entropy. The results demonstrate the average performance in terms of accuracy achieved by the classification algorithms: Neural Network (NN) achieved 99.74%, K-Nearest Neighbor (KNN) 98.98%, Support Vector Machine (SVM) 99.37%, Random Forest (RF) 99.76 % and Naïve Bayes (NB) 98.66% respectively. We believe that this study paves the way for further research, offering a framework for identifying cardiac health conditions through machine learning techniques.
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Affiliation(s)
- Qurat-Ul-Ain Mastoi
- School of Computer Science and Creative Technologies, University of the West of England, Bristol, BS16QY, UK
| | - Ali Alqahtani
- Department of Networks and Communications Engineering, College of Computer Science and Information Systems, Najran University, 61441, Najran, Najran, Saudi Arabia
| | - Sultan Almakdi
- Department of Computer Science, College of Computer Science and Information Systems, Najran University, 61441, Najran, Saudi Arabia
| | - Adel Sulaiman
- Department of Computer Science, College of Computer Science and Information Systems, Najran University, 61441, Najran, Saudi Arabia.
| | - Adel Rajab
- Department of Computer Science, College of Computer Science and Information Systems, Najran University, 61441, Najran, Saudi Arabia
| | - Asadullah Shaikh
- Department of Information Systems, College of Computer Science and Information Systems, Najran University, 61441, Najran, Saudi Arabia
| | - Samar M Alqhtani
- Department of Information Systems, College of Computer Science and Information Systems, Najran University, 61441, Najran, Saudi Arabia
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Dokeroglu T, Deniz A, Kiziloz HE. A comprehensive survey on recent metaheuristics for feature selection. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.04.083] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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Wang H, Niu B, Tan L. Bacterial colony algorithm with adaptive attribute learning strategy for feature selection in classification of customers for personalized recommendation. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2020.07.142] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Li G, Tan Z, Xu W, Xu F, Wang L, Chen J, Wu K. A particle swarm optimization improved BP neural network intelligent model for electrocardiogram classification. BMC Med Inform Decis Mak 2021; 21:99. [PMID: 34330266 PMCID: PMC8322832 DOI: 10.1186/s12911-021-01453-6] [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: 01/24/2021] [Accepted: 02/22/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND As proven to reflect the work state of heart and physiological situation objectively, electrocardiogram (ECG) is widely used in the assessment of human health, especially the diagnosis of heart disease. The accuracy and reliability of abnormal ECG (AECG) decision depend to a large extent on the feature extraction. However, it is often uneasy or even impossible to obtain accurate features, as the detection process of ECG is easily disturbed by the external environment. And AECG got many species and great variation. What's more, the ECG result obtained after a long time past, which can not reach the purpose of early warning or real-time disease diagnosis. Therefore, developing an intelligent classification model with an accurate feature extraction method to identify AECG is of quite significance. This study aimed to explore an accurate feature extraction method of ECG and establish a suitable model for identifying AECG and the diagnosis of heart disease. METHODS In this research, the wavelet combined with four operations and adaptive threshold methods were applied to filter the ECG and extract its feature waves first. Then, a BP neural network (BPNN) intelligent model and a particle swarm optimization (PSO) improved BPNN (PSO-BPNN) intelligent model based on MIT-BIH open database was established to identify ECG. To reduce the complexity of the model, the principal component analysis (PCA) was used to minimize the feature dimension. RESULTS Wavelet transforms combined four operations and adaptive threshold methods were capable of ECG filtering and feature extraction. PCA can significantly deduce the modeling feature dimension to minimize the complexity and save classification time. The PSO-BPNN intelligent model was suitable for identifying five types of ECG and showed better effects while comparing it with the BPNN model. CONCLUSION In summary, it was further concluded that the PSO-BPNN intelligent model would be a suitable way to identify AECG and provide a tool for the diagnosis of heart disease.
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Affiliation(s)
- Guixiang Li
- National Engineering Research Center for Healthcare Devices, Guangdong Key Lab of Medical Electronic Instruments and Polymer Material Products, Guangdong Institute of Medical Instruments, Institute of Medicine and Health, Guangdong Academy of Sciences, Guangzhou, 510500, China.,Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou, 510500, China
| | - Zhongwei Tan
- National Engineering Research Center for Healthcare Devices, Guangdong Key Lab of Medical Electronic Instruments and Polymer Material Products, Guangdong Institute of Medical Instruments, Institute of Medicine and Health, Guangdong Academy of Sciences, Guangzhou, 510500, China
| | - Weikang Xu
- National Engineering Research Center for Healthcare Devices, Guangdong Key Lab of Medical Electronic Instruments and Polymer Material Products, Guangdong Institute of Medical Instruments, Institute of Medicine and Health, Guangdong Academy of Sciences, Guangzhou, 510500, China
| | - Fei Xu
- National Engineering Research Center for Healthcare Devices, Guangdong Key Lab of Medical Electronic Instruments and Polymer Material Products, Guangdong Institute of Medical Instruments, Institute of Medicine and Health, Guangdong Academy of Sciences, Guangzhou, 510500, China
| | - Lei Wang
- Department of Artificial Intelligence, College of Information and Communication Engineering, Hainan University, Haikou, 570228, China.
| | - Jun Chen
- National Engineering Research Center for Healthcare Devices, Guangdong Key Lab of Medical Electronic Instruments and Polymer Material Products, Guangdong Institute of Medical Instruments, Institute of Medicine and Health, Guangdong Academy of Sciences, Guangzhou, 510500, China. .,Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou, 510500, China.
| | - Kai Wu
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, 510006, China. .,Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou, 510500, China. .,Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, 510370, China. .,The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou, 510370, China. .,National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou, 510006, China. .,Key Laboratory of Biomedical Engineering of Guangdong Province, South China University of Technology, Guangzhou, 510006, China. .,Department of Nuclear Medicine and Radiology, Institute of Development, Aging and Cancer, Tohoku University, Sendai, 980-8575, Japan.
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Dai H, Hwang HG, Tseng VS. Convolutional neural network based automatic screening tool for cardiovascular diseases using different intervals of ECG signals. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 203:106035. [PMID: 33770545 DOI: 10.1016/j.cmpb.2021.106035] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Accepted: 02/28/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVE Automatic screening tools can be applied to detect cardiovascular diseases (CVDs), which are the leading cause of death worldwide. As an effective and non-invasive method, electrocardiogram (ECG) based approaches are widely used to identify CVDs. Hence, this paper proposes a deep convolutional neural network (CNN) to classify five CVDs using standard 12-lead ECG signals. METHODS The Physiobank (PTB) ECG database is used in this study. Firstly, ECG signals are segmented into different intervals (one-second, two-seconds and three-seconds), without any wave detection, and three datasets are obtained. Secondly, as an alternative to any complex preprocessing, durations of raw ECG signals have been considered as input with simple min-max normalization. Lastly, a ten-fold cross-validation method is employed for one-second ECG signals and also tested on other two datasets (two-seconds and three-seconds). RESULTS Comparing to the competing approaches, the proposed CNN acquires the highest performance, having an accuracy, sensitivity, and specificity of 99.59%, 99.04%, and 99.87%, respectively, with one-second ECG signals. The overall accuracy, sensitivity, and specificity obtained are 99.80%, 99.48%, and 99.93%, respectively, using two-seconds of signals with pre-trained proposed models. The accuracy, sensitivity, and specificity of segmented ECG tested by three-seconds signals are 99.84%, 99.52%, and 99.95%, respectively. CONCLUSION The results of this study indicate that the proposed system accomplishes high performance and keeps the characterizations in brief with flexibility at the same time, which means that it has the potential for implementation in a practical, real-time medical environment.
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Affiliation(s)
- Hao Dai
- Institute of Information Management, National Chiao Tung University, Hsinchu, Taiwan.
| | - Hsin-Ginn Hwang
- Institute of Information Management, National Chiao Tung University, Hsinchu, Taiwan
| | - Vincent S Tseng
- Department of Computer Science, National Chiao Tung University, Hsinchu, Taiwan
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An Adapting Chemotaxis Bacterial Foraging Optimization Algorithm for Feature Selection in Classification. LECTURE NOTES IN COMPUTER SCIENCE 2020. [PMCID: PMC7354779 DOI: 10.1007/978-3-030-53956-6_25] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Efficient classification methods can improve the data quality or relevance to better optimize some Internet applications such as fast searching engine and accurate identification. However, in the big data era, difficulties and volumes of data processing increase drastically. To decrease the huge computational cost, heuristic algorithms have been used. In this paper, an Adapting Chemotaxis Bacterial Foraging Optimization (ACBFO) algorithm is proposed based on basic Bacterial Foraging Optimization (BFO) algorithm. The aim of this work is to design a modified algorithm which is more suitable for data classification. The proposed algorithm has two updating strategies and one structural changing. First, the adapting chemotaxis step updating strategy is responsible to increase the flexibility of searching. Second, the feature subsets updating strategy better combines the proposed heuristic algorithm with the KNN classifier. Third, the nesting structure of BFO has been simplified to reduce the computation complexity. The ACBFO has been compared with BFO, BFOLIW and BPSO by testing on 12 widely used benchmark datasets. The result shows that ACBFO has a good ability of solving classification problems and gets higher accuracy than the other comparation algorithm.
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Kora P, Abraham A, Meenakshi K. Heart disease detection using hybrid of bacterial foraging and particle swarm optimization. EVOLVING SYSTEMS 2019. [DOI: 10.1007/s12530-019-09312-6] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Houssein EH, Ewees AA, ElAziz MA. Improving Twin Support Vector Machine Based on Hybrid Swarm Optimizer for Heartbeat Classification. PATTERN RECOGNITION AND IMAGE ANALYSIS 2018; 28:243-253. [DOI: 10.1134/s1054661818020037] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2017] [Indexed: 09/02/2023]
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Kora P. ECG based Myocardial Infarction detection using Hybrid Firefly Algorithm. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 152:141-148. [PMID: 29054254 DOI: 10.1016/j.cmpb.2017.09.015] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2017] [Revised: 07/27/2017] [Accepted: 09/16/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND AND OBJECTIVE Myocardial Infarction (MI) is one of the most frequent diseases, and can also cause demise, disability and monetary loss in patients who suffer from cardiovascular disorder. Diagnostic methods of this ailment by physicians are typically invasive, even though they do not fulfill the required detection accuracy. METHODS Recent feature extraction methods, for example, Auto Regressive (AR) modelling; Magnitude Squared Coherence (MSC); Wavelet Coherence (WTC) using Physionet database, yielded a collection of huge feature set. A large number of these features may be inconsequential containing some excess and non-discriminative components that present excess burden in computation and loss of execution performance. So Hybrid Firefly and Particle Swarm Optimization (FFPSO) is directly used to optimise the raw ECG signal instead of extracting features using the above feature extraction techniques. RESULTS Provided results in this paper show that, for the detection of MI class, the FFPSO algorithm with ANN gives 99.3% accuracy, sensitivity of 99.97%, and specificity of 98.7% on MIT-BIH database by including NSR database also. CONCLUSIONS The proposed approach has shown that methods that are based on the feature optimization of the ECG signals are the perfect to diagnosis the condition of the heart patients.
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Affiliation(s)
- Padmavathi Kora
- Gokaraju Rangaraju Institute of Engineering and Technology (GRIET), Hyderabad, India.
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Wang H, Jing X, Niu B. A discrete bacterial algorithm for feature selection in classification of microarray gene expression cancer data. Knowl Based Syst 2017. [DOI: 10.1016/j.knosys.2017.04.004] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Detection of Bundle Branch Block using Adaptive Bacterial Foraging Optimization and Neural Network. EGYPTIAN INFORMATICS JOURNAL 2017. [DOI: 10.1016/j.eij.2016.04.004] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Kora P, Sri Rama Krishna K. Detection of Bundle Branch Block Using Bat Algorithm and Levenberg Marquardt Neural Network. PROCEEDINGS OF FIRST INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGY FOR INTELLIGENT SYSTEMS: VOLUME 1 2016. [DOI: 10.1007/978-3-319-30933-0_55] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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Kora P, Kalva SR. Improved Bat algorithm for the detection of myocardial infarction. SPRINGERPLUS 2015; 4:666. [PMID: 26558169 PMCID: PMC4631839 DOI: 10.1186/s40064-015-1379-7] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2015] [Accepted: 09/25/2015] [Indexed: 11/10/2022]
Abstract
The medical practitioners study the electrical activity of the human heart in order to detect heart diseases from the electrocardiogram (ECG) of the heart patients. A myocardial infarction (MI) or heart attack is a heart disease, that occurs when there is a block (blood clot) in the pathway of one or more coronary blood vessels (arteries) that supply blood to the heart muscle. The abnormalities in the heart can be identified by the changes in the ECG signal. The first step in the detection of MI is Preprocessing of ECGs which removes noise by using filters. Feature extraction is the next key process in detecting the changes in the ECG signals. This paper presents a method for extracting key features from each cardiac beat using Improved Bat algorithm. Using this algorithm best features are extracted, then these best (reduced) features are applied to the input of the neural network classifier. It has been observed that the performance of the classifier is improved with the help of the optimized features.
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Affiliation(s)
- Padmavathi Kora
- Department of ECE, GRIET, Bachupally, 500090 Hyderabad, India
| | - Sri Ramakrishna Kalva
- Department of ECE, Velagapudi Ramakrishna Siddhartha Engineering College, Kanuru, Vijayawada, India
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