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Kim J, Im J, Shin W, Lee S, Oh S, Kwon D, Jung G, Choi WY, Lee JH. Demonstration of In-Memory Biosignal Analysis: Novel High-Density and Low-Power 3D Flash Memory Array for Arrhythmia Detection. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024:e2308460. [PMID: 38709909 DOI: 10.1002/advs.202308460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 02/08/2024] [Indexed: 05/08/2024]
Abstract
Smart healthcare systems integrated with advanced deep neural networks enable real-time health monitoring, early disease detection, and personalized treatment. In this work, a novel 3D AND-type flash memory array with a rounded double channel for computing-in-memory (CIM) architecture to overcome the limitations of conventional smart healthcare systems: the necessity of high area and energy efficiency while maintaining high classification accuracy is proposed. The fabricated array, characterized by low-power operations and high scalability with double independent channels per floor, exhibits enhanced cell density and energy efficiency while effectively emulating the features of biological synapses. The CIM architecture leveraging the fabricated array achieves high classification accuracy (93.5%) for electrocardiogram signals, ensuring timely detection of potentially life-threatening arrhythmias. Incorporated with a simplified spike-timing-dependent plasticity learning rule, the CIM architecture is suitable for robust, area- and energy-efficient in-memory arrhythmia detection systems. This work effectively addresses the challenges of conventional smart healthcare systems, paving the way for a more refined healthcare paradigm.
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Affiliation(s)
- Jangsaeng Kim
- Department of Electrical and Computer Engineering and Inter-university Semiconductor Research Center, Seoul National University, Seoul, 08826, Republic of Korea
| | - Jiseong Im
- Department of Electrical and Computer Engineering and Inter-university Semiconductor Research Center, Seoul National University, Seoul, 08826, Republic of Korea
| | - Wonjun Shin
- Department of Electrical and Computer Engineering and Inter-university Semiconductor Research Center, Seoul National University, Seoul, 08826, Republic of Korea
| | - Soochang Lee
- Department of Electrical and Computer Engineering and Inter-university Semiconductor Research Center, Seoul National University, Seoul, 08826, Republic of Korea
| | - Seongbin Oh
- Department of Electrical and Computer Engineering and Inter-university Semiconductor Research Center, Seoul National University, Seoul, 08826, Republic of Korea
| | - Dongseok Kwon
- Department of Electrical and Computer Engineering and Inter-university Semiconductor Research Center, Seoul National University, Seoul, 08826, Republic of Korea
| | - Gyuweon Jung
- Department of Electrical and Computer Engineering and Inter-university Semiconductor Research Center, Seoul National University, Seoul, 08826, Republic of Korea
| | - Woo Young Choi
- Department of Electrical and Computer Engineering and Inter-university Semiconductor Research Center, Seoul National University, Seoul, 08826, Republic of Korea
| | - Jong-Ho Lee
- Department of Electrical and Computer Engineering and Inter-university Semiconductor Research Center, Seoul National University, Seoul, 08826, Republic of Korea
- Ministry of Science and ICT, Sejong, 30121, Republic of Korea
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A novel technique for the detection of myocardial dysfunction using ECG signals based on CEEMD, DWT, PSR and neural networks. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10262-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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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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Uddin J, Ghazali R, H. Abawajy J, Shah H, Husaini NA, Zeb A. Rough set based information theoretic approach for clustering uncertain categorical data. PLoS One 2022; 17:e0265190. [PMID: 35559954 PMCID: PMC9106167 DOI: 10.1371/journal.pone.0265190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 02/27/2022] [Indexed: 12/02/2022] Open
Abstract
Motivation Many real applications such as businesses and health generate large categorical datasets with uncertainty. A fundamental task is to efficiently discover hidden and non-trivial patterns from such large uncertain categorical datasets. Since the exact value of an attribute is often unknown in uncertain categorical datasets, conventional clustering analysis algorithms do not provide a suitable means for dealing with categorical data, uncertainty, and stability. Problem statement The ability of decision making in the presence of vagueness and uncertainty in data can be handled using Rough Set Theory. Though, recent categorical clustering techniques based on Rough Set Theory help but they suffer from low accuracy, high computational complexity, and generalizability especially on data sets where they sometimes fail or hardly select their best clustering attribute. Objectives The main objective of this research is to propose a new information theoretic based Rough Purity Approach (RPA). Another objective of this work is to handle the problems of traditional Rough Set Theory based categorical clustering techniques. Hence, the ultimate goal is to cluster uncertain categorical datasets efficiently in terms of the performance, generalizability and computational complexity. Methods The RPA takes into consideration information-theoretic attribute purity of the categorical-valued information systems. Several extensive experiments are conducted to evaluate the efficiency of RPA using a real Supplier Base Management (SBM) and six benchmark UCI datasets. The proposed RPA is also compared with several recent categorical data clustering techniques. Results The experimental results show that RPA outperforms the baseline algorithms. The significant percentage improvement with respect to time (66.70%), iterations (83.13%), purity (10.53%), entropy (14%), and accuracy (12.15%) as well as Rough Accuracy of clusters show that RPA is suitable for practical usage. Conclusion We conclude that as compared to other techniques, the attribute purity of categorical-valued information systems can better cluster the data. Hence, RPA technique can be recommended for large scale clustering in multiple domains and its performance can be enhanced for further research.
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Affiliation(s)
- Jamal Uddin
- Qurtuba University of Science & IT, Peshawar, Pakistan
- * E-mail:
| | - Rozaida Ghazali
- Universiti Tun Hussien Onn Malaysia, Batu Pahat, Johor, Malaysia
| | | | | | | | - Asim Zeb
- Abbottabad University of Science & Technology, Abbottabad, Pakistan
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5
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Shariatnia S, Ziaratban M, Rajabi A, Salehi A, Abdi Zarrini K, Vakili M. Modeling the diagnosis of coronary artery disease by discriminant analysis and logistic regression: a cross-sectional study. BMC Med Inform Decis Mak 2022; 22:85. [PMID: 35351098 PMCID: PMC8966192 DOI: 10.1186/s12911-022-01823-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2021] [Accepted: 03/24/2022] [Indexed: 12/15/2022] Open
Abstract
PURPOSE Coronary artery disease (CAD) is one of the most significant cardiovascular diseases that requires accurate angiography to diagnose. Angiography is an invasive approach involving risks like death, heart attack, and stroke. An appropriate alternative for diagnosis of the disease is to use statistical or data mining methods. The purpose of the study was to predict CAD by using discriminant analysis and compared with the logistic regression. MATERIALS AND METHODS This cross-sectional study included 758 cases admitted to Fatemeh Zahra Teaching Hospital (Sari, Iran) for examination and coronary angiography for evaluation of CAD in 2019. A logistics discriminant, Quadratic Discriminant Analysis (QDA) and Linear Discriminant Analysis (LDA) model and K-Nearest Neighbor (KNN) were fitted for prognosis of CAD with the help of clinical and laboratory information of patients. RESULTS Out of the 758 examined cases, 250 (32.98%) cases were non-CAD and 508 (67.22%) were diagnosed with CAD disease. The results indicated that the indices of accuracy, sensitivity, specificity and area under the ROC curve (AUC) in the linear discriminant analysis (LDA) were 78.6, 81.3, 71.3, and 81.9%, respectively. The results obtained by the quadratic discriminant analysis were respectively 64.6, 88.2, 47.9, and 81%. The values of the metrics in K-nearest neighbor method were 74, 77.5, 63.7, and 82%, respectively. Finally, the logistic regression reached 77, 87.6, 55.6, and 82%, respectively for the evaluation metrics. CONCLUSIONS The LDA method is superior to the Quadratic Discriminant Analysis (QDA), K-Nearest Neighbor (KNN) and Logistic Regression (LR) methods in differentiating CAD patients. Therefore, in addition to common non-invasive diagnostic methods, LDA technique is recommended as a predictive model with acceptable accuracy, sensitivity, and specificity for the diagnosis of CAD. However, given that the differences between the models are small, it is recommended to use each model to predict CAD disease.
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Affiliation(s)
- Sahar Shariatnia
- Department of Biostatistics and Epidemiology, Faculty of Health, Golestan University of Medica Science, Gorgan, Iran
| | - Majid Ziaratban
- Department of Electrical Engineering, Faculty of Engineering, Golestan University, Gorgan, Iran
| | - Abdolhalim Rajabi
- Health Management and Social Development Research Center, Department of Biostatistics and Epidemiology, Faculty of Health, Golestan University of Medical Sciences, Gorgan, Iran
| | - Aref Salehi
- Ischemic Disorders Research Center, Golestan University of Medical Sciences, Gorgan, Iran
| | - Kobra Abdi Zarrini
- Intensive Care Unit of Fatemeh Zahra Hospital, Mazandaran University Medical Sciences, Sari, Iran
| | - Mohammadali Vakili
- Health Management and Social Development Research Center, Department of Biostatistics and Epidemiology, Faculty of Health, Golestan University of Medical Sciences, Gorgan, Iran.
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6
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Zeng W, Yuan C. ECG arrhythmia classification based on variational mode decomposition, Shannon energy envelope and deterministic learning. INT J MACH LEARN CYB 2021. [DOI: 10.1007/s13042-021-01389-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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7
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Tan X, Dai Y, Humayun AI, Chen H, Allen GI, Jain PN. Detection of Junctional Ectopic Tachycardia by Central Venous Pressure. ACTA ACUST UNITED AC 2021; 12721:258-262. [PMID: 34278383 PMCID: PMC8281976 DOI: 10.1007/978-3-030-77211-6_29] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
Central venous pressure (CVP) is the blood pressure in the venae cavae, near the right atrium of the heart. This signal waveform is commonly collected in clinical settings, and yet there has been limited discussion of using this data for detecting arrhythmia and other cardiac events. In this paper, we develop a signal processing and feature engineering pipeline for CVP waveform analysis. Through a case study on pediatric junctional ectopic tachycardia (JET), we show that our extracted CVP features reliably detect JET with comparable results to the more commonly used electrocardiogram (ECG) features. This machine learning pipeline can thus improve the clinical diagnosis and ICU monitoring of arrhythmia. It also corroborates and complements the ECG-based diagnosis, especially when the ECG measurements are unavailable or corrupted.
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Affiliation(s)
- Xin Tan
- Department of Statistics, Rice University, Houston, TX, USA
| | - Yanwan Dai
- Department of Statistics, Rice University, Houston, TX, USA
| | - Ahmed Imtiaz Humayun
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA
| | - Haoze Chen
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA
| | - Genevera I Allen
- Departments of ECE, Statistics, and Computer Science, Rice University, TX, USA.,Neurological Research Institute, Baylor College of Medicine, Houston, TX, USA
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8
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Zeng W, Yuan J, Yuan C, Wang Q, Liu F, Wang Y. A novel technique for the detection of myocardial dysfunction using ECG signals based on hybrid signal processing and neural networks. Soft comput 2021. [DOI: 10.1007/s00500-020-05465-8] [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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9
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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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10
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Zeng W, Yuan J, Yuan C, Wang Q, Liu F, Wang Y. Classification of myocardial infarction based on hybrid feature extraction and artificial intelligence tools by adopting tunable-Q wavelet transform (TQWT), variational mode decomposition (VMD) and neural networks. Artif Intell Med 2020; 106:101848. [PMID: 32593387 DOI: 10.1016/j.artmed.2020.101848] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Revised: 02/16/2020] [Accepted: 03/20/2020] [Indexed: 12/18/2022]
Abstract
Cardiovascular diseases (CVD) is the leading cause of human mortality and morbidity around the world, in which myocardial infarction (MI) is a silent condition that irreversibly damages the heart muscles. Currently, electrocardiogram (ECG) is widely used by the clinicians to diagnose MI patients due to its inexpensiveness and non-invasive nature. Pathological alterations provoked by MI cause slow conduction by increasing axial resistance on coupling between cells. This issue may cause abnormal patterns in the dynamics of the tip of the cardiac vector in the ECG signals. However, manual interpretation of the pathological alternations induced by MI is a time-consuming, tedious and subjective task. To overcome such disadvantages, computer-aided diagnosis techniques including signal processing and artificial intelligence tools have been developed. In this study we propose a novel technique for automatic detection of MI based on hybrid feature extraction and artificial intelligence tools. Tunable quality factor (Q-factor) wavelet transform (TQWT), variational mode decomposition (VMD) and phase space reconstruction (PSR) are utilized to extract representative features to form cardiac vectors with synthesis of the standard 12-lead and Frank XYZ leads. They are combined with neural networks to model, identify and detect abnormal patterns in the dynamics of cardiac system caused by MI. First, 12-lead ECG signals are reduced to 3-dimensional VCG signals, which are synthesized with Frank XYZ leads to build a hybrid 4-dimensional cardiac vector. Second, this vector is decomposed into a set of frequency subbands with a number of decomposition levels by using the TQWT method. Third, VMD is employed to decompose the subband of the 4-dimensional cardiac vector into different intrinsic modes, in which the first intrinsic mode contains the majority of the cardiac vector's energy and is considered to be the predominant intrinsic mode. It is selected to construct the reference variable for analysis. Fourth, phase space of the reference variable is reconstructed, in which the properties associated with the nonlinear cardiac system dynamics are preserved. Three-dimensional (3D) PSR together with Euclidean distance (ED) has been utilized to derive features, which demonstrate significant difference in cardiac system dynamics between normal (healthy) and MI cardiac vector signals. Fifth, cardiac system dynamics can be modeled and identified using neural networks, which employ the ED of 3D PSR of the reference variable as the input features. The difference of cardiac system dynamics between healthy control and MI cardiac vector is computed and used for the detection of MI based on a bank of estimators. Finally, data sets, which include conventional 12-lead and Frank XYZ leads ECG signal fragments from 148 patients with MI and 52 healthy controls from PTB diagnostic ECG database, are used for evaluation. By using the 10-fold cross-validation style, the achieved average classification accuracy is reported to be 97.98%. Currently, ST segment evaluation is one of the major and traditional ways for the MI detection. However, there exist weak or even undetectable ST segments in many ECG signals. Since the proposed method does not rely on the information of ST waves, it can serve as a complementary MI detection algorithm in the intensive care unit (ICU) of hospitals to assist the clinicians in confirming their diagnosis. Overall, our results verify that the proposed features may satisfactorily reflect cardiac system dynamics, and are complementary to the existing ECG features for automatic cardiac function analysis.
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Affiliation(s)
- Wei Zeng
- School of Physics and Mechanical and Electrical Engineering, Longyan University, Longyan 364012, PR China.
| | - Jian Yuan
- School of Physics and Mechanical and Electrical Engineering, Longyan University, Longyan 364012, PR China
| | - Chengzhi Yuan
- Department of Mechanical, Industrial and Systems Engineering, University of Rhode Island, Kingston, RI 02881, USA
| | - Qinghui Wang
- School of Physics and Mechanical and Electrical Engineering, Longyan University, Longyan 364012, PR China
| | - Fenglin Liu
- School of Physics and Mechanical and Electrical Engineering, Longyan University, Longyan 364012, PR China
| | - Ying Wang
- School of Physics and Mechanical and Electrical Engineering, Longyan University, Longyan 364012, PR China
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Li F, Chen K, Ling J, Zhan Y, Manogaran G. Automatic diagnosis of cardiac arrhythmia in electrocardiograms via multigranulation computing. Appl Soft Comput 2019. [DOI: 10.1016/j.asoc.2019.04.007] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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12
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Ayatollahi H, Gholamhosseini L, Salehi M. Predicting coronary artery disease: a comparison between two data mining algorithms. BMC Public Health 2019; 19:448. [PMID: 31035958 PMCID: PMC6489351 DOI: 10.1186/s12889-019-6721-5] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2018] [Accepted: 03/28/2019] [Indexed: 12/14/2022] Open
Abstract
Background Cardiovascular diseases (CADs) are the first leading cause of death across the world. World Health Organization has estimated that morality rate caused by heart diseases will mount to 23 million cases by 2030. Hence, the use of data mining algorithms could be useful in predicting coronary artery diseases. Therefore, the present study aimed to compare the positive predictive value (PPV) of CAD using artificial neural network (ANN) and SVM algorithms and their distinction in terms of predicting CAD in the selected hospitals. Methods The present study was conducted by using data mining techniques. The research sample was the medical records of the patients with coronary artery disease who were hospitalized in three hospitals affiliated to AJA University of Medical Sciences between March 2016 and March 2017 (n = 1324). The dataset and the predicting variables used in this study was the same for both data mining techniques. Totally, 25 variables affecting CAD were selected and related data were extracted. After normalizing and cleaning the data, they were entered into SPSS (V23.0) and Excel 2013. Then, R 3.3.2 was used for statistical computing. Results The SVM model had lower MAPE (112.03), higher Hosmer-Lemeshow test’s result (16.71), and higher sensitivity (92.23). Moreover, variables affecting CAD (74.42) yielded better goodness of fit in SVM model and provided more accurate result than the ANN model. On the other hand, since the area under the receiver operating characteristic (ROC) curve in the SVM algorithm was more than this area in ANN model, it could be concluded that SVM model had higher accuracy than the ANN model. Conclusion According to the results, the SVM algorithm presented higher accuracy and better performance than the ANN model and was characterized with higher power and sensitivity. Overall, it provided a better classification for the prediction of CAD. The use of other data mining algorithms are suggested to improve the positive predictive value of the disease prediction.
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Affiliation(s)
- Haleh Ayatollahi
- Health Management and Economics Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Leila Gholamhosseini
- Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran. .,School of Paramedical Sciences, AJA University of Medical Sciences, Tehran, Iran.
| | - Masoud Salehi
- Department of Biostatistics, School of Public Health, Iran University of Medical Sciences, Tehran, Iran
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13
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A Systematic Mapping Study of Data Preparation in Heart Disease Knowledge Discovery. J Med Syst 2018; 43:17. [PMID: 30542772 DOI: 10.1007/s10916-018-1134-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2018] [Accepted: 12/03/2018] [Indexed: 01/25/2023]
Abstract
The increasing amount of data produced by various biomedical and healthcare systems has led to a need for methodologies related to knowledge data discovery. Data mining (DM) offers a set of powerful techniques that allow the identification and extraction of relevant information from medical datasets, thus enabling doctors and patients to greatly benefit from DM, particularly in the case of diseases with high mortality and morbidity rates, such as heart disease (HD). Nonetheless, the use of raw medical data implies several challenges, such as missing data, noise, redundancy and high dimensionality, which make the extraction of useful and relevant information difficult and challenging. Intensive research has, therefore, recently begun in order to prepare raw healthcare data before knowledge extraction. In any knowledge data discovery (KDD) process, data preparation is the step prior to DM that deals with data imperfectness in order to improve its quality so as to satisfy the requirements and improve the performances of DM techniques. The objective of this paper is to perform a systematic mapping study (SMS) on data preparation for KDD in cardiology so as to provide an overview of the quantity and type of research carried out in this respect. The SMS consisted of a set of 58 selected papers published in the period January 2000 and December 2017. The selected studies were analyzed according to six criteria: year and channel of publication, preparation task, medical task, DM objective, research type and empirical type. The results show that a high amount of data preparation research was carried out in order to improve the performance of DM-based decision support systems in cardiology. Researchers were mainly interested in the data reduction preparation task and particularly in feature selection. Moreover, the majority of the selected studies focused on classification for the diagnosis of HD. Two main research types were identified in the selected studies: solution proposal and evaluation research, and the most frequently used empirical type was that of historical-based evaluation.
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14
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Rahman MA, Islam MZ. Application of a density based clustering technique on biomedical datasets. Appl Soft Comput 2018. [DOI: 10.1016/j.asoc.2018.09.012] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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15
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An EigenECG Network Approach Based on PCANet for Personal Identification from ECG Signal. SENSORS 2018; 18:s18114024. [PMID: 30453697 PMCID: PMC6263947 DOI: 10.3390/s18114024] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2018] [Revised: 11/11/2018] [Accepted: 11/14/2018] [Indexed: 11/17/2022]
Abstract
We herein propose an EigenECG Network (EECGNet) based on the principal component analysis network (PCANet) for the personal identification of electrocardiogram (ECG) from human biosignal data. The EECGNet consists of three stages. In the first stage, ECG signals are preprocessed by normalization and spike removal. The R peak points in the preprocessed ECG signals are detected. Subsequently, ECG signals are transformed into two-dimensional images to use as the input to the EECGNet. Further, we perform patch-mean removal and PCA algorithm similar to the PCANet from the transformed two-dimensional images. The second stage is almost the same as the first stage, where the mean removal and PCA process are repeatedly performed in the cascaded network. In the final stage, the binary quantization, block sliding, and histogram computation are performed. Thus, this EECGNet performs well without the use of back-propagation to obtain features from the visual content. We constructed a Chosun University (CU)-ECG database from an ECG sensor implemented by ourselves. Further, we used the well-known MIT Beth Israel Hospital (BIH) ECG database. The experimental results clearly reveal the good performance and effectiveness of the proposed method compared with conventional algorithms such as PCA, auto-encoder (AE), extreme learning machine (ELM), and ensemble extreme learning machine (EELM).
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16
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Idri A, Benhar H, Fernández-Alemán JL, Kadi I. A systematic map of medical data preprocessing in knowledge discovery. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 162:69-85. [PMID: 29903496 DOI: 10.1016/j.cmpb.2018.05.007] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2017] [Revised: 04/25/2018] [Accepted: 05/03/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND AND OBJECTIVE Datamining (DM) has, over the last decade, received increased attention in the medical domain and has been widely used to analyze medical datasets in order to extract useful knowledge and previously unknown patterns. However, historical medical data can often comprise inconsistent, noisy, imbalanced, missing and high dimensional data. These challenges lead to a serious bias in predictive modeling and reduce the performance of DM techniques. Data preprocessing is, therefore, an essential step in knowledge discovery as regards improving the quality of data and making it appropriate and suitable for DM techniques. The objective of this paper is to review the use of preprocessing techniques in clinical datasets. METHODS We performed a systematic map of studies regarding the application of data preprocessing to healthcare and published between January 2000 and December 2017. A search string was determined on the basis of the mapping questions and the PICO categories. The search string was then applied in digital databases covering the fields of computer science and medical informatics in order to identify relevant studies. The studies were initially selected by reading their titles, abstracts and keywords. Those that were selected at that stage were then reviewed using a set of inclusion and exclusion criteria in order to eliminate any that were not relevant. This process resulted in 126 primary studies. RESULTS Selected studies were analyzed and classified according to their publication years and channels, research type, empirical type and contribution type. The findings of this mapping study revealed that researchers have paid a considerable amount of attention to preprocessing in medical DM in last decade. A significant number of the selected studies used data reduction and cleaning preprocessing tasks. Moreover, the disciplines in which preprocessing have received most attention are: cardiology, endocrinology and oncology. CONCLUSIONS Researchers should develop and implement standards for an effective integration of multiple medical data types. Moreover, we identified the need to perform literature reviews.
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Affiliation(s)
- A Idri
- Software Project Management Research Team, ENSIAS, University Mohammed V of Rabat, Morocco.
| | - H Benhar
- Software Project Management Research Team, ENSIAS, University Mohammed V of Rabat, Morocco.
| | - J L Fernández-Alemán
- Department of Informatics and Systems, Faculty of Computer Science, University of Murcia, Spain.
| | - I Kadi
- Software Project Management Research Team, ENSIAS, University Mohammed V of Rabat, Morocco.
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17
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Abawajy J, Kelarev A, Yi X, Jelinek HF. Minimal ensemble based on subset selection using ECG to diagnose categories of CAN. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 160:85-94. [PMID: 29728250 DOI: 10.1016/j.cmpb.2018.01.019] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2017] [Revised: 12/06/2017] [Accepted: 01/15/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND AND OBJECTIVE Early diagnosis of cardiac autonomic neuropathy (CAN) is critical for reversing or decreasing its progression and prevent complications. Diagnostic accuracy or precision is one of the core requirements of CAN detection. As the standard Ewing battery tests suffer from a number of shortcomings, research in automating and improving the early detection of CAN has recently received serious attention in identifying additional clinical variables and designing advanced ensembles of classifiers to improve the accuracy or precision of CAN diagnostics. Although large ensembles are commonly proposed for the automated diagnosis of CAN, large ensembles are characterized by slow processing speed and computational complexity. This paper applies ECG features and proposes a new ensemble-based approach for diagnosis of CAN progression. METHODS We introduce a Minimal Ensemble Based On Subset Selection (MEBOSS) for the diagnosis of all categories of CAN including early, definite and atypical CAN. MEBOSS is based on a novel multi-tier architecture applying classifier subset selection as well as the training subset selection during several steps of its operation. Our experiments determined the diagnostic accuracy or precision obtained in 5 × 2 cross-validation for various options employed in MEBOSS and other classification systems. RESULTS The experiments demonstrate the operation of the MEBOSS procedure invoking the most effective classifiers available in the open source software environment SageMath. The results of our experiments show that for the large DiabHealth database of CAN related parameters MEBOSS outperformed other classification systems available in SageMath and achieved 94% to 97% precision in 5 × 2 cross-validation correctly distinguishing any two CAN categories to a maximum of five categorizations including control, early, definite, severe and atypical CAN. CONCLUSIONS These results show that MEBOSS architecture is effective and can be recommended for practical implementations in systems for the diagnosis of CAN progression.
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Affiliation(s)
- Jemal Abawajy
- School of Information Technology, Deakin University, 221 Burwood Hwy, Victoria 3125, Australia.
| | - Andrei Kelarev
- School of Information Technology, Deakin University, 221 Burwood Hwy, Victoria 3125, Australia; School of Science, RMIT University, GPO Box 2476, Melbourne, VIC 3001, Australia.
| | - Xun Yi
- School of Science, RMIT University, GPO Box 2476, Melbourne, VIC 3001, Australia.
| | - Herbert F Jelinek
- School of Community Health, Charles Sturt University, PO Box 789, Albury, NSW 2640, Australia.
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18
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A Survey of Data Mining and Deep Learning in Bioinformatics. J Med Syst 2018; 42:139. [DOI: 10.1007/s10916-018-1003-9] [Citation(s) in RCA: 81] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2018] [Accepted: 06/21/2018] [Indexed: 12/13/2022]
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19
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Kaplan Berkaya S, Uysal AK, Sora Gunal E, Ergin S, Gunal S, Gulmezoglu MB. A survey on ECG analysis. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2018.03.003] [Citation(s) in RCA: 197] [Impact Index Per Article: 32.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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20
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V. Kelarev A, Yi X, Cui H, Rylands L, F. Jelinek H. A survey of state-of-the-art methods for securing medical databases. AIMS MEDICAL SCIENCE 2018. [DOI: 10.3934/medsci.2018.1.1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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21
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Kadi I, Idri A, Fernandez-Aleman JL. Systematic mapping study of data mining–based empirical studies in cardiology. Health Informatics J 2017; 25:741-770. [DOI: 10.1177/1460458217717636] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Data mining provides the methodology and technology to transform huge amount of data into useful information for decision making. It is a powerful process to extract knowledge and discover new patterns embedded in large data sets. Data mining has been increasingly used in medicine, particularly in cardiology. In fact, data mining applications can greatly benefits all parts involved in cardiology such as patients, cardiologists and nurses. This article aims to perform a systematic mapping study so as to analyze and synthesize empirical studies on the application of data mining techniques in cardiology. A total of 142 articles published between 2000 and 2015 were therefore selected, studied and analyzed according to the four following criteria: year and channel of publication, research type, medical task and empirical type. The results of this mapping study are discussed and a list of recommendations for researchers and cardiologists is provided.
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Affiliation(s)
| | - Ali Idri
- Mohammed V University in Rabat, Morocco
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22
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Kadi I, Idri A, Fernandez-Aleman J. Knowledge discovery in cardiology: A systematic literature review. Int J Med Inform 2017; 97:12-32. [DOI: 10.1016/j.ijmedinf.2016.09.005] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2016] [Revised: 09/01/2016] [Accepted: 09/11/2016] [Indexed: 11/24/2022]
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23
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24
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Reyes-Galaviz OF, Pedrycz W. Granular fuzzy modeling with evolving hyperboxes in multi-dimensional space of numerical data. Neurocomputing 2015. [DOI: 10.1016/j.neucom.2015.05.102] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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25
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Wang J, Sun X, Nahavandi S, Kouzani A, Wu Y, She M. Multichannel biomedical time series clustering via hierarchical probabilistic latent semantic analysis. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2014; 117:238-246. [PMID: 25023531 DOI: 10.1016/j.cmpb.2014.06.014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2013] [Revised: 06/20/2014] [Accepted: 06/22/2014] [Indexed: 06/03/2023]
Abstract
Biomedical time series clustering that automatically groups a collection of time series according to their internal similarity is of importance for medical record management and inspection such as bio-signals archiving and retrieval. In this paper, a novel framework that automatically groups a set of unlabelled multichannel biomedical time series according to their internal structural similarity is proposed. Specifically, we treat a multichannel biomedical time series as a document and extract local segments from the time series as words. We extend a topic model, i.e., the Hierarchical probabilistic Latent Semantic Analysis (H-pLSA), which was originally developed for visual motion analysis to cluster a set of unlabelled multichannel time series. The H-pLSA models each channel of the multichannel time series using a local pLSA in the first layer. The topics learned in the local pLSA are then fed to a global pLSA in the second layer to discover the categories of multichannel time series. Experiments on a dataset extracted from multichannel Electrocardiography (ECG) signals demonstrate that the proposed method performs better than previous state-of-the-art approaches and is relatively robust to the variations of parameters including length of local segments and dictionary size. Although the experimental evaluation used the multichannel ECG signals in a biometric scenario, the proposed algorithm is a universal framework for multichannel biomedical time series clustering according to their structural similarity, which has many applications in biomedical time series management.
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Affiliation(s)
- Jin Wang
- School of Computer Science & Software Engineering, The University of Western Australia, Australia; Center for Intelligent Systems Research, Deakin University, Australia.
| | - Xiangping Sun
- Center for Intelligent Systems Research, Deakin University, Australia; School of Engineering, Deakin University, Australia
| | - Saeid Nahavandi
- Center for Intelligent Systems Research, Deakin University, Australia
| | | | - Yuchuan Wu
- School of Mechanical Engineering and Automation, Wuhan Textile University, Wuhan, PR China
| | - Mary She
- Center for Intelligent Systems Research, Deakin University, Australia; School of Engineering, Deakin University, Australia
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26
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Abawajy J, Kelarev A, Chowdhury MU, Jelinek HF. Enhancing Predictive Accuracy of Cardiac Autonomic Neuropathy Using Blood Biochemistry Features and Iterative Multitier Ensembles. IEEE J Biomed Health Inform 2014; 20:408-15. [PMID: 25347890 DOI: 10.1109/jbhi.2014.2363177] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Blood biochemistry attributes form an important class of tests, routinely collected several times per year for many patients with diabetes. The objective of this study is to investigate the role of blood biochemistry for improving the predictive accuracy of the diagnosis of cardiac autonomic neuropathy (CAN) progression. Blood biochemistry contributes to CAN, and so it is a causative factor that can provide additional power for the diagnosis of CAN especially in the absence of a complete set of Ewing tests. We introduce automated iterative multitier ensembles (AIME) and investigate their performance in comparison to base classifiers and standard ensemble classifiers for blood biochemistry attributes. AIME incorporate diverse ensembles into several tiers simultaneously and combine them into one automatically generated integrated system so that one ensemble acts as an integral part of another ensemble. We carried out extensive experimental analysis using large datasets from the diabetes screening research initiative (DiScRi) project. The results of our experiments show that several blood biochemistry attributes can be used to supplement the Ewing battery for the detection of CAN in situations where one or more of the Ewing tests cannot be completed because of the individual difficulties faced by each patient in performing the tests. The results show that AIME provide higher accuracy as a multitier CAN classification paradigm. The best predictive accuracy of 99.57% has been obtained by the AIME combining decorate on top tier with bagging on middle tier based on random forest. Practitioners can use these findings to increase the accuracy of CAN diagnosis.
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27
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Lee SH, Lim JS, Kim JK, Yang J, Lee Y. Classification of normal and epileptic seizure EEG signals using wavelet transform, phase-space reconstruction, and Euclidean distance. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2014; 116:10-25. [PMID: 24837641 DOI: 10.1016/j.cmpb.2014.04.012] [Citation(s) in RCA: 67] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2012] [Revised: 04/18/2014] [Accepted: 04/21/2014] [Indexed: 06/03/2023]
Abstract
This paper proposes new combined methods to classify normal and epileptic seizure EEG signals using wavelet transform (WT), phase-space reconstruction (PSR), and Euclidean distance (ED) based on a neural network with weighted fuzzy membership functions (NEWFM). WT, PSR, ED, and statistical methods that include frequency distributions and variation, were implemented to extract 24 initial features to use as inputs. Of the 24 initial features, 4 minimum features with the highest accuracy were selected using a non-overlap area distribution measurement method supported by the NEWFM. These 4 minimum features were used as inputs for the NEWFM and this resulted in performance sensitivity, specificity, and accuracy of 96.33%, 100%, and 98.17%, respectively. In addition, the area under Receiver Operating Characteristic (ROC) curve was used to measure the performances of NEWFM both without and with feature selections.
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Affiliation(s)
- Sang-Hong Lee
- Department of Computer Science & Engineering, Anyang University, Anyang-si, Republic of Korea.
| | - Joon S Lim
- IT College, Gachon University, Seongnam-si, Republic of Korea.
| | - Jae-Kwon Kim
- Department of Computer Science & Engineering, Inha University, Inchon-si, Republic of Korea.
| | - Junggi Yang
- IT College, Gachon University, Seongnam-si, Republic of Korea.
| | - Youngho Lee
- IT College, Gachon University, Seongnam-si, Republic of Korea.
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28
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Chiang HS, Shih DH, Lin B, Shih MH. An APN model for Arrhythmic beat classification. Bioinformatics 2014; 30:1739-46. [PMID: 24535096 DOI: 10.1093/bioinformatics/btu101] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Changes in the normal rhythm of a human heart may result in different cardiac arrhythmias, which may be immediately fatal or cause irreparable damage to the heart sustained over long periods of time. Therefore, the ability to automatically identify arrhythmias from ECG recordings is important for clinical diagnosis and treatment. In this article, classification by using associative Petri net (APN) for personalized ECG-arrhythmia-pattern identification is proposed for the first time in literature. RESULTS A rule-based classification model and reasoning algorithm of APN are created for ECG arrhythmias classification. The performance evaluation using MIT-BIH arrhythmia database shows that our approach compares well with other reported studies.
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Affiliation(s)
- Hsiu-Sen Chiang
- Department of Information Management, National Taichung University of Science and Technology, 129, Section 3, Sanmin Road, Taichung City 404, Taiwan, Department of Information Management, National Yunlin University of Science and Technology, 123, Section 3, University Road, Douliu City, Yunlin County, Taiwan, College of Business Administration, BE321, Louisiana State University in Shreveport, Shreveport, LA 71115, USA and Department of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011, USA
| | - Dong-Her Shih
- Department of Information Management, National Taichung University of Science and Technology, 129, Section 3, Sanmin Road, Taichung City 404, Taiwan, Department of Information Management, National Yunlin University of Science and Technology, 123, Section 3, University Road, Douliu City, Yunlin County, Taiwan, College of Business Administration, BE321, Louisiana State University in Shreveport, Shreveport, LA 71115, USA and Department of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011, USA
| | - Binshan Lin
- Department of Information Management, National Taichung University of Science and Technology, 129, Section 3, Sanmin Road, Taichung City 404, Taiwan, Department of Information Management, National Yunlin University of Science and Technology, 123, Section 3, University Road, Douliu City, Yunlin County, Taiwan, College of Business Administration, BE321, Louisiana State University in Shreveport, Shreveport, LA 71115, USA and Department of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011, USA
| | - Ming-Hung Shih
- Department of Information Management, National Taichung University of Science and Technology, 129, Section 3, Sanmin Road, Taichung City 404, Taiwan, Department of Information Management, National Yunlin University of Science and Technology, 123, Section 3, University Road, Douliu City, Yunlin County, Taiwan, College of Business Administration, BE321, Louisiana State University in Shreveport, Shreveport, LA 71115, USA and Department of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011, USA
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29
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da Silva HP, Lourenço A, Fred A, Raposo N, Aires-de-Sousa M. Check your biosignals here: a new dataset for off-the-person ECG biometrics. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2014; 113:503-514. [PMID: 24377903 DOI: 10.1016/j.cmpb.2013.11.017] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2013] [Revised: 11/26/2013] [Accepted: 11/28/2013] [Indexed: 06/03/2023]
Abstract
The Check Your Biosignals Here initiative (CYBHi) was developed as a way of creating a dataset and consistently repeatable acquisition framework, to further extend research in electrocardiographic (ECG) biometrics. In particular, our work targets the novel trend towards off-the-person data acquisition, which opens a broad new set of challenges and opportunities both for research and industry. While datasets with ECG signals collected using medical grade equipment at the chest can be easily found, for off-the-person ECG data the solution is generally for each team to collect their own corpus at considerable expense of resources. In this paper we describe the context, experimental considerations, methods, and preliminary findings of two public datasets created by our team, one for short-term and another for long-term assessment, with ECG data collected at the hand palms and fingers.
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Affiliation(s)
- Hugo Plácido da Silva
- Instituto de Telecomunicações, Instituto Superior Técnico, 1049-001 Lisboa, Portugal.
| | - André Lourenço
- Instituto de Telecomunicações, Instituto Superior Técnico, 1049-001 Lisboa, Portugal; Instituto Superior de Engenharia de Lisboa, 1959-007 Lisboa, Portugal.
| | - Ana Fred
- Instituto de Telecomunicações, Instituto Superior Técnico, 1049-001 Lisboa, Portugal.
| | - Nuno Raposo
- Escola Superior de Saúde, Cruz Vermelha Portuguesa, 1300-125 Lisboa, Portugal.
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