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Li H, Han J, Zhang H, Zhang X, Si Y, Zhang Y, Liu Y, Yang H. Clinical knowledge-based ECG abnormalities detection using dual-view CNN-Transformer and external attention mechanism. Comput Biol Med 2024; 178:108751. [PMID: 38936078 DOI: 10.1016/j.compbiomed.2024.108751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Revised: 06/09/2024] [Accepted: 06/10/2024] [Indexed: 06/29/2024]
Abstract
BACKGROUND Automatic abnormalities detection based on Electrocardiogram (ECG) contributes greatly to early prevention, computer aided diagnosis, and dynamic analysis of cardiovascular diseases. In order to achieve cardiologist-level performance, deep neural networks have been widely utilized to extract abstract feature representations. However, the mechanical stacking of numerous computationally intensive operations makes traditional deep neural networks suffer from inadequate learning, poor interpretability, and high complexity. METHOD To address these limitations, a clinical knowledge-based ECG abnormalities detection model using dual-view CNN-Transformer and external attention mechanism is proposed by mimicking the diagnosis of the clinicians. Considering the clinical knowledge that both the detailed waveform changes within a single heartbeat and the global changes throughout the entire recording have complementary roles in abnormalities detection, we presented a dual-view CNN-Transformer to extract and fuse spatial-temporal features from different views. In addition, the locations of the ECG where abnormalities occur provide more information than other areas. Therefore, two external attention mechanisms are designed and added to the corresponding views to help the network learn efficiently. RESULTS Experiment results on the 9-class dataset show that the proposed model achieves an average F1-score of 0.854±0.01 with a higher interpretability and a lower complexity, outperforming the state-of-the-art model. CONCLUSIONS Combining all these excellent features, this study provides a credible solution for automatic ECG abnormalities detection.
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Affiliation(s)
- Hui Li
- School of Life Sciences, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, China; Engineering Research Center of Chinese Ministry of Education for Biological Diagnosis, Treatment and Protection Technology, Xi'an, Shaanxi 710072, China
| | - Jiyang Han
- School of Life Sciences, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, China; Engineering Research Center of Chinese Ministry of Education for Biological Diagnosis, Treatment and Protection Technology, Xi'an, Shaanxi 710072, China
| | - Honghao Zhang
- School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, China
| | - Xi Zhang
- School of Life Sciences, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, China; Engineering Research Center of Chinese Ministry of Education for Biological Diagnosis, Treatment and Protection Technology, Xi'an, Shaanxi 710072, China
| | - Yingjun Si
- School of Life Sciences, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, China; Engineering Research Center of Chinese Ministry of Education for Biological Diagnosis, Treatment and Protection Technology, Xi'an, Shaanxi 710072, China
| | - Yu Zhang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, China
| | - Yu Liu
- Department of Cardiology, Nanjing University Medical School Affiliated Nanjing Drum Tower Hospital, Nanjing 210008, China
| | - Hui Yang
- School of Life Sciences, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, China; Engineering Research Center of Chinese Ministry of Education for Biological Diagnosis, Treatment and Protection Technology, Xi'an, Shaanxi 710072, China.
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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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3
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Zhang A, Yang X, Li T, Dou M, Yang H. Classification Method of ECG Signals Based on RANet. Cardiovasc Eng Technol 2024:10.1007/s13239-024-00730-5. [PMID: 38653933 DOI: 10.1007/s13239-024-00730-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 04/05/2024] [Indexed: 04/25/2024]
Abstract
BACKGROUND Electrocardiograms (ECG) are an important source of information on human heart health and are widely used to detect different types of arrhythmias. OBJECTIVE With the advancement of deep learning, end-to-end ECG classification models based on neural networks have been developed. However, deeper network layers lead to gradient vanishing. Moreover, different channels and periods of an ECG signal hold varying significance for identifying different types of ECG abnormalities. METHODS To solve these two problems, an ECG classification method based on a residual attention neural network is proposed in this paper. The residual network (ResNet) is used to solve the gradient vanishing problem. Moreover, it has fewer model parameters, and its structure is simpler. An attention mechanism is added to focus on key information, integrate channel features, and improve voting methods to alleviate the problem of data imbalance. RESULTS Experiments and verifications are conducted using the PhysioNet/CinC Challenge 2017 dataset. The average F1 value is 0.817, which is 0.064 higher than that for the ResNet model. Compared with the mainstream methods, the performance is excellent.
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Affiliation(s)
- Aoxiang Zhang
- Faculty of Information, Beijing University of Technology, Beijing, China
| | - Xinwu Yang
- Faculty of Information, Beijing University of Technology, Beijing, China.
| | - Tong Li
- Faculty of Information, Beijing University of Technology, Beijing, China
| | - Mengfei Dou
- Faculty of Information, Beijing University of Technology, Beijing, China
| | - Hongxiao Yang
- Faculty of Information, Beijing University of Technology, Beijing, China
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4
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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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5
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Ansari Y, Mourad O, Qaraqe K, Serpedin E. Deep learning for ECG Arrhythmia detection and classification: an overview of progress for period 2017-2023. Front Physiol 2023; 14:1246746. [PMID: 37791347 PMCID: PMC10542398 DOI: 10.3389/fphys.2023.1246746] [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: 06/26/2023] [Accepted: 08/28/2023] [Indexed: 10/05/2023] Open
Abstract
Cardiovascular diseases are a leading cause of mortality globally. Electrocardiography (ECG) still represents the benchmark approach for identifying cardiac irregularities. Automatic detection of abnormalities from the ECG can aid in the early detection, diagnosis, and prevention of cardiovascular diseases. Deep Learning (DL) architectures have been successfully employed for arrhythmia detection and classification and offered superior performance to traditional shallow Machine Learning (ML) approaches. This survey categorizes and compares the DL architectures used in ECG arrhythmia detection from 2017-2023 that have exhibited superior performance. Different DL models such as Convolutional Neural Networks (CNNs), Multilayer Perceptrons (MLPs), Transformers, and Recurrent Neural Networks (RNNs) are reviewed, and a summary of their effectiveness is provided. This survey provides a comprehensive roadmap to expedite the acclimation process for emerging researchers willing to develop efficient algorithms for detecting ECG anomalies using DL models. Our tailored guidelines bridge the knowledge gap allowing newcomers to align smoothly with the prevailing research trends in ECG arrhythmia detection. We shed light on potential areas for future research and refinement in model development and optimization, intending to stimulate advancement in ECG arrhythmia detection and classification.
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Affiliation(s)
- Yaqoob Ansari
- ECEN Program, Texas A&M University at Qatar, Doha, Qatar
| | | | - Khalid Qaraqe
- ECEN Program, Texas A&M University at Qatar, Doha, Qatar
| | - Erchin Serpedin
- ECEN Department, Texas A&M University, College Station, TX, United States
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6
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Celard P, Iglesias EL, Sorribes-Fdez JM, Romero R, Vieira AS, Borrajo L. A survey on deep learning applied to medical images: from simple artificial neural networks to generative models. Neural Comput Appl 2022; 35:2291-2323. [PMID: 36373133 PMCID: PMC9638354 DOI: 10.1007/s00521-022-07953-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 10/12/2022] [Indexed: 11/06/2022]
Abstract
Deep learning techniques, in particular generative models, have taken on great importance in medical image analysis. This paper surveys fundamental deep learning concepts related to medical image generation. It provides concise overviews of studies which use some of the latest state-of-the-art models from last years applied to medical images of different injured body areas or organs that have a disease associated with (e.g., brain tumor and COVID-19 lungs pneumonia). The motivation for this study is to offer a comprehensive overview of artificial neural networks (NNs) and deep generative models in medical imaging, so more groups and authors that are not familiar with deep learning take into consideration its use in medicine works. We review the use of generative models, such as generative adversarial networks and variational autoencoders, as techniques to achieve semantic segmentation, data augmentation, and better classification algorithms, among other purposes. In addition, a collection of widely used public medical datasets containing magnetic resonance (MR) images, computed tomography (CT) scans, and common pictures is presented. Finally, we feature a summary of the current state of generative models in medical image including key features, current challenges, and future research paths.
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Affiliation(s)
- P. Celard
- Computer Science Department, Universidade de Vigo, Escuela Superior de Ingeniería Informática, Campus Universitario As Lagoas, 32004 Ourense, Spain
- CINBIO - Biomedical Research Centre, Universidade de Vigo, Campus Universitario Lagoas-Marcosende, 36310 Vigo, Spain
- SING Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
| | - E. L. Iglesias
- Computer Science Department, Universidade de Vigo, Escuela Superior de Ingeniería Informática, Campus Universitario As Lagoas, 32004 Ourense, Spain
- CINBIO - Biomedical Research Centre, Universidade de Vigo, Campus Universitario Lagoas-Marcosende, 36310 Vigo, Spain
- SING Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
| | - J. M. Sorribes-Fdez
- Computer Science Department, Universidade de Vigo, Escuela Superior de Ingeniería Informática, Campus Universitario As Lagoas, 32004 Ourense, Spain
- CINBIO - Biomedical Research Centre, Universidade de Vigo, Campus Universitario Lagoas-Marcosende, 36310 Vigo, Spain
- SING Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
| | - R. Romero
- Computer Science Department, Universidade de Vigo, Escuela Superior de Ingeniería Informática, Campus Universitario As Lagoas, 32004 Ourense, Spain
- CINBIO - Biomedical Research Centre, Universidade de Vigo, Campus Universitario Lagoas-Marcosende, 36310 Vigo, Spain
- SING Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
| | - A. Seara Vieira
- Computer Science Department, Universidade de Vigo, Escuela Superior de Ingeniería Informática, Campus Universitario As Lagoas, 32004 Ourense, Spain
- CINBIO - Biomedical Research Centre, Universidade de Vigo, Campus Universitario Lagoas-Marcosende, 36310 Vigo, Spain
- SING Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
| | - L. Borrajo
- Computer Science Department, Universidade de Vigo, Escuela Superior de Ingeniería Informática, Campus Universitario As Lagoas, 32004 Ourense, Spain
- CINBIO - Biomedical Research Centre, Universidade de Vigo, Campus Universitario Lagoas-Marcosende, 36310 Vigo, Spain
- SING Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
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Tao Y, Li Z, Gu C, Jiang B, Zhang Y. ECG-based expert-knowledge attention network to tachyarrhythmia recognition. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103649] [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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8
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Arrhythmia detection and classification using ECG and PPG techniques: a review. Phys Eng Sci Med 2021; 44:1027-1048. [PMID: 34727361 DOI: 10.1007/s13246-021-01072-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 10/25/2021] [Indexed: 12/26/2022]
Abstract
Electrocardiogram (ECG) and photoplethysmograph (PPG) are non-invasive techniques that provide electrical and hemodynamic information of the heart, respectively. This information is advantageous in the diagnosis of various cardiac abnormalities. Arrhythmia is the most common cardiovascular disease, manifested as single or multiple irregular heartbeats. However, due to the continuous manual observation, it becomes troublesome for experts sometimes to identify the paroxysmal nature of arrhythmia correctly. Moreover, due to advancements in technology, there is an inclination towards wearable sensors which monitor such patients continuously. Thus, there is a need for automatic detection techniques for the identification of arrhythmia. In the presented work, ECG and PPG-based state-of-the-art methods have been described, including preprocessing, feature extraction, and classification techniques for the detection of various arrhythmias. Additionally, this review exhibits various wearable sensors used in the literature and public databases available for the evaluation of results. The study also highlights the limitations of the current techniques and pragmatic solutions to improvise the ongoing effort.
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Abstract
The ambulatory ECG (AECG) is an important diagnostic tool for many heart electrophysiology-related cases. AECG covers a wide spectrum of devices and applications. At the core of these devices and applications are the algorithms responsible for signal conditioning, ECG beat detection and classification, and event detections. Over the years, there has been huge progress for algorithm development and implementation thanks to great efforts by researchers, engineers, and physicians, alongside the rapid development of electronics and signal processing, especially machine learning (ML). The current efforts and progress in machine learning fields are unprecedented, and many of these ML algorithms have also been successfully applied to AECG applications. This review covers some key AECG applications of ML algorithms. However, instead of doing a general review of ML algorithms, we are focusing on the central tasks of AECG and discussing what ML can bring to solve the key challenges AECG is facing. The center tasks of AECG signal processing listed in the review include signal preprocessing, beat detection and classification, event detection, and event prediction. Each AECG device/system might have different portions and forms of those signal components depending on its application and the target, but these are the topics most relevant and of greatest concern to the people working in this area.
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Zhu H, Zhao Y, Pan Y, Xie H, Wu F, Huan R. Robust Heartbeat Classification for Wearable Single-Lead ECG via Extreme Gradient Boosting. SENSORS 2021; 21:s21165290. [PMID: 34450733 PMCID: PMC8398252 DOI: 10.3390/s21165290] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 07/28/2021] [Accepted: 08/02/2021] [Indexed: 11/16/2022]
Abstract
Wearable electrocardiogram (ECG) monitoring devices have enabled everyday ECG collection in our daily lives. However, the condition of ECG signal acquisition using wearable devices varies and wearable ECG signals could be interfered with by severe noises, resulting in great challenges of computer-aided automated ECG analysis, especially for single-lead ECG signals without spare channels as references. There remains room for improvement of the beat-level single-lead ECG diagnosis regarding accuracy and efficiency. In this paper, we propose new morphological features of heartbeats for an extreme gradient boosting-based beat-level ECG analysis method to carry out the five-class heartbeat classification according to the Association for the Advancement of Medical Instrumentation standard. The MIT-BIH Arrhythmia Database (MITDB) and a self-collected wearable single-lead ECG dataset are used for performance evaluation in the static and wearable ECG monitoring conditions, respectively. The results show that our method outperforms other state-of-the-art models with an accuracy of 99.14% on the MITDB and maintains robustness with an accuracy of 98.68% in the wearable single-lead ECG analysis.
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Affiliation(s)
- Huaiyu Zhu
- College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China; (H.Z.); (Y.Z.)
| | - Yisheng Zhao
- College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China; (H.Z.); (Y.Z.)
| | - Yun Pan
- College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China; (H.Z.); (Y.Z.)
- Correspondence:
| | - Hanshuang Xie
- R&D Department, Hangzhou Proton Technology Co., Ltd., Hangzhou 310012, China; (H.X.); (F.W.)
| | - Fan Wu
- R&D Department, Hangzhou Proton Technology Co., Ltd., Hangzhou 310012, China; (H.X.); (F.W.)
| | - Ruohong Huan
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China;
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Li Z, Derksen H, Gryak J, Jiang C, Gao Z, Zhang W, Ghanbari H, Gunaratne P, Najarian K. Prediction of cardiac arrhythmia using deterministic probabilistic finite-state automata. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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12
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Soft Computing in Bioinformatics. Adv Bioinformatics 2021. [DOI: 10.1007/978-981-33-6191-1_23] [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] Open
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13
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de Campos Souza PV. Fuzzy neural networks and neuro-fuzzy networks: A review the main techniques and applications used in the literature. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2020.106275] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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14
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Pruning Fuzzy Neural Network Applied to the Construction of Expert Systems to Aid in the Diagnosis of the Treatment of Cryotherapy and Immunotherapy. BIG DATA AND COGNITIVE COMPUTING 2019. [DOI: 10.3390/bdcc3020022] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Human papillomavirus (HPV) infection is related to frequent cases of cervical cancer and genital condyloma in humans. Up to now, numerous methods have come into existence for the prevention and treatment of this disease. In this context, this paper aims to help predict the susceptibility of the patient to forms treatment using both cryotherapy and immunotherapy. These studies facilitate the choice of medications, which can be painful and embarrassing for patients who have warts on intimate parts. However, the use of intelligent models generates efficient results but does not allow a better interpretation of the results. To solve the problem, we present the method of a fuzzy neural network (FNN). A hybrid model capable of solving complex problems and extracting knowledge from the database will pruned through F-score techniques to perform pattern classification in the treatment of warts, and to produce a specialist system based on if/then rules, according to the experience obtained from the database collected through medical research. Finally, binary pattern-classification tests realized in the FNN and compared with other models commonly used for classification tasks capture results of greater accuracy than the current state of the art for this type of problem (84.32% for immunotherapy, and 88.64% for cryotherapy), and extract fuzzy rules from the problem database. It was found that the hybrid approach based on neural networks and fuzzy systems can be an excellent tool to aid the prediction of cryotherapy and immunotherapy treatments.
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Yang T, Yu L, Jin Q, Wu L, He B. Localization of Origins of Premature Ventricular Contraction by Means of Convolutional Neural Network From 12-Lead ECG. IEEE Trans Biomed Eng 2018; 65:1662-1671. [PMID: 28952932 PMCID: PMC6089373 DOI: 10.1109/tbme.2017.2756869] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
OBJECTIVE This paper proposes a novel method to localize origins of premature ventricular contractions (PVCs) from 12-lead electrocardiography (ECG) using convolutional neural network (CNN) and a realistic computer heart model. METHODS The proposed method consists of two CNNs (Segment CNN and Epi-Endo CNN) to classify among ventricular sources from 25 segments and from epicardium (Epi) or endocardium (Endo). The inputs are the full time courses and the first half of QRS complexes of 12-lead ECG, respectively. After registering the ventricle computer model with an individual patient's heart, the training datasets were generated by multiplying ventricular current dipoles derived from single pacing at various locations with patient-specific lead field. The origins of PVC are localized by calculating the weighted center of gravity of classification returned by the CNNs. A number of computer simulations were conducted to evaluate the proposed method under a variety of noise levels and heart registration errors. Furthermore, the proposed method was evaluated on 90 PVC beats from nine human patients with PVCs and compared against ablation outcome in the same patients. RESULTS The computer simulation evaluation returned relatively high accuracies for Segment CNN (∼78%) and Epi-Endo CNN (∼90%). Clinical testing in nine PVC patients resulted an averaged localization error of 11 mm. CONCLUSION Our simulation and clinical evaluation results demonstrate the capability and merits of the proposed CNN-based method for localization of PVC. SIGNIFICANCE This paper suggests a new approach for cardiac source localization of origin of arrhythmias using only the 12-lead ECG by means of CNN, and may have important applications for future real-time monitoring and localizing origins of cardiac arrhythmias guiding ablation treatment.
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Mastoi QUA, Wah TY, Gopal Raj R, Iqbal U. Automated Diagnosis of Coronary Artery Disease: A Review and Workflow. Cardiol Res Pract 2018; 2018:2016282. [PMID: 29507812 PMCID: PMC5817359 DOI: 10.1155/2018/2016282] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/27/2017] [Accepted: 12/19/2017] [Indexed: 11/21/2022] Open
Abstract
Coronary artery disease (CAD) is the most dangerous heart disease which may lead to sudden cardiac death. However, CAD diagnoses are quite expensive and time-consuming procedures which a patient need to go through. The aim of our paper is to present a unique review of state-of-the-art methods up to 2017 for automatic CAD classification. The protocol of review methods is identifying best methods and classifier for CAD identification. The study proposes two workflows based on two parameter sets for instances A and B. It is necessary to follow the proper procedure, for future evaluation process of automatic diagnosis of CAD. The initial two stages of the parameter set A workflow are preprocessing and feature extraction. Subsequently, stages (feature selection and classification) are same for both workflows. In literature, the SVM classifier represents a promising approach for CAD classification. Moreover, the limitation leads to extract proper features from noninvasive signals.
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Affiliation(s)
- Qurat-ul-ain Mastoi
- Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia
| | - Teh Ying Wah
- Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia
| | - Ram Gopal Raj
- Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia
| | - Uzair Iqbal
- Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia
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17
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Lyon A, Mincholé A, Martínez JP, Laguna P, Rodriguez B. Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances. J R Soc Interface 2018; 15:20170821. [PMID: 29321268 PMCID: PMC5805987 DOI: 10.1098/rsif.2017.0821] [Citation(s) in RCA: 74] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Accepted: 12/08/2017] [Indexed: 01/09/2023] Open
Abstract
Widely developed for clinical screening, electrocardiogram (ECG) recordings capture the cardiac electrical activity from the body surface. ECG analysis can therefore be a crucial first step to help diagnose, understand and predict cardiovascular disorders responsible for 30% of deaths worldwide. Computational techniques, and more specifically machine learning techniques and computational modelling are powerful tools for classification, clustering and simulation, and they have recently been applied to address the analysis of medical data, especially ECG data. This review describes the computational methods in use for ECG analysis, with a focus on machine learning and 3D computer simulations, as well as their accuracy, clinical implications and contributions to medical advances. The first section focuses on heartbeat classification and the techniques developed to extract and classify abnormal from regular beats. The second section focuses on patient diagnosis from whole recordings, applied to different diseases. The third section presents real-time diagnosis and applications to wearable devices. The fourth section highlights the recent field of personalized ECG computer simulations and their interpretation. Finally, the discussion section outlines the challenges of ECG analysis and provides a critical assessment of the methods presented. The computational methods reported in this review are a strong asset for medical discoveries and their translation to the clinical world may lead to promising advances.
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Affiliation(s)
- Aurore Lyon
- Department of Computer Science, British Heart Foundation, Oxford, UK
| | - Ana Mincholé
- Department of Computer Science, British Heart Foundation, Oxford, UK
| | - Juan Pablo Martínez
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, University of Zaragoza, CIBER-BBN, Zaragoza, Spain
| | - Pablo Laguna
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, University of Zaragoza, CIBER-BBN, Zaragoza, Spain
| | - Blanca Rodriguez
- Department of Computer Science, British Heart Foundation, Oxford, UK
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Documenting and predicting topic changes in Computers in Biology and Medicine: A bibliometric keyword analysis from 1990 to 2017. INFORMATICS IN MEDICINE UNLOCKED 2018. [DOI: 10.1016/j.imu.2018.03.002] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
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Sadrawi M, Lin CH, Lin YT, Hsieh Y, Kuo CC, Chien JC, Haraikawa K, Abbod MF, Shieh JS. Arrhythmia Evaluation in Wearable ECG Devices. SENSORS 2017; 17:s17112445. [PMID: 29068369 PMCID: PMC5712868 DOI: 10.3390/s17112445] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/19/2017] [Revised: 10/20/2017] [Accepted: 10/21/2017] [Indexed: 11/16/2022]
Abstract
This study evaluates four databases from PhysioNet: The American Heart Association database (AHADB), Creighton University Ventricular Tachyarrhythmia database (CUDB), MIT-BIH Arrhythmia database (MITDB), and MIT-BIH Noise Stress Test database (NSTDB). The ANSI/AAMI EC57:2012 is used for the evaluation of the algorithms for the supraventricular ectopic beat (SVEB), ventricular ectopic beat (VEB), atrial fibrillation (AF), and ventricular fibrillation (VF) via the evaluation of the sensitivity, positive predictivity and false positive rate. Sample entropy, fast Fourier transform (FFT), and multilayer perceptron neural network with backpropagation training algorithm are selected for the integrated detection algorithms. For this study, the result for SVEB has some improvements compared to a previous study that also utilized ANSI/AAMI EC57. In further, VEB sensitivity and positive predictivity gross evaluations have greater than 80%, except for the positive predictivity of the NSTDB database. For AF gross evaluation of MITDB database, the results show very good classification, excluding the episode sensitivity. In advanced, for VF gross evaluation, the episode sensitivity and positive predictivity for the AHADB, MITDB, and CUDB, have greater than 80%, except for MITDB episode positive predictivity, which is 75%. The achieved results show that the proposed integrated SVEB, VEB, AF, and VF detection algorithm has an accurate classification according to ANSI/AAMI EC57:2012. In conclusion, the proposed integrated detection algorithm can achieve good accuracy in comparison with other previous studies. Furthermore, more advanced algorithms and hardware devices should be performed in future for arrhythmia detection and evaluation.
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Affiliation(s)
- Muammar Sadrawi
- Department of Mechanical Engineering and Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Taoyuan, Chung-Li 32003, Taiwan.
| | - Chien-Hung Lin
- Healthcare and Beauty RD Center, Kinpo Electronics, Inc., New Taipei City 222, Taiwan.
| | - Yin-Tsong Lin
- Healthcare and Beauty RD Center, Kinpo Electronics, Inc., New Taipei City 222, Taiwan.
| | - Yita Hsieh
- Healthcare and Beauty RD Center, Kinpo Electronics, Inc., New Taipei City 222, Taiwan.
| | - Chia-Chun Kuo
- Healthcare and Beauty RD Center, Kinpo Electronics, Inc., New Taipei City 222, Taiwan.
| | - Jen Chien Chien
- Healthcare and Beauty RD Center, Kinpo Electronics, Inc., New Taipei City 222, Taiwan.
| | - Koichi Haraikawa
- Healthcare and Beauty RD Center, Kinpo Electronics, Inc., New Taipei City 222, Taiwan.
| | - Maysam F Abbod
- Department of Electronic and Computer Engineering, Brunel University London, Uxbridge UB8 3PH, UK.
| | - Jiann-Shing Shieh
- Department of Mechanical Engineering and Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Taoyuan, Chung-Li 32003, Taiwan.
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Inter-Patient ECG Heartbeat Classification with Temporal VCG Optimized by PSO. Sci Rep 2017; 7:10543. [PMID: 28874683 PMCID: PMC5585360 DOI: 10.1038/s41598-017-09837-3] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2017] [Accepted: 07/31/2017] [Indexed: 11/12/2022] Open
Abstract
Classifying arrhythmias can be a tough task for a human being and automating this task is highly desirable. Nevertheless fully automatic arrhythmia classification through Electrocardiogram (ECG) signals is a challenging task when the inter-patient paradigm is considered. For the inter-patient paradigm, classifiers are evaluated on signals of unknown subjects, resembling the real world scenario. In this work, we explore a novel ECG representation based on vectorcardiogram (VCG), called temporal vectorcardiogram (TVCG), along with a complex network for feature extraction. We also fine-tune the SVM classifier and perform feature selection with a particle swarm optimization (PSO) algorithm. Results for the inter-patient paradigm show that the proposed method achieves the results comparable to state-of-the-art in MIT-BIH database (53% of Positive predictive (+P) for the Supraventricular ectopic beat (S) class and 87.3% of Sensitivity (Se) for the Ventricular ectopic beat (V) class) that TVCG is a richer representation of the heartbeat and that it could be useful for problems involving the cardiac signal and pattern recognition.
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Yaghoobi Karimui R, Azadi S. Cardiac arrhythmia classification using the phase space sorted by Poincare sections. Biocybern Biomed Eng 2017. [DOI: 10.1016/j.bbe.2017.08.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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24
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Haldar NAH, Khan FA, Ali A, Abbas H. Arrhythmia classification using Mahalanobis distance based improved Fuzzy C-Means clustering for mobile health monitoring systems. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2016.08.042] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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25
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Luz EJDS, Schwartz WR, Cámara-Chávez G, Menotti D. ECG-based heartbeat classification for arrhythmia detection: A survey. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016; 127:144-64. [PMID: 26775139 DOI: 10.1016/j.cmpb.2015.12.008] [Citation(s) in RCA: 256] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2015] [Revised: 11/08/2015] [Accepted: 12/17/2015] [Indexed: 05/20/2023]
Abstract
An electrocardiogram (ECG) measures the electric activity of the heart and has been widely used for detecting heart diseases due to its simplicity and non-invasive nature. By analyzing the electrical signal of each heartbeat, i.e., the combination of action impulse waveforms produced by different specialized cardiac tissues found in the heart, it is possible to detect some of its abnormalities. In the last decades, several works were developed to produce automatic ECG-based heartbeat classification methods. In this work, we survey the current state-of-the-art methods of ECG-based automated abnormalities heartbeat classification by presenting the ECG signal preprocessing, the heartbeat segmentation techniques, the feature description methods and the learning algorithms used. In addition, we describe some of the databases used for evaluation of methods indicated by a well-known standard developed by the Association for the Advancement of Medical Instrumentation (AAMI) and described in ANSI/AAMI EC57:1998/(R)2008 (ANSI/AAMI, 2008). Finally, we discuss limitations and drawbacks of the methods in the literature presenting concluding remarks and future challenges, and also we propose an evaluation process workflow to guide authors in future works.
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Affiliation(s)
| | - William Robson Schwartz
- Universidade Federal de Minas Gerais, Computer Science Department, Belo Horizonte, MG, Brazil.
| | | | - David Menotti
- Universidade Federal de Ouro Preto, Computing Department, Ouro Preto, MG, Brazil; Universidade Federal do Paraná, Department of Informatics, Curitiba, PR, Brazil.
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DESAI USHA, MARTIS ROSHANJOY, GURUDAS NAYAK C, SESHIKALA G, SARIKA K, SHETTY K. RANJAN. DECISION SUPPORT SYSTEM FOR ARRHYTHMIA BEATS USING ECG SIGNALS WITH DCT, DWT AND EMD METHODS: A COMPARATIVE STUDY. J MECH MED BIOL 2016. [DOI: 10.1142/s0219519416400121] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Electrocardiogram (ECG) signal is a non-invasive method, used to diagnose the patients with cardiac abnormalities. The subjective evaluation of interval and amplitude of ECG by physician can be tedious, time consuming, and susceptible to observer bias. ECG signals are generated due to the excitation of many cardiac myocytes and hence resultant signals are non-linear in nature. These subtle changes can be well represented and discriminated in transform and non-linear domains. In this paper, performance of Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT) and Empirical Mode Decomposition (EMD) methods are compared for automated diagnosis of five classes namely Non-ectopic (N), Supraventricular ectopic (S), Ventricular ectopic (V), Fusion (F) and Unknown (U) beats. Six different approaches: (i) Principal Components (PCs) on DCT, (ii) Independent Components (ICs) on DCT, (iii) PCs on DWT, (iv) ICs on DWT, (v) PCs on EMD and (vi) ICs on EMD are employed in this work. Clinically significant features are selected using ANOVA test ([Formula: see text]) and fed to k-Nearest Neighbor (k-NN) classifier. We have obtained a classification accuracy of 99.77% using ICs on DWT method. Consistency of performance is evaluated using Cohen’s kappa statistic. Developed approach is robust, accurate and can be employed for mass diagnosis of cardiac healthcare.
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Affiliation(s)
- USHA DESAI
- Department of Electronics and Communication Engineering, NMAM Institute of Technology, Nitte, Udupi, Karnataka 574110, India
- School of Electronics and Communication Engineering, REVA University, Bengaluru 560064, India
| | - ROSHAN JOY MARTIS
- Department of Electronics and Communication Engineering, St. Joseph Engineering College, Mangaloru 575028, India
| | - C. GURUDAS NAYAK
- Department of Instrumentation and Control Engineering, MIT, Manipal University, Manipal 576104, India
| | - G. SESHIKALA
- School of Electronics and Communication Engineering, REVA University, Bengaluru 560064, India
| | - K. SARIKA
- Department of Electronics and Communication Engineering, NMAM Institute of Technology, Nitte, Udupi, Karnataka 574110, India
| | - RANJAN SHETTY K.
- Department of Cardiology, Kasturba Medical College, Manipal University, Manipal 576104, India
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27
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Wavelet Based Method for Congestive Heart Failure Recognition by Three Confirmation Functions. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2016; 2016:7359516. [PMID: 26949412 PMCID: PMC4754477 DOI: 10.1155/2016/7359516] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/30/2015] [Revised: 09/23/2015] [Accepted: 10/15/2015] [Indexed: 11/17/2022]
Abstract
An investigation of the electrocardiogram (ECG) signals and arrhythmia characterization by wavelet energy is proposed. This study employs a wavelet based feature extraction method for congestive heart failure (CHF) obtained from the percentage energy (PE) of terminal wavelet packet transform (WPT) subsignals. In addition, the average framing percentage energy (AFE) technique is proposed, termed WAFE. A new classification method is introduced by three confirmation functions. The confirmation methods are based on three concepts: percentage root mean square difference error (PRD), logarithmic difference signal ratio (LDSR), and correlation coefficient (CC). The proposed method showed to be a potential effective discriminator in recognizing such clinical syndrome. ECG signals taken from MIT-BIH arrhythmia dataset and other databases are utilized to analyze different arrhythmias and normal ECGs. Several known methods were studied for comparison. The best recognition rate selection obtained was for WAFE. The recognition performance was accomplished as 92.60% accurate. The Receiver Operating Characteristic curve as a common tool for evaluating the diagnostic accuracy was illustrated, which indicated that the tests are reliable. The performance of the presented system was investigated in additive white Gaussian noise (AWGN) environment, where the recognition rate was 81.48% for 5 dB.
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28
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Alshraideh H, Otoom M, Al-Araida A, Bawaneh H, Bravo J. A Web Based Cardiovascular Disease Detection System. J Med Syst 2015; 39:122. [PMID: 26293754 DOI: 10.1007/s10916-015-0290-7] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2015] [Accepted: 07/20/2015] [Indexed: 11/26/2022]
Abstract
Cardiovascular Disease (CVD) is one of the most catastrophic and life threatening health issue nowadays. Early detection of CVD is an important solution to reduce its devastating effects on health. In this paper, an efficient CVD detection algorithm is identified. The algorithm uses patient demographic data as inputs, along with several ECG signal features extracted automatically through signal processing techniques. Cross-validation results show a 98.29 % accuracy for the decision tree classification algorithm. The algorithm has been integrated into a web based system that can be used at anytime by patients to check their heart health status. At one end of the system is the ECG sensor attached to the patient's body, while at the other end is the detection algorithm. Communication between the two ends is done through an Android application.
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Affiliation(s)
- Hussam Alshraideh
- Department of Industrial Engineering, Jordan University of Science and Technology, Irbid, Jordan,
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29
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Lam H, Ekong U, Xiao B, Ouyang G, Liu H, Chan K, Ho Ling S. Variable weight neural networks and their applications on material surface and epilepsy seizure phase classifications. Neurocomputing 2015. [DOI: 10.1016/j.neucom.2014.09.011] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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30
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Nayak J, Naik B, Behera HS. Fuzzy C-Means (FCM) Clustering Algorithm: A Decade Review from 2000 to 2014. COMPUTATIONAL INTELLIGENCE IN DATA MINING - VOLUME 2 2015. [DOI: 10.1007/978-81-322-2208-8_14] [Citation(s) in RCA: 75] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
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31
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Martis RJ, Acharya UR, Adeli H. Current methods in electrocardiogram characterization. Comput Biol Med 2014; 48:133-49. [DOI: 10.1016/j.compbiomed.2014.02.012] [Citation(s) in RCA: 100] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2013] [Revised: 02/15/2014] [Accepted: 02/17/2014] [Indexed: 10/25/2022]
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32
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Daqrouq K, Alkhateeb A, Ajour MN, Morfeq A. Neural network and wavelet average framing percentage energy for atrial fibrillation classification. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2014; 113:919-926. [PMID: 24503178 DOI: 10.1016/j.cmpb.2013.12.002] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2012] [Revised: 12/07/2013] [Accepted: 12/09/2013] [Indexed: 06/03/2023]
Abstract
ECG signals are an important source of information in the diagnosis of atrial conduction pathology. Nevertheless, diagnosis by visual inspection is a difficult task. This work introduces a novel wavelet feature extraction method for atrial fibrillation derived from the average framing percentage energy (AFE) of terminal wavelet packet transform (WPT) sub signals. Probabilistic neural network (PNN) is used for classification. The presented method is shown to be a potentially effective discriminator in an automated diagnostic process. The ECG signals taken from the MIT-BIH database are used to classify different arrhythmias together with normal ECG. Several published methods were investigated for comparison. The best recognition rate selection was obtained for AFE. The classification performance achieved accuracy 97.92%. It was also suggested to analyze the presented system in an additive white Gaussian noise (AWGN) environment; 55.14% for 0dB and 92.53% for 5dB. It was concluded that the proposed approach of automating classification is worth pursuing with larger samples to validate and extend the present study.
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Affiliation(s)
- K Daqrouq
- Electrical and Computer Engineering Department, King Abdulaziz University, Saudi Arabia.
| | - A Alkhateeb
- Electrical and Computer Engineering Department, King Abdulaziz University, Saudi Arabia.
| | - M N Ajour
- Electrical and Computer Engineering Department, King Abdulaziz University, Saudi Arabia.
| | - A Morfeq
- Electrical and Computer Engineering Department, King Abdulaziz University, Saudi Arabia.
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33
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Sansone M, Fusco R, Pepino A, Sansone C. Electrocardiogram pattern recognition and analysis based on artificial neural networks and support vector machines: a review. JOURNAL OF HEALTHCARE ENGINEERING 2014; 4:465-504. [PMID: 24287428 DOI: 10.1260/2040-2295.4.4.465] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
Computer systems for Electrocardiogram (ECG) analysis support the clinician in tedious tasks (e.g., Holter ECG monitored in Intensive Care Units) or in prompt detection of dangerous events (e.g., ventricular fibrillation). Together with clinical applications (arrhythmia detection and heart rate variability analysis), ECG is currently being investigated in biometrics (human identification), an emerging area receiving increasing attention. Methodologies for clinical applications can have both differences and similarities with respect to biometrics. This paper reviews methods of ECG processing from a pattern recognition perspective. In particular, we focus on features commonly used for heartbeat classification. Considering the vast literature in the field and the limited space of this review, we dedicated a detailed discussion only to a few classifiers (Artificial Neural Networks and Support Vector Machines) because of their popularity; however, other techniques such as Hidden Markov Models and Kalman Filtering will be also mentioned.
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Affiliation(s)
- Mario Sansone
- Department of Electrical Engineering and Information Technologies, University "Federico II" of Naples, Italy
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34
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Hoseini Sabzevari SA, Moavenian M. QRS complex detection based on simple robust 2-D pictorial-geometrical feature. J Med Eng Technol 2014; 38:16-22. [DOI: 10.3109/03091902.2013.845699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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35
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Javadi M. Combining neural networks and ANFIS classifiers for supervised examining of electrocardiogram beats. J Med Eng Technol 2013; 37:484-97. [DOI: 10.3109/03091902.2013.831493] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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A novel automatic detection system for ECG arrhythmias using maximum margin clustering with immune evolutionary algorithm. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2013; 2013:453402. [PMID: 23690875 PMCID: PMC3652208 DOI: 10.1155/2013/453402] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2013] [Revised: 04/01/2013] [Accepted: 04/02/2013] [Indexed: 11/17/2022]
Abstract
This paper presents a novel maximum margin clustering method with immune evolution (IEMMC) for automatic diagnosis of electrocardiogram (ECG) arrhythmias. This diagnostic system consists of signal processing, feature extraction, and the IEMMC algorithm for clustering of ECG arrhythmias. First, raw ECG signal is processed by an adaptive ECG filter based on wavelet transforms, and waveform of the ECG signal is detected; then, features are extracted from ECG signal to cluster different types of arrhythmias by the IEMMC algorithm. Three types of performance evaluation indicators are used to assess the effect of the IEMMC method for ECG arrhythmias, such as sensitivity, specificity, and accuracy. Compared with K-means and iterSVR algorithms, the IEMMC algorithm reflects better performance not only in clustering result but also in terms of global search ability and convergence ability, which proves its effectiveness for the detection of ECG arrhythmias.
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38
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Classification of ECG arrhythmia by a modular neural network based on Mixture of Experts and Negatively Correlated Learning. Biomed Signal Process Control 2013. [DOI: 10.1016/j.bspc.2012.10.005] [Citation(s) in RCA: 83] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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39
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Kaur M, Arora AS. Classification of ECG signals using LDA with factor analysis method as feature reduction technique. J Med Eng Technol 2013; 36:411-20. [PMID: 23075309 DOI: 10.3109/03091902.2012.702851] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
The analysis of ECG signal, especially the QRS complex as the most characteristic wave in ECG, is a widely accepted approach to study and to classify cardiac dysfunctions. In this paper, first wavelet coefficients calculated for QRS complex are taken as features. Next, factor analysis procedures without rotation and with orthogonal rotation (varimax, equimax and quartimax) are used for feature reduction. The procedure uses the 'Principal Component Method' to estimate component loadings. Further, classification has been done with a LDA classifier. The MIT-BIH arrhythmia database is used and five types of beats (normal, PVC, paced, LBBB and RBBB) are considered for analysis. Accuracy, sensitivity and positive predictivity are performance parameters used for comparing performance of feature reduction techniques. Results demonstrate that the equimax rotation method yields maximum average accuracy of 99.056% for unknown data sets among other used methods.
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40
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YANG GUANGYING. ELECTROCARDIOGRAM ARRHYTHMIA PATTERN RECOGNITION BASED ON AN IMPROVED WAVELET NEURAL NETWORK. J MECH MED BIOL 2013. [DOI: 10.1142/s0219519413500188] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Electrocardiography (ECG) is a transthoracic interpretation of the electrical activity of the heart over a period of time, as detected by electrodes attached to the outer surface of the skin and recorded by a device external to the body. ECG signal classification is very important for the clinical detection of arrhythmia. This paper presents an application of an improved wavelet neural network structure to the classification of the ECG beats, because of the high precision and fast learning rate. Feature extraction method in this paper is wavelet transform. Our experimental data set is taken from the MIT-BIH arrhythmia database. The correct detection rate of QRS wave is 95% by testing the data of MIT-BIH database. The proposed methods are applied to a large number of ECG signals consisting of 600 training samples and 120 test samples from the MIT-BIH database. The samples equally represent six different ECG signal types, including normal beat, atrial premature beat, ventricular premature beat, left bundle branch block, right bundle branch block and paced beat. In comparison with pattern recognition methods of BP neural networks, RBF neural networks and Support Vector Machines (SVM), the results in this experiment prove that the wavelet neural network method has a better recognition rate when classifying electrocardiogram signals. The experimental results prove that supposed method in this paper is effective for arrhythmia pattern recognition field.
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Affiliation(s)
- GUANGYING YANG
- School of Physical and Electronics Engineering, Taizhou University, Taizhou, People's Republic of China
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41
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Lee SH, Ko HC, Yoon YR. Classification of ventricular arrhythmia using a support vector machine based on morphological features. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2013; 2013:5785-5788. [PMID: 24111053 DOI: 10.1109/embc.2013.6610866] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
This paper proposes a method for the classification of ventricular arrhythmia using support vector machines (SVM). The features used in the SVMs were extracted automatically based on morphological information. Three different features were extracted: RR interval, QRS slope, and QRS shape similarity. Then, the SVM was used to classify five different electrocardiogram (ECG) heartbeat episodes. The Gaussian Radial Basis Function was utilized for the kernel function because the ECG beat episodes were treated as a non-linear pattern. The sensitivity of the classification used for the five beat episodes was 93.16%.
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42
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A new ECG beat clustering method based on kernelized fuzzy c-means and hybrid ant colony optimization for continuous domains. Appl Soft Comput 2012. [DOI: 10.1016/j.asoc.2012.07.007] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Mateo J, Torres A, Rieta JJ. An efficient method for ectopic beats cancellation based on radial basis function. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2012; 2011:6947-50. [PMID: 22255936 DOI: 10.1109/iembs.2011.6091756] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
The analysis of the surface Electrocardiogram (ECG) is the most extended noninvasive technique in cardiological diagnosis. In order to properly use the ECG, we need to cancel out ectopic beats. These beats may occur in both normal subjects and patients with heart disease, and their presence represents an important source of error which must be handled before any other analysis. This paper presents a method for electrocardiogram ectopic beat cancellation based on Radial Basis Function Neural Network (RBFNN). A train-able neural network ensemble approach to develop customized electrocardiogram beat classifier in an effort to further improve the performance of ECG processing and to offer individualized health care is presented. Six types of beats including: Normal Beats (NB); Premature Ventricular Contractions (PVC); Left Bundle Branch Blocks (LBBB); Right Bundle Branch Blocks (RBBB); Paced Beats (PB) and Ectopic Beats (EB) are obtained from the MIT-BIH arrhythmia database. Four morphological features are extracted from each beat after the preprocessing of the selected records. Average Results for the RBFNN based method provided an ectopic beat reduction (EBR) of (mean ± std) EBR = 7, 23 ± 2.18 in contrast to traditional compared methods that, for the best case, yielded EBR = 4.05 ± 2.13. The results prove that RBFNN based methods are able to obtain a very accurate reduction of ectopic beats together with low distortion of the QRST complex.
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Affiliation(s)
- Jorge Mateo
- Innovation in Bioengineering Research Group, University of Castilla-La Mancha, Campus Universitario, 16071 Cuenca, Spain.
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Ebrahimpour R, Sadeghnejad N, Sajedin A, Mohammadi N. Electrocardiogram beat classification via coupled boosting by filtering and preloaded mixture of experts. Neural Comput Appl 2012. [DOI: 10.1007/s00521-012-1063-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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45
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Bashir MEA, Dong Gyu Lee, Meijing Li, Jang-Whan Bae, Shon HS, Myung Chan Cho, Keun Ho Ryu. Trigger Learning and ECG Parameter Customization for Remote Cardiac Clinical Care Information System. ACTA ACUST UNITED AC 2012; 16:561-71. [DOI: 10.1109/titb.2012.2188812] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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46
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Jovic A, Bogunovic N. Evaluating and comparing performance of feature combinations of heart rate variability measures for cardiac rhythm classification. Biomed Signal Process Control 2012. [DOI: 10.1016/j.bspc.2011.10.001] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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47
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Javadi M, Ebrahimpour R, Sajedin A, Faridi S, Zakernejad S. Improving ECG classification accuracy using an ensemble of neural network modules. PLoS One 2011; 6:e24386. [PMID: 22046232 PMCID: PMC3202523 DOI: 10.1371/journal.pone.0024386] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2011] [Accepted: 08/08/2011] [Indexed: 11/23/2022] Open
Abstract
This paper illustrates the use of a combined neural network model based on Stacked Generalization method for classification of electrocardiogram (ECG) beats. In conventional Stacked Generalization method, the combiner learns to map the base classifiers' outputs to the target data. We claim adding the input pattern to the base classifiers' outputs helps the combiner to obtain knowledge about the input space and as the result, performs better on the same task. Experimental results support our claim that the additional knowledge according to the input space, improves the performance of the proposed method which is called Modified Stacked Generalization. In particular, for classification of 14966 ECG beats that were not previously seen during training phase, the Modified Stacked Generalization method reduced the error rate for 12.41% in comparison with the best of ten popular classifier fusion methods including Max, Min, Average, Product, Majority Voting, Borda Count, Decision Templates, Weighted Averaging based on Particle Swarm Optimization and Stacked Generalization.
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Affiliation(s)
- Mehrdad Javadi
- Islamic Azad University, South Tehran Branch, Tehran, Iran.
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Rodríguez-Sotelo JL, Delgado-Trejos E, Peluffo-Ordóñez D, Cuesta-Frau D, Castellanos-Domínguez G. Weighted-PCA for unsupervised classification of cardiac arrhythmias. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2010; 2010:1906-9. [PMID: 21096570 DOI: 10.1109/iembs.2010.5627321] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
A method that improves the feature selection stage for non-supervised analysis of Holter ECG signals is presented. The method corresponds to WPCA approach developed mainly in two stages. First, the weighting of the feature set through a weight vector based on M-inner product as distance measure and a quadratic optimization function. The second one is the linear projection of weighted data using principal components. In the clustering stage, some procedures are considered: estimation of the number of groups, initialization of centroids and grouping by means a soft clustering algorithm. In order to decrease the procedure computational cost, segment analysis, grouping contiguous segments and establishing union and exclusion criteria per each cluster, is carried out. This work is focused to classify cardiac arrhythmias into 5 groups, according to the standard of the AAMI (ANSI/AAMI EC57:1998/ 2003). To validate the method, some recordings from MIT/BIH arrhythmia database are used. By employing the labels of each recording, the performance is assessed with supervised measures (Se = 90.1%, Sp = 98.9% y Cp = 97.4%), enhancing other works in the literature that do not take into account all heartbeat types.
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Affiliation(s)
- J L Rodríguez-Sotelo
- Faculty of Electrical and Electronic Engineering, Universidad Nacional de Colombia sede Manizales, Colombia.
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49
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Ainon RN, Bulgiba AM, Lahsasna A. AMI Screening Using Linguistic Fuzzy Rules. J Med Syst 2010; 36:463-73. [DOI: 10.1007/s10916-010-9491-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2009] [Accepted: 04/06/2010] [Indexed: 10/19/2022]
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50
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Haas SL, Müller R, Fernandes A, Dzeyk-Boycheva K, Würl S, Hohmann J, Hemberger S, Elmas E, Brückmann M, Bugert P, Backhaus J. Spectroscopic diagnosis of myocardial infarction and heart failure by Fourier transform infrared spectroscopy in serum samples. APPLIED SPECTROSCOPY 2010; 64:262-267. [PMID: 20223059 DOI: 10.1366/000370210790918508] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
Cardiovascular disease is the leading cause of death in Western civilization. In this pilot study we evaluated a new method for the diagnosis of myocardial infarction and heart failure by determining the typical fingerprint in the infrared (IR) spectrum of 1 microL of a dried patient serum sample by Fourier transform IR spectroscopy. For classification, cluster analysis and artificial neural networks (ANN) were applied. In this study 567 subjects were enrolled, comprising 225 controls (Co) and 342 patients with myocardial infarction (MI) (n = 157) and heart failure (HF) (n = 185). By applying artificial neural network algorithms, the following sensitivities and specificities of the same spectra were determined: MI versus Co (98%, 97%), HF versus Co (98%, 100%), MI versus HF (100%, 100%), and MI plus HF versus Co (100%, 100%). Based on our data, mid-IR spectroscopy appears to be a promising new method to diagnose heart diseases from serum samples. Artificial neural network algorithms proved to be superior to cluster analysis for correct prediction.
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Affiliation(s)
- Stephan L Haas
- Department of Medicine II, Mannheim Medical Faculty of the University Heidelberg, Theodor-Kutzer-Ufer 1-3, D-68167 Mannheim, Germany
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