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Ullah H, Heyat MBB, Akhtar F, Muaad AY, Ukwuoma CC, Bilal M, Miraz MH, Bhuiyan MAS, Wu K, Damaševičius R, Pan T, Gao M, Lin Y, Lai D. An Automatic Premature Ventricular Contraction Recognition System Based on Imbalanced Dataset and Pre-Trained Residual Network Using Transfer Learning on ECG Signal. Diagnostics (Basel) 2022; 13:diagnostics13010087. [PMID: 36611379 PMCID: PMC9818233 DOI: 10.3390/diagnostics13010087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Revised: 12/05/2022] [Accepted: 12/23/2022] [Indexed: 12/29/2022] Open
Abstract
The development of automatic monitoring and diagnosis systems for cardiac patients over the internet has been facilitated by recent advancements in wearable sensor devices from electrocardiographs (ECGs), which need the use of patient-specific approaches. Premature ventricular contraction (PVC) is a common chronic cardiovascular disease that can cause conditions that are potentially fatal. Therefore, for the diagnosis of likely heart failure, precise PVC detection from ECGs is crucial. In the clinical settings, cardiologists typically employ long-term ECGs as a tool to identify PVCs, where a cardiologist must put in a lot of time and effort to appropriately assess the long-term ECGs which is time consuming and cumbersome. By addressing these issues, we have investigated a deep learning method with a pre-trained deep residual network, ResNet-18, to identify PVCs automatically using transfer learning mechanism. Herein, features are extracted by the inner layers of the network automatically compared to hand-crafted feature extraction methods. Transfer learning mechanism handles the difficulties of required large volume of training data for a deep model. The pre-trained model is evaluated on the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia and Institute of Cardiological Technics (INCART) datasets. First, we used the Pan-Tompkins algorithm to segment 44,103 normal and 6423 PVC beats, as well as 106,239 normal and 9987 PVC beats from the MIT-BIH Arrhythmia and IN-CART datasets, respectively. The pre-trained model employed the segmented beats as input after being converted into 2D (two-dimensional) images. The method is optimized with the using of weighted random samples, on-the-fly augmentation, Adam optimizer, and call back feature. The results from the proposed method demonstrate the satisfactory findings without the using of any complex pre-processing and feature extraction technique as well as design complexity of model. Using LOSOCV (leave one subject out cross-validation), the received accuracies on MIT-BIH and INCART are 99.93% and 99.77%, respectively, suppressing the state-of-the-art methods for PVC recognition on unseen data. This demonstrates the efficacy and generalizability of the proposed method on the imbalanced datasets. Due to the absence of device-specific (patient-specific) information at the evaluating stage on the target datasets in this study, the method might be used as a general approach to handle the situations in which ECG signals are obtained from different patients utilizing a variety of smart sensor devices.
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Affiliation(s)
- Hadaate Ullah
- State Key Laboratory of Electronic Thin Films and Integrated Devices, School of Materials and Energy, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Md Belal Bin Heyat
- IoT Research Center, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
- Correspondence: (M.B.B.H.); (R.D.); (Y.L.); (D.L.)
| | - Faijan Akhtar
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | | | - Chiagoziem C. Ukwuoma
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Muhammad Bilal
- College of Pharmacy, Liaquat University of Medical and Health Sciences, Jamshoro 76090, Pakistan
| | - Mahdi H. Miraz
- School of Computing and Data Science, Xiamen University Malaysia, Bandar Sunsuria, Sepang 43900, Malaysia
- School of Computing, Glyndŵr University, Wrexham LL11 2AW, UK
| | | | - Kaishun Wu
- IoT Research Center, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
| | - Robertas Damaševičius
- Department of Software Engineering, Kaunas University of Technology, 44249 Kaunas, Lithuania
- Correspondence: (M.B.B.H.); (R.D.); (Y.L.); (D.L.)
| | - Taisong Pan
- State Key Laboratory of Electronic Thin Films and Integrated Devices, School of Materials and Energy, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Min Gao
- State Key Laboratory of Electronic Thin Films and Integrated Devices, School of Materials and Energy, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Yuan Lin
- State Key Laboratory of Electronic Thin Films and Integrated Devices, School of Materials and Energy, University of Electronic Science and Technology of China, Chengdu 610054, China
- Medico-Engineering Corporation on Applied Medicine Research Center, University of Electronic Science and Technology of China, Chengdu 610054, China
- Correspondence: (M.B.B.H.); (R.D.); (Y.L.); (D.L.)
| | - Dakun Lai
- Biomedical Imaging and Electrophysiology Laboratory, School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
- Correspondence: (M.B.B.H.); (R.D.); (Y.L.); (D.L.)
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Premature Ventricular Contraction Recognition Based on a Deep Learning Approach. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:1450723. [PMID: 35378947 PMCID: PMC8976634 DOI: 10.1155/2022/1450723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 02/25/2022] [Accepted: 02/28/2022] [Indexed: 11/18/2022]
Abstract
Electrocardiogram signal (ECG) is considered a significant biological signal employed to diagnose heart diseases. An ECG signal allows the demonstration of the cyclical contraction and relaxation of human heart muscles. This signal is a primary and noninvasive tool employed to recognize the actual life threat related to the heart. Abnormal ECG heartbeat and arrhythmia are the possible symptoms of severe heart diseases that can lead to death. Premature ventricular contraction (PVC) is one of the most common arrhythmias which begins from the lower chamber of the heart and can cause cardiac arrest, palpitation, and other symptoms affecting all activities of a patient. Nowadays, computer-assisted techniques reduce doctors' burden to assess heart arrhythmia and heart disease automatically. In this study, we propose a PVC recognition based on a deep learning approach using the MIT-BIH arrhythmia database. Firstly, 10 heartbeat and statistical features including three morphological features (RS amplitude, QR amplitude, and QRS width) and seven statistical features are computed for each signal. The extraction process of these features is conducted for 20 s of ECG data that create a feature vector. Next, these features are fed into a convolutional neural network (CNN) to find unique patterns and classify them more effectively. The obtained results prove that our pipeline improves the diagnosis performance more effectively.
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Cai Z, Wang T, Shen Y, Xing Y, Yan R, Li J, Liu C. Robust PVC Identification by Fusing Expert System and Deep Learning. BIOSENSORS 2022; 12:185. [PMID: 35448245 PMCID: PMC9025768 DOI: 10.3390/bios12040185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Revised: 03/18/2022] [Accepted: 03/18/2022] [Indexed: 06/14/2023]
Abstract
Premature ventricular contraction (PVC) is one of the common ventricular arrhythmias, which may cause stroke or sudden cardiac death. Automatic long-term electrocardiogram (ECG) analysis algorithms could provide diagnosis suggestion and even early warning for physicians. However, they are mutually exclusive in terms of robustness, generalization and low complexity. In this study, a novel PVC recognition algorithm that combines deep learning-based heartbeat template clusterer and expert system-based heartbeat classifier is proposed. A long short-term memory-based auto-encoder (LSTM-AE) network was used to extract features from ECG heartbeats for K-means clustering. Thus, the templates were constructed and determined based on clustering results. Finally, the PVC heartbeats were recognized based on a combination of multiple rules, including template matching and rhythm characteristics. Three quantitative parameters, sensitivity (Se), positive predictive value (P+) and accuracy (ACC), were used to evaluate the performances of the proposed method on the MIT-BIH Arrhythmia database and the St. Petersburg Institute of Cardiological Technics database. Se on the two test databases was 87.51% and 87.92%, respectively; P+ was 92.47% and 93.18%, respectively; and ACC was 98.63% and 97.89%, respectively. The PVC scores on the third China Physiological Signal Challenge 2020 training set and hidden test set were 36,256 and 46,706, respectively, which could rank first in the open-source codes. The results showed that the combination strategy of expert system and deep learning can provide new insights for robust and generalized PVC identification from long-term single-lead ECG recordings.
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Affiliation(s)
- Zhipeng Cai
- School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China; (Z.C.); (T.W.); (Y.S.); (Y.X.)
| | - Tiantian Wang
- School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China; (Z.C.); (T.W.); (Y.S.); (Y.X.)
| | - Yumin Shen
- School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China; (Z.C.); (T.W.); (Y.S.); (Y.X.)
| | - Yantao Xing
- School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China; (Z.C.); (T.W.); (Y.S.); (Y.X.)
| | - Ruqiang Yan
- School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China; (Z.C.); (T.W.); (Y.S.); (Y.X.)
- School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 714009, China;
| | - Jianqing Li
- School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 714009, China;
| | - Chengyu Liu
- School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China; (Z.C.); (T.W.); (Y.S.); (Y.X.)
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Naseri Jahfari A, Tax D, Reinders M, van der Bilt I. Machine Learning for Cardiovascular Outcomes From Wearable Data: Systematic Review From a Technology Readiness Level Point of View. JMIR Med Inform 2022; 10:e29434. [PMID: 35044316 PMCID: PMC8811688 DOI: 10.2196/29434] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 11/22/2021] [Accepted: 12/04/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Wearable technology has the potential to improve cardiovascular health monitoring by using machine learning. Such technology enables remote health monitoring and allows for the diagnosis and prevention of cardiovascular diseases. In addition to the detection of cardiovascular disease, it can exclude this diagnosis in symptomatic patients, thereby preventing unnecessary hospital visits. In addition, early warning systems can aid cardiologists in timely treatment and prevention. OBJECTIVE This study aims to systematically assess the literature on detecting and predicting outcomes of patients with cardiovascular diseases by using machine learning with data obtained from wearables to gain insights into the current state, challenges, and limitations of this technology. METHODS We searched PubMed, Scopus, and IEEE Xplore on September 26, 2020, with no restrictions on the publication date and by using keywords such as "wearables," "machine learning," and "cardiovascular disease." Methodologies were categorized and analyzed according to machine learning-based technology readiness levels (TRLs), which score studies on their potential to be deployed in an operational setting from 1 to 9 (most ready). RESULTS After the removal of duplicates, application of exclusion criteria, and full-text screening, 55 eligible studies were included in the analysis, covering a variety of cardiovascular diseases. We assessed the quality of the included studies and found that none of the studies were integrated into a health care system (TRL<6), prospective phase 2 and phase 3 trials were absent (TRL<7 and 8), and group cross-validation was rarely used. These issues limited these studies' ability to demonstrate the effectiveness of their methodologies. Furthermore, there seemed to be no agreement on the sample size needed to train these studies' models, the size of the observation window used to make predictions, how long participants should be observed, and the type of machine learning model that is suitable for predicting cardiovascular outcomes. CONCLUSIONS Although current studies show the potential of wearables to monitor cardiovascular events, their deployment as a diagnostic or prognostic cardiovascular clinical tool is hampered by the lack of a realistic data set and proper systematic and prospective evaluation.
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Affiliation(s)
- Arman Naseri Jahfari
- Pattern Recognition and Bioinformatics, Delft University of Technology, Delft, Netherlands.,Department of Cardiology, Haga Teaching Hospital, The Hague, Netherlands
| | - David Tax
- Pattern Recognition and Bioinformatics, Delft University of Technology, Delft, Netherlands
| | - Marcel Reinders
- Pattern Recognition and Bioinformatics, Delft University of Technology, Delft, Netherlands
| | - Ivo van der Bilt
- Department of Cardiology, Haga Teaching Hospital, The Hague, Netherlands
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Rahul J, Sora M, Sharma LD, Bohat VK. An improved cardiac arrhythmia classification using an RR interval-based approach. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2021.04.004] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
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Cai Z, Li J, Johnson AEW, Zhang X, Shen Q, Zhang J, Liu C. Rule-based rough-refined two-step-procedure for real-time premature beat detection in single-lead ECG. Physiol Meas 2020; 41:054004. [DOI: 10.1088/1361-6579/ab87b4] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Malek AS, Elnahrawy A, Anwar H, Naeem M. Automated detection of premature ventricular contraction in ECG signals using enhanced template matching algorithm. Biomed Phys Eng Express 2020; 6:015024. [PMID: 33438612 DOI: 10.1088/2057-1976/ab6995] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Nowadays, with the increasing number of people who suffer from cardiovascular diseases such as irregular heartbeats (arrhythmia), there is a vital need to pay more attention to healthcare conditions. Therefore, the production of smart biomedical garments becomes of great necessity. The first step of manufacturing such smart garments is to build an electrocardiogram (ECG) analysis system. In this paper, the premature ventricular contraction (PVC), which is a serious life-threatening cardiovascular condition, is recognised. In addition, an improved template matching technique is developed, implemented, and evaluated to identify the irregularity of PVC beats in the QRS complex and T wave. The improvement in this technique is that a PVC recogniser is established by analysing the maximum and minimum correlation coefficient values instead of the maximum values only. Moreover, a sufficient number of features are relied upon for the accurate detection of PVC beats. The template matching algorithm is evaluated on the MIT-BIH arrhythmia, St. Petersburg Institute of Cardiological Technics (INCART), QT, MIT-BIH Supraventricular Arrhythmia, and Fantasia databases. The results show a valuable accuracy enhancement when compared with those of other recent approaches.
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Affiliation(s)
- Abdel Salam Malek
- Textile Department, Faculty of Industrial Education, Suez University, Egypt
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