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Wen W, Zhang H, Wang Z, Gao X, Wu P, Lin J, Zeng N. Enhanced multi-label cardiology diagnosis with channel-wise recurrent fusion. Comput Biol Med 2024; 171:108210. [PMID: 38417383 DOI: 10.1016/j.compbiomed.2024.108210] [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: 01/08/2024] [Revised: 02/08/2024] [Accepted: 02/25/2024] [Indexed: 03/01/2024]
Abstract
The timely detection of abnormal electrocardiogram (ECG) signals is vital for preventing heart disease. However, traditional automated cardiology diagnostic methods have the limitation of being unable to simultaneously identify multiple diseases in a segment of ECG signals, and do not consider the potential correlations between the 12-lead ECG signals. To address these issues, this paper presents a novel network architecture, denoted as Branched Convolution and Channel Fusion Network (BCCF-Net), designed for the multi-label diagnosis of ECG cardiology to achieve simultaneous identification of multiple diseases. Among them, the BCCF-Net incorporates the Channel-wise Recurrent Fusion (CRF) network, which is designed to enhance the ability to explore potential correlation information between 12 leads. Furthermore, the utilization of the squeeze and excitation (SE) attention mechanism maximizes the potential of the convolutional neural network (CNN). In order to efficiently capture complex patterns in space and time across various scales, the multi branch convolution (MBC) module has been developed. Through extensive experiments on two public datasets with seven subtasks, the efficacy and robustness of the proposed ECG multi-label classification framework have been comprehensively evaluated. The results demonstrate the superior performance of the BCCF-Net compared to other state-of-the-art algorithms. The developed framework holds practical application in clinical settings, allowing for the refined diagnosis of cardiac arrhythmias through ECG signal analysis.
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Affiliation(s)
- Weimin Wen
- School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Xiamen 361024, China
| | - Hongyi Zhang
- School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Xiamen 361024, China
| | - Zidong Wang
- Department of Computer Science, Brunel University London, Uxbridge UB8 3PH, UK.
| | - Xingen Gao
- School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Xiamen 361024, China
| | - Peishu Wu
- Department of Instrumental and Electrical Engineering, Xiamen University, Fujian 361005, China
| | - Juqiang Lin
- School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Xiamen 361024, China
| | - Nianyin Zeng
- Department of Instrumental and Electrical Engineering, Xiamen University, Fujian 361005, China.
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2
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Vasconcelos L, Martinez BP, Kent M, Ansari S, Ghanbari H, Nenadic I. Multi-center atrial fibrillation electrocardiogram (ECG) classification using Fourier space convolutional neural networks (FD-CNN) and transfer learning. J Electrocardiol 2023; 81:201-206. [PMID: 37778217 DOI: 10.1016/j.jelectrocard.2023.09.010] [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: 07/07/2023] [Revised: 09/05/2023] [Accepted: 09/17/2023] [Indexed: 10/03/2023]
Abstract
There has been a proliferation of machine learning (ML) electrocardiogram (ECG) classification algorithms reaching >85% accuracy for various cardiac pathologies. Despite the high accuracy at individual institutions, challenges remain when it comes to multi-center deployment. Transfer learning (TL) is a technique in which a model trained for a specific task is repurposed for another related task, in this case ECG ML model trained at one institution is fine-tuned to be utilized to classify ECGs at another institution. Models trained at one institution, however, might not be generalizable for accurate classification when deployed broadly due to differences in type, time, and sampling rate of traditional ECG acquisition. In this study, we evaluate the performance of time domain (TD) and frequency domain (FD) convolutional neural network (CNN) classification models in an inter-institutional scenario leveraging three different publicly available datasets. The larger PTB-XL ECG dataset was used to initially train TD and FD CNN models for atrial fibrillation (AFIB) classification. The models were then tested on two different data sets, Lobachevsky University Electrocardiography Database (LUDB) and Korea University Medical Center database (KURIAS). The FD model was able to retain most of its performance (>0.81 F1-score), whereas TD was highly affected (<0.53 F1-score) by the dataset variations, even with TL applied. The FD CNN showed superior robustness to cross-institutional variability and has potential for widespread application with no compromise to ECG classification performance.
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Affiliation(s)
| | | | - Madeline Kent
- Division of Cardiology, Henry Ford Hospital, Detroit, MI, USA
| | - Sardar Ansari
- Department of Emergency Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Hamid Ghanbari
- Cardiovascular Disease, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Ivan Nenadic
- Department of Radiology, Mayo Clinic, Rochester, MN, USA; Duke Cardiology, Duke University Medical Center, Durham, NC, USA
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3
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Liu F, Li H, Wu T, Lin H, Lin C, Han G. Automatic classification of arrhythmias using multi-branch convolutional neural networks based on channel-based attention and bidirectional LSTM. ISA TRANSACTIONS 2023; 138:397-407. [PMID: 36898911 DOI: 10.1016/j.isatra.2023.02.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 02/13/2023] [Accepted: 02/25/2023] [Indexed: 06/16/2023]
Abstract
Cardiac arrhythmia is an abnormal rhythm of the heartbeat and can be life-threatening Electrocardiogram (ECG) is a technology that uses an electrocardiograph machine to record a graph of the changes in electrical activity produced by the heart at each cardiac cycle. ECG can generally be used to check whether the examinee has arrhythmia, ion channel disease, cardiomyopathy, electrolyte disorder and other diseases. To reduce the workload of doctors and improve the accuracy of ECG signal recognition, a novel and lightweight automatic ECG classification method based on Convolutional Neural Network (CNN) is proposed. The multi-branch network with different receptive fields is used to extract the multi-spatial deep features of heartbeats. The Channel Attention Module (CAM) and Bidirectional Long Short-Term Memory neural network (BLSTM) module are used to filter redundant ECG features. CAM and BLSTM are beneficial for distinguishing different categories of heartbeats. In the experiments, a four-fold cross-validation technique is used to improve the generalization capability of the network, and it shows good performance on the testing set. This method divides heartbeats into five categories according to the American Advancement of Medical Instrumentation (AAMI) criteria, which is validated in the MIT-BIH arrhythmia database. The sensitivity of this method to Ventricular Ectopic Beat (VEB) is 98.5% and the F1 score is 98.2%. The precision of the Supraventricular Ectopic Beat (SVEB) is 91.1%, and the corresponding F1 score is 90.8%. The proposed method has high classification performance and a lightweight feature. In a word, it has broad application prospects in clinical medicine and health testing.
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Affiliation(s)
- Fengqing Liu
- School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, People's Republic of China
| | - Huaidong Li
- School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, People's Republic of China
| | - Teng Wu
- School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, People's Republic of China
| | - Hong Lin
- School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, People's Republic of China
| | - Chenyu Lin
- School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, People's Republic of China
| | - Guoqiang Han
- School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, People's Republic of China.
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4
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Li N, Liu L, Yang Z, Qin S. A self-adjusting ant colony clustering algorithm for ECG arrhythmia classification based on a correction mechanism. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 235:107519. [PMID: 37040683 DOI: 10.1016/j.cmpb.2023.107519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 03/17/2023] [Accepted: 03/28/2023] [Indexed: 05/08/2023]
Abstract
BACKGROUND AND OBJECTIVE As a representative type of cardiovascular disease, persistent arrhythmias can often become life-threatening. In recent years, machine learning-based ECG arrhythmia classification aided methods have been effective in assisting physicians with their diagnosis, but these methods have problems such as complex model structures, poor feature perception ability, and low classification accuracy. METHODS In this paper, a self-adjusting ant colony clustering algorithm for ECG arrhythmia classification based on a correction mechanism is proposed. This method does not distinguish between subjects when establishing the dataset in order to reduce the effect of differences in ECG signal features between individuals, thus improving the robustness of the model. When the classification is achieved, a correction mechanism is introduced to correct outliers caused by the accumulation of errors in the classification process in order to improve the classification accuracy of the model. According to the principle that the flow rate of gas can be increased under the convergence channel, a dynamically updated pheromone volatilization coefficient ρ, namely the increased flow rate ρ, is introduced to help the model converge more stably and faster. As the ants move, the next transfer target is selected by a truly self-adjusting transfer method, and the transfer probability is dynamically adjusted according to the pheromone concentration and the path distance. RESULTS Based on the MIT-BIH arrhythmia dataset, the new algorithm achieved classification of five heart rhythm types, with an overall accuracy of 99.00%. Compared to other experimental models, the classification accuracy of the proposed method represents a 0.2% to 16.6% improvement, and compared to other current studies, the classification accuracy of the proposed method is 0.65% to 7.5% better. CONCLUSIONS This paper addresses the shortcomings of ECG arrhythmia classification methods based on feature engineering, traditional machine learning and deep learning, and presents a self-adjusting ant colony clustering algorithm for ECG arrhythmia classification based on a correction mechanism. Experiments demonstrate the superiority of the proposed method compared to basic models as well as those with improved partial structures. Furthermore, the proposed method achieves very high classification accuracy with a simple structure and fewer iterations than other current methods.
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Affiliation(s)
- Ning Li
- Xi'an University of Technology, Xi'an, China.
| | - Linyue Liu
- Xi'an Technological University, Xi'an, China
| | | | - Shuguang Qin
- The Second Affiliate Hospital of Xi'an Jiaotong University, Xi'an, China
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5
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Neri L, Oberdier MT, van Abeelen KCJ, Menghini L, Tumarkin E, Tripathi H, Jaipalli S, Orro A, Paolocci N, Gallelli I, Dall’Olio M, Beker A, Carrick RT, Borghi C, Halperin HR. Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:4805. [PMID: 37430719 PMCID: PMC10223364 DOI: 10.3390/s23104805] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 05/09/2023] [Accepted: 05/12/2023] [Indexed: 07/12/2023]
Abstract
Worldwide, population aging and unhealthy lifestyles have increased the incidence of high-risk health conditions such as cardiovascular diseases, sleep apnea, and other conditions. Recently, to facilitate early identification and diagnosis, efforts have been made in the research and development of new wearable devices to make them smaller, more comfortable, more accurate, and increasingly compatible with artificial intelligence technologies. These efforts can pave the way to the longer and continuous health monitoring of different biosignals, including the real-time detection of diseases, thus providing more timely and accurate predictions of health events that can drastically improve the healthcare management of patients. Most recent reviews focus on a specific category of disease, the use of artificial intelligence in 12-lead electrocardiograms, or on wearable technology. However, we present recent advances in the use of electrocardiogram signals acquired with wearable devices or from publicly available databases and the analysis of such signals with artificial intelligence methods to detect and predict diseases. As expected, most of the available research focuses on heart diseases, sleep apnea, and other emerging areas, such as mental stress. From a methodological point of view, although traditional statistical methods and machine learning are still widely used, we observe an increasing use of more advanced deep learning methods, specifically architectures that can handle the complexity of biosignal data. These deep learning methods typically include convolutional and recurrent neural networks. Moreover, when proposing new artificial intelligence methods, we observe that the prevalent choice is to use publicly available databases rather than collecting new data.
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Affiliation(s)
- Luca Neri
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA; (L.N.)
- Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy
| | - Matt T. Oberdier
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA; (L.N.)
| | - Kirsten C. J. van Abeelen
- Department of Informatics, Systems, and Communication, University of Milano-Bicocca, 20126 Milan, Italy
- Department of Internal Medicine, Radboud University Medical Center, 6525 AJ Nijmegen, The Netherlands
| | - Luca Menghini
- Department of Psychology and Cognitive Science, University of Trento, 38068 Rovereto, Italy
| | - Ethan Tumarkin
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA; (L.N.)
| | - Hemantkumar Tripathi
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA; (L.N.)
| | - Sujai Jaipalli
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Alessandro Orro
- Institute of Biomedical Technologies, National Research Council, 20054 Segrate, Italy
| | - Nazareno Paolocci
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA; (L.N.)
| | - Ilaria Gallelli
- Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy
| | - Massimo Dall’Olio
- Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy
| | - Amir Beker
- AccYouRate Group S.p.A., 67100 L’Aquila, Italy
| | - Richard T. Carrick
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA; (L.N.)
| | - Claudio Borghi
- Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy
| | - Henry R. Halperin
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA; (L.N.)
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Radiology, Johns Hopkins University, Baltimore, MD 21205, USA
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6
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Huang Y, Li H, Yu X. A novel time representation input based on deep learning for ECG classification. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
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7
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Sun L, Wu J, Xu Y, Zhang Y. A federated learning and blockchain framework for physiological signal classification based on continual learning. Inf Sci (N Y) 2023. [DOI: 10.1016/j.ins.2023.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
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8
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Premalatha G, Bai VT. Design and implementation of intelligent patient in-house monitoring system based on efficient XGBoost-CNN approach. Cogn Neurodyn 2022; 16:1135-1149. [PMID: 36237411 PMCID: PMC9508314 DOI: 10.1007/s11571-021-09754-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 11/01/2021] [Accepted: 11/08/2021] [Indexed: 11/28/2022] Open
Abstract
Because of the scarcity of caregivers and the high cost of medical devices, it is difficult to keep track of the aging population and provide assistance. To avoid deterioration of health issues, continuous monitoring of personal health should be done prior to the intervention. If a problem is discovered, the IoT platform collects and presents the caretaker with graphical data. The death rates of older patients are reduced when projections are made ahead of time. Patients can die as a result of minor abnormalities in their ECG. The cardiac dysrhythmia/irregular heart rate is classified with several multilayer parameters using a deep convolutional neural network (CNN) approach in this paper. The key benefit of utilizing this CNN approach is that it can handle databases that have been purposefully oversampled. Using the XGBoost approach, these are oversampled to deal with difficulties like minority class and imbalance. XGBoost is a decision tree-based ensemble learning algorithm that uses a gradient boosting framework. It uses an artificial neural network and predicts the unstructured data in a structured manner. This CNN-based supervised learning model is tested and simulated on a real-time elderly heart patient IoT dataset. The proposed methodology has a recall value of 100%, an F1-Score of 94.8%, a precision of 98%, and an accuracy of 98%, which is higher than existing approaches like decision trees, random forests, and Support Vector Machine. The results reveal that the proposed model outperforms state-of-the-art methodologies and improves elderly heart disease patient monitoring with a low error rate.
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Affiliation(s)
- G. Premalatha
- Department of ECE, Prathyusha Engineering College, Anna University, Chennai, India
| | - V. Thulasi Bai
- Department of ECE, KCG College of Technology, Chennai, India
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9
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A Deep Neural Network Ensemble Classifier with Focal Loss for Automatic Arrhythmia Classification. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:9370517. [PMID: 36118121 PMCID: PMC9481402 DOI: 10.1155/2022/9370517] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 07/29/2022] [Accepted: 08/09/2022] [Indexed: 11/18/2022]
Abstract
Automated electrocardiogram classification techniques play an important role in assisting physicians in diagnosing arrhythmia. Among these, the automatic classification of single-lead heartbeats has received wider attention due to the urgent need for portable ECG monitoring devices. Although many heartbeat classification studies performed well in intrapatient assessment, they do not perform as well in interpatient assessment. In particular, for supraventricular ectopic heartbeats (S), most models do not classify them well. To solve these challenges, this article provides an automated arrhythmia classification algorithm. There are three key components of the algorithm. First, a new heartbeat segmentation method is used, which improves the algorithm’s capacity to classify S substantially. Second, to overcome the problems created by data imbalance, a combination of traditional sampling and focal loss is applied. Finally, using the interpatient evaluation paradigm, a deep convolutional neural network ensemble classifier is built to perform classification validation. The experimental results show that the overall accuracy of the method is 91.89%, the sensitivity is 85.37%, the positive productivity is 59.51%, and the specificity is 93.15%. In particular, for the supraventricular ectopic heartbeat(s), the method achieved a sensitivity of 80.23%, a positivity of 49.40%, and a specificity of 96.85%, exceeding most existing studies. Even without any manually extracted features or heartbeat preprocessing, the technique achieved high classification performance in the interpatient assessment paradigm.
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10
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A Micro Neural Network for Healthcare Sensor Data Stream Classification in Sustainable and Smart Cities. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:4270295. [PMID: 35785086 PMCID: PMC9249444 DOI: 10.1155/2022/4270295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Accepted: 06/06/2022] [Indexed: 11/17/2022]
Abstract
A smart city is an intelligent space, in which large amounts of data are collected and analyzed using low-cost sensors and automatic algorithms. The application of artificial intelligence and Internet of Things (IoT) technologies in electronic health (E-health) can efficiently promote the development of sustainable and smart cities. The IoT sensors and intelligent algorithms enable the remote monitoring and analyzing of the healthcare data of patients, which reduces the medical and travel expenses in cities. Existing deep learning-based methods for healthcare sensor data classification have made great achievements. However, these methods take much time and storage space for model training and inference. They are difficult to be deployed in small devices to classify the physiological signal of patients in real time. To solve the above problems, this paper proposes a micro time series classification model called the micro neural network (MicroNN). The proposed model is micro enough to be deployed on tiny edge devices. MicroNN can be applied to long-term physiological signal monitoring based on edge computing devices. We conduct comprehensive experiments to evaluate the classification accuracy and computation complexity of MicroNN. Experiment results show that MicroNN performs better than the state-of-the-art methods. The accuracies on the two datasets (MIT-BIH-AR and INCART) are 98.4% and 98.1%, respectively. Finally, we present an application to show how MicroNN can improve the development of sustainable and smart cities.
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11
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Shafi I, Aziz A, Din S, Ashraf I. Reduced features set neural network approach based on high-resolution time-frequency images for cardiac abnormality detection. Comput Biol Med 2022; 145:105425. [DOI: 10.1016/j.compbiomed.2022.105425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 03/17/2022] [Accepted: 03/18/2022] [Indexed: 11/03/2022]
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12
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Qiu L, Cai W, Zhang M, Dong Y, Zhu W, Wang L. Supraventricular ectopic beats and ventricular ectopic beats detection based on improved U-net. Physiol Meas 2022; 43. [PMID: 35472766 DOI: 10.1088/1361-6579/ac6aa2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 04/26/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Supraventricular ectopic beats (SVEB) or ventricular ectopic beats (VEB) are common arrhythmia with uncertain occurrence and morphological diversity, so realizing their automatic localization is of great significance in clinical diagnosis. METHODS We propose a modified U-net network: USV-net, it can simultaneously realize the automatic positioning of VEB and SVEB. The improvement consists of three parts: Firstly, we reconstruct part of the convolutional layer in U-net using group convolution to reduce the expression of redundant features. Secondly, a plug-and-play multi-scale 2D deformable convolution (MSDC) module is designed to extract cross-channel features of different scales. Thirdly, in addition to conventional output of U-net, we also compress and output the bottom feature map of U-net, the dual-output is trained through Dice-loss to take into account the learning of high/low resolution features of the model. We used the MIT-BIH arrhythmia database for patient-specific training, and used Sensitivity, Positive prediction rate and F1-scores to evaluate the effectiveness of our method. MAIN RESULT The F1-scores of SVEB and VEB achieve the best results compared with other studies in different testing records. It is worth noting that the F1-scores of SVEB and VEB reached 81.3 and 95.4 in the 24 testing records. Moreover, our method is also at the forefront in Sensitivity and Positive prediction rate. SIGNIFICANCE The method proposed in this paper has great potential in the detection of SVEB and VEB. We anticipate efficiency and accuracy of clinical detection of ectopic beats would be improved.
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Affiliation(s)
- Lishen Qiu
- School of Biomedical Engineering (Suzhou),Division of Life Sciences and medicine, University of Science and Technology of China, No. 88, Keling Road, Science and Technology City, Suzhou High-tech Zone, suzhou, 230026, CHINA
| | - Wenqiang Cai
- Soochow University, No. 333 Ganjiang East Road, Gusu District, Suzhou City, Suzhou, 215000, CHINA
| | - Miao Zhang
- , SIBET, No. 88, Keling Road, Science and Technology City, Suzhou High-tech Zone, Suzhou, 215000, CHINA
| | - Yanfang Dong
- School of Biomedical Engineering (suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, No. 88, Keling Road, Science and Technology City, Suzhou High-tech Zone, Hefei, 215000, CHINA
| | - Wenliang Zhu
- , SIBET, No. 88, Keling Road, Science and Technology City, Suzhou High-tech Zone, Suzhou, 215000, CHINA
| | - Lirong Wang
- Soochow University, No. 333 Ganjiang East Road, Gusu District, Suzhou City, Suzhou, 215006, CHINA
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13
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Wang X, Chen B, Zeng M, Wang Y, Liu H, Liu R, Tian L, Lu X. An ECG Signal Denoising Method Using Conditional Generative Adversarial Net. IEEE J Biomed Health Inform 2022; 26:2929-2940. [PMID: 35446775 DOI: 10.1109/jbhi.2022.3169325] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
In this paper, a novel denoising method for electrocardiogram (ECG) signal is proposed to improve performance and availability under multiple noise cases. The method is based on the framework of conditional generative adversarial network (CGAN), and we improved the CGAN framework for ECG denoising. The proposed framework consists of two networks: a generator that is composed of the optimized convolutional auto-encoder (CAE) and a discriminator that is composed of four convolution layers and one full connection layer. As the convolutional layers of CAE can preserve spatial locality and the neighborhood relations in the latent higher-level feature representations of ECG signal, and the skip connection facilitates the gradient propagation in the denoising training process, the trained denoising model has good performance and generalization ability. The extensive experimental results on MIT-BIH databases show that for single noise and mixed noises, the average signal-to-noise ratio (SNR) of denoised ECG signal is above 39dB, and it is better than that of the state-of-the-art methods. Furthermore, the denoised classification results of four cardiac diseases show that the average accuracy increased above 32% under multiple noises under SNR=0dB. So, the proposed method can remove noise effectively as well as keep the details of the features of ECG signals.
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14
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De Melo Ribeiro H, Arnold A, Howard JP, Shun-Shin MJ, Zhang Y, Francis DP, Lim PB, Whinnett Z, Zolgharni M. ECG-based real-time arrhythmia monitoring using quantized deep neural networks: A feasibility study. Comput Biol Med 2022; 143:105249. [PMID: 35091363 DOI: 10.1016/j.compbiomed.2022.105249] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 01/18/2022] [Accepted: 01/19/2022] [Indexed: 11/23/2022]
Abstract
Continuous ambulatory cardiac monitoring plays a critical role in early detection of abnormality in at-risk patients, thereby increasing the chance of early intervention. In this study, we present an automated ECG classification approach for distinguishing between healthy heartbeats and pathological rhythms. The proposed lightweight solution uses quantized one-dimensional deep convolutional neural networks and is ideal for real-time continuous monitoring of cardiac rhythm, capable of providing one output prediction per second. Raw ECG data is used as the input to the classifier, eliminating the need for complex data preprocessing on low-powered wearable devices. In contrast to many compute-intensive approaches, the data analysis can be carried out locally on edge devices, providing privacy and portability. The proposed lightweight solution is accurate (sensitivity of 98.5% and specificity of 99.8%), and implemented on a smartphone, it is energy-efficient and fast, requiring 5.85 mJ and 7.65 ms per prediction, respectively.
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Affiliation(s)
| | - Ahran Arnold
- National Heart and Lung Institute, Imperial College London, UK
| | - James P Howard
- National Heart and Lung Institute, Imperial College London, UK
| | | | - Ying Zhang
- School of Computing and Engineering, University of West London, UK
| | | | - Phang B Lim
- National Heart and Lung Institute, Imperial College London, UK
| | | | - Massoud Zolgharni
- School of Computing and Engineering, University of West London, UK; National Heart and Lung Institute, Imperial College London, UK
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15
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Chen SW, Wang SL, Qi XZ, Samuri SM, Yang C. Review of ECG detection and classification based on deep learning: Coherent taxonomy, motivation, open challenges and recommendations. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103493] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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Liu Y, Li Q, He R, Wang K, Liu J, Yuan Y, Xia Y, Zhang H. Generalizable Beat-by-Beat Arrhythmia Detection by Using Weakly Supervised Deep Learning. Front Physiol 2022; 13:850951. [PMID: 35480046 PMCID: PMC9037749 DOI: 10.3389/fphys.2022.850951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Accepted: 03/07/2022] [Indexed: 11/24/2022] Open
Abstract
Beat-by-beat arrhythmia detection in ambulatory electrocardiogram (ECG) monitoring is critical for the evaluation and prognosis of cardiac arrhythmias, however, it is a highly professional demanding and time-consuming task. Current methods for automatic beat-by-beat arrhythmia detection suffer from poor generalization ability due to the lack of large-sample and finely-annotated (labels are given to each beat) ECG data for model training. In this work, we propose a weakly supervised deep learning framework for arrhythmia detection (WSDL-AD), which permits training a fine-grained (beat-by-beat) arrhythmia detector with the use of large amounts of coarsely annotated ECG data (labels are given to each recording) to improve the generalization ability. In this framework, heartbeat classification and recording classification are integrated into a deep neural network for end-to-end training with only recording labels. Several techniques, including knowledge-based features, masked aggregation, and supervised pre-training, are proposed to improve the accuracy and stability of the heartbeat classification under weak supervision. The developed WSDL-AD model is trained for the detection of ventricular ectopic beats (VEB) and supraventricular ectopic beats (SVEB) on five large-sample and coarsely-annotated datasets and the model performance is evaluated on three independent benchmarks according to the recommendations from the Association for the Advancement of Medical Instrumentation (AAMI). The experimental results show that our method improves the F1 score of supraventricular ectopic beats detection by 8%–290% and the F1 of ventricular ectopic beats detection by 4%–11% on the benchmarks compared with the state-of-the-art methods of supervised learning. It demonstrates that the WSDL-AD framework can leverage the abundant coarsely-labeled data to achieve a better generalization ability than previous methods while retaining fine detection granularity. Therefore, this framework has a great potential to be used in clinical and telehealth applications. The source code is available at https://github.com/sdnjly/WSDL-AD.
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Affiliation(s)
- Yang Liu
- School of Computer Science and Technology, Harbin Institute of Technology (HIT), Harbin, China
| | - Qince Li
- School of Computer Science and Technology, Harbin Institute of Technology (HIT), Harbin, China
- Peng Cheng Laboratory, Shenzhen, China
- *Correspondence: Qince Li, ; Henggui Zhang,
| | - Runnan He
- Peng Cheng Laboratory, Shenzhen, China
| | - Kuanquan Wang
- School of Computer Science and Technology, Harbin Institute of Technology (HIT), Harbin, China
| | - Jun Liu
- School of Computer Science and Technology, Harbin Institute of Technology (HIT), Harbin, China
| | - Yongfeng Yuan
- School of Computer Science and Technology, Harbin Institute of Technology (HIT), Harbin, China
| | - Yong Xia
- School of Computer Science and Technology, Harbin Institute of Technology (HIT), Harbin, China
| | - Henggui Zhang
- Peng Cheng Laboratory, Shenzhen, China
- School of Physics and Astronomy, The University of Manchester, Manchester, United Kingdom
- Key Laboratory of Medical Electrophysiology of Ministry of Education and Medical Electrophysiological Key Laboratory of Sichuan Province, Institute of Cardiovascular Research, Southwest Medical University, Luzhou, China
- *Correspondence: Qince Li, ; Henggui Zhang,
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Jiao Y, Qi H, Wu J. Capsule network assisted electrocardiogram classification model for smart healthcare. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2022.03.006] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
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18
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Labib MI, Nahid AA. OptRPC: A novel and optimized recurrence plot-based system for ECG beat classification. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103328] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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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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Malik J, Devecioglu OC, Kiranyaz S, Ince T, Gabbouj M. Real-Time Patient-Specific ECG Classification by 1D Self-Operational Neural Networks. IEEE Trans Biomed Eng 2021; 69:1788-1801. [PMID: 34910628 DOI: 10.1109/tbme.2021.3135622] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Despite the proliferation of numerous deep learning methods proposed for generic ECG classification and arrhythmia detection, compact systems with the real-time ability and high accuracy for classifying patient-specific ECG are still few. Particularly, the scarcity of patient-specific data poses an ultimate challenge to any classifier. Recently, compact 1D Convolutional Neural Networks (CNNs) have achieved the state-of-the-art performance level for the accurate classification of ventricular and supraventricular ectopic beats. However, several studies have demonstrated the fact that the learning performance of the conventional CNNs is limited because they are homogenous networks with a basic (linear) neuron model. In order to address this deficiency and further boost the patient-specific ECG classification performance, in this study, we propose 1D Self-organized Operational Neural Networks (1D Self-ONNs). Due to its self-organization capability, Self-ONNs have the utmost advantage and superiority over conventional ONNs where the prior operator search within the operator set library to find the best possible set of operators is entirely avoided. As the first study where 1D Self-ONNs are ever proposed for a classification task, our results over the MIT-BIH arrhythmia benchmark database demonstrate that 1D Self-ONNs can surpass 1D CNNs with a significant margin while having a similar computational complexity. Under AAMI recommendations and with minimal common training data used, over the entire MIT-BIH dataset 1D Self-ONNs have achieved 98% and 99.04% average accuracies, 76.6% and 93.7% average F1 scores on supra-ventricular and ventricular ectopic beat (VEB) classifications, respectively, which is the highest performance level ever reported.
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Ge Z, Jiang X, Tong Z, Feng P, Zhou B, Xu M, Wang Z, Pang Y. Multi-label correlation guided feature fusion network for abnormal ECG diagnosis. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2021.107508] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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22
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Bhaskar N, Suchetha M. A Computationally Efficient Correlational Neural Network for Automated Prediction of Chronic Kidney Disease. Ing Rech Biomed 2021. [DOI: 10.1016/j.irbm.2020.07.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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24
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Tutuko B, Nurmaini S, Tondas AE, Rachmatullah MN, Darmawahyuni A, Esafri R, Firdaus F, Sapitri AI. AFibNet: an implementation of atrial fibrillation detection with convolutional neural network. BMC Med Inform Decis Mak 2021; 21:216. [PMID: 34261486 PMCID: PMC8281594 DOI: 10.1186/s12911-021-01571-1] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 06/29/2021] [Indexed: 11/27/2022] Open
Abstract
Background Generalization model capacity of deep learning (DL) approach for atrial fibrillation (AF) detection remains lacking. It can be seen from previous researches, the DL model formation used only a single frequency sampling of the specific device. Besides, each electrocardiogram (ECG) acquisition dataset produces a different length and sampling frequency to ensure sufficient precision of the R–R intervals to determine the heart rate variability (HRV). An accurate HRV is the gold standard for predicting the AF condition; therefore, a current challenge is to determine whether a DL approach can be used to analyze raw ECG data in a broad range of devices. This paper demonstrates powerful results for end-to-end implementation of AF detection based on a convolutional neural network (AFibNet). The method used a single learning system without considering the variety of signal lengths and frequency samplings. For implementation, the AFibNet is processed with a computational cloud-based DL approach. This study utilized a one-dimension convolutional neural networks (1D-CNNs) model for 11,842 subjects. It was trained and validated with 8232 records based on three datasets and tested with 3610 records based on eight datasets. The predicted results, when compared with the diagnosis results indicated by human practitioners, showed a 99.80% accuracy, sensitivity, and specificity. Result Meanwhile, when tested using unseen data, the AF detection reaches 98.94% accuracy, 98.97% sensitivity, and 98.97% specificity at a sample period of 0.02 seconds using the DL Cloud System. To improve the confidence of the AFibNet model, it also validated with 18 arrhythmias condition defined as Non-AF-class. Thus, the data is increased from 11,842 to 26,349 instances for three-class, i.e., Normal sinus (N), AF and Non-AF. The result found 96.36% accuracy, 93.65% sensitivity, and 96.92% specificity. Conclusion These findings demonstrate that the proposed approach can use unknown data to derive feature maps and reliably detect the AF periods. We have found that our cloud-DL system is suitable for practical deployment
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Affiliation(s)
- Bambang Tutuko
- Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang, 30139, Indonesia
| | - Siti Nurmaini
- Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang, 30139, Indonesia.
| | - Alexander Edo Tondas
- Department of Cardiology and Vascular Medicine, Dr. Mohammad Hoesin Hospital, Palembang, Indonesia
| | - Muhammad Naufal Rachmatullah
- Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang, 30139, Indonesia
| | - Annisa Darmawahyuni
- Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang, 30139, Indonesia
| | - Ria Esafri
- Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang, 30139, Indonesia
| | - Firdaus Firdaus
- Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang, 30139, Indonesia
| | - Ade Iriani Sapitri
- Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang, 30139, Indonesia
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Tong Y, Sun Y, Zhou P, Shen Y, Jiang H, Sha X, Chang S. Locating abnormal heartbeats in ECG segments based on deep weakly supervised learning. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102674] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Identification of Arrhythmia by Using a Decision Tree and Gated Network Fusion Model. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:6665357. [PMID: 34194537 PMCID: PMC8181111 DOI: 10.1155/2021/6665357] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Revised: 04/10/2021] [Accepted: 05/03/2021] [Indexed: 11/17/2022]
Abstract
In recent years, deep learning (DNN) based methods have made leapfrogging level breakthroughs in detecting cardiac arrhythmias as the cost effectiveness of arithmetic power, and data size has broken through the tipping point. However, the inability of these methods to provide a basis for modeling decisions limits clinicians' confidence on such methods. In this paper, a Gate Recurrent Unit (GRU) and decision tree fusion model, referred to as (T-GRU), was designed to explore the problem of arrhythmia recognition and to improve the credibility of deep learning methods. The fusion model multipathway processing time-frequency domain featured the introduction of decision tree probability analysis of frequency domain features, the regularization of GRU model parameters and weight control to improve the decision tree model output weights. The MIT-BIH arrhythmia database was used for validation. Results showed that the low-frequency band features dominated the model prediction. The fusion model had an accuracy of 98.31%, sensitivity of 96.85%, specificity of 98.81%, and precision of 96.73%, indicating its high reliability and clinical significance.
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An ECG Denoising Method Based on the Generative Adversarial Residual Network. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021. [DOI: 10.1155/2021/5527904] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
High-quality and high-fidelity removal of noise in the Electrocardiogram (ECG) signal is of great significance to the auxiliary diagnosis of ECG diseases. In view of the single function of traditional denoising methods and the insufficient performance of signal details after denoising, a new method of ECG denoising based on the combination of the Generative Adversarial Network (GAN) and Residual Network is proposed. The method adopted in this paper is based on the GAN structure, and it restructures the generator and discriminator. In the generator network, residual blocks and Skip-Connecting are used to deepen the network structure and better capture the in-depth information in the ECG signal. In the discriminator network, the ResNet framework is used. In order to optimize the noise reduction process and solve the lack of local relevance considering the global ECG problem, the differential function and overall function of the maximum local difference are added in the loss function in this paper. The experimental results prove that the method used in this article has better performance than the current excellent S-Transform (S-T) algorithm, Wavelet Transform (WT) algorithm, Stacked Denoising Autoencoder (S-DAE) algorithm, and Improved Denoising Autoencoder (I-DAE) algorithm. Experiments show that the Root Mean Square Error (RMSE) of this method in the Massachusetts Institute of Technology and Beth Israel Hospital (MIT-BIH) noise pressure database is 0.0102, and the Signal-to-Noise Ratio (SNR) is 40.8526 dB, which is compared with that of the most advanced experimental methods. Our method improves the SNR by 88.57% on average. Besides the three noise intensities for comparison experiments, additional noise reduction experiments are also performed under four noise intensities in our paper. The experimental results verify the scientific nature of the model, which is that our method can effectively retain the important information conveyed by the original signal.
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Mastoi QUA, Memon MS, Lakhan A, Mohammed MA, Qabulio M, Al-Turjman F, Abdulkareem KH. Machine learning-data mining integrated approach for premature ventricular contraction prediction. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-05820-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Pandey SK, Janghel RR. Classification of electrocardiogram signal using an ensemble of deep learning models. DATA TECHNOLOGIES AND APPLICATIONS 2021. [DOI: 10.1108/dta-05-2020-0108] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
PurposeAccording to the World Health Organization, arrhythmia is one of the primary causes of deaths across the globe. In order to reduce mortality rate, cardiovascular disease should be properly identified and the proper treatment for the same should be immediately provided to the patients. The objective of this paper was to implement a better heartbeat classification model which will work better than the other implemented heartbeat classification methods.Design/methodology/approachIn this paper, the ensemble of two deep learning models is proposed to classify the MIT-BIH arrhythmia database into four different classes according to ANSI-AAMI standards. First, a convolutional neural network (CNN) model is used to classify heartbeats on a raw data set. Secondly, four features (wavelets, R-R intervals, morphological and higher-order statistics) are extracted from the data set and then applied to a long short-term memory (LSTM) model to classify the heartbeats. Finally, the ensemble of CNN and LSTM model with sum rule, product rule and majority voting has been used to identify the heartbeat classes.FindingsAmong these, the highest accuracy obtained is 98.58% using ensemble method with product rule. The results show that the ensemble of CNN and BLSTM has offered satisfactory performance compared to other techniques discussed in this study.Originality/valueIn this study, we have developed a new combination of two deep learning models to enhance the performance of arrhythmia classification using segmentation of input ECG signals. The contributions of this study are as follows: First, a deep CNN model is built to classify ECG heartbeat using a raw data set. Second, four types of features (R-R interval, HOS, morphological and wavelet) were extracted from the raw data set and then applied to the bidirectional LSTM model to classify the ECG heartbeat. Third, combination rules (sum rules, product rules and majority voting rules) were tested to ensure the accumulated probabilities of the CNN and LSTM models.
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Ensemble of kernel extreme learning machine based random forest classifiers for automatic heartbeat classification. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102138] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Yildirim O, Talo M, Ciaccio EJ, Tan RS, Acharya UR. Accurate deep neural network model to detect cardiac arrhythmia on more than 10,000 individual subject ECG records. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 197:105740. [PMID: 32932129 PMCID: PMC7477611 DOI: 10.1016/j.cmpb.2020.105740] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Accepted: 08/31/2020] [Indexed: 05/04/2023]
Abstract
BACKGROUND AND OBJECTIVE Cardiac arrhythmia, which is an abnormal heart rhythm, is a common clinical problem in cardiology. Detection of arrhythmia on an extended duration electrocardiogram (ECG) is done based on initial algorithmic software screening, with final visual validation by cardiologists. It is a time consuming and subjective process. Therefore, fully automated computer-assisted detection systems with a high degree of accuracy have an essential role in this task. In this study, we proposed an effective deep neural network (DNN) model to detect different rhythm classes from a new ECG database. METHODS Our DNN model was designed for high performance on all ECG leads. The proposed model, which included both representation learning and sequence learning tasks, showed promising results on all 12-lead inputs. Convolutional layers and sub-sampling layers were used in the representation learning phase. The sequence learning part involved a long short-term memory (LSTM) unit after representation of learning layers. RESULTS We performed two different class scenarios, including reduced rhythms (seven rhythm types) and merged rhythms (four rhythm types) according to the records from the database. Our trained DNN model achieved 92.24% and 96.13% accuracies for the reduced and merged rhythm classes, respectively. CONCLUSION Recently, deep learning algorithms have been found to be useful because of their high performance. The main challenge is the scarcity of appropriate training and testing resources because model performance is dependent on the quality and quantity of case samples. In this study, we used a new public arrhythmia database comprising more than 10,000 records. We constructed an efficient DNN model for automated detection of arrhythmia using these records.
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Affiliation(s)
- Ozal Yildirim
- Department of Computer Engineering, Munzur University, Tunceli,62000, Turkey
| | - Muhammed Talo
- Department of Software Engineering, Firat University, Elazig, Turkey
| | - Edward J Ciaccio
- Department of Medicine, Division of Cardiology, Columbia University Medical Center, New York, NY 10032, USA
| | - Ru San Tan
- National Heart Centre Singapore, Singapore; Duke-NUS Medical School, Singapore
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan; School of Management and Enterprise University of Southern Queensland, Springfield, Australia.
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Knowledge-shot learning: An interpretable deep model for classifying imbalanced electrocardiography data. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.07.045] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Gao Y, Wang H, Liu Z. A Novel Approach for Atrial Fibrillation Signal Identification Based on Temporal Attention Mechanism. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:316-319. [PMID: 33017992 DOI: 10.1109/embc44109.2020.9175823] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Atrial fibrillation (AF) is a common heart rhythm which occurs when the upper chambers of the heart beat irregularly. With the rapid development of the deep learning algorithm, the Convolutional Neural Networks (CNN) is widely investigated for the ECG classification task. However, for AF detection, the performance of CNN is greatly limited due to the lack of consideration for temporal characteristic of the ECG signal. In order to improve the discriminative ability of CNN, we introduce the attention mechanism to help the network concentrate on the informative parts and obtain the temporal features of the signals. Inspired by this idea, we propose a temporal attention block (TA-block) and a temporal attention convolutional neural network (TACNN) for the AF detection tasks. The TA-block can adaptively learn the temporal features of the signal and generate the attention weights to enhance informative features. With a stack architecture of TA-blocks, the TA-CNN obtains better performance as a result of paying more attention to the informative parts of the signal. We validate our approach on the single lead ECG classification dataset of The PhysioNet Computing in Cardiology Challenge 2017. The experimental results indicate that the proposed framework outperform state-of-the-arts classification networks.Clinical Relevance-The proposed algorithm can be potentially applied to the portable cardiovascular monitoring devices reducing the danger of AF.
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Ebrahimi Z, Loni M, Daneshtalab M, Gharehbaghi A. A review on deep learning methods for ECG arrhythmia classification. ACTA ACUST UNITED AC 2020. [DOI: 10.1016/j.eswax.2020.100033] [Citation(s) in RCA: 71] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
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Hong S, Zhou Y, Shang J, Xiao C, Sun J. Opportunities and challenges of deep learning methods for electrocardiogram data: A systematic review. Comput Biol Med 2020; 122:103801. [PMID: 32658725 DOI: 10.1016/j.compbiomed.2020.103801] [Citation(s) in RCA: 108] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2019] [Revised: 04/30/2020] [Accepted: 04/30/2020] [Indexed: 12/15/2022]
Abstract
BACKGROUND The electrocardiogram (ECG) is one of the most commonly used diagnostic tools in medicine and healthcare. Deep learning methods have achieved promising results on predictive healthcare tasks using ECG signals. OBJECTIVE This paper presents a systematic review of deep learning methods for ECG data from both modeling and application perspectives. METHODS We extracted papers that applied deep learning (deep neural network) models to ECG data that were published between January 1st of 2010 and February 29th of 2020 from Google Scholar, PubMed, and the Digital Bibliography & Library Project. We then analyzed each article according to three factors: tasks, models, and data. Finally, we discuss open challenges and unsolved problems in this area. RESULTS The total number of papers extracted was 191. Among these papers, 108 were published after 2019. Different deep learning architectures have been used in various ECG analytics tasks, such as disease detection/classification, annotation/localization, sleep staging, biometric human identification, and denoising. CONCLUSION The number of works on deep learning for ECG data has grown explosively in recent years. Such works have achieved accuracy comparable to that of traditional feature-based approaches and ensembles of multiple approaches can achieve even better results. Specifically, we found that a hybrid architecture of a convolutional neural network and recurrent neural network ensemble using expert features yields the best results. However, there are some new challenges and problems related to interpretability, scalability, and efficiency that must be addressed. Furthermore, it is also worth investigating new applications from the perspectives of datasets and methods. SIGNIFICANCE This paper summarizes existing deep learning research using ECG data from multiple perspectives and highlights existing challenges and problems to identify potential future research directions.
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Affiliation(s)
- Shenda Hong
- Department of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, USA.
| | - Yuxi Zhou
- School of Electronics Engineering and Computer Science, Peking University, Beijing, China; Key Laboratory of Machine Perception (Ministry of Education), Peking University, Beijing, China.
| | - Junyuan Shang
- School of Electronics Engineering and Computer Science, Peking University, Beijing, China; Key Laboratory of Machine Perception (Ministry of Education), Peking University, Beijing, China.
| | - Cao Xiao
- Analytics Center of Excellence, IQVIA, Cambridge, USA.
| | - Jimeng Sun
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, USA.
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Application of deep learning techniques for heartbeats detection using ECG signals-analysis and review. Comput Biol Med 2020; 120:103726. [DOI: 10.1016/j.compbiomed.2020.103726] [Citation(s) in RCA: 70] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Revised: 03/16/2020] [Accepted: 03/21/2020] [Indexed: 01/03/2023]
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38
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Li Y, Pang Y, Wang K, Li X. Toward improving ECG biometric identification using cascaded convolutional neural networks. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.01.019] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Tan C, Wu C, Huang Y, Wu C, Chen H. Identification of different species of Zanthoxyli Pericarpium based on convolution neural network. PLoS One 2020; 15:e0230287. [PMID: 32282810 PMCID: PMC7153909 DOI: 10.1371/journal.pone.0230287] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Accepted: 02/25/2020] [Indexed: 11/18/2022] Open
Abstract
Zanthoxyli Pericarpium (ZP) are the dried ripe peel of Zanthoxylum schinifolium Sieb. et Zucc (ZC) or Zanthoxylum bungeanum Maxim (ZB). It has wide range of uses both medicine and food, and favorable market value. The diverse specifications of components of ZP is exceptional, and the common aims of adulteration for economic profit is conducted. In this work, a novel method for the identification different species of ZP is proposed using convolutional neural networks (CNNs). The data used for the experiment is 5 classes obtained from camera and mobile phones. Firstly, the data considering 2 categories are trained to detect the labels by YOLO. Then, the multiple deep learning including VGG, ResNet, Inception v4, and DenseNet are introduced to identify the different species of ZP (HZB, DZB, OZB, ZA and JZC). In order to assess the performance of CNNs, compared with two traditional identification models including Support Vector Machines (SVM) and Back Propagation (BP). The experimental results demonstrate that the CNN model have a better performance to identify different species of ZP and the highest identification accuracy is 99.35%. The present study is proved to be a useful strategy for the discrimination of different traditional Chinese medicines (TCMs).
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Affiliation(s)
- Chaoqun Tan
- National Key Laboratory of Fundamental Science on Synthetic Vision, College of Computer Science, Sichuan University, Chengdu, China
- College of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Chong Wu
- National Key Laboratory of Fundamental Science on Synthetic Vision, College of Computer Science, Sichuan University, Chengdu, China
| | - Yongliang Huang
- Affiliated Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Chunjie Wu
- College of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Hu Chen
- National Key Laboratory of Fundamental Science on Synthetic Vision, College of Computer Science, Sichuan University, Chengdu, China
- * E-mail:
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Asgharzadeh-Bonab A, Amirani MC, Mehri A. Spectral entropy and deep convolutional neural network for ECG beat classification. Biocybern Biomed Eng 2020. [DOI: 10.1016/j.bbe.2020.02.004] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Zhao W, Hu J, Jia D, Wang H, Li Z, Yan C, You T. Deep Learning Based Patient-Specific Classification of Arrhythmia on ECG signal. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:1500-1503. [PMID: 31946178 DOI: 10.1109/embc.2019.8856650] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The classification of the heartbeat type is an essential function in the automatical electrocardiogram (ECG) analysis algorithm. The guideline of the ANSI/AAMI EC57 defined five types of heartbeat: non-ectopic or paced beat (N), supraventricular ectopic beat (S), ventricular ectopic beat (V), fusion of a ventricular and normal beat (F), pace beat or fusion of a paced and a normal or beat that cannot be classified (Q). In the work, a deep neural network based method was proposed to classify these five types of heartbeat. After removing the noise from ECG signals by a low-pass filter, the two-lead heartbeat segments with 2-s length were generated on the filtered signals, and classified by an adaptive ResNet model. The proposed method was evaluated on the MIT-BIH Arrhythmia Database with the patient-specific pattern. The overall accuracy was 98.6% and sensitivity of N, S, V, F were 99.4%, 85.4%, 96.6%, 90.6% respectively. Experimental results show that the proposed method achieved a good performance, and would be useful in the clinic practice.
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Data analytics for the sustainable use of resources in hospitals: Predicting the length of stay for patients with chronic diseases. INFORMATION & MANAGEMENT 2020. [DOI: 10.1016/j.im.2020.103282] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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Vasanthselvakumar R, Balasubramanian M, Sathiya S. Automatic Detection and Classification of Chronic Kidney Diseases Using CNN Architecture. ADVANCES IN INTELLIGENT SYSTEMS AND COMPUTING 2020. [DOI: 10.1007/978-981-15-1097-7_62] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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Shi H, Qin C, Xiao D, Zhao L, Liu C. Automated heartbeat classification based on deep neural network with multiple input layers. Knowl Based Syst 2020. [DOI: 10.1016/j.knosys.2019.105036] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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Xu SS, Mak MW, Cheung CC. I-Vector-Based Patient Adaptation of Deep Neural Networks for Automatic Heartbeat Classification. IEEE J Biomed Health Inform 2019; 24:717-727. [PMID: 31150349 DOI: 10.1109/jbhi.2019.2919732] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Automatic classification of electrocardiogram (ECG) signals is important for diagnosing heart arrhythmias. A big challenge in automatic ECG classification is the variation in the waveforms and characteristics of ECG signals among different patients. To address this issue, this paper proposes adapting a patient-independent deep neural network (DNN) using the information in the patient-dependent identity vectors (i-vectors). The adapted networks, namely i-vector adapted patient-specific DNNs (iAP-DNNs), are tuned toward the ECG characteristics of individual patients. For each patient, his/her ECG waveforms are compressed into an i-vector using a factor analysis model. Then, this i-vector is injected into the middle hidden layer of the patient-independent DNN. Stochastic gradient descent is then applied to fine-tune the whole network to form a patient-specific classifier. As a result, the adaptation makes use of not only the raw ECG waveforms from the specific patient but also the compact representation of his/her ECG characteristics through the i-vector. Analysis on the hidden-layer activations shows that by leveraging the information in the i-vectors, the iAP-DNNs are more capable of discriminating normal heartbeats against arrhythmic heartbeats than the networks that use the patient-specific ECG only for the adaptation. Experimental results based on the MIT-BIH database suggest that the iAP-DNNs perform better than existing patient-specific classifiers in terms of various performance measures. In particular, the sensitivity and specificity of the existing methods are all under the receiver operating characteristic curves of the iAP-DNNs.
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Offloading and Transmission Strategies for IoT Edge Devices and Networks. SENSORS 2019; 19:s19040835. [PMID: 30781650 PMCID: PMC6412226 DOI: 10.3390/s19040835] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2019] [Revised: 02/13/2019] [Accepted: 02/14/2019] [Indexed: 11/21/2022]
Abstract
We present a machine and deep learning method to offload trained deep learning model and transmit packets efficiently on resource-constrained internet of things (IoT) edge devices and networks. Recently, the types of IoT devices have become diverse and the volume of data has been increasing, such as images, voice, and time-series sensory signals generated by various devices. However, transmitting large amounts of data to a server or cloud becomes expensive owing to limited bandwidth, and leads to latency for time-sensitive operations. Therefore, we propose a novel offloading and transmission policy considering energy-efficiency, execution time, and the number of generated packets for resource-constrained IoT edge devices that run a deep learning model and a reinforcement learning method to find an optimal contention window size for effective channel access using a contention-based medium access control (MAC) protocol. A Reinforcement learning is used to improve the performance of the applied MAC protocol. Our proposed method determines the offload and transmission strategies that are better to directly send fragmented packets of raw data or to send the extracted feature vector or the final output of deep learning networks, considering the operation performance and power consumption of the resource-constrained microprocessor, as well as the power consumption of the radio transceiver and latency for transmitting the all the generated packets. In the performance evaluation, we measured the performance parameters of ARM Cortex-M4 and Cortex-M7 processors for the network simulation. The evaluation results show that our proposed adaptive channel access and learning-based offload and transmission methods outperform conventional role-based channel access schemes. They transmit packets of raw data and are effective for IoT edge devices and network protocols.
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