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Rahman S, Pal S, Yearwood J, Karmakar C. Robustness of Deep Learning models in electrocardiogram noise detection and classification. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 253:108249. [PMID: 38815528 DOI: 10.1016/j.cmpb.2024.108249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 05/20/2024] [Accepted: 05/22/2024] [Indexed: 06/01/2024]
Abstract
BACKGROUND AND OBJECTIVE Automatic electrocardiogram (ECG) signal analysis for heart disease detection has gained significant attention due to busy lifestyles. However, ECG signals are susceptible to noise, which adversely affects the performance of ECG signal analysers. Traditional blind filtering methods use predefined noise frequency and filter order, but they alter ECG biomarkers. Several Deep Learning-based ECG noise detection and classification methods exist, but no study compares recurrent neural network (RNN) and convolutional neural network (CNN) architectures and their complexity. METHODS This paper introduces a knowledge-based ECG filtering system using Deep Learning to classify ECG noise types and compare popular computer vision model architectures in a practical Internet of Medical Things (IoMT) framework. Experimental results demonstrate that the CNN-based ECG noise classifier outperforms the RNN-based model in terms of performance and training time. RESULTS The study shows that AlexNet, visual geometry group (VGG), and residual network (ResNet) achieved over 70% accuracy, specificity, sensitivity, and F1 score across six datasets. VGG and ResNet performances were comparable, but VGG was more complex than ResNet, with only a 4.57% less F1 score. CONCLUSIONS This paper introduces a Deep Learning (DL) based ECG noise classifier for a knowledge-driven ECG filtering system, offering selective filtering to reduce signal distortion. Evaluation of various CNN and RNN-based models reveals VGG and Resnet outperform. Further, the VGG model is superior in terms of performance. But Resnet performs comparably to VGG with less model complexity.
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Affiliation(s)
- Saifur Rahman
- School of Information Technology, Deakin University, Melbourne, Victoria, Australia.
| | - Shantanu Pal
- School of Information Technology, Deakin University, Melbourne, Victoria, Australia.
| | - John Yearwood
- School of Information Technology, Deakin University, Melbourne, Victoria, Australia.
| | - Chandan Karmakar
- School of Information Technology, Deakin University, Melbourne, Victoria, Australia.
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Mäkynen M, Ng GA, Li X, Schlindwein FS, Pearce TC. Compressed Deep Learning Models for Wearable Atrial Fibrillation Detection through Attention. SENSORS (BASEL, SWITZERLAND) 2024; 24:4787. [PMID: 39123835 PMCID: PMC11314646 DOI: 10.3390/s24154787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Revised: 07/02/2024] [Accepted: 07/03/2024] [Indexed: 08/12/2024]
Abstract
Deep learning (DL) models have shown promise for the accurate detection of atrial fibrillation (AF) from electrocardiogram/photoplethysmography (ECG/PPG) data, yet deploying these on resource-constrained wearable devices remains challenging. This study proposes integrating a customized channel attention mechanism to compress DL neural networks for AF detection, allowing the model to focus only on the most salient time-series features. The results demonstrate that applying compression through channel attention significantly reduces the total number of model parameters and file size while minimizing loss in detection accuracy. Notably, after compression, performance increases for certain model variants in key AF databases (ADB and C2017DB). Moreover, analyzing the learned channel attention distributions after training enhances the explainability of the AF detection models by highlighting the salient temporal ECG/PPG features most important for its diagnosis. Overall, this research establishes that integrating attention mechanisms is an effective strategy for compressing large DL models, making them deployable on low-power wearable devices. We show that this approach yields compressed, accurate, and explainable AF detectors ideal for wearables. Incorporating channel attention enables simpler yet more accurate algorithms that have the potential to provide clinicians with valuable insights into the salient temporal biomarkers of AF. Our findings highlight that the use of attention is an important direction for the future development of efficient, high-performing, and interpretable AF screening tools for wearable technology.
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Affiliation(s)
- Marko Mäkynen
- Biomedical Engineering Research Group, School of Engineering, University of Leicester, Leicester LE1 7RH, UK; (M.M.); (X.L.); (F.S.S.)
| | - G. Andre Ng
- National Institute for Health Research Leicester Cardiovascular Biomedical Research Centre, Glenfield Hospital, Leicester LE5 4PW, UK;
- Department of Cardiovascular Sciences, University of Leicester, Leicester LE1 7RH, UK
| | - Xin Li
- Biomedical Engineering Research Group, School of Engineering, University of Leicester, Leicester LE1 7RH, UK; (M.M.); (X.L.); (F.S.S.)
| | - Fernando S. Schlindwein
- Biomedical Engineering Research Group, School of Engineering, University of Leicester, Leicester LE1 7RH, UK; (M.M.); (X.L.); (F.S.S.)
| | - Timothy C. Pearce
- Biomedical Engineering Research Group, School of Engineering, University of Leicester, Leicester LE1 7RH, UK; (M.M.); (X.L.); (F.S.S.)
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3
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Cosentino N, Zhang X, Farrar EJ, Yapici HO, Coffeng R, Vaananen H, Beard JW. Performance comparison of 6 in-hospital patient monitoring systems in the detection and alarm of ventricular cardiac arrhythmias. CARDIOVASCULAR DIGITAL HEALTH JOURNAL 2024; 5:70-77. [PMID: 38765622 PMCID: PMC11096657 DOI: 10.1016/j.cvdhj.2024.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/22/2024] Open
Abstract
Background Patient monitoring devices are critical for alerting of potential cardiac arrhythmias during hospitalization; however, there are concerns of alarm fatigue due to high false alarm rates. Objective The purpose of this study was to evaluate the sensitivity and false alarm rate of hospital-based continuous electrocardiographic (ECG) monitoring technologies. Methods Six commonly used multiparameter bedside monitoring systems available in the United States were evaluated: B125M (GE HealthCare), ePM10 and iPM12 (Mindray), Efficia and IntelliVue (Philips), and Life Scope (Nihon Kohden). Sensitivity was tested using ECG recordings containing 57 true ventricular tachycardia (VT) events. False-positive rate testing used 205 patient-hours of ECG recordings containing no cardiac arrhythmias. Signals from ECG recordings were fed to devices simultaneously; high-severity arrhythmia alarms were tracked. Sensitivity to true VT events and false-positive rates were determined. Differences were assessed using Fisher exact tests (sensitivity) and Z-tests (false-positive rates). Results B125M raised 56 total alarms for 57 annotated VT events and had the highest sensitivity (98%; P <.05), followed by iPM12 (84%), Life Scope (81%), Efficia (79%), ePM10 (77%), and IntelliVue (75%). B125M raised 20 false alarms, which was significantly lower (P <.0001) than iPM12 (284), Life Scope (292), IntelliVue (304), ePM10 (324), and Efficia (493). The most common false alarm was VT, followed by nonsustained VT. Conclusion We found significant performance differences among multiparameter bedside ECG monitoring systems using previously collected recordings. B125M had the highest sensitivity in detecting true VT events and lowest false alarm rate. These results can assist in minimizing alarm fatigue and optimizing patient safety by careful selection of in-hospital continuous monitoring technology.
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Affiliation(s)
| | - Xuan Zhang
- Boston Strategic Partners Inc., Boston, Massachusetts
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4
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Talukdar D, De Deus LF, Sehgal N. Evaluation of Atrial Fibrillation Detection in Short-Term Photoplethysmography (PPG) Signals Using Artificial Intelligence. Cureus 2023; 15:e45111. [PMID: 37842400 PMCID: PMC10568237 DOI: 10.7759/cureus.45111] [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] [Accepted: 09/12/2023] [Indexed: 10/17/2023] Open
Abstract
Background Atrial fibrillation (AFIB) is a common atrial arrhythmia that affects millions of people worldwide. However, most of the time, AFIB is paroxysmal and can pass unnoticed in medical exams; therefore, regular screening is required. This paper proposes machine learning (ML) methods to detect AFIB from short-term electrocardiogram (ECG) and photoplethysmography (PPG) signals. Aim Several experiments were conducted across five different databases, with three of them containing ECG signals and the other two consisting of only PPG signals. Experiments were conducted to investigate the hypothesis that an ML model trained to predict AFIB from ECG segments could be used to predict AFIB from PPG segments. Materials and methods A random forest (RF) ML algorithm achieved the best accuracy and achieved a 90% accuracy rate on the University of Mississippi Medical Center (UMMC) dataset (216 samples) and a 97% accuracy rate on the Medical Information Mart for Intensive Care (MIMIC)-III datasets (2,134 samples). Results A total of 269,842 signal segments were analyzed across all datasets (212,266 were of normal sinus rhythm (NSR) and 57,576 corresponded to AFIB segments). Conclusions The ability to detect AFIB with significant accuracy using ML algorithms from PPG signals, which can be acquired via non-invasive contact or contactless, is a promising step forward toward the goal of achieving large-scale screening for AFIB.
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Affiliation(s)
- Debjyoti Talukdar
- Medical Research, Mkhitar Gosh Armenian-Russian International University, Yerevan, ARM
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5
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Jahmunah V, Ng EYK, Tan RS, Oh SL, Acharya UR. Uncertainty quantification in DenseNet model using myocardial infarction ECG signals. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 229:107308. [PMID: 36535127 DOI: 10.1016/j.cmpb.2022.107308] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 12/11/2022] [Accepted: 12/11/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND AND OBJECTIVE Myocardial infarction (MI) is a life-threatening condition diagnosed acutely on the electrocardiogram (ECG). Several errors, such as noise, can impair the prediction of automated ECG diagnosis. Therefore, quantification and communication of model uncertainty are essential for reliable MI diagnosis. METHODS A Dirichlet DenseNet model that could analyze out-of-distribution data and detect misclassification of MI and normal ECG signals was developed. The DenseNet model was first trained with the pre-processed MI ECG signals (from the best lead V6) acquired from the Physikalisch-Technische Bundesanstalt (PTB) database, using the reverse Kullback-Leibler (KL) divergence loss. The model was then tested with newly synthesized ECG signals with added em and ma noise samples. Predictive entropy was used as an uncertainty measure to determine the misclassification of normal and MI signals. Model performance was evaluated using four uncertainty metrics: uncertainty sensitivity (UNSE), uncertainty specificity (UNSP), uncertainty accuracy (UNAC), and uncertainty precision (UNPR); the classification threshold was set at 0.3. RESULTS The UNSE of the DenseNet model was low but increased over the studied decremental noise range (-6 to 24 dB), indicating that the model grew more confident in classifying the signals as they got less noisy. The model became more certain in its predictions from SNR values of 12 dB and 18 dB onwards, yielding UNAC values of 80% and 82.4% for em and ma noise signals, respectively. UNSP and UNPR values were close to 100% for em and ma noise signals, indicating that the model was self-aware of what it knew and didn't. CONCLUSION Through this work, it has been established that the model is reliable as it was able to convey when it was not confident in the diagnostic information it was presenting. Thus, the model is trustworthy and can be used in healthcare applications, such as the emergency diagnosis of MI on ECGs.
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Affiliation(s)
- V Jahmunah
- School of Mechanical and Aerospace Engineering, Nanyang Technological University
| | - E Y K Ng
- School of Mechanical and Aerospace Engineering, Nanyang Technological University
| | | | - Shu Lih Oh
- School of Engineering, Ngee Ann Polytechnic, Singapore
| | - U Rajendra Acharya
- School of Engineering, Ngee Ann Polytechnic, Singapore; Biomedical Engineering, School of Social Science and Technology, Singapore University of Social Sciences, Singapore; International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, Japan; Department Bioinformatics and Medical Engineering, Asia University, Taiwan.; School of Management and Enterprise, University of Southern Queensland, Australia.
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6
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Chen B, Javadi G, Hamilton A, Sibley S, Laird P, Abolmaesumi P, Maslove D, Mousavi P. Quantifying deep neural network uncertainty for atrial fibrillation detection with limited labels. Sci Rep 2022; 12:20140. [PMID: 36418604 PMCID: PMC9684456 DOI: 10.1038/s41598-022-24574-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Accepted: 11/17/2022] [Indexed: 11/25/2022] Open
Abstract
Atrial fibrillation (AF) is the most common arrhythmia found in the intensive care unit (ICU), and is associated with many adverse outcomes. Effective handling of AF and similar arrhythmias is a vital part of modern critical care, but obtaining knowledge about both disease burden and effective interventions often requires costly clinical trials. A wealth of continuous, high frequency physiological data such as the waveforms derived from electrocardiogram telemetry are promising sources for enriching clinical research. Automated detection using machine learning and in particular deep learning has been explored as a solution for processing these data. However, a lack of labels, increased presence of noise, and inability to assess the quality and trustworthiness of many machine learning model predictions pose challenges to interpretation. In this work, we propose an approach for training deep AF models on limited, noisy data and report uncertainty in their predictions. Using techniques from the fields of weakly supervised learning, we leverage a surrogate model trained on non-ICU data to create imperfect labels for a large ICU telemetry dataset. We combine these weak labels with techniques to estimate model uncertainty without the need for extensive human data annotation. AF detection models trained using this process demonstrated higher classification performance (0.64-0.67 F1 score) and improved calibration (0.05-0.07 expected calibration error).
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Affiliation(s)
- Brian Chen
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Golara Javadi
- Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada
| | | | - Stephanie Sibley
- Department of Critical Care Medicine, Queen's University, Kingston, ON, Canada
| | - Philip Laird
- Department of Critical Care Medicine, Queen's University, Kingston, ON, Canada
| | - Purang Abolmaesumi
- Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada
| | - David Maslove
- Department of Critical Care Medicine, Queen's University, Kingston, ON, Canada
| | - Parvin Mousavi
- School of Computing, Queen's University, Kingston, ON, Canada.
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7
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A two-step pre-processing tool to remove Gaussian and ectopic noise for heart rate variability analysis. Sci Rep 2022; 12:18396. [PMID: 36319659 PMCID: PMC9626590 DOI: 10.1038/s41598-022-21776-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 10/04/2022] [Indexed: 11/22/2022] Open
Abstract
Artifacts in the Electrocardiogram (ECG) degrade the quality of the recorded signal and are not conducive to heart rate variability (HRV) analysis. The two types of noise most often found in ECG recordings are technical and physiological artifacts. Current preprocessing methods primarily attend to ectopic beats but do not consider technical issues that affect the ECG. A secondary aim of this study was to investigate the effect of increasing increments of artifacts on 24 of the most used HRV measures. A two-step preprocessing approach for denoising HRV is introduced which targets each type of noise separately. First, the technical artifacts in the ECG are eliminated by applying complete ensemble empirical mode decomposition with adaptive noise. The second step removes physiological artifacts from the HRV signal using a combination filter of single dependent rank order mean and an adaptive filtering algorithm. The performance of the two-step pre-processing tool showed a high correlation coefficient of 0.846 and RMSE value of 7.69 × 10-5 for 6% of added ectopic beats and 6 dB Gaussian noise. All HRV measures studied except HF peak and LF peak are significantly affected by both types of noise. Frequency measures of Total power, HF power, and LF power and fragmentation measures; PAS, PIP, and PSS are the most sensitive to both types of noise.
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8
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Li M, Du S. Current status and trends in researches based on public intensive care databases: A scientometric investigation. Front Public Health 2022; 10:912151. [PMID: 36187634 PMCID: PMC9521614 DOI: 10.3389/fpubh.2022.912151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 08/08/2022] [Indexed: 01/22/2023] Open
Abstract
Objective Public intensive care databases cover a wide range of data that are produced in intensive care units (ICUs). Public intensive care databases draw great attention from researchers since they were time-saving and money-saving in obtaining data. This study aimed to explore the current status and trends of publications based on public intensive care databases. Methods Articles and reviews based on public intensive care databases, published from 2001 to 2021, were retrieved from the Web of Science Core Collection (WoSCC) for investigation. Scientometric software (CiteSpace and VOSviewer) were used to generate network maps and reveal hot spots of studies based on public intensive care databases. Results A total of 456 studies were collected. Zhang Zhongheng from Zhejiang University (China) and Leo Anthony Celi from Massachusetts Institute of Technology (MIT, USA) occupied important positions in studies based on public intensive care databases. Closer cooperation was observed between institutions in the same country. Six Research Topics were concluded through keyword analysis. Result of citation burst indicated that this field was in the stage of rapid development, with more diseases and clinical problems being investigated. Machine learning is still the hot research method in this field. Conclusions This is the first time that scientometrics has been used in the investigation of studies based on public intensive databases. Although more and more studies based on public intensive care databases were published, public intensive care databases may not be fully explored. Moreover, it could also help researchers directly perceive the current status and trends in this field. Public intensive care databases could be fully explored with more researchers' knowledge of this field.
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9
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Mohagheghian F, Han D, Peitzsch A, Nishita N, Ding E, Dickson EL, DiMezza D, Otabil EM, Noorishirazi K, Scott J, Lessard D, Wang Z, Whitcomb C, Tran KV, Fitzgibbons TP, McManus DD, Chon KH. Optimized Signal Quality Assessment for Photoplethysmogram Signals Using Feature Selection. IEEE Trans Biomed Eng 2022; 69:2982-2993. [PMID: 35275809 PMCID: PMC9478959 DOI: 10.1109/tbme.2022.3158582] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
OBJECTIVE With the increasing use of wearable healthcare devices for remote patient monitoring, reliable signal quality assessment (SQA) is required to ensure the high accuracy of interpretation and diagnosis on the recorded data from patients. Photoplethysmographic (PPG) signals non-invasively measured by wearable devices are extensively used to provide information about the cardiovascular system and its associated diseases. In this study, we propose an approach to optimize the quality assessment of the PPG signals. METHODS We used an ensemble-based feature selection scheme to enhance the prediction performance of the classification model to assess the quality of the PPG signals. Our approach for feature and subset size selection yielded the best-suited feature subset, which was optimized to differentiate between the clean and artifact corrupted PPG segments. CONCLUSION A high discriminatory power was achieved between two classes on the test data by the proposed feature selection approach, which led to strong performance on all dependent and independent test datasets. We achieved accuracy, sensitivity, and specificity rates of higher than 0.93, 0.89, and 0.97, respectively, for dependent test datasets, independent of heartbeat type, i.e., atrial fibrillation (AF) or non-AF data including normal sinus rhythm (NSR), premature atrial contraction (PAC), and premature ventricular contraction (PVC). For independent test datasets, accuracy, sensitivity, and specificity rates were greater than 0.93, 0.89, and 0.97, respectively, on PPG data recorded from AF and non-AF subjects. These results were found to be more accurate than those of all of the contemporary methods cited in this work. SIGNIFICANCE As the results illustrate, the advantage of our proposed scheme is its robustness against dynamic variations in the PPG signal during long-term 14-day recordings accompanied with different types of physical activities and a diverse range of fluctuations and waveforms caused by different individual hemodynamic characteristics, and various types of recording devices. This robustness instills confidence in the application of the algorithm to various kinds of wearable devices as a reliable PPG signal quality assessment approach.
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10
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Charlton PH, Kotzen K, Mejía-Mejía E, Aston PJ, Budidha K, Mant J, Pettit C, Behar JA, Kyriacou PA. Detecting beats in the photoplethysmogram: benchmarking open-source algorithms. Physiol Meas 2022; 43:085007. [PMID: 35853440 PMCID: PMC9393905 DOI: 10.1088/1361-6579/ac826d] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 07/07/2022] [Accepted: 07/19/2022] [Indexed: 11/12/2022]
Abstract
The photoplethysmogram (PPG) signal is widely used in pulse oximeters and smartwatches. A fundamental step in analysing the PPG is the detection of heartbeats. Several PPG beat detection algorithms have been proposed, although it is not clear which performs best.Objective:This study aimed to: (i) develop a framework with which to design and test PPG beat detectors; (ii) assess the performance of PPG beat detectors in different use cases; and (iii) investigate how their performance is affected by patient demographics and physiology.Approach:Fifteen beat detectors were assessed against electrocardiogram-derived heartbeats using data from eight datasets. Performance was assessed using theF1score, which combines sensitivity and positive predictive value.Main results:Eight beat detectors performed well in the absence of movement withF1scores of ≥90% on hospital data and wearable data collected at rest. Their performance was poorer during exercise withF1scores of 55%-91%; poorer in neonates than adults withF1scores of 84%-96% in neonates compared to 98%-99% in adults; and poorer in atrial fibrillation (AF) withF1scores of 92%-97% in AF compared to 99%-100% in normal sinus rhythm.Significance:Two PPG beat detectors denoted 'MSPTD' and 'qppg' performed best, with complementary performance characteristics. This evidence can be used to inform the choice of PPG beat detector algorithm. The algorithms, datasets, and assessment framework are freely available.
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Affiliation(s)
- Peter H Charlton
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, United Kingdom
- Research Centre for Biomedical Engineering, City, University of London, London, EC1V 0HB, United Kingdom
| | - Kevin Kotzen
- Faculty of Biomedical Engineering, Technion-IIT, Israel
| | - Elisa Mejía-Mejía
- Research Centre for Biomedical Engineering, City, University of London, London, EC1V 0HB, United Kingdom
| | - Philip J Aston
- Department of Mathematics, University of Surrey, Guildford, Surrey GU2 7XH, United Kingdom
| | - Karthik Budidha
- Research Centre for Biomedical Engineering, City, University of London, London, EC1V 0HB, United Kingdom
| | - Jonathan Mant
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, United Kingdom
| | - Callum Pettit
- Department of Mathematics, University of Surrey, Guildford, Surrey GU2 7XH, United Kingdom
| | | | - Panicos A Kyriacou
- Research Centre for Biomedical Engineering, City, University of London, London, EC1V 0HB, United Kingdom
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Xie J, Wang Z, Yu Z, Guo B. Enabling Timely Medical Intervention by Exploring Health-Related Multivariate Time Series with a Hybrid Attentive Model. SENSORS (BASEL, SWITZERLAND) 2022; 22:6104. [PMID: 36015865 PMCID: PMC9414519 DOI: 10.3390/s22166104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 08/03/2022] [Accepted: 08/10/2022] [Indexed: 06/15/2023]
Abstract
Modern healthcare practice, especially in intensive care units, produces a vast amount of multivariate time series of health-related data, e.g., multi-lead electrocardiogram (ECG), pulse waveform, blood pressure waveform and so on. As a result, timely and accurate prediction of medical intervention (e.g., intravenous injection) becomes possible, by exploring such semantic-rich time series. Existing works mainly focused on onset prediction at the granularity of hours that was not suitable for medication intervention in emergency medicine. This research proposes a Multi-Variable Hybrid Attentive Model (MVHA) to predict the impending need of medical intervention, by jointly mining multiple time series. Specifically, a two-level attention mechanism is designed to capture the pattern of fluctuations and trends of different time series. This work applied MVHA to the prediction of the impending intravenous injection need of critical patients at the intensive care units. Experiments on the MIMIC Waveform Database demonstrated that the proposed model achieves a prediction accuracy of 0.8475 and an ROC-AUC of 0.8318, which significantly outperforms baseline models.
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Affiliation(s)
| | - Zhu Wang
- School of Computer Science, Northwestern Polytechnical University, 1 Dongxiang Road, Chang’an District, Xi’an 710129, China
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12
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An Innovative Machine Learning Approach for Classifying ECG Signals in Healthcare Devices. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:7194419. [PMID: 35463679 PMCID: PMC9020932 DOI: 10.1155/2022/7194419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Revised: 02/20/2022] [Accepted: 02/23/2022] [Indexed: 12/24/2022]
Abstract
An ECG is a diagnostic technique that examines and records the heart's electrical impulses. It is easy to categorise and prevent computational abstractions in the ECG signal using the conventional method for obtaining ECG features. It is a significant issue, but it is also a difficult and time-consuming chore for cardiologists and medical professionals. The proposed classifier eliminates all of the following limitations. Machine learning in healthcare equipment reduces moral transgressions. This study's primary purpose is to calculate the R-R interval and analyze the blockage utilising simple algorithms and approaches that give high accuracy. The MIT-BIH dataset may be used to rebuild the data. The acquired data may include both normal and abnormal ECGs. A Gabor filter is employed to generate a noiseless signal, and DCT-DOST is used to calculate the signal's amplitude. The amplitude is computed to detect any cardiac anomalies. A genetic algorithm derives the main highlights from the R peak and cycle segment length underlying the ECG signal. So, combining data with specific qualities maximises identification. The genetic algorithm aids in hereditary computations, which aids in multitarget improvement. Finally, Radial Basis Function Neural Network (RBFNN) is presented as an example. An efficient feedforward neural network lowers the number of local minima in the signal. It shows progress in identifying both normal and abnormal ECG signals.
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13
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Butkuviene M, Petrenas A, Solosenko A, Martin-Yebra A, Marozas V, Sornmo L. Considerations on Performance Evaluation of Atrial Fibrillation Detectors. IEEE Trans Biomed Eng 2021; 68:3250-3260. [PMID: 33750686 DOI: 10.1109/tbme.2021.3067698] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
OBJECTIVE A large number of atrial fibrillation (AF) detectors have been published in recent years, signifying that the comparison of detector performance plays a central role, though not always consistent. The aim of this study is to shed needed light on aspects crucial to the evaluation of detection performance. METHODS Three types of AF detector, using either information on rhythm, rhythm and morphology, or segments of ECG samples, are implemented and studied on both real and simulated ECG signals. The properties of different performance measures are investigated, for example, in relation to dataset imbalance. RESULTS The results show that performance can differ considerably depending on the way detector output is compared to database annotations, i.e., beat-to-beat, segment-to-segment, or episode-to-episode comparison. Moreover, depending on the type of detector, the results substantiate that physiological and technical factors, e.g., changes in ECG morphology, rate of atrial premature beats, and noise level, can have a considerable influence on performance. CONCLUSION The present study demonstrates overall strengths and weaknesses of different types of detector, highlights challenges in AF detection, and proposes five recommendations on how to handle data and characterize performance.
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14
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Halvaei H, Sörnmo L, Stridh M. Signal Quality Assessment of a Novel ECG Electrode for Motion Artifact Reduction. SENSORS (BASEL, SWITZERLAND) 2021; 21:5548. [PMID: 34450990 PMCID: PMC8402297 DOI: 10.3390/s21165548] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 08/10/2021] [Accepted: 08/15/2021] [Indexed: 12/20/2022]
Abstract
BACKGROUND The presence of noise is problematic in the analysis and interpretation of the ECG, especially in ambulatory monitoring. Restricting the analysis to high-quality signal segments only comes with the risk of excluding significant arrhythmia episodes. Therefore, the development of novel electrode technology, robust to noise, continues to be warranted. METHODS The signal quality of a novel wet ECG electrode (Piotrode) is assessed and compared to a commercially available, commonly used electrode (Ambu). The assessment involves indices of QRS detection and atrial fibrillation detection performance, as well as signal quality indices (ensemble standard deviation and time-frequency repeatability), computed from ECGs recorded simultaneously from 20 healthy subjects performing everyday activities. RESULTS The QRS detection performance using the Piotrode was considerably better than when using the Ambu, especially for running but also for lighter activities. The two signal quality indices demonstrated similar trends: the gap in quality became increasingly larger as the subjects became increasingly more active. CONCLUSIONS The novel wet ECG electrode produces signals with less motion artifacts, thereby offering the potential to reduce the review burden, and accordingly the cost, associated with ambulatory monitoring.
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Affiliation(s)
- Hesam Halvaei
- Department of Biomedical Engineering, Lund University, SE-22100 Lund, Sweden;
| | - Leif Sörnmo
- Department of Biomedical Engineering, Lund University, SE-22100 Lund, Sweden;
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The Comparison Features of ECG Signal with Different Sampling Frequencies and Filter Methods for Real-Time Measurement. Symmetry (Basel) 2021. [DOI: 10.3390/sym13081461] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Electrocardiogram (ECG) signals have been used to monitor and diagnose signs of cardiovascular disease and abnormal signals about the human body. ECG signals are typically characterized by the PR, QRS, QT interval, ST-segment, and heart rate (HR) parameters. ECG devices are widely used for many applications, especially for the elderly. However, ECG signals are often affected by noises from the environment. There are mainly two types of noises that affect the ECG signals: low frequencies from muscle activity and 50/60 Hz from the electrical grid. Removing these noises is important for improving the quality of the ECG signal. A clear ECG signal makes it easy to diagnose cardiovascular problems. ECG signals with high sampling frequency are more accurate. However, the noises in the signal will be more obvious and it will be difficult to remove these noises with filters. We analyzed the symmetrical correlation between the sampling frequency of the signal and the parameters of the signal such as signal to noise ratio (SNR) and signal amplitude. This study will compare characterization of ECG signals performed at different sampling frequencies before and after applying infinite impulse response (IIR) and symmetric finite impulse response (FIR) filters. Therefore, it is critical that the sampling frequency is consistent at the same frequency of the ECG signal for accurate diagnosis. Furthermore, the approach can be also important for the device to help reduce the device’s computing power and hardware resources. Our results were tested with the MIT/ BIH database at 360 Hz sampling frequency with 11-bit resolution. We also experimented with the device operating in real-time with a sampling frequency from 100 Hz to 2133 Hz and a 24-bit resolution. The test results show the advantages of the symmetric FIR filter over IIR when applied to the filtering of ECG signals. The study’s conclusions can be applied to real-world devices to improve the quality of ECG signals.
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16
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Zhu W, Qiu L, Cai W, Yu J, Li D, Li W, Zhong J, Wang Y, Wang L. A novel method to reduce false alarms in ECG diagnostic systems: capture and quantification of noisy signals. Physiol Meas 2021; 42. [PMID: 33878739 DOI: 10.1088/1361-6579/abf9f4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 04/20/2021] [Indexed: 11/11/2022]
Abstract
Objective. Muscle artifacts (MA) and electrode motion artifacts (EMA) in electrocardiogram (ECG) signals lead to a large number of false alarms from cardiac diagnostic systems. To reduce false alarms, it is necessary to improve the performance of the diagnostic algorithm in noisy environments by removing excessively noisy signals. However, existing methods focus on signal quality assessment and contain too many artificial features. Here, we present a novel method to flexibly eliminate noisy signals without any artificial features.Approach. Our method contains an improved lightweight deep neural network (DNN) to capture the signal portions damaged by EMA and MA, uses the sample entropy to quantize noisy portions, and discards those portions that exceed a defined threshold. Our method was tested in conjunction with Pan-Tompkins (PT), Filter Bank (FB), and 'UNSW' R-peak detection algorithms along with two heartbeat classification algorithms on datasets synthesized from the MIT-BIH Noise Stress Test Database, the China Physiological Signal Challenge 2018 Database and the MIT-BIH Arrhythmia Database.Main results. For PT R-peak detection algorithms, the sensitivity (Se) increased noticeably from 89.01% to 99.42% in the synthesized datasets with a signal-to-noise ratio of 6 dB. With the same datasets, the Se of the FB algorithm increased about 9.29%, and a 3.64% increase occurred in the Se of the 'UNSW' algorithm. For heartbeat classification algorithms, the overall F1-score increased about 6% in the synthesized one-heartbeat datasets. It is the first study to utilize a DNN to capture noisy segments of the ECG signal.Significance. Too many false alarms can cause alarm fatigue. Our method can be utilized as the preprocessing before signal analysis, thereby reducing false alarms from the ECG diagnostic systems.
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Affiliation(s)
- Wenliang Zhu
- School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China, People's Republic of China.,Suzhou Institute of Biomedical Engineering and Technology, China Academy of Sciences, People's Republic of China
| | - Lishen Qiu
- School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China, People's Republic of China.,Suzhou Institute of Biomedical Engineering and Technology, China Academy of Sciences, People's Republic of China
| | - Wenqiang Cai
- School of Electronics and Information Technology, Soochow University, People's Republic of China
| | - Jie Yu
- School of Electronics and Information Technology, Soochow University, People's Republic of China
| | - Deyin Li
- School of Electronics and Information Technology, Soochow University, People's Republic of China
| | - Wanyue Li
- School of Electronics and Information Technology, Soochow University, People's Republic of China
| | - Jun Zhong
- Suzhou Institute of Biomedical Engineering and Technology, China Academy of Sciences, People's Republic of China
| | - Yan Wang
- Suzhou Institute of Biomedical Engineering and Technology, China Academy of Sciences, People's Republic of China
| | - Lirong Wang
- Suzhou Institute of Biomedical Engineering and Technology, China Academy of Sciences, People's Republic of China.,School of Electronics and Information Technology, Soochow University, People's Republic of China
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Halvaei H, Svennberg E, Sörnmo L, Stridh M. Identification of Transient Noise to Reduce False Detections in Screening for Atrial Fibrillation. Front Physiol 2021; 12:672875. [PMID: 34149452 PMCID: PMC8212862 DOI: 10.3389/fphys.2021.672875] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 04/19/2021] [Indexed: 11/13/2022] Open
Abstract
Screening for atrial fibrillation (AF) with a handheld device for recording the ECG is becoming increasingly popular. The poorer signal quality of such ECGs may lead to false detection of AF, often caused by transient noise. Consequently, the need for expert review in AF screening can become extensive. A convolutional neural network (CNN) is proposed for transient noise identification in AF detection. The network is trained using the events produced by a QRS detector, classified into either true beat detections or false detections. The CNN and a low-complexity AF detector are trained and tested using the StrokeStop I database, containing 30-s ECGs from mass screening for AF in the elderly population. Performance evaluation of the CNN-based quality control using a subset of the database resulted in sensitivity, specificity, and accuracy of 96.4, 96.9, and 96.9%, respectively. By inserting the CNN before the AF detector, the false AF detections were reduced by 22.5% without any loss in sensitivity. The results show that the number of recordings calling for expert review can be significantly reduced thanks to the identification of transient noise. The reduction of false AF detections is directly linked to the time and cost spent on expert review.
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Affiliation(s)
- Hesam Halvaei
- Department of Biomedical Engineering, Lund University, Lund, Sweden
| | - Emma Svennberg
- Department of Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Leif Sörnmo
- Department of Biomedical Engineering, Lund University, Lund, Sweden
| | - Martin Stridh
- Department of Biomedical Engineering, Lund University, Lund, Sweden
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18
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Feasibility of atrial fibrillation detection from a novel wearable armband device. CARDIOVASCULAR DIGITAL HEALTH JOURNAL 2021; 2:179-191. [PMID: 35265907 PMCID: PMC8890073 DOI: 10.1016/j.cvdhj.2021.05.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
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19
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Design of a nearly linear-phase IIR filter and JPEG compression ECG signal in real-time system. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102431] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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20
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Walkey AJ, Bashar SK, Hossain MB, Ding E, Albuquerque D, Winter M, Chon KH, McManus DD. Development and Validation of an Automated Algorithm to Detect Atrial Fibrillation Within Stored Intensive Care Unit Continuous Electrocardiographic Data: Observational Study. JMIR Cardio 2021; 5:e18840. [PMID: 33587041 PMCID: PMC8411425 DOI: 10.2196/18840] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2020] [Revised: 06/28/2020] [Accepted: 11/11/2020] [Indexed: 11/24/2022] Open
Abstract
Background Atrial fibrillation (AF) is the most common arrhythmia during critical illness, representing a sepsis-defining cardiac dysfunction associated with adverse outcomes. Large burdens of premature beats and noisy signal during sepsis may pose unique challenges to automated AF detection. Objective The objective of this study is to develop and validate an automated algorithm to accurately identify AF within electronic health care data among critically ill patients with sepsis. Methods This is a retrospective cohort study of patients hospitalized with sepsis identified from Medical Information Mart for Intensive Care (MIMIC III) electronic health data with linked electrocardiographic (ECG) telemetry waveforms. Within 3 separate cohorts of 50 patients, we iteratively developed and validated an automated algorithm that identifies ECG signals, removes noise, and identifies irregular rhythm and premature beats in order to identify AF. We compared the automated algorithm to current methods of AF identification in large databases, including ICD-9 (International Classification of Diseases, 9th edition) codes and hourly nurse annotation of heart rhythm. Methods of AF identification were tested against gold-standard manual ECG review. Results AF detection algorithms that did not differentiate AF from premature atrial and ventricular beats performed modestly, with 76% (95% CI 61%-87%) accuracy. Performance improved (P=.02) with the addition of premature beat detection (validation set accuracy: 94% [95% CI 83%-99%]). Median time between automated and manual detection of AF onset was 30 minutes (25th-75th percentile 0-208 minutes). The accuracy of ICD-9 codes (68%; P=.002 vs automated algorithm) and nurse charting (80%; P=.02 vs algorithm) was lower than that of the automated algorithm. Conclusions An automated algorithm using telemetry ECG data can feasibly and accurately detect AF among critically ill patients with sepsis, and represents an improvement in AF detection within large databases.
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Affiliation(s)
- Allan J Walkey
- Boston University School of Medicine, The Pulmonary Center, Boston, MA, United States
| | - Syed K Bashar
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT, United States
| | - Md Billal Hossain
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT, United States
| | - Eric Ding
- University of Massachusetts Medical School, Worcester, MA, United States
| | | | - Michael Winter
- Boston University School of Public Health, Boston, MA, United States
| | - Ki H Chon
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT, United States
| | - David D McManus
- University of Massachusetts Medical School, Worcester, MA, United States
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21
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Bashar SK, Han D, Zieneddin F, Ding E, Fitzgibbons TP, Walkey AJ, McManus DD, Javidi B, Chon KH. Novel Density Poincaré Plot Based Machine Learning Method to Detect Atrial Fibrillation From Premature Atrial/Ventricular Contractions. IEEE Trans Biomed Eng 2021; 68:448-460. [PMID: 32746035 PMCID: PMC7863548 DOI: 10.1109/tbme.2020.3004310] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
OBJECTIVE Detection of Atrial fibrillation (AF) from premature atrial contraction (PAC) and premature ventricular contraction (PVC) is difficult as frequent occurrences of these ectopic beats can mimic the typical irregular patterns of AF. In this paper, we present a novel density Poincaré plot-based machine learning method to detect AF from PAC/PVCs using electrocardiogram (ECG) recordings. METHODS First, we propose the generation of this new density Poincaré plot which is derived from the difference of the heart rate (DHR) and provides the overlapping phase-space trajectory information of the DHR. Next, from this density Poincaré plot, several image processing domain-based approaches including statistical central moments, template correlation, Zernike moment, discrete wavelet transform and Hough transform features are used to extract suitable features. Subsequently, the infinite latent feature selection algorithm is implemented to rank the features. Finally, classification of AF vs. PAC/PVC is performed using K-Nearest Neighbor, Support vector machine (SVM) and Random Forest (RF) classifiers. Our method is developed and validated using a subset of Medical Information Mart for Intensive Care (MIMIC) III database containing 10 AF and 10 PAC/PVC subjects. Results- During the segment-wise 10-fold cross-validation, SVM achieved the best performance with 98.99% sensitivity, 95.18% specificity and 97.45% accuracy with the extracted features. In subject-wise scenario, RF achieved the highest accuracy of 91.93%. Moreover, we further validated the proposed method using two other databases: wearable armband ECG data and the Physionet AFPDB. 100% PAC detection accuracy was obtained for both databases without any further training. CONCLUSION Our proposed density Poincaré plot-based method showed superior performance when compared with four existing algorithms; thus showing the efficacy of the extracted image domain-based features. SIGNIFICANCE From intensive care unit's ECG to wearable armband ECGs, the proposed method is shown to discriminate PAC/PVCs from AF with high accuracy.
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Bashar SK, Hossain MB, Ding E, Walkey AJ, McManus DD, Chon KH. Atrial Fibrillation Detection During Sepsis: Study on MIMIC III ICU Data. IEEE J Biomed Health Inform 2020; 24:3124-3135. [PMID: 32750900 PMCID: PMC7670858 DOI: 10.1109/jbhi.2020.2995139] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Sepsis is defined by life-threatening organ dysfunction during infection and is one of the leading causes of critical illness. During sepsis, there is high risk that new-onset of atrial fibrillation (AF) can occur, which is associated with significant morbidity and mortality. As a result, computer aided automated and reliable detection of new-onset AF during sepsis is crucial, especially for the critically ill patients in the intensive care unit (ICU). In this paper, a novel automated and robust two-step algorithm to detect AF from ICU patients using electrocardiogram (ECG) signals is presented. First, several statistical parameters including root mean square of successive differences, Shannon entropy, and sample entropy were calculated from the heart rate for the screening of possible AF segments. Next, Poincaré plot-based features along with P-wave characteristics were used to reduce false positive detection of AF, caused by the premature atrial and ventricular beats. A subset of the Medical Information Mart for Intensive Care (MIMIC) III database containing 198 subjects was used in this study. During the training and validation phases, both the simple thresholding as well as machine learning classifiers achieved very high segment-wise AF classification performance. Finally, we tested the performance of our proposed algorithm using two independent test data sets and compared the performance with two state-of-the-art methods. The algorithm achieved an overall 100% sensitivity, 98% specificity, 98.99% accuracy, 98% positive predictive value, and 100% negative predictive value on the subject-wise AF detection, thus showing the efficacy of our proposed algorithm in critically ill sepsis patients. The annotations of the data have been made publicly available for other investigators.
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Bashar SK, Han D, Zieneddin F, Ding E, Walkey AJ, McManus DD, Chon KH. Preliminary Results on Density Poincare Plot Based Atrial Fibrillation Detection from Premature Atrial/Ventricular Contractions .. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:2594-2597. [PMID: 33018537 DOI: 10.1109/embc44109.2020.9175216] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Detection of Atrial fibrillation (AF) from premature atrial contraction (PAC) and premature ventricular contraction (PVC) is challenging as frequent occurrences of these ectopic beats can mimic the typical irregular patterns of AF. In this paper, we present a preliminary study of using density Poincare plot based machine learning method to detect AF from PAC/PVCs using electrocardiogram (ECG) recordings. First, we propose creation of this new density Poincare plot which is derived from the difference of the heart rate. Next, from this density Poincare plot, template correlation and discrete wavelet transform are used to extract suitable image-based features, which is followed by infinite latent feature selection algorithm to rank the features. Finally, classification of AF vs PAC/PVC is performed using K-Nearest Neighbor, discriminant analysis and support vector machine (SVM) classifiers. Our method is developed and validated using a subset of Medical Information Mart for Intensive Care (MIMIC) III database containing 8 AF and 8 PAC/PVC subjects. Both 10-fold and leave-one-subject-out cross validations are performed to show the robustness of our proposed method. During the 10-fold cross-validation, SVM achieved the best performance with 99.49% sensitivity, 94.51% specificity and 97.29% accuracy with the extracted features while for the leave-one-subject-out, the highest overall accuracy is 90.91%. Moreover, when compared with two state-of-the-art methods, the proposed algorithm achieves superior AF vs. PAC/PVC discrimination performance.Clinical Relevance-This preliminary study shows that with the help of density Poincare plot, AF can be separated from PAC/PVC with better accuracy.
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A Deep Learning Approach for Featureless Robust Quality Assessment of Intermittent Atrial Fibrillation Recordings from Portable and Wearable Devices. ENTROPY 2020; 22:e22070733. [PMID: 33286505 PMCID: PMC7517279 DOI: 10.3390/e22070733] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 06/27/2020] [Accepted: 06/28/2020] [Indexed: 01/03/2023]
Abstract
Atrial fibrillation (AF) is the most common heart rhythm disturbance in clinical practice. It often starts with asymptomatic and very short episodes, which are extremely difficult to detect without long-term monitoring of the patient’s electrocardiogram (ECG). Although recent portable and wearable devices may become very useful in this context, they often record ECG signals strongly corrupted with noise and artifacts. This impairs automatized ulterior analyses that could only be conducted reliably through a previous stage of automatic identification of high-quality ECG intervals. So far, a variety of techniques for ECG quality assessment have been proposed, but poor performances have been reported on recordings from patients with AF. This work introduces a novel deep learning-based algorithm to robustly identify high-quality ECG segments within the challenging environment of single-lead recordings alternating sinus rhythm, AF episodes and other rhythms. The method is based on the high learning capability of a convolutional neural network, which has been trained with 2-D images obtained when turning ECG signals into wavelet scalograms. For its validation, almost 100,000 ECG segments from three different databases have been analyzed during 500 learning-testing iterations, thus involving more than 320,000 ECGs analyzed in total. The obtained results have revealed a discriminant ability to detect high-quality and discard low-quality ECG excerpts of about 93%, only misclassifying around 5% of clean AF segments as noisy ones. In addition, the method has also been able to deal with raw ECG recordings, without requiring signal preprocessing or feature extraction as previous stages. Consequently, it is particularly suitable for portable and wearable devices embedding, facilitating early detection of AF as well as other automatized diagnostic facilities by reliably providing high-quality ECG excerpts to further processing stages.
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