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Hashimoto K, Fujii K, Ueda D, Sumiyoshi A, Hasegawa K, Fukuhara R, Otagaki M, Okamura A, Yamamoto W, Kawano N, Yamamoto A, Miki Y, Shiojima I. Development and Validation of Artificial Intelligence-Based Algorithms for Predicting the Segments Debulked by Rotational Atherectomy Using Intravascular Ultrasound Images. Am J Cardiol 2024; 223:S0002-9149(24)00394-1. [PMID: 38782227 DOI: 10.1016/j.amjcard.2024.05.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 04/22/2024] [Accepted: 05/17/2024] [Indexed: 05/25/2024]
Abstract
We develop and evaluate an artificial intelligence (AI)-based algorithm that uses pre-rotation atherectomy (RA) intravascular ultrasound (IVUS) images to automatically predict regions debulked by RA. A total of 2106 IVUS cross-sections from 60 patients with de novo severely calcified coronary lesions who underwent IVUS-guided RA were consecutively collected. The 2 identical IVUS images of pre- and post-RA were merged, and the orientations of the debulked segments identified in the merged images were marked on the outer circle of each IVUS image. The AI model was developed based on ResNet (deep residual learning for image recognition). The architecture connected 36 fully connected layers, each corresponding to 1 of the 36 orientations segmented every 10°, to a single feature extractor. In each cross-sectional analysis, our AI model achieved an average sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 81%, 72%, 46%, 90%, and 75%, respectively. In conclusion, the AI-based algorithm can use information from pre-RA IVUS images to accurately predict regions debulked by RA and will assist interventional cardiologists in determining the treatment strategies for severely calcified coronary lesions.
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Affiliation(s)
- Kenta Hashimoto
- Division of Cardiology, Department of Medicine II, Kansai Medical University, Hirakata, Japan
| | - Kenichi Fujii
- Division of Cardiology, Department of Medicine II, Kansai Medical University, Hirakata, Japan.
| | - Daiju Ueda
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan
| | - Akinori Sumiyoshi
- Cardiovascular Vascular Center, Sakurabashi Watanabe Hospital, Osaka, Japan
| | - Katsuyuki Hasegawa
- Department of Cardiology, Higashi Takarazuka Satoh Hospital, Takarazuka, Japan
| | - Rei Fukuhara
- Department of Cardiovascular Medicine, Hyogo Prefectural Amagasaki General Medical Center, Amagasaki, Japan
| | - Munemitsu Otagaki
- Department of Cardiology, Kansai Medical University Medical Center, Moriguchi, Japan
| | - Atsunori Okamura
- Cardiovascular Vascular Center, Sakurabashi Watanabe Hospital, Osaka, Japan
| | - Wataru Yamamoto
- Department of Cardiology, Higashi Takarazuka Satoh Hospital, Takarazuka, Japan
| | - Naoki Kawano
- Department of Cardiovascular Medicine, Hyogo Prefectural Amagasaki General Medical Center, Amagasaki, Japan
| | - Akira Yamamoto
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan
| | - Yukio Miki
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan
| | - Iichiro Shiojima
- Division of Cardiology, Department of Medicine II, Kansai Medical University, Hirakata, Japan
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Wong CK, Lau YM, Lui HW, Chan WF, San WC, Zhou M, Cheng Y, Huang D, Lai WH, Lau YM, Siu CW. Automatic detection of cardiac conditions from photos of electrocardiogram captured by smartphones. Heart 2024:heartjnl-2023-323822. [PMID: 38768982 DOI: 10.1136/heartjnl-2023-323822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Accepted: 05/02/2024] [Indexed: 05/22/2024] Open
Abstract
BACKGROUND Researchers have developed machine learning-based ECG diagnostic algorithms that match or even surpass cardiologist level of performance. However, most of them cannot be used in real-world, as older generation ECG machines do not permit installation of new algorithms. OBJECTIVE To develop a smartphone application that automatically extract ECG waveforms from photos and to convert them to voltage-time series for downstream analysis by a variety of diagnostic algorithms built by researchers. METHODS A novel approach of using objective detection and image segmentation models to automatically extract ECG waveforms from photos taken by clinicians was devised. Modular machine learning models were developed to sequentially perform waveform identification, gridline removal, and scale calibration. The extracted data were then analysed using a machine learning-based cardiac rhythm classifier. RESULTS Waveforms from 40 516 scanned and 444 photographed ECGs were automatically extracted. 12 828 of 13 258 (96.8%) scanned and 5399 of 5743 (94.0%) photographed waveforms were correctly cropped and labelled. 11 604 of 12 735 (91.1%) scanned and 5062 of 5752 (88.0%) photographed waveforms achieved successful voltage-time signal extraction after automatic gridline and background noise removal. In a proof-of-concept demonstration, an atrial fibrillation diagnostic algorithm achieved 91.3% sensitivity, 94.2% specificity, 95.6% positive predictive value, 88.6% negative predictive value and 93.4% F1 score, using photos of ECGs as input. CONCLUSION Object detection and image segmentation models allow automatic extraction of ECG signals from photos for downstream diagnostics. This novel pipeline circumvents the need for costly ECG hardware upgrades, thereby paving the way for large-scale implementation of machine learning-based diagnostic algorithms.
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Affiliation(s)
- Chun-Ka Wong
- Cardiology Division, Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Yuk Ming Lau
- Cardiology Division, Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Hin Wai Lui
- Cardiology Division, Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Wai Fung Chan
- Cardiology Division, Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Wing Chun San
- Cardiology Division, Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Mi Zhou
- Cardiology Division, Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Yangyang Cheng
- Cardiology Division, Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Duo Huang
- Cardiology Division, Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Wing Hon Lai
- Cardiology Division, Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Yee Man Lau
- Cardiology Division, Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Chung Wah Siu
- Cardiology Division, Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
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Ding C, Guo Z, Rudin C, Xiao R, Shah A, Do DH, Lee RJ, Clifford G, Nahab FB, Hu X. Learning From Alarms: A Robust Learning Approach for Accurate Photoplethysmography-Based Atrial Fibrillation Detection Using Eight Million Samples Labeled With Imprecise Arrhythmia Alarms. IEEE J Biomed Health Inform 2024; 28:2650-2661. [PMID: 38300786 DOI: 10.1109/jbhi.2024.3360952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2024]
Abstract
Atrial fibrillation (AF) is a common cardiac arrhythmia with serious health consequences if not detected and treated early. Detecting AF using wearable devices with photoplethysmography (PPG) sensors and deep neural networks has demonstrated some success using proprietary algorithms in commercial solutions. However, to improve continuous AF detection in ambulatory settings towards a population-wide screening use case, we face several challenges, one of which is the lack of large-scale labeled training data. To address this challenge, we propose to leverage AF alarms from bedside patient monitors to label concurrent PPG signals, resulting in the largest PPG-AF dataset so far (8.5 M 30-second records from 24,100 patients) and demonstrating a practical approach to build large labeled PPG datasets. Furthermore, we recognize that the AF labels thus obtained contain errors because of false AF alarms generated from imperfect built-in algorithms from bedside monitors. Dealing with label noise with unknown distribution characteristics in this case requires advanced algorithms. We, therefore, introduce and open-source a novel loss design, the cluster membership consistency (CMC) loss, to mitigate label errors. By comparing CMC with state-of-the-art methods selected from a noisy label competition, we demonstrate its superiority in handling label noise in PPG data, resilience to poor-quality signals, and computational efficiency.
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Makimoto H, Kohro T. Adopting artificial intelligence in cardiovascular medicine: a scoping review. Hypertens Res 2024; 47:685-699. [PMID: 37907600 DOI: 10.1038/s41440-023-01469-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Revised: 09/03/2023] [Accepted: 09/26/2023] [Indexed: 11/02/2023]
Abstract
Recent years have witnessed significant transformations in cardiovascular medicine, driven by the rapid evolution of artificial intelligence (AI). This scoping review was conducted to capture the breadth of AI applications within cardiovascular science. Employing a structured approach, we sourced relevant articles from PubMed, with an emphasis on journals encompassing general cardiology and digital medicine. We applied filters to highlight cardiovascular articles published in journals focusing on general internal medicine, cardiology and digital medicine, thereby identifying the prevailing trends in the field. Following a comprehensive full-text screening, a total of 140 studies were identified. Over the preceding 5 years, cardiovascular medicine's interplay with AI has seen an over tenfold augmentation. This expansive growth encompasses multiple cardiovascular subspecialties, including but not limited to, general cardiology, ischemic heart disease, heart failure, and arrhythmia. Deep learning emerged as the predominant methodology. The majority of AI endeavors in this domain have been channeled toward enhancing diagnostic and prognostic capabilities, utilizing resources such as hospital datasets, electrocardiograms, and echocardiography. A significant uptrend was observed in AI's application for omics data analysis. However, a clear gap persists in AI's full-scale integration into the clinical decision-making framework. AI, particularly deep learning, has demonstrated robust applications across cardiovascular subspecialties, indicating its transformative potential in this field. As we continue on this trajectory, ensuring the alignment of technological progress with medical ethics becomes crucial. The abundant digital health data today further accentuates the need for meticulous systematic reviews, tailoring them to each cardiovascular subspecialty.
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Affiliation(s)
- Hisaki Makimoto
- Data Science Center/Cardiovascular Center, Jichi Medical University, Shimotsuke, Japan.
| | - Takahide Kohro
- Data Science Center/Cardiovascular Center, Jichi Medical University, Shimotsuke, Japan
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Antiperovitch P, Mortara D, Barrios J, Avram R, Yee K, Khaless AN, Cristal A, Tison G, Olgin J. Continuous Atrial Fibrillation Monitoring From Photoplethysmography: Comparison Between Supervised Deep Learning and Heuristic Signal Processing. JACC Clin Electrophysiol 2024; 10:334-345. [PMID: 38340117 DOI: 10.1016/j.jacep.2024.01.008] [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: 08/16/2023] [Revised: 10/19/2023] [Accepted: 10/24/2023] [Indexed: 02/12/2024]
Abstract
BACKGROUND Continuous monitoring for atrial fibrillation (AF) using photoplethysmography (PPG) from smartwatches or other wearables is challenging due to periods of poor signal quality during motion or suboptimal wearing. As a result, many consumer wearables sample infrequently and only analyze when the user is at rest, which limits the ability to perform continuous monitoring or to quantify AF. OBJECTIVES This study aimed to compare 2 methods of continuous monitoring for AF in free-living patients: a well-validated signal processing (SP) heuristic and a convolutional deep neural network (DNN) trained on raw signal. METHODS We collected 4 weeks of continuous PPG and electrocardiography signals in 204 free-living patients. Both SP and DNN models were developed and validated both on holdout patients and an external validation set. RESULTS The results show that the SP model demonstrated receiver-operating characteristic area under the curve (AUC) of 0.972 (sensitivity 99.6%, specificity: 94.4%), which was similar to the DNN receiver-operating characteristic AUC of 0.973 (sensitivity 92.2, specificity: 95.5%); however, the DNN classified significantly more data (95% vs 62%), revealing its superior tolerance of tracings prone to motion artifact. Explainability analysis revealed that the DNN automatically suppresses motion artifacts, evaluates irregularity, and learns natural AF interbeat variability. The DNN performed better and analyzed more signal in the external validation cohort using a different population and PPG sensor (AUC, 0.994; 97% analyzed vs AUC, 0.989; 88% analyzed). CONCLUSIONS DNNs perform at least as well as SP models, classify more data, and thus may be better for continuous PPG monitoring.
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Affiliation(s)
- Pavel Antiperovitch
- Division of Cardiology, Department of Medicine and Cardiovascular Research Institute, University of California-San Francisco, San Francisco, California, USA
| | - David Mortara
- Division of Cardiology, Department of Medicine and Cardiovascular Research Institute, University of California-San Francisco, San Francisco, California, USA
| | - Joshua Barrios
- Division of Cardiology, Department of Medicine and Cardiovascular Research Institute, University of California-San Francisco, San Francisco, California, USA; Bakar Computational Health Sciences Institute, University of California-San Francisco, San Francisco, California, USA
| | - Robert Avram
- Division of Cardiology, Department of Medicine and Cardiovascular Research Institute, University of California-San Francisco, San Francisco, California, USA; Montreal Heart Institute, Department of Medicine, University of Montreal, Montreal, Quebec, Canada; Heartwise.ai Laboratory, Montreal, Quebec, Canada
| | - Kimberly Yee
- Division of Cardiology, Department of Medicine and Cardiovascular Research Institute, University of California-San Francisco, San Francisco, California, USA
| | - Armeen Namjou Khaless
- Division of Cardiology, Department of Medicine and Cardiovascular Research Institute, University of California-San Francisco, San Francisco, California, USA
| | - Ashley Cristal
- Division of Cardiology, Department of Medicine and Cardiovascular Research Institute, University of California-San Francisco, San Francisco, California, USA
| | - Geoffrey Tison
- Division of Cardiology, Department of Medicine and Cardiovascular Research Institute, University of California-San Francisco, San Francisco, California, USA; Bakar Computational Health Sciences Institute, University of California-San Francisco, San Francisco, California, USA
| | - Jeffrey Olgin
- Division of Cardiology, Department of Medicine and Cardiovascular Research Institute, University of California-San Francisco, San Francisco, California, USA.
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Qureshi A, Lip GYH, Nordsletten DA, Williams SE, Aslanidi O, de Vecchi A. Imaging and biophysical modelling of thrombogenic mechanisms in atrial fibrillation and stroke. Front Cardiovasc Med 2023; 9:1074562. [PMID: 36733827 PMCID: PMC9887999 DOI: 10.3389/fcvm.2022.1074562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 12/29/2022] [Indexed: 01/18/2023] Open
Abstract
Atrial fibrillation (AF) underlies almost one third of all ischaemic strokes, with the left atrial appendage (LAA) identified as the primary thromboembolic source. Current stroke risk stratification approaches, such as the CHA2DS2-VASc score, rely mostly on clinical comorbidities, rather than thrombogenic mechanisms such as blood stasis, hypercoagulability and endothelial dysfunction-known as Virchow's triad. While detection of AF-related thrombi is possible using established cardiac imaging techniques, such as transoesophageal echocardiography, there is a growing need to reliably assess AF-patient thrombogenicity prior to thrombus formation. Over the past decade, cardiac imaging and image-based biophysical modelling have emerged as powerful tools for reproducing the mechanisms of thrombogenesis. Clinical imaging modalities such as cardiac computed tomography, magnetic resonance and echocardiographic techniques can measure blood flow velocities and identify LA fibrosis (an indicator of endothelial dysfunction), but imaging remains limited in its ability to assess blood coagulation dynamics. In-silico cardiac modelling tools-such as computational fluid dynamics for blood flow, reaction-diffusion-convection equations to mimic the coagulation cascade, and surrogate flow metrics associated with endothelial damage-have grown in prevalence and advanced mechanistic understanding of thrombogenesis. However, neither technique alone can fully elucidate thrombogenicity in AF. In future, combining cardiac imaging with in-silico modelling and integrating machine learning approaches for rapid results directly from imaging data will require development under a rigorous framework of verification and clinical validation, but may pave the way towards enhanced personalised stroke risk stratification in the growing population of AF patients. This Review will focus on the significant progress in these fields.
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Affiliation(s)
- Ahmed Qureshi
- School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom,*Correspondence: Ahmed Qureshi,
| | - Gregory Y. H. Lip
- Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool, United Kingdom
| | - David A. Nordsletten
- School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom,Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States
| | - Steven E. Williams
- School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom,Centre for Cardiovascular Science, The University of Edinburgh, Edinburgh, United Kingdom
| | - Oleg Aslanidi
- School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom
| | - Adelaide de Vecchi
- School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom
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Gill S, Bunting KV, Sartini C, Cardoso VR, Ghoreishi N, Uh HW, Williams JA, Suzart-Woischnik K, Banerjee A, Asselbergs FW, Eijkemans M, Gkoutos GV, Kotecha D. Smartphone detection of atrial fibrillation using photoplethysmography: a systematic review and meta-analysis. Heart 2022; 108:1600-1607. [PMID: 35277454 PMCID: PMC9554073 DOI: 10.1136/heartjnl-2021-320417] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Accepted: 01/24/2022] [Indexed: 11/24/2022] Open
Abstract
OBJECTIVES Timely diagnosis of atrial fibrillation (AF) is essential to reduce complications from this increasingly common condition. We sought to assess the diagnostic accuracy of smartphone camera photoplethysmography (PPG) compared with conventional electrocardiogram (ECG) for AF detection. METHODS This is a systematic review of MEDLINE, EMBASE and Cochrane (1980-December 2020), including any study or abstract, where smartphone PPG was compared with a reference ECG (1, 3 or 12-lead). Random effects meta-analysis was performed to pool sensitivity/specificity and identify publication bias, with study quality assessed using the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies-2) risk of bias tool. RESULTS 28 studies were included (10 full-text publications and 18 abstracts), providing 31 comparisons of smartphone PPG versus ECG for AF detection. 11 404 participants were included (2950 in AF), with most studies being small and based in secondary care. Sensitivity and specificity for AF detection were high, ranging from 81% to 100%, and from 85% to 100%, respectively. 20 comparisons from 17 studies were meta-analysed, including 6891 participants (2299 with AF); the pooled sensitivity was 94% (95% CI 92% to 95%) and specificity 97% (96%-98%), with substantial heterogeneity (p<0.01). Studies were of poor quality overall and none met all the QUADAS-2 criteria, with particular issues regarding selection bias and the potential for publication bias. CONCLUSION PPG provides a non-invasive, patient-led screening tool for AF. However, current evidence is limited to small, biased, low-quality studies with unrealistically high sensitivity and specificity. Further studies are needed, preferably independent from manufacturers, in order to advise clinicians on the true value of PPG technology for AF detection.
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Affiliation(s)
- Simrat Gill
- Institute of Cardiovascular Sciences, University of Birmingham, Birmingham, UK
- Health Data Research UK Midlands Site, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Karina V Bunting
- Institute of Cardiovascular Sciences, University of Birmingham, Birmingham, UK
- Health Data Research UK Midlands Site, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Claudio Sartini
- Medical Affairs and Pharmacovigilance, Pharmaceuticals, Integrated Evidence Generation, Bayer AG, Leverkusen, Nordrhein-Westfalen, Germany
| | - Victor Roth Cardoso
- Institute of Cardiovascular Sciences, University of Birmingham, Birmingham, UK
- Health Data Research UK Midlands Site, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
| | - Narges Ghoreishi
- Medical Affairs and Pharmacovigilance, Pharmaceuticals, Integrated Evidence Generation, Bayer AG, Leverkusen, Nordrhein-Westfalen, Germany
| | - Hae-Won Uh
- Julius Center for Health Sciences and Primary Care, University Medical Centre, Utrecht, Netherlands
| | - John A Williams
- Health Data Research UK Midlands Site, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
| | - Kiliana Suzart-Woischnik
- Medical Affairs and Pharmacovigilance, Pharmaceuticals, Integrated Evidence Generation, Bayer AG, Leverkusen, Nordrhein-Westfalen, Germany
| | - Amitava Banerjee
- Farr Institute of Health Informatics Research, University College London, London, UK
| | - Folkert W Asselbergs
- Department of Cardiology, University Medical Centre Utrecht Department of Cardiology, Utrecht, Netherlands
- Department of Cardiology, University College London Faculty of Population Health Sciences, London, UK
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK
| | - Mjc Eijkemans
- Julius Center for Health Sciences and Primary Care, University Medical Centre, Utrecht, Netherlands
| | - Georgios V Gkoutos
- Health Data Research UK Midlands Site, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
| | - Dipak Kotecha
- Institute of Cardiovascular Sciences, University of Birmingham, Birmingham, UK
- Health Data Research UK Midlands Site, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Department of Cardiology, University Medical Centre Utrecht Department of Cardiology, Utrecht, Netherlands
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Sun Z, Junttila J, Tulppo M, Seppanen T, Li X. Non-Contact Atrial Fibrillation Detection From Face Videos by Learning Systolic Peaks. IEEE J Biomed Health Inform 2022; 26:4587-4598. [PMID: 35867368 DOI: 10.1109/jbhi.2022.3193117] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
OBJECTIVE We propose a non-contact approach for atrial fibrillation (AF) detection from face videos. METHODS Face videos, electrocardiography (ECG), and contact photoplethysmography (PPG) from 100 healthy subjects and 100 AF patients are recorded. Data recordings from healthy subjects are all labeled as healthy. Two cardiologists evaluated ECG recordings of patients and labeled each recording as AF, sinus rhythm (SR), or atrial flutter (AFL). We use the 3D convolutional neural network for remote PPG monitoring and propose a novel loss function (Wasserstein distance) to use the timing of systolic peaks from contact PPG as the label for our model training. Then a set of heart rate variability (HRV) features are calculated from the inter-beat intervals, and a support vector machine (SVM) classifier is trained with HRV features. RESULTS Our proposed method can accurately extract systolic peaks from face videos for AF detection. The proposed method is trained with subject-independent 10-fold cross-validation with 30s video clips and tested on two tasks. 1) Classification of healthy versus AF: the accuracy, sensitivity, and specificity are 96.00%, 95.36%, and 96.12%. 2) Classification of SR versus AF: the accuracy, sensitivity, and specificity are 95.23%, 98.53%, and 91.12%. In addition, we also demonstrate the feasibility of non-contact AFL detection. CONCLUSION We achieve good performance of non-contact AF detection by learning systolic peaks. SIGNIFICANCE non-contact AF detection can be used for self-screening of AF symptoms for suspectable populations at home or self-monitoring of AF recurrence after treatment for chronic patients.
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Sivanandarajah P, Wu H, Bajaj N, Khan S, Ng FS. Is machine learning the future for atrial fibrillation screening? CARDIOVASCULAR DIGITAL HEALTH JOURNAL 2022; 3:136-145. [PMID: 35720677 PMCID: PMC9204790 DOI: 10.1016/j.cvdhj.2022.04.001] [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: 11/29/2022] Open
Abstract
Atrial fibrillation (AF) is the most common arrhythmia and causes significant morbidity and mortality. Early identification of AF may lead to early treatment of AF and may thus prevent AF-related strokes and complications. However, there is no current formal, cost-effective strategy for population screening for AF. In this review, we give a brief overview of targeted screening for AF, AF risk score models used for screening and describe the different screening tools. We then go on to extensively discuss the potential applications of machine learning in AF screening.
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Affiliation(s)
- Pavidra Sivanandarajah
- National Heart and Lung Institute, Imperial College London, London, United Kingdom.,Chelsea and Westminster NHS Foundation Trust, London, United Kingdom
| | - Huiyi Wu
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Nikesh Bajaj
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Sadia Khan
- Chelsea and Westminster NHS Foundation Trust, London, United Kingdom
| | - Fu Siong Ng
- National Heart and Lung Institute, Imperial College London, London, United Kingdom.,Chelsea and Westminster NHS Foundation Trust, London, United Kingdom
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10
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Wong CK, Un KC, Zhou M, Cheng Y, Lau YM, Shea PC, Lui HW, Zuo ML, Yin LX, Chan EW, Wong ICK, Sin SWC, Yeung PPN, Chen H, Wibowo S, Wei TLN, Lee SM, Chow A, Tong RCF, Hai J, Tam FCC, Siu CW. Daily ambulatory remote monitoring system for drug escalation in chronic heart failure with reduced ejection fraction: pilot phase of DAVID-HF study. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2022; 3:284-295. [PMID: 36713022 PMCID: PMC9708020 DOI: 10.1093/ehjdh/ztac024] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 03/27/2022] [Accepted: 04/11/2022] [Indexed: 05/04/2023]
Abstract
AIMS Underutilization of guideline-directed heart failure with reduced ejection fraction (HFrEF) medications contributes to poor outcomes. METHODS AND RESULTS A pilot study to evaluate the safety and efficacy of a home-based remote monitoring system for HFrEF management was performed. The system included wearable armband monitors paired with the smartphone application. An HFrEF medication titration algorithm was used to adjust medication daily. The primary endpoint was HFrEF medication utilization at 120 days. Twenty patients (60.5 ± 8.2 years, men: 85%) with HFrEF were recruited. All received angiotensin-converting enzyme inhibitor (ACEI)/angiotensin receptor blocker (ARB)/angiotensin receptor-neprilysin inhibitor (ARNI) at recruitment; 45% received ≥50% maximal targeted dose (MTD) with % MTD of 44.4 ± 31.7%. At baseline, 90 and 70% received beta-adrenergic blocker and mineralocorticoid receptor antagonist (MRA), 35% received ≥50% MTD beta-adrenergic blocker with % MTD of 34.1 ± 29.6%, and 25% received ≥50% MTD MRA with % MTD of 25.0 ± 19.9%. At 120 days, 70% received ≥50% MTD ACEI/ARB/ARNI (P = 0.110) with % MTD increased to 64.4 ± 33.5% (P = 0.060). The proportion receiving ≥50% MTD ARNI increased from 15 to 55% (P = 0.089) with % MTD ARNI increased from 20.6 ± 30.9 to 53.1 ± 39.5% (P = 0.006*). More patients received ≥50% MTD MRA (65 vs. 25%, P = 0.011*) with % MTD MRA increased from 25.0 ± 19.9 to 46.2 ± 28.8% (P = 0.009*). Ninety-five per cent of patients had reduced NT-proBNP with the percentage reduction of 26.7 ± 19.7%. CONCLUSION Heart failure with reduced ejection fraction medication escalation with remote monitoring appeared feasible.
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Affiliation(s)
- Chun Ka Wong
- Division of Cardiology, Department of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Ka Chun Un
- Division of Cardiology, Department of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Mi Zhou
- Division of Cardiology, Department of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Yangyang Cheng
- Division of Cardiology, Department of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Yuk Ming Lau
- Division of Cardiology, Department of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Puigi Catherine Shea
- Division of Cardiology, Department of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Hin Wai Lui
- Division of Cardiology, Department of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Ming Liang Zuo
- Department of Echocardiography & Non-invasive Cardiology Laboratory, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, China
| | - Li Xue Yin
- Department of Echocardiography & Non-invasive Cardiology Laboratory, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, China
| | - Esther W Chan
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Ian C K Wong
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Simon Wai Ching Sin
- Respiratory Division, Department of Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Pauline Pui Ning Yeung
- Respiratory Division, Department of Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Hao Chen
- Biofourmis Singapore Pte Ltd, Singapore, Singapore
| | - Sandi Wibowo
- Biofourmis Singapore Pte Ltd, Singapore, Singapore
| | | | | | | | | | - Jojo Hai
- Division of Cardiology, Department of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Frankie Chor Cheung Tam
- Division of Cardiology, Department of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Chung Wah Siu
- Corresponding author. Tel: +86 (852) 2255 4694, Fax: +86 (852) 2818 6304,
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11
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Chu J, Yang WT, Chang YT, Yang FL. Visual Reassessment with Flux-Interval Plot Configuration after Automatic Classification for Accurate Atrial Fibrillation Detection by Photoplethysmography. Diagnostics (Basel) 2022; 12:diagnostics12061304. [PMID: 35741114 PMCID: PMC9221814 DOI: 10.3390/diagnostics12061304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 05/13/2022] [Accepted: 05/19/2022] [Indexed: 12/10/2022] Open
Abstract
Atrial fibrillation (AFib) is a common type of arrhythmia that is often clinically asymptomatic, which increases the risk of stroke significantly but can be prevented with anticoagulation. The photoplethysmogram (PPG) has recently attracted a lot of attention as a surrogate for electrocardiography (ECG) on atrial fibrillation (AFib) detection, with its out-of-hospital usability for rapid screening or long-term monitoring. Previous studies on AFib detection via PPG signals have achieved good results, but were short of intuitive criteria like ECG p-wave absence or not, especially while using interval randomness to detect AFib suffering from conjunction with premature contractions (PAC/PVC). In this study, we newly developed a PPG flux (pulse amplitude) and interval plots-based methodology, simply comprising an irregularity index threshold of 20 and regression error threshold of 0.06 for the precise automatic detection of AFib. The proposed method with automated detection on AFib shows a combined sensitivity, specificity, accuracy, and precision of 1, 0.995, 0.995, and 0.952 across the 460 samples. Furthermore, the flux-interval plot configuration also acts as a very intuitive tool for visual reassessment to confirm the automatic detection of AFib by its distinctive plot pattern compared to other cardiac rhythms. The study demonstrated that exclusive 2 false-positive cases could be corrected after the reassessment. With the methodology’s background theory well established, the detection process automated and visualized, and the PPG sensors already extensively used, this technology is very user-friendly and convincing for promoted to in-house AFib diagnostics.
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Affiliation(s)
- Justin Chu
- Research Center for Applied Sciences, Academia Sinica, 128 Academia Rd., Sec. 2, Nankang, Taipei City 115-29, Taiwan; (J.C.); (W.-T.Y.)
| | - Wen-Tse Yang
- Research Center for Applied Sciences, Academia Sinica, 128 Academia Rd., Sec. 2, Nankang, Taipei City 115-29, Taiwan; (J.C.); (W.-T.Y.)
- Department of Biomechatronics Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei City 10607, Taiwan
| | - Yao-Ting Chang
- Division of Cardiology, Department of Internal Medicine, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, No. 289, Jianguo Rd., Xindian Dist., New Taipei City 231-42, Taiwan
- Correspondence: (Y.-T.C.); (F.-L.Y.)
| | - Fu-Liang Yang
- Research Center for Applied Sciences, Academia Sinica, 128 Academia Rd., Sec. 2, Nankang, Taipei City 115-29, Taiwan; (J.C.); (W.-T.Y.)
- Correspondence: (Y.-T.C.); (F.-L.Y.)
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12
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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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13
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Liu Z, Zhou B, Jiang Z, Chen X, Li Y, Tang M, Miao F. Multiclass Arrhythmia Detection and Classification From Photoplethysmography Signals Using a Deep Convolutional Neural Network. J Am Heart Assoc 2022; 11:e023555. [PMID: 35322685 PMCID: PMC9075456 DOI: 10.1161/jaha.121.023555] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background Studies have reported the use of photoplethysmography signals to detect atrial fibrillation; however, the use of photoplethysmography signals in classifying multiclass arrhythmias has rarely been reported. Our study investigated the feasibility of using photoplethysmography signals and a deep convolutional neural network to classify multiclass arrhythmia types. Methods and Results ECG and photoplethysmography signals were collected simultaneously from a group of patients who underwent radiofrequency ablation for arrhythmias. A deep convolutional neural network was developed to classify multiple rhythms based on 10‐second photoplethysmography waveforms. Classification performance was evaluated by calculating the area under the microaverage receiver operating characteristic curve, overall accuracy, sensitivity, specificity, and positive and negative predictive values against annotations on the rhythm of arrhythmias provided by 2 cardiologists consulting the ECG results. A total of 228 patients were included; 118 217 pairs of 10‐second photoplethysmography and ECG waveforms were used. When validated against an independent test data set (23 384 photoplethysmography waveforms from 45 patients), the DCNN achieved an overall accuracy of 85.0% for 6 rhythm types (sinus rhythm, premature ventricular contraction, premature atrial contraction, ventricular tachycardia, supraventricular tachycardia, and atrial fibrillation); the microaverage area under the microaverage receiver operating characteristic curve was 0.978; the average sensitivity, specificity, and positive and negative predictive values were 75.8%, 96.9%, 75.2%, and 97.0%, respectively. Conclusions This study demonstrated the feasibility of classifying multiclass arrhythmias from photoplethysmography signals using deep learning techniques. The approach is attractive for population‐based screening and may hold promise for the long‐term surveillance and management of arrhythmia. Registration URL: www.chictr.org.cn. Identifier: ChiCTR2000031170.
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Affiliation(s)
- Zengding Liu
- Key Laboratory for Health Informatics Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Shenzhen China.,University of Chinese Academy of Sciences Beijing China
| | - Bin Zhou
- Department of Cardiology Laboratory of Heart Center Zhujiang HospitalSouthern Medical University Guangzhou China.,Fuwai HospitalNational Center for Cardiovascular DiseaseState Key Lab of Cardiovascular DiseaseNational Clinical Research Center of Cardiovascular DiseasesChinese Academy of Medical Sciences and Peking Union Medical College Beijing China
| | - Zhiming Jiang
- Key Laboratory for Health Informatics Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Shenzhen China
| | - Xi Chen
- Key Laboratory for Health Informatics Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Shenzhen China
| | - Ye Li
- Key Laboratory for Health Informatics Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Shenzhen China.,Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Shenzhen China
| | - Min Tang
- Fuwai HospitalNational Center for Cardiovascular DiseaseState Key Lab of Cardiovascular DiseaseNational Clinical Research Center of Cardiovascular DiseasesChinese Academy of Medical Sciences and Peking Union Medical College Beijing China
| | - Fen Miao
- Key Laboratory for Health Informatics Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Shenzhen China
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14
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Sehrawat O, Kashou AH, Noseworthy PA. Artificial Intelligence and Atrial Fibrillation. J Cardiovasc Electrophysiol 2022; 33:1932-1943. [PMID: 35258136 PMCID: PMC9717694 DOI: 10.1111/jce.15440] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/25/2021] [Revised: 02/03/2022] [Accepted: 03/01/2022] [Indexed: 11/30/2022]
Abstract
In the context of atrial fibrillation (AF), traditional clinical practices have thus far fallen short in several domains such as identifying patients at risk of incident AF or patients with concomitant undetected paroxysmal AF. Novel approaches leveraging artificial intelligence have the potential to provide new tools to deal with some of these old problems. In this review we focus on the roles of artificial intelligence-enabled ECG pertaining to AF, potential roles of deep learning (DL) models in the context of current knowledge gaps, as well as limitations of these models. One key area where DL models can translate to better patient outcomes is through automated ECG interpretation. Further, we overview some of the challenges facing AF screening and the harms and benefits of screening. In this context, a unique model was developed to detect underlying hidden AF from sinus rhythm and is discussed in detail with its potential uses. Knowledge gaps also remain regarding the best ways to monitor patients with embolic stroke of undetermined source (ESUS) and who would benefit most from oral anticoagulation. The AI-enabled AF model is one potential way to tackle this complex problem as it could be used to identify a subset of high-risk ESUS patients likely to benefit from empirical oral anticoagulation. Role of DL models assessing AF burden from long duration ECG data is also discussed as a way of guiding management. There is a trend towards the use of consumer-grade wristbands and watches to detect AF from photoplethysmography data. However, ECG currently remains the gold standard to detect arrythmias including AF. Lastly, role of adequate external validation of the models and clinical trials to study true performance is discussed. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Ojasav Sehrawat
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Anthony H Kashou
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
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15
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Park J, Seok HS, Kim SS, Shin H. Photoplethysmogram Analysis and Applications: An Integrative Review. Front Physiol 2022; 12:808451. [PMID: 35300400 PMCID: PMC8920970 DOI: 10.3389/fphys.2021.808451] [Citation(s) in RCA: 49] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 12/21/2021] [Indexed: 12/03/2022] Open
Abstract
Beyond its use in a clinical environment, photoplethysmogram (PPG) is increasingly used for measuring the physiological state of an individual in daily life. This review aims to examine existing research on photoplethysmogram concerning its generation mechanisms, measurement principles, clinical applications, noise definition, pre-processing techniques, feature detection techniques, and post-processing techniques for photoplethysmogram processing, especially from an engineering point of view. We performed an extensive search with the PubMed, Google Scholar, Institute of Electrical and Electronics Engineers (IEEE), ScienceDirect, and Web of Science databases. Exclusion conditions did not include the year of publication, but articles not published in English were excluded. Based on 118 articles, we identified four main topics of enabling PPG: (A) PPG waveform, (B) PPG features and clinical applications including basic features based on the original PPG waveform, combined features of PPG, and derivative features of PPG, (C) PPG noise including motion artifact baseline wandering and hypoperfusion, and (D) PPG signal processing including PPG preprocessing, PPG peak detection, and signal quality index. The application field of photoplethysmogram has been extending from the clinical to the mobile environment. Although there is no standardized pre-processing pipeline for PPG signal processing, as PPG data are acquired and accumulated in various ways, the recently proposed machine learning-based method is expected to offer a promising solution.
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Affiliation(s)
- Junyung Park
- Department of Biomedical Engineering, Chonnam National University, Yeosu, South Korea
| | - Hyeon Seok Seok
- Department of Biomedical Engineering, Chonnam National University, Yeosu, South Korea
| | - Sang-Su Kim
- Department of Biomedical Engineering, Chonnam National University, Yeosu, South Korea
| | - Hangsik Shin
- Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
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16
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Isaksen JL, Baumert M, Hermans ANL, Maleckar M, Linz D. Artificial intelligence for the detection, prediction, and management of atrial fibrillation. Herzschrittmacherther Elektrophysiol 2022; 33:34-41. [PMID: 35147766 PMCID: PMC8853037 DOI: 10.1007/s00399-022-00839-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 01/17/2022] [Indexed: 11/07/2022]
Abstract
The present article reviews the state of the art of machine learning algorithms for the detection, prediction, and management of atrial fibrillation (AF), as well as of the development and evaluation of artificial intelligence (AI) in cardiology and beyond. Today, AI detects AF with a high accuracy using 12-lead or single-lead electrocardiograms or photoplethysmography. The prediction of paroxysmal or future AF currently operates at a level of precision that is too low for clinical use. Further studies are needed to determine whether patient selection for interventions may be possible with machine learning.
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Affiliation(s)
- Jonas L Isaksen
- Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Mathias Baumert
- School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, SA, Australia
| | - Astrid N L Hermans
- Department of Cardiology, Maastricht University Medical Center and Cardiovascular Research Institute Maastricht, Maastricht, The Netherlands
| | - Molly Maleckar
- Department of Computational Physiology, Simula Research Laboratory, Oslo, Norway
| | - Dominik Linz
- Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark. .,Department of Cardiology, Maastricht University Medical Center and Cardiovascular Research Institute Maastricht, Maastricht, The Netherlands.
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17
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Attia ZI, Harmon DM, Behr ER, Friedman PA. Application of artificial intelligence to the electrocardiogram. Eur Heart J 2021; 42:4717-4730. [PMID: 34534279 PMCID: PMC8500024 DOI: 10.1093/eurheartj/ehab649] [Citation(s) in RCA: 86] [Impact Index Per Article: 28.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 06/18/2021] [Accepted: 09/02/2021] [Indexed: 01/02/2023] Open
Abstract
Artificial intelligence (AI) has given the electrocardiogram (ECG) and clinicians reading them super-human diagnostic abilities. Trained without hard-coded rules by finding often subclinical patterns in huge datasets, AI transforms the ECG, a ubiquitous, non-invasive cardiac test that is integrated into practice workflows, into a screening tool and predictor of cardiac and non-cardiac diseases, often in asymptomatic individuals. This review describes the mathematical background behind supervised AI algorithms, and discusses selected AI ECG cardiac screening algorithms including those for the detection of left ventricular dysfunction, episodic atrial fibrillation from a tracing recorded during normal sinus rhythm, and other structural and valvular diseases. The ability to learn from big data sets, without the need to understand the biological mechanism, has created opportunities for detecting non-cardiac diseases as COVID-19 and introduced challenges with regards to data privacy. Like all medical tests, the AI ECG must be carefully vetted and validated in real-world clinical environments. Finally, with mobile form factors that allow acquisition of medical-grade ECGs from smartphones and wearables, the use of AI may enable massive scalability to democratize healthcare.
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Affiliation(s)
- Zachi I Attia
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - David M Harmon
- Department of Internal Medicine, Mayo Clinic School of Graduate Medical Education, 200 First Street SW, Rochester, MN 55905, USA
| | - Elijah R Behr
- Cardiology Research Center and Cardiovascular Clinical Academic Group, Molecular and Clinical Sciences Institute, St. George’s University of London and St. George’s University Hospitals NHS Foundation Trust, Blackshaw Rd, London SW17 0QT, UK
- Mayo Clinic Healthcare, 15 Portland Pl, London W1B 1PT, UK
| | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
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18
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Cheng X, Manandhar I, Aryal S, Joe B. Application of Artificial Intelligence in Cardiovascular Medicine. Compr Physiol 2021; 11:2455-2466. [PMID: 34558666 DOI: 10.1002/cphy.c200034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The advent of advances in machine learning (ML)-based techniques has popularized wide applications of artificial intelligence (AI) in various fields ranging from robotics to medicine. In recent years, there has been a surge in the application of AI to research in cardiovascular medicine, which is largely driven by the availability of large-scale clinical and multi-omics datasets. Such applications are providing a new perspective for a better understanding of cardiovascular disease (CVD), which could be used to develop novel diagnostic and therapeutic strategies. For example, studies have shown that ML has a substantial potential for early diagnosis of different types of CVD, prediction of adverse disease outcomes such as heart failure, and development of newer and personalized treatments. In this article, we provide an overview and discuss the current status of a wide range of AI applications, including machine learning, reinforcement learning, and deep learning, in cardiovascular medicine. © 2021 American Physiological Society. Compr Physiol 11:1-12, 2021.
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Affiliation(s)
- Xi Cheng
- Bioinformatics & Artificial Intelligence Laboratory, Center for Hypertension and Precision Medicine, Program in Physiological Genomics, Department of Physiology and Pharmacology, University of Toledo College of Medicine and Life Sciences, Toledo, Ohio, USA
| | - Ishan Manandhar
- Bioinformatics & Artificial Intelligence Laboratory, Center for Hypertension and Precision Medicine, Program in Physiological Genomics, Department of Physiology and Pharmacology, University of Toledo College of Medicine and Life Sciences, Toledo, Ohio, USA
| | - Sachin Aryal
- Bioinformatics & Artificial Intelligence Laboratory, Center for Hypertension and Precision Medicine, Program in Physiological Genomics, Department of Physiology and Pharmacology, University of Toledo College of Medicine and Life Sciences, Toledo, Ohio, USA
| | - Bina Joe
- Bioinformatics & Artificial Intelligence Laboratory, Center for Hypertension and Precision Medicine, Program in Physiological Genomics, Department of Physiology and Pharmacology, University of Toledo College of Medicine and Life Sciences, Toledo, Ohio, USA
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19
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Kareem M, Lei N, Ali A, Ciaccio EJ, Acharya UR, Faust O. A review of patient-led data acquisition for atrial fibrillation detection to prevent stroke. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102818] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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20
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Abstract
Arrhythmia management has been revolutionized by the ability to monitor the cardiac rhythm in a patient's home environment in real-time using high-fidelity prescription-grade and commercially available wearable electrodes. The vast amount of digitally acquired electrophysiological signals has generated the need for scalable and efficient data processing with actionable output that can be provided directly to clinicians and patients. In this setting, artificial intelligence applications are increasingly important in arrhythmia monitoring, ranging from conventional algorithmic analysis for rhythm determination to more complex deep machine learning methods that have led to the realization of fully automated humanlike rhythm determination in real-time.
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Affiliation(s)
- Konstantinos C Siontis
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street Southwest, Rochester, MN 55905, USA
| | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street Southwest, Rochester, MN 55905, USA.
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21
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Lopez Perales CR, Van Spall HGC, Maeda S, Jimenez A, Laţcu DG, Milman A, Kirakoya-Samadoulougou F, Mamas MA, Muser D, Casado Arroyo R. Mobile health applications for the detection of atrial fibrillation: a systematic review. Europace 2021; 23:11-28. [PMID: 33043358 DOI: 10.1093/europace/euaa139] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Indexed: 12/21/2022] Open
Abstract
AIMS Atrial fibrillation (AF) is the most common sustained arrhythmia and an important risk factor for stroke and heart failure. We aimed to conduct a systematic review of the literature and summarize the performance of mobile health (mHealth) devices in diagnosing and screening for AF. METHODS AND RESULTS We conducted a systematic search of MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials. Forty-three studies met the inclusion criteria and were divided into two groups: 28 studies aimed at validating smart devices for AF diagnosis, and 15 studies used smart devices to screen for AF. Evaluated technologies included smartphones, with photoplethysmographic (PPG) pulse waveform measurement or accelerometer sensors, smartbands, external electrodes that can provide a smartphone single-lead electrocardiogram (iECG), such as AliveCor, Zenicor and MyDiagnostick, and earlobe monitor. The accuracy of these devices depended on the technology and the population, AliveCor and smartphone PPG sensors being the most frequent systems analysed. The iECG provided by AliveCor demonstrated a sensitivity and specificity between 66.7% and 98.5% and 99.4% and 99.0%, respectively. The PPG sensors detected AF with a sensitivity of 85.0-100% and a specificity of 93.5-99.0%. The incidence of newly diagnosed arrhythmia ranged from 0.12% in a healthy population to 8% among hospitalized patients. CONCLUSION Although the evidence for clinical effectiveness is limited, these devices may be useful in detecting AF. While mHealth is growing in popularity, its clinical, economic, and policy implications merit further investigation. More head-to-head comparisons between mHealth and medical devices are needed to establish their comparative effectiveness.
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Affiliation(s)
- Carlos Ruben Lopez Perales
- Department of Cardiology, Hopital Erasme, Université Libre de Bruxelles, Route de Lennik 808, 1070 Brussels, Belgium.,Servicio de Cardiología, Hospital Universitario Miguel Servet, Isabel La Catolica 1-3, Zaragoza 50009, Spain
| | - Harriette G C Van Spall
- Division of Cardiology, Department of Medicine, Population Health Research Institute, McMaster University, 1280 Main Street West, Hamilton, Ontario, Canada, Canada
| | - Shingo Maeda
- Advanced Arrhythmia Research, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, 113-8519 Tokyo, Japan
| | - Alejandro Jimenez
- Division of Cardiology, University of Maryland Medical Center, 22 S. Greene Street, Baltimore, MD 21201, USA
| | - Decebal Gabriel Laţcu
- Department of Cardiology, Centre Hospitalier Princesse Grace, Avenue Pasteur, 98000, Monaco, Monaco (Principalty)
| | - Anat Milman
- Department of Cardiology, Leviev Heart Institute, The Chaim Sheba Medical Center, Tel Hashomer, and Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Fati Kirakoya-Samadoulougou
- Centre de Recherche en Epidémiologie, Biostatistiques et Recherche Clinique, Ecole de Santé Publique, Université librede Bruxelles, Avenue Franklin Roosevelt 50 - 1050, Brussels, Belgium
| | - Mamas A Mamas
- Keele Cardiovascular Research Group, Keele University, Stoke-on-Trent, Keele, Newcastle ST5 5BG, UK.,Royal Stoke University Hospital, Newcastle Rd, Stoke-on-Trent ST4 6QG, UK
| | - Daniele Muser
- Section of Cardiac Electrophysiology, Hospital of the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104, USA
| | - Ruben Casado Arroyo
- Department of Cardiology, Hopital Erasme, Université Libre de Bruxelles, Route de Lennik 808, 1070 Brussels, Belgium
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22
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Masterson Creber R, Turchioe MR. Returning Cardiac Rhythm Data to Patients: Opportunities and Challenges. Card Electrophysiol Clin 2021; 13:555-567. [PMID: 34330381 PMCID: PMC8328196 DOI: 10.1016/j.ccep.2021.05.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Spurred by federal legislation, professional organizations, and patients themselves, patient access to data from electronic cardiac devices is increasingly transparent. Patients can collect data through consumer devices and access data traditionally shared only with health care providers. These data may improve screening, self-management, and shared decision-making for cardiac arrhythmias, but challenges remain, including patient comprehension, communication with providers, and sustained engagement. Ways to address these challenges include leveraging visualizations that support comprehension, involving patients in designing and developing patient-facing digital tools, and establishing clear practices and goals for data exchange with health care providers.
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Affiliation(s)
- Ruth Masterson Creber
- Division of Health Informatics, Weill Cornell Medicine, 425 E 61st St, Floor 3, New York, NY 10065, USA.
| | - Meghan Reading Turchioe
- Division of Health Informatics, Weill Cornell Medicine, 425 E 61st St, Floor 3, New York, NY 10065, USA
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23
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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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24
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Shibutani H, Fujii K, Ueda D, Kawakami R, Imanaka T, Kawai K, Matsumura K, Hashimoto K, Yamamoto A, Hao H, Hirota S, Miki Y, Shiojima I. Automated classification of coronary atherosclerotic plaque in optical frequency domain imaging based on deep learning. Atherosclerosis 2021; 328:100-105. [PMID: 34126504 DOI: 10.1016/j.atherosclerosis.2021.06.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 04/23/2021] [Accepted: 06/03/2021] [Indexed: 10/21/2022]
Abstract
BACKGROUND AND AIMS We developed a deep learning (DL) model for automated atherosclerotic plaque categorization using optical frequency domain imaging (OFDI) and performed quantitative and visual evaluations. METHODS A total of 1103 histological cross-sections from 45 autopsy hearts were examined to compare the ex vivo OFDI scans. The images were segmented and annotated considering four histological categories: pathological intimal thickening (PIT), fibrous cap atheroma (FA), fibrocalcific plaque (FC), and healed erosion/rupture (HER). The DL model was developed based on pyramid scene parsing network (PSPNet). Given an input image, a convolutional neural network (ResNet50) was used as an encoder to generate feature maps of the last convolutional layer. RESULTS For the quantitative evaluation, the mean F-score and IoU values, which are used to evaluate how close the predicted results are to the ground truth, were used. The validation and test dataset had F-score and IoU values of 0.63, 0.49, and 0.66, 0.52, respectively. For the section-level diagnostic accuracy, the areas under the receiver-operating characteristic curve produced by the DL model for FC, PIT, FA, and HER were 0.91, 0.85, 0.86, and 0.86, respectively, and were comparable to those of an expert observer. CONCLUSIONS DL semantic segmentation of coronary plaques in OFDI images was used as a tool to automatically categorize atherosclerotic plaques using histological findings as the gold standard. The proposed method can support interventional cardiologists in understanding histological properties of plaques.
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Affiliation(s)
- Hiroki Shibutani
- Division of Cardiology, Department of Medicine II, Kansai Medical University, Hirakata, Japan
| | - Kenichi Fujii
- Division of Cardiology, Department of Medicine II, Kansai Medical University, Hirakata, Japan.
| | - Daiju Ueda
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Rika Kawakami
- Division of Surgical Pathology, Hyogo College of Medicine, Nishinomiya, Japan
| | - Takahiro Imanaka
- Division of Cardiovascular Medicine and Coronary Heart Disease, Hyogo College of Medicine, Nishinomiya, Japan
| | - Kenji Kawai
- Division of Cardiovascular Medicine and Coronary Heart Disease, Hyogo College of Medicine, Nishinomiya, Japan
| | - Koichiro Matsumura
- Division of Cardiology, Department of Medicine II, Kansai Medical University, Hirakata, Japan
| | - Kenta Hashimoto
- Division of Cardiology, Department of Medicine II, Kansai Medical University, Hirakata, Japan
| | - Akira Yamamoto
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Hiroyuki Hao
- Division of Human Pathology, Department of Pathology and Microbiology, Nihon University School of Medicine, Tokyo, Japan
| | - Seiichi Hirota
- Division of Surgical Pathology, Hyogo College of Medicine, Nishinomiya, Japan
| | - Yukio Miki
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Ichiro Shiojima
- Division of Cardiology, Department of Medicine II, Kansai Medical University, Hirakata, Japan
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25
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Taniguchi H, Takata T, Takechi M, Furukawa A, Iwasawa J, Kawamura A, Taniguchi T, Tamura Y. Explainable Artificial Intelligence Model for Diagnosis of Atrial Fibrillation Using Holter Electrocardiogram Waveforms. Int Heart J 2021; 62:534-539. [PMID: 34053998 DOI: 10.1536/ihj.21-094] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Atrial fibrillation is a clinically important arrhythmia. There are some reports on machine learning models for AF diagnosis using electrocardiogram data. However, few reports have proposed an eXplainable Artificial Intelligence (XAI) model to enable physicians to easily understand the machine learning model's diagnosis results.We developed and validated an XAI-enabled atrial fibrillation diagnosis model based on a convolutional neural network (CNN) algorithm. We used Holter electrocardiogram monitoring data and the gradient-weighted class activation mapping (Grad-CAM) method.Electrocardiogram data recorded from patients between January 4, 2016, and October 31, 2019, totaling 57,273 electrocardiogram waveform slots of 30 seconds each with diagnostic information annotated by cardiologists, were used for training our proposed model. Performance metrics of our AI model for AF diagnosis are as follows: sensitivity, 97.1% (95% CI: 0.969-0.972); specificity, 94.5% (95% CI: 0.943-0.946); accuracy, 95.3% (95% CI: 0.952-0.955); positive predictive value, 89.3% (95% CI: 0.892-0.897); and F-value, 93.1% (95% CI: 0.929-0.933). The area under the receiver operating characteristic curve for AF detection using our model was 0.988 (95% CI: 0.987-0.988). Furthermore, using the XAI method, 94.5 ± 3.5% of the areas identified as regions of interest using our machine learning model were identified as characteristic sites for AF diagnosis by cardiologists.AF was accurately diagnosed and favorably explained with Holter ECG waveforms using our proposed CNN-based XAI model. Our study presents another step toward realizing a viable XAI-based detection model for AF diagnoses for use by physicians.
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Affiliation(s)
- Hirohisa Taniguchi
- Department of Cardiology, International University of Health and Welfare School of Medicine
| | | | - Mineki Takechi
- Pulmonary Hypertension Center, International University of Health and Welfare Mita Hospital
| | - Asuka Furukawa
- Pulmonary Hypertension Center, International University of Health and Welfare Mita Hospital
| | - Jin Iwasawa
- Department of Cardiology, International University of Health and Welfare School of Medicine
| | - Akio Kawamura
- Department of Cardiology, International University of Health and Welfare School of Medicine
| | - Tadahiro Taniguchi
- Department of Information Science and Engineering, Ritsumeikan University
| | - Yuichi Tamura
- Department of Cardiology, International University of Health and Welfare School of Medicine.,CardioIntelligence Inc.,Pulmonary Hypertension Center, International University of Health and Welfare Mita Hospital
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26
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Recent Research for Unobtrusive Atrial Fibrillation Detection Methods Based on Cardiac Dynamics Signals: A Survey. SENSORS 2021; 21:s21113814. [PMID: 34072986 PMCID: PMC8199222 DOI: 10.3390/s21113814] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 05/20/2021] [Accepted: 05/26/2021] [Indexed: 11/16/2022]
Abstract
Atrial fibrillation (AF) is the most common cardiac arrhythmia. It tends to cause multiple cardiac conditions, such as cerebral artery blockage, stroke, and heart failure. The morbidity and mortality of AF have been progressively increasing over the past few decades, which has raised widespread concern about unobtrusive AF detection in routine life. The up-to-date non-invasive AF detection methods include electrocardiogram (ECG) signals and cardiac dynamics signals, such as the ballistocardiogram (BCG) signal, the seismocardiogram (SCG) signal and the photoplethysmogram (PPG) signal. Cardiac dynamics signals can be collected by cushions, mattresses, fabrics, or even cameras, which is more suitable for long-term monitoring. Therefore, methods for AF detection by cardiac dynamics signals bring about extensive attention for recent research. This paper reviews the current unobtrusive AF detection methods based on the three cardiac dynamics signals, summarized as data acquisition and preprocessing, feature extraction and selection, classification and diagnosis. In addition, the drawbacks and limitations of the existing methods are analyzed, and the challenges in future work are discussed.
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27
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Muffoletto M, Qureshi A, Zeidan A, Muizniece L, Fu X, Zhao J, Roy A, Bates PA, Aslanidi O. Toward Patient-Specific Prediction of Ablation Strategies for Atrial Fibrillation Using Deep Learning. Front Physiol 2021; 12:674106. [PMID: 34122144 PMCID: PMC8187921 DOI: 10.3389/fphys.2021.674106] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Accepted: 04/26/2021] [Indexed: 12/20/2022] Open
Abstract
Atrial fibrillation (AF) is a common cardiac arrhythmia that affects 1% of the population worldwide and is associated with high levels of morbidity and mortality. Catheter ablation (CA) has become one of the first line treatments for AF, but its success rates are suboptimal, especially in the case of persistent AF. Computational approaches have shown promise in predicting the CA strategy using simulations of atrial models, as well as applying deep learning to atrial images. We propose a novel approach that combines image-based computational modelling of the atria with deep learning classifiers trained on patient-specific atrial models, which can be used to assist in CA therapy selection. Therefore, we trained a deep convolutional neural network (CNN) using a combination of (i) 122 atrial tissue images obtained by unfolding patient LGE-MRI datasets, (ii) 157 additional synthetic images derived from the patient data to enhance the training dataset, and (iii) the outcomes of 558 CA simulations to terminate several AF scenarios in the corresponding image-based atrial models. Four CNN classifiers were trained on this patient-specific dataset balanced using several techniques to predict three common CA strategies from the patient atrial images: pulmonary vein isolation (PVI), rotor-based ablation (Rotor) and fibrosis-based ablation (Fibro). The training accuracy for these classifiers ranged from 96.22 to 97.69%, while the validation accuracy was from 78.68 to 86.50%. After training, the classifiers were applied to predict CA strategies for an unseen holdout test set of atrial images, and the results were compared to outcomes of the respective image-based simulations. The highest success rate was observed in the correct prediction of the Rotor and Fibro strategies (100%), whereas the PVI class was predicted in 33.33% of the cases. In conclusion, this study provides a proof-of-concept that deep neural networks can learn from patient-specific MRI datasets and image-derived models of AF, providing a novel technology to assist in tailoring CA therapy to a patient.
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Affiliation(s)
- Marica Muffoletto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Biomolecular Modelling Laboratory, The Francis Crick Institute, London, United Kingdom
| | - Ahmed Qureshi
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Aya Zeidan
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Laila Muizniece
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Xiao Fu
- Biomolecular Modelling Laboratory, The Francis Crick Institute, London, United Kingdom
| | - Jichao Zhao
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Aditi Roy
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | - Paul A. Bates
- Biomolecular Modelling Laboratory, The Francis Crick Institute, London, United Kingdom
| | - Oleg Aslanidi
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
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28
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Olier I, Ortega-Martorell S, Pieroni M, Lip GYH. How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management. Cardiovasc Res 2021; 117:1700-1717. [PMID: 33982064 PMCID: PMC8477792 DOI: 10.1093/cvr/cvab169] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 05/11/2021] [Indexed: 02/01/2023] Open
Abstract
There has been an exponential growth of artificial intelligence (AI) and machine learning (ML) publications aimed at advancing our understanding of atrial fibrillation (AF), which has been mainly driven by the confluence of two factors: the advances in deep neural networks (DeepNNs) and the availability of large, open access databases. It is observed that most of the attention has centred on applying ML for dvsetecting AF, particularly using electrocardiograms (ECGs) as the main data modality. Nearly a third of them used DeepNNs to minimize or eliminate the need for transforming the ECGs to extract features prior to ML modelling; however, we did not observe a significant advantage in following this approach. We also found a fraction of studies using other data modalities, and others centred in aims, such as risk prediction, AF management, and others. From the clinical perspective, AI/ML can help expand the utility of AF detection and risk prediction, especially for patients with additional comorbidities. The use of AI/ML for detection and risk prediction into applications and smart mobile health (mHealth) technology would enable ‘real time’ dynamic assessments. AI/ML could also adapt to treatment changes over time, as well as incident risk factors. Incorporation of a dynamic AI/ML model into mHealth technology would facilitate ‘real time’ assessment of stroke risk, facilitating mitigation of modifiable risk factors (e.g. blood pressure control). Overall, this would lead to an improvement in clinical care for patients with AF.
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Affiliation(s)
- Ivan Olier
- School of Computer Science and Mathematics, Liverpool John Moores University, 3 Byrom Street, Liverpool L3 3AF, UK.,Liverpool Centre for Cardiovascular Science, Liverpool John Moores University, Liverpool, UK
| | - Sandra Ortega-Martorell
- School of Computer Science and Mathematics, Liverpool John Moores University, 3 Byrom Street, Liverpool L3 3AF, UK.,Liverpool Centre for Cardiovascular Science, Liverpool John Moores University, Liverpool, UK
| | - Mark Pieroni
- School of Computer Science and Mathematics, Liverpool John Moores University, 3 Byrom Street, Liverpool L3 3AF, UK.,Liverpool Centre for Cardiovascular Science, Liverpool John Moores University, Liverpool, UK
| | - Gregory Y H Lip
- Liverpool Centre for Cardiovascular Science, Liverpool John Moores University, Liverpool, UK.,Liverpool Heart and Chest Hospital, Liverpool, UK
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29
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Zhang O, Ding C, Pereira T, Xiao R, Gadhoumi K, Meisel K, Lee RJ, Chen Y, Hu X. Explainability Metrics of Deep Convolutional Networks for Photoplethysmography Quality Assessment. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:29736-29745. [PMID: 33747683 PMCID: PMC7978398 DOI: 10.1109/access.2021.3054613] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Photoplethysmography (PPG) is a noninvasive way to monitor various aspects of the circulatory system, and is becoming more and more widespread in biomedical processing. Recently, deep learning methods for analyzing PPG have also become prevalent, achieving state of the art results on heart rate estimation, atrial fibrillation detection, and motion artifact identification. Consequently, a need for interpretable deep learning has arisen within the field of biomedical signal processing. In this paper, we pioneer novel explanatory metrics which leverage domain-expert knowledge to validate a deep learning model. We visualize model attention over a whole testset using saliency methods and compare it to human expert annotations. Congruence, our first metric, measures the proportion of model attention within expert-annotated regions. Our second metric, Annotation Classification, measures how much of the expert annotations our deep learning model pays attention to. Finally, we apply our metrics to compare between a signal based model and an image based model for PPG signal quality classification. Both models are deep convolutional networks based on the ResNet architectures. We show that our signal-based one dimensional model acts in a more explainable manner than our image based model; on average 50.78% of the one dimensional model's attention are within expert annotations, whereas 36.03% of the two dimensional model's attention are within expert annotations. Similarly, when thresholding the one dimensional model attention, one can more accurately predict if each pixel of the PPG is annotated as artifactual by an expert. Through this testcase, we demonstrate how our metrics can provide a quantitative and dataset-wide analysis of how explainable the model is.
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Affiliation(s)
- Oliver Zhang
- Department of Computer Science, Stanford University, Stanford, CA 94305, USA
| | - Cheng Ding
- Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA
| | - Tania Pereira
- Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal
| | - Ran Xiao
- School of Nursing, Duke University, Durham, NC 27708, USA
| | - Kais Gadhoumi
- School of Nursing, Duke University, Durham, NC 27708, USA
| | - Karl Meisel
- Department of Neurology School of Medicine, University of California at San Francisco, San Francisco, CA 94110, USA
| | - Randall J Lee
- School of Medicine, University of California at San Francisco, San Francisco, CA 94110, USA
| | - Yiran Chen
- Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA
| | - Xiao Hu
- Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA
- School of Nursing, Duke University, Durham, NC 27708, USA
- Department of Biostatistics and Bioinformatics, School of Medicine, Duke University, Durham, NC 27708, USA
- Department of Surgery, School of Medicine, Duke University, Durham, NC 27708, USA
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30
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Diao X, Huo Y, Yan Z, Wang H, Yuan J, Wang Y, Cai J, Zhao W. An Application of Machine Learning to Etiological Diagnosis of Secondary Hypertension: Retrospective Study Using Electronic Medical Records. JMIR Med Inform 2021; 9:e19739. [PMID: 33492233 PMCID: PMC7870351 DOI: 10.2196/19739] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 09/16/2020] [Accepted: 10/28/2020] [Indexed: 12/12/2022] Open
Abstract
Background Secondary hypertension is a kind of hypertension with a definite etiology and may be cured. Patients with suspected secondary hypertension can benefit from timely detection and treatment and, conversely, will have a higher risk of morbidity and mortality than those with primary hypertension. Objective The aim of this study was to develop and validate machine learning (ML) prediction models of common etiologies in patients with suspected secondary hypertension. Methods The analyzed data set was retrospectively extracted from electronic medical records of patients discharged from Fuwai Hospital between January 1, 2016, and June 30, 2019. A total of 7532 unique patients were included and divided into 2 data sets by time: 6302 patients in 2016-2018 as the training data set for model building and 1230 patients in 2019 as the validation data set for further evaluation. Extreme Gradient Boosting (XGBoost) was adopted to develop 5 models to predict 4 etiologies of secondary hypertension and occurrence of any of them (named as composite outcome), including renovascular hypertension (RVH), primary aldosteronism (PA), thyroid dysfunction, and aortic stenosis. Both univariate logistic analysis and Gini Impurity were used for feature selection. Grid search and 10-fold cross-validation were used to select the optimal hyperparameters for each model. Results Validation of the composite outcome prediction model showed good performance with an area under the receiver-operating characteristic curve (AUC) of 0.924 in the validation data set, while the 4 prediction models of RVH, PA, thyroid dysfunction, and aortic stenosis achieved AUC of 0.938, 0.965, 0.959, and 0.946, respectively, in the validation data set. A total of 79 clinical indicators were identified in all and finally used in our prediction models. The result of subgroup analysis on the composite outcome prediction model demonstrated high discrimination with AUCs all higher than 0.890 among all age groups of adults. Conclusions The ML prediction models in this study showed good performance in detecting 4 etiologies of patients with suspected secondary hypertension; thus, they may potentially facilitate clinical diagnosis decision making of secondary hypertension in an intelligent way.
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Affiliation(s)
- Xiaolin Diao
- Department of Information Center, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yanni Huo
- Department of Information Center, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zhanzheng Yan
- Department of Information Center, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Haibin Wang
- Department of Information Center, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jing Yuan
- Department of Information Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yuxin Wang
- Department of Information Center, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jun Cai
- Hypertension Center, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wei Zhao
- Department of Information Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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31
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Yan BP, Lai WHS, Chan CKY, Au ACK, Freedman B, Poh YC, Poh MZ. High-Throughput, Contact-Free Detection of Atrial Fibrillation From Video With Deep Learning. JAMA Cardiol 2021; 5:105-107. [PMID: 31774461 DOI: 10.1001/jamacardio.2019.4004] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Affiliation(s)
- Bryan P Yan
- Division of Cardiology, Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China.,Division of Cardiology, Department of Medicine and Therapeutics, Prince of Wales Hospital, Hong Kong SAR, China.,Heart and Vascular Institute, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - William H S Lai
- Division of Cardiology, Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Christy K Y Chan
- Division of Cardiology, Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Alex C K Au
- Division of Cardiology, Department of Medicine and Therapeutics, Prince of Wales Hospital, Hong Kong SAR, China
| | - Ben Freedman
- Heart Research Institute, Charles Perkins Centre, and Concord Hospital Cardiology, University of Sydney, Australia
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32
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Using machine learning to predict anticoagulation control in atrial fibrillation: A UK Clinical Practice Research Datalink study. INFORMATICS IN MEDICINE UNLOCKED 2021. [DOI: 10.1016/j.imu.2021.100688] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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33
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Murata K, Endo K, Aihara T, Suzuki H, Sawaji Y, Matsuoka Y, Nishimura H, Takamatsu T, Konishi T, Maekawa A, Yamauchi H, Kanazawa K, Endo H, Tsuji H, Inoue S, Fukushima N, Kikuchi H, Sato H, Yamamoto K. Artificial intelligence for the detection of vertebral fractures on plain spinal radiography. Sci Rep 2020; 10:20031. [PMID: 33208824 PMCID: PMC7674499 DOI: 10.1038/s41598-020-76866-w] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Accepted: 10/29/2020] [Indexed: 12/26/2022] Open
Abstract
Vertebral fractures (VFs) cause serious problems, such as substantial functional loss and a high mortality rate, and a delayed diagnosis may further worsen the prognosis. Plain thoracolumbar radiography (PTLR) is an essential method for the evaluation of VFs. Therefore, minimizing the diagnostic errors of VFs on PTLR is crucial. Image identification based on a deep convolutional neural network (DCNN) has been recognized to be potentially effective as a diagnostic strategy; however, the accuracy for detecting VFs has not been fully investigated. A DCNN was trained with PTLR images of 300 patients (150 patients with and 150 without VFs). The accuracy, sensitivity, and specificity of diagnosis of the model were calculated and compared with those of orthopedic residents, orthopedic surgeons, and spine surgeons. The DCNN achieved accuracy, sensitivity, and specificity rates of 86.0% [95% confidence interval (CI) 82.0-90.0%], 84.7% (95% CI 78.8-90.5%), and 87.3% (95% CI 81.9-92.7%), respectively. Both the accuracy and sensitivity of the model were suggested to be noninferior to those of orthopedic surgeons. The DCNN can assist clinicians in the early identification of VFs and in managing patients, to prevent further invasive interventions and a decreased quality of life.
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Affiliation(s)
- Kazuma Murata
- Department of Orthopedic Surgery, Tokyo Medical University, 6-7-1 Nishishinjuku, Shinjuku-ku, Tokyo, 160-0023, Japan.
| | - Kenji Endo
- Department of Orthopedic Surgery, Tokyo Medical University, 6-7-1 Nishishinjuku, Shinjuku-ku, Tokyo, 160-0023, Japan
| | - Takato Aihara
- Department of Orthopedic Surgery, Tokyo Medical University, 6-7-1 Nishishinjuku, Shinjuku-ku, Tokyo, 160-0023, Japan
| | - Hidekazu Suzuki
- Department of Orthopedic Surgery, Tokyo Medical University, 6-7-1 Nishishinjuku, Shinjuku-ku, Tokyo, 160-0023, Japan
| | - Yasunobu Sawaji
- Department of Orthopedic Surgery, Tokyo Medical University, 6-7-1 Nishishinjuku, Shinjuku-ku, Tokyo, 160-0023, Japan
| | - Yuji Matsuoka
- Department of Orthopedic Surgery, Tokyo Medical University, 6-7-1 Nishishinjuku, Shinjuku-ku, Tokyo, 160-0023, Japan
| | - Hirosuke Nishimura
- Department of Orthopedic Surgery, Tokyo Medical University, 6-7-1 Nishishinjuku, Shinjuku-ku, Tokyo, 160-0023, Japan
| | - Taichiro Takamatsu
- Department of Orthopedic Surgery, Tokyo Medical University, 6-7-1 Nishishinjuku, Shinjuku-ku, Tokyo, 160-0023, Japan
| | - Takamitsu Konishi
- Department of Orthopedic Surgery, Tokyo Medical University, 6-7-1 Nishishinjuku, Shinjuku-ku, Tokyo, 160-0023, Japan
| | - Asato Maekawa
- Department of Orthopedic Surgery, Tokyo Medical University, 6-7-1 Nishishinjuku, Shinjuku-ku, Tokyo, 160-0023, Japan
| | - Hideya Yamauchi
- Department of Orthopedic Surgery, Tokyo Medical University, 6-7-1 Nishishinjuku, Shinjuku-ku, Tokyo, 160-0023, Japan
| | - Kei Kanazawa
- Department of Orthopedic Surgery, Tokyo Medical University, 6-7-1 Nishishinjuku, Shinjuku-ku, Tokyo, 160-0023, Japan
| | - Hiroo Endo
- Department of Orthopedic Surgery, Tokyo Medical University, 6-7-1 Nishishinjuku, Shinjuku-ku, Tokyo, 160-0023, Japan
| | - Hanako Tsuji
- Department of Orthopedic Surgery, Tokyo Medical University, 6-7-1 Nishishinjuku, Shinjuku-ku, Tokyo, 160-0023, Japan
| | - Shigeru Inoue
- Department of Preventive Medicine and Public Health, Tokyo Medical University, Tokyo, Japan
| | - Noritoshi Fukushima
- Department of Preventive Medicine and Public Health, Tokyo Medical University, Tokyo, Japan
| | - Hiroyuki Kikuchi
- Department of Preventive Medicine and Public Health, Tokyo Medical University, Tokyo, Japan
| | - Hiroki Sato
- Department of Preventive Medicine and Public Health, Tokyo Medical University, Tokyo, Japan
| | - Kengo Yamamoto
- Department of Orthopedic Surgery, Tokyo Medical University, 6-7-1 Nishishinjuku, Shinjuku-ku, Tokyo, 160-0023, Japan
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Dall'Olio L, Curti N, Remondini D, Safi Harb Y, Asselbergs FW, Castellani G, Uh HW. Prediction of vascular aging based on smartphone acquired PPG signals. Sci Rep 2020; 10:19756. [PMID: 33184391 PMCID: PMC7661535 DOI: 10.1038/s41598-020-76816-6] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Accepted: 10/23/2020] [Indexed: 02/04/2023] Open
Abstract
Photoplethysmography (PPG) measured by smartphone has the potential for a large scale, non-invasive, and easy-to-use screening tool. Vascular aging is linked to increased arterial stiffness, which can be measured by PPG. We investigate the feasibility of using PPG to predict healthy vascular aging (HVA) based on two approaches: machine learning (ML) and deep learning (DL). We performed data preprocessing, including detrending, demodulating, and denoising on the raw PPG signals. For ML, ridge penalized regression has been applied to 38 features extracted from PPG, whereas for DL several convolutional neural networks (CNNs) have been applied to the whole PPG signals as input. The analysis has been conducted using the crowd-sourced Heart for Heart data. The prediction performance of ML using two features (AUC of 94.7%) – the a wave of the second derivative PPG and tpr, including four covariates, sex, height, weight, and smoking – was similar to that of the best performing CNN, 12-layer ResNet (AUC of 95.3%). Without having the heavy computational cost of DL, ML might be advantageous in finding potential biomarkers for HVA prediction. The whole workflow of the procedure is clearly described, and open software has been made available to facilitate replication of the results.
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Affiliation(s)
- Lorenzo Dall'Olio
- Department of Physics and Astronomy, University of Bologna, 40126, Bologna, BO, Italy
| | - Nico Curti
- Department of Specialised, Diagnostic and Experimental Medicine, University of Bologna, 40126, Bologna, BO, Italy
| | - Daniel Remondini
- Department of Physics and Astronomy, University of Bologna, 40126, Bologna, BO, Italy
| | | | - Folkert W Asselbergs
- Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, 3584 CX, Utrecht, The Netherlands.,Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, WC1E 6BT, UK.,Health Data Research UK and Institute of Health Informatics, University College London, London, NW1 2BE, UK
| | - Gastone Castellani
- Department of Specialised, Diagnostic and Experimental Medicine, University of Bologna, 40126, Bologna, BO, Italy
| | - Hae-Won Uh
- Department of Biostatistics and Research Support, University Medical Center Utrecht, 3584 CX, Utrecht, The Netherlands.
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35
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Xie L, Li Z, Zhou Y, He Y, Zhu J. Computational Diagnostic Techniques for Electrocardiogram Signal Analysis. SENSORS (BASEL, SWITZERLAND) 2020; 20:E6318. [PMID: 33167558 PMCID: PMC7664289 DOI: 10.3390/s20216318] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 10/27/2020] [Accepted: 11/04/2020] [Indexed: 12/25/2022]
Abstract
Cardiovascular diseases (CVDs), including asymptomatic myocardial ischemia, angina, myocardial infarction, and ischemic heart failure, are the leading cause of death globally. Early detection and treatment of CVDs significantly contribute to the prevention or delay of cardiovascular death. Electrocardiogram (ECG) records the electrical impulses generated by heart muscles, which reflect regular or irregular beating activity. Computer-aided techniques provide fast and accurate tools to identify CVDs using a patient's ECG signal, which have achieved great success in recent years. Latest computational diagnostic techniques based on ECG signals for estimating CVDs conditions are summarized here. The procedure of ECG signals analysis is discussed in several subsections, including data preprocessing, feature engineering, classification, and application. In particular, the End-to-End models integrate feature extraction and classification into learning algorithms, which not only greatly simplifies the process of data analysis, but also shows excellent accuracy and robustness. Portable devices enable users to monitor their cardiovascular status at any time, bringing new scenarios as well as challenges to the application of ECG algorithms. Computational diagnostic techniques for ECG signal analysis show great potential for helping health care professionals, and their application in daily life benefits both patients and sub-healthy people.
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Affiliation(s)
- Liping Xie
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China; (Z.L.); (Y.Z.); (Y.H.); (J.Z.)
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Goh CH, Tan LK, Lovell NH, Ng SC, Tan MP, Lim E. Robust PPG motion artifact detection using a 1-D convolution neural network. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 196:105596. [PMID: 32580054 DOI: 10.1016/j.cmpb.2020.105596] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2019] [Accepted: 06/01/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND OBJECTIVES Continuous monitoring of physiological parameters such as photoplethysmography (PPG) has attracted increased interest due to advances in wearable sensors. However, PPG recordings are susceptible to various artifacts, and thus reducing the reliability of PPG-driven parameters, such as oxygen saturation, heart rate, blood pressure and respiration. This paper proposes a one-dimensional convolution neural network (1-D-CNN) to classify five-second PPG segments into clean or artifact-affected segments, avoiding data-dependent pulse segmentation techniques and heavy manual feature engineering. METHODS Continuous raw PPG waveforms were blindly allocated into segments with an equal length (5s) without leveraging any pulse location information and were normalized with Z-score normalization methods. A 1-D-CNN was designed to automatically learn the intrinsic features of the PPG waveform, and perform the required classification. Several training hyperparameters (initial learning rate and gradient threshold) were varied to investigate the effect of these parameters on the performance of the network. Subsequently, this proposed network was trained and validated with 30 subjects, and then tested with eight subjects, with our local dataset. Moreover, two independent datasets downloaded from the PhysioNet MIMIC II database were used to evaluate the robustness of the proposed network. RESULTS A 13 layer 1-D-CNN model was designed. Within our local study dataset evaluation, the proposed network achieved a testing accuracy of 94.9%. The classification accuracy of two independent datasets also achieved satisfactory accuracy of 93.8% and 86.7% respectively. Our model achieved a comparable performance with most reported works, with the potential to show good generalization as the proposed network was evaluated with multiple cohorts (overall accuracy of 94.5%). CONCLUSION This paper demonstrated the feasibility and effectiveness of applying blind signal processing and deep learning techniques to PPG motion artifact detection, whereby manual feature thresholding was avoided and yet a high generalization ability was achieved.
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Affiliation(s)
- Choon-Hian Goh
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia; Graduate School of Biomedical Engineering, Faculty of Engineering, UNSW Sydney, New South Wales 2052, Australia; Department of Mechatronics and Biomedical Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, 43000 Kajang, Selangor Darul Ehsan, Malaysia
| | - Li Kuo Tan
- Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia
| | - Nigel H Lovell
- Graduate School of Biomedical Engineering, Faculty of Engineering, UNSW Sydney, New South Wales 2052, Australia
| | - Siew-Cheok Ng
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
| | - Maw Pin Tan
- Department of Medicine, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia; Department Medical Sciences, Faculty of Healthcare and Medical Sciences, Sunway University, 47500 Bandar Sunway, Malaysia
| | - Einly Lim
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia.
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Hill NR, Arden C, Beresford-Hulme L, Camm AJ, Clifton D, Davies DW, Farooqui U, Gordon J, Groves L, Hurst M, Lawton S, Lister S, Mallen C, Martin AC, McEwan P, Pollock KG, Rogers J, Sandler B, Sugrue DM, Cohen AT. Identification of undiagnosed atrial fibrillation patients using a machine learning risk prediction algorithm and diagnostic testing (PULsE-AI): Study protocol for a randomised controlled trial. Contemp Clin Trials 2020; 99:106191. [PMID: 33091585 PMCID: PMC7571442 DOI: 10.1016/j.cct.2020.106191] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 10/14/2020] [Accepted: 10/16/2020] [Indexed: 12/29/2022]
Abstract
Atrial fibrillation (AF) is associated with an increased risk of stroke, enhanced stroke severity, and other comorbidities. However, AF is often asymptomatic, and frequently remains undiagnosed until complications occur. Current screening approaches for AF lack either cost-effectiveness or diagnostic sensitivity; thus, there is interest in tools that could be used for population screening. An AF risk prediction algorithm, developed using machine learning from a UK dataset of 2,994,837 patients, was found to be more effective than existing models at identifying patients at risk of AF. Therefore, the aim of the trial is to assess the effectiveness of this risk prediction algorithm combined with diagnostic testing for the identification of AF in a real-world primary care setting. Eligible participants (aged ≥30 years and without an existing AF diagnosis) registered at participating UK general practices will be randomised into intervention and control arms. Intervention arm participants identified at highest risk of developing AF (algorithm risk score ≥ 7.4%) will be invited for a 12‑lead electrocardiogram (ECG) followed by two-weeks of home-based ECG monitoring with a KardiaMobile device. Control arm participants will be used for comparison and will be managed routinely. The primary outcome is the number of AF diagnoses in the intervention arm compared with the control arm during the research window. If the trial is successful, there is potential for the risk prediction algorithm to be implemented throughout primary care for narrowing the population considered at highest risk for AF who could benefit from more intensive screening for AF. Trial Registration: NCT04045639.
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Affiliation(s)
- Nathan R Hill
- Bristol Myers Squibb Pharmaceutical Ltd, Uxbridge, UK.
| | - Chris Arden
- Park Surgery, Chandlers Ford, Hampshire, UK.
| | | | - A John Camm
- Cardiology Clinical Academic Group, Molecular & Clinical Sciences Research Institute, St. George's University of London, London, UK.
| | - David Clifton
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, UK.
| | | | | | - Jason Gordon
- Health Economics and Outcomes Research Ltd, Cardiff, UK.
| | - Lara Groves
- Health Economics and Outcomes Research Ltd, Cardiff, UK.
| | - Michael Hurst
- Health Economics and Outcomes Research Ltd, Cardiff, UK.
| | - Sarah Lawton
- School of Medicine, Keele University, Staffordshire, UK.
| | - Steven Lister
- Bristol Myers Squibb Pharmaceutical Ltd, Uxbridge, UK.
| | | | - Anne-Celine Martin
- Université de Paris, Innovative Therapies in Haemostasis, INSERM, Hôpital Européen Georges Pompidou, Service de Cardiologie, 20 rue Leblanc, Paris, France
| | - Phil McEwan
- Health Economics and Outcomes Research Ltd, Cardiff, UK.
| | | | | | | | | | - Alexander T Cohen
- Department of Haematological Medicine, Guys and St Thomas' NHS Foundation Trust, King's College London, London, UK.
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Prasitlumkum N, Cheungpasitporn W, Chokesuwattanaskul A, Thangjui S, Thongprayoon C, Bathini T, Vallabhajosyula S, Kanitsoraphan C, Leesutipornchai T, Chokesuwattanaskul R. Diagnostic accuracy of smart gadgets/wearable devices in detecting atrial fibrillation: A systematic review and meta-analysis. Arch Cardiovasc Dis 2020; 114:4-16. [PMID: 32921618 DOI: 10.1016/j.acvd.2020.05.015] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Revised: 05/16/2020] [Accepted: 05/19/2020] [Indexed: 01/23/2023]
Abstract
BACKGROUND Recently, smart devices have been used for medical purposes, particularly to screen for atrial fibrillation. However, current data on the diagnostic performance of these devices are scarce. AIMS We performed a systemic review and meta-analysis to assess the accuracy of atrial fibrillation diagnosis by smart gadgets/wearable devices. METHODS We comprehensively searched the MEDLINE, EMBASE and Cochrane databases for all works since the inception of each database until January 2020. Included in this review were published observational studies of the diagnostic accuracy of smartphones or smartwatches in detecting atrial fibrillation. We calculated the area under the summary receiver operating characteristic curves and pooled sensitivities and specificities. RESULTS Participants in our study were from the general population or were patients with underlying atrial fibrillation. In the overall analyses, the areas under the summary receiver operating characteristic curves were 0.96 and 0.94 for smartphones and smartwatches, respectively. Smartphones had a sensitivity of 94% and a specificity of 96%, and smartwatches showed similar diagnostic accuracy, with a specificity of 94% and a sensitivity of 93%. In subgroup analyses, we found no difference in diagnostic accuracy between photoplethysmography and single-lead electrocardiography. CONCLUSIONS This study suggests that smart devices have similar diagnostic accuracies. Regarding atrial fibrillation detection methods, there was also no difference between photoplethysmography and single-lead electrocardiography. However, further studies are warranted to determine their clinical implications in atrial fibrillation management.
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Affiliation(s)
- Narut Prasitlumkum
- Department of Cardiology, University of California Riverside, 92521 Riverside, CA, USA.
| | - Wisit Cheungpasitporn
- Department of Medicine, University of Mississippi Medical Centre, 39216 Jackson, MS, USA
| | - Anthipa Chokesuwattanaskul
- Faculty of Medicine, King-Chulalongkorn Memorial Hospital, Chulalongkorn University, 10330 Bangkok, Thailand
| | - Sittinun Thangjui
- Faculty of Medicine, King-Chulalongkorn Memorial Hospital, Chulalongkorn University, 10330 Bangkok, Thailand
| | | | - Tarun Bathini
- Department of Internal Medicine, University of Arizona, 85724 Tucson, AZ, USA
| | | | | | - Thiratest Leesutipornchai
- Faculty of Medicine, King-Chulalongkorn Memorial Hospital, Chulalongkorn University, 10330 Bangkok, Thailand
| | - Ronpichai Chokesuwattanaskul
- Faculty of Medicine, King-Chulalongkorn Memorial Hospital, Chulalongkorn University, 10330 Bangkok, Thailand; Division of Cardiac Electrophysiology, University of Michigan Health Care, 48109 Ann Arbor, MI, USA
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Torres-Soto J, Ashley EA. Multi-task deep learning for cardiac rhythm detection in wearable devices. NPJ Digit Med 2020; 3:116. [PMID: 32964139 PMCID: PMC7481177 DOI: 10.1038/s41746-020-00320-4] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Accepted: 07/22/2020] [Indexed: 11/18/2022] Open
Abstract
Wearable devices enable theoretically continuous, longitudinal monitoring of physiological measurements such as step count, energy expenditure, and heart rate. Although the classification of abnormal cardiac rhythms such as atrial fibrillation from wearable devices has great potential, commercial algorithms remain proprietary and tend to focus on heart rate variability derived from green spectrum LED sensors placed on the wrist, where noise remains an unsolved problem. Here we develop DeepBeat, a multitask deep learning method to jointly assess signal quality and arrhythmia event detection in wearable photoplethysmography devices for real-time detection of atrial fibrillation. The model is trained on approximately one million simulated unlabeled physiological signals and fine-tuned on a curated dataset of over 500 K labeled signals from over 100 individuals from 3 different wearable devices. We demonstrate that, in comparison with a single-task model, our architecture using unsupervised transfer learning through convolutional denoising autoencoders dramatically improves the performance of atrial fibrillation detection from a F1 score of 0.54 to 0.96. We also include in our evaluation a prospectively derived replication cohort of ambulatory participants where the algorithm performed with high sensitivity (0.98), specificity (0.99), and F1 score (0.93). We show that two-stage training can help address the unbalanced data problem common to biomedical applications, where large-scale well-annotated datasets are hard to generate due to the expense of manual annotation, data acquisition, and participant privacy.
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Affiliation(s)
| | - Euan A. Ashley
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University, Stanford, CA USA
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40
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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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Sekelj S, Sandler B, Johnston E, Pollock KG, Hill NR, Gordon J, Tsang C, Khan S, Ng FS, Farooqui U. Detecting undiagnosed atrial fibrillation in UK primary care: Validation of a machine learning prediction algorithm in a retrospective cohort study. Eur J Prev Cardiol 2020; 28:598-605. [PMID: 34021576 DOI: 10.1177/2047487320942338] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Accepted: 06/24/2020] [Indexed: 02/01/2023]
Abstract
AIMS To evaluate the ability of a machine learning algorithm to identify patients at high risk of atrial fibrillation in primary care. METHODS A retrospective cohort study was undertaken using the DISCOVER registry to validate an algorithm developed using a Clinical Practice Research Datalink (CPRD) dataset. The validation dataset included primary care patients in London, England aged ≥30 years from 1 January 2006 to 31 December 2013, without a diagnosis of atrial fibrillation in the prior 5 years. Algorithm performance metrics were sensitivity, specificity, positive predictive value, negative predictive value (NPV) and number needed to screen (NNS). Subgroup analysis of patients aged ≥65 years was also performed. RESULTS Of 2,542,732 patients in DISCOVER, the algorithm identified 604,135 patients suitable for risk assessment. Of these, 3.0% (17,880 patients) had a diagnosis of atrial fibrillation recorded before study end. The area under the curve of the receiver operating characteristic was 0.87, compared with 0.83 in algorithm development. The NNS was nine patients, matching the CPRD cohort. In patients aged ≥30 years, the algorithm correctly identified 99.1% of patients who did not have atrial fibrillation (NPV) and 75.0% of true atrial fibrillation cases (sensitivity). Among patients aged ≥65 years (n = 117,965), the NPV was 96.7% with 91.8% sensitivity. CONCLUSIONS This atrial fibrillation risk prediction algorithm, based on machine learning methods, identified patients at highest risk of atrial fibrillation. It performed comparably in a large, real-world population-based cohort and the developmental registry cohort. If implemented in primary care, the algorithm could be an effective tool for narrowing the population who would benefit from atrial fibrillation screening in the United Kingdom.
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Affiliation(s)
- Sara Sekelj
- Imperial College Health Partners, London, UK
| | | | | | | | - Nathan R Hill
- Uxbridge, Bristol-Myers Squibb Pharmaceuticals Ltd., UK
| | - Jason Gordon
- Health Economics and Outcomes Research Ltd, Cardiff, UK
| | - Carmen Tsang
- Health Economics and Outcomes Research Ltd, Cardiff, UK
| | - Sadia Khan
- Chelsea & Westminster Hospital NHS Foundation Trust, London, UK
| | - Fu Siong Ng
- Chelsea & Westminster Hospital NHS Foundation Trust, London, UK.,Faculty of Medicine, National Heart and Lung Institute, Imperial College London, UK
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Skaria R, Parvaneh S, Zhou S, Kim J, Wanjiru S, Devers G, Konhilas J, Khalpey Z. Path to precision: prevention of post-operative atrial fibrillation. J Thorac Dis 2020; 12:2735-2746. [PMID: 32642182 PMCID: PMC7330352 DOI: 10.21037/jtd-19-3875] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Development of post-operative atrial fibrillation (POAF) following open-heart surgery is a significant clinical and economic burden. Despite advancements in medical therapies, the incidence of POAF remains elevated at 25-40%. Early work focused on detecting arrhythmias from electrocardiograms as well as identifying pre-operative risk factors from medical records. However, further progress has been stagnant, and a deeper understanding of pathogenesis and significant influences is warranted. With the advent of more complex machine learning (ML) algorithms and high-throughput sequencing, we have an unprecedented ability to capture and predict POAF in real-time. Integration of multimodal heterogeneous data and application of ML can generate a paradigm shift for diagnosis and treatment. This will require a concerted effort to consolidate and streamline real-time data. Herein, we will review the current literature and emerging opportunities aimed at predictive targets and new insights into the mechanisms underlying long-term sequelae of POAF.
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Affiliation(s)
- Rinku Skaria
- University of Arizona, College of Medicine, Tucson, AZ, USA
| | | | - Sophia Zhou
- Philips Research North America, Cambridge, MA, USA
| | - James Kim
- University of Arizona, College of Medicine, Tucson, AZ, USA
| | | | | | - John Konhilas
- University of Arizona, College of Medicine, Tucson, AZ, USA
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Diller GP, Kempny A, Babu-Narayan SV, Henrichs M, Brida M, Uebing A, Lammers AE, Baumgartner H, Li W, Wort SJ, Dimopoulos K, Gatzoulis MA. Machine learning algorithms estimating prognosis and guiding therapy in adult congenital heart disease: data from a single tertiary centre including 10 019 patients. Eur Heart J 2020; 40:1069-1077. [PMID: 30689812 DOI: 10.1093/eurheartj/ehy915] [Citation(s) in RCA: 106] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/10/2018] [Revised: 11/23/2018] [Accepted: 12/31/2018] [Indexed: 12/15/2022] Open
Abstract
AIMS To assess the utility of machine learning algorithms on estimating prognosis and guiding therapy in a large cohort of patients with adult congenital heart disease (ACHD) or pulmonary hypertension at a single, tertiary centre. METHODS AND RESULTS We included 10 019 adult patients (age 36.3 ± 17.3 years) under follow-up at our institution between 2000 and 2018. Clinical and demographic data, ECG parameters, cardiopulmonary exercise testing, and selected laboratory markers where collected and included in deep learning (DL) algorithms. Specific DL-models were built based on raw data to categorize diagnostic group, disease complexity, and New York Heart Association (NYHA) class. In addition, models were developed to estimate need for discussion at multidisciplinary team (MDT) meetings and to gauge prognosis of individual patients. Overall, the DL-algorithms-based on over 44 000 medical records-categorized diagnosis, disease complexity, and NYHA class with an accuracy of 91.1%, 97.0%, and 90.6%, respectively in the test sample. Similarly, patient presentation at MDT-meetings was predicted with a test sample accuracy of 90.2%. During a median follow-up time of 8 years, 785 patients died. The automatically derived disease severity-score derived from clinical information was related to survival on Cox analysis independently of demographic, exercise, laboratory, and ECG parameters. CONCLUSION We present herewith the utility of machine learning algorithms trained on large datasets to estimate prognosis and potentially to guide therapy in ACHD. Due to the largely automated process involved, these DL-algorithms can easily be scaled to multi-institutional datasets to further improve accuracy and ultimately serve as online based decision-making tools.
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Affiliation(s)
- Gerhard-Paul Diller
- Adult Congenital Heart Centre, National Centre for Pulmonary Hypertension, Royal Brompton Hospital, Sydney Street, London, UK.,National Heart and Lung Institute, Imperial College School of Medicine, Dovehouse Street, London, UK.,Department of Cardiology III - Adult Congenital and Valvular Heart Disease, University Hospital Muenster, Albert-Schweitzer-Campus 1, Muenster, Germany.,Competence Network for Congenital Heart Defects, DZHK (German Centre for Cardiovascular Research), Augustenburger Platz 1, Berlin, Germany
| | - Aleksander Kempny
- Adult Congenital Heart Centre, National Centre for Pulmonary Hypertension, Royal Brompton Hospital, Sydney Street, London, UK.,National Heart and Lung Institute, Imperial College School of Medicine, Dovehouse Street, London, UK
| | - Sonya V Babu-Narayan
- Adult Congenital Heart Centre, National Centre for Pulmonary Hypertension, Royal Brompton Hospital, Sydney Street, London, UK.,National Heart and Lung Institute, Imperial College School of Medicine, Dovehouse Street, London, UK
| | - Marthe Henrichs
- Department of Cardiology III - Adult Congenital and Valvular Heart Disease, University Hospital Muenster, Albert-Schweitzer-Campus 1, Muenster, Germany
| | - Margarita Brida
- Adult Congenital Heart Centre, National Centre for Pulmonary Hypertension, Royal Brompton Hospital, Sydney Street, London, UK.,Division of Valvular Heart Disease and Adult Congenital Heart Disease, Department of Cardiovascular Medicine, University Hospital Centre Zagreb, Kispaticeva 12, Zagreb, Croatia
| | - Anselm Uebing
- Adult Congenital Heart Centre, National Centre for Pulmonary Hypertension, Royal Brompton Hospital, Sydney Street, London, UK.,Division of Paediatric Cardiology, University Hospital Muenster, Albert-Schweitzer-Campus 1, Muenster, Germany
| | - Astrid E Lammers
- Division of Paediatric Cardiology, University Hospital Muenster, Albert-Schweitzer-Campus 1, Muenster, Germany
| | - Helmut Baumgartner
- Department of Cardiology III - Adult Congenital and Valvular Heart Disease, University Hospital Muenster, Albert-Schweitzer-Campus 1, Muenster, Germany.,Competence Network for Congenital Heart Defects, DZHK (German Centre for Cardiovascular Research), Augustenburger Platz 1, Berlin, Germany
| | - Wei Li
- Adult Congenital Heart Centre, National Centre for Pulmonary Hypertension, Royal Brompton Hospital, Sydney Street, London, UK.,National Heart and Lung Institute, Imperial College School of Medicine, Dovehouse Street, London, UK
| | - Stephen J Wort
- Adult Congenital Heart Centre, National Centre for Pulmonary Hypertension, Royal Brompton Hospital, Sydney Street, London, UK.,National Heart and Lung Institute, Imperial College School of Medicine, Dovehouse Street, London, UK
| | - Konstantinos Dimopoulos
- Adult Congenital Heart Centre, National Centre for Pulmonary Hypertension, Royal Brompton Hospital, Sydney Street, London, UK.,National Heart and Lung Institute, Imperial College School of Medicine, Dovehouse Street, London, UK
| | - Michael A Gatzoulis
- Adult Congenital Heart Centre, National Centre for Pulmonary Hypertension, Royal Brompton Hospital, Sydney Street, London, UK.,National Heart and Lung Institute, Imperial College School of Medicine, Dovehouse Street, London, UK
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Tadesse GA, Zhu T, Liu Y, Zhou Y, Chen J, Tian M, Clifton D. Cardiovascular disease diagnosis using cross-domain transfer learning. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:4262-4265. [PMID: 31946810 DOI: 10.1109/embc.2019.8857737] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
While cardiovascular diseases (CVDs) are commonly diagnosed by cardiologists via inspecting electrocardiogram (ECG) waveforms, these decisions can be supported by a data-driven approach, which may automate this process. An automatic diagnostic approach often employs hand-crafted features extracted from ECG waveforms. These features, however, do not generalise well, challenged by variation in acquisition settings such as sampling rate and mounting points. Existing deep learning (DL) approaches, on the other hand, extract features from ECG automatically but require construction of dedicated networks that require huge data and computational resource if trained from scratch. Here we propose an end-to-end trainable cross-domain transfer learning for CVD classification from ECG waveforms, by utilising existing vision-based CNN frameworks as feature extractors, followed by ECG feature learning layers. Because these frameworks are designed for image inputs, we employ a stacked spectrogram representation of multi-lead ECG waveforms as a preprocessing step. We also proposed a fusion of multiple ECG leads, using plausible stacking arrangements of the spectrograms, to encode their spatial relations. The proposed approach is validated on multiple ECG datasets and competitive performance is achieved.
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Kwon S, Hong J, Choi EK, Lee B, Baik C, Lee E, Jeong ER, Koo BK, Oh S, Yi Y. Detection of Atrial Fibrillation Using a Ring-Type Wearable Device (CardioTracker) and Deep Learning Analysis of Photoplethysmography Signals: Prospective Observational Proof-of-Concept Study. J Med Internet Res 2020; 22:e16443. [PMID: 32348254 PMCID: PMC7273241 DOI: 10.2196/16443] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 02/24/2020] [Accepted: 03/21/2020] [Indexed: 12/23/2022] Open
Abstract
Background Continuous photoplethysmography (PPG) monitoring with a wearable device may aid the early detection of atrial fibrillation (AF). Objective We aimed to evaluate the diagnostic performance of a ring-type wearable device (CardioTracker, CART), which can detect AF using deep learning analysis of PPG signals. Methods Patients with persistent AF who underwent cardioversion were recruited prospectively. We recorded PPG signals at the finger with CART and a conventional pulse oximeter before and after cardioversion over a period of 15 min (each instrument). Cardiologists validated the PPG rhythms with simultaneous single-lead electrocardiography. The PPG data were transmitted to a smartphone wirelessly and analyzed with a deep learning algorithm. We also validated the deep learning algorithm in 20 healthy subjects with sinus rhythm (SR). Results In 100 study participants, CART generated a total of 13,038 30-s PPG samples (5850 for SR and 7188 for AF). Using the deep learning algorithm, the diagnostic accuracy, sensitivity, specificity, positive-predictive value, and negative-predictive value were 96.9%, 99.0%, 94.3%, 95.6%, and 98.7%, respectively. Although the diagnostic accuracy decreased with shorter sample lengths, the accuracy was maintained at 94.7% with 10-s measurements. For SR, the specificity decreased with higher variability of peak-to-peak intervals. However, for AF, CART maintained consistent sensitivity regardless of variability. Pulse rates had a lower impact on sensitivity than on specificity. The performance of CART was comparable to that of the conventional device when using a proper threshold. External validation showed that 94.99% (16,529/17,400) of the PPG samples from the control group were correctly identified with SR. Conclusions A ring-type wearable device with deep learning analysis of PPG signals could accurately diagnose AF without relying on electrocardiography. With this device, continuous monitoring for AF may be promising in high-risk populations. Trial Registration ClinicalTrials.gov NCT04023188; https://clinicaltrials.gov/ct2/show/NCT04023188
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Affiliation(s)
- Soonil Kwon
- Seoul National University Hospital, Seoul, Republic of Korea
| | - Joonki Hong
- Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Eue-Keun Choi
- Seoul National University Hospital, Seoul, Republic of Korea
| | | | | | - Euijae Lee
- Seoul National University Hospital, Seoul, Republic of Korea
| | - Eui-Rim Jeong
- Department of Information and Communication Engineering, Hanbat National University, Daejeon, Republic of Korea
| | - Bon-Kwon Koo
- Seoul National University Hospital, Seoul, Republic of Korea
| | - Seil Oh
- Seoul National University Hospital, Seoul, Republic of Korea
| | - Yung Yi
- Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
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Turchioe MR, Jimenez V, Isaac S, Alshalabi M, Slotwiner D, Creber RM. Review of mobile applications for the detection and management of atrial fibrillation. Heart Rhythm O2 2020; 1:35-43. [PMID: 32656542 PMCID: PMC7351352 DOI: 10.1016/j.hroo.2020.02.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
BACKGROUND Free mobile applications (apps) that use photoplethysmography (PPG) waveforms may extend atrial fibrillation (AF) detection to underserved populations, but they have not been rigorously evaluated. OBJECTIVE The purpose of this study was to systematically review and evaluate the quality, functionality, and adherence to self-management behaviors of existing mobile apps for AF. METHODS We systematically searched 3 app stores for apps that were free, available in English, and intended for use by patients to detect and manage AF. A minimum of 2 reviewers evaluated (1) app quality, using the Mobile Application Rating Scale (MARS); (2) functionality using published criteria; and (3) features that support 4 self-management behaviors (including PPG waveform monitoring) identified using evidence-based guidelines. Interrater reliability between the reviewers was calculated. RESULTS Of 12 included apps, 5 (42%) scored above average for quality (MARS score ≥3.0). App quality was highest for their ease of use, navigation, layout, and visual appeal (eg, functionality and aesthetics) and lowest for their behavioral change support and subjective impressions of quality. The most common app functionalities were capturing and graphically displaying user-entered data (n = 9 [75%]). Nearly all apps (n = 11 [92%]) supported PPG waveform monitoring, but only 2 (17%) supported all 4 self-management behaviors. Interrater reliability was high (0.75-0.83). CONCLUSION The reviewed apps had wide variability in quality, functionality, and adherence to self-management behaviors. Given the accessibility of these apps to underserved populations and the tremendous potential they hold for improving AF detection and management, high priority should be given to improving app quality and functionality.
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Affiliation(s)
- Meghan Reading Turchioe
- Address reprint requests and correspondence: Dr Meghan Reading Turchioe, Department of Population Health Sciences, Division of Health Informatics, Weill Cornell Medicine, 425 East 61st Street, Suite 301, New York, NY 10065.
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Pang Y, Yang Z, Yang Y, Ren TL. Wearable Electronics Based on 2D Materials for Human Physiological Information Detection. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2020; 16:e1901124. [PMID: 31364311 DOI: 10.1002/smll.201901124] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Revised: 05/07/2019] [Indexed: 05/12/2023]
Abstract
Recently, advancement in materials production, device fabrication, and flexible circuit has led to the huge prosperity of wearable electronics for human healthcare monitoring and medical diagnosis. Particularly, with the emergence of 2D materials many merits including light weight, high stretchability, excellent biocompatibility, and high performance are used for those potential applications. Thus, it is urgent to review the wearable electronics based on 2D materials for the detection of various human signals. In this work, the typical graphene-based materials, transition-metal dichalcogenides, and transition metal carbides or carbonitrides used for the wearable electronics are discussed. To well understand the human physiological information, it is divided into two dominated categories, namely, the human physical and the human chemical signals. The monitoring of body temperature, electrograms, subtle signals, and limb motions is described for the physical signals while the detection of body fluid including sweat, breathing gas, and saliva is reviewed for the chemical signals. Recent progress and development toward those specific utilizations are highlighted in the Review with the representative examples. The future outlook of wearable healthcare techniques is briefly discussed for their commercialization.
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Affiliation(s)
- Yu Pang
- Institute of Microelectronics, Tsinghua University, Beijing, 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, China
| | - Zhen Yang
- Institute of Microelectronics, Tsinghua University, Beijing, 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, China
| | - Yi Yang
- Institute of Microelectronics, Tsinghua University, Beijing, 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, China
| | - Tian-Ling Ren
- Institute of Microelectronics, Tsinghua University, Beijing, 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, China
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48
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Hood RL, Rubinsky B. Special Issue: Medical Devices for Economically Disadvantaged People and Populations: Perspective Problems and Prospective Solutions. J Med Device 2020. [DOI: 10.1115/1.4046008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Affiliation(s)
- R. Lyle Hood
- Department of Mechanical Engineering, University of Texas at San Antonio, San Antonio, TX 78249; Graduate Program in Biomedical Engineering, University of Texas at San Antonio and UT Health San Antonio, San Antonio, TX 78229
| | - Boris Rubinsky
- Department of Mechanical Engineering, University of California, Berkeley, CA 94720
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49
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A human-in-the-loop deep learning paradigm for synergic visual evaluation in children. Neural Netw 2020; 122:163-173. [DOI: 10.1016/j.neunet.2019.10.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Revised: 08/11/2019] [Accepted: 10/01/2019] [Indexed: 11/20/2022]
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50
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Millán CA, Girón NA, Lopez DM. Analysis of Relevant Features from Photoplethysmographic Signals for Atrial Fibrillation Classification. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:E498. [PMID: 31941071 PMCID: PMC7013739 DOI: 10.3390/ijerph17020498] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 12/23/2019] [Accepted: 12/24/2019] [Indexed: 11/16/2022]
Abstract
Atrial Fibrillation (AF) is the most common cardiac arrhythmia found in clinical practice. It affects an estimated 33.5 million people, representing approximately 0.5% of the world's population. Electrocardiogram (ECG) is the main diagnostic criterion for AF. Recently, photoplethysmography (PPG) has emerged as a simple and portable alternative for AF detection. However, it is not completely clear which are the most important features of the PPG signal to perform this process. The objective of this paper is to determine which are the most relevant features for PPG signal analysis in the detection of AF. This study is divided into two stages: (a) a systematic review carried out following the Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies (PRISMA-DTA) statement in six databases, in order to identify the features of the PPG signal reported in the literature for the detection of AF, and (b) an experimental evaluation of them, using machine learning, in order to determine which have the greatest influence on the process of detecting AF. Forty-four features were found when analyzing the signal in the time, frequency, or time-frequency domains. From those 44 features, 27 were implemented, and through machine learning, it was found that only 11 are relevant in the detection process. An algorithm was developed for the detection of AF based on these 11 features, which obtained an optimal performance in terms of sensitivity (98.43%), specificity (99.52%), and accuracy (98.97%).
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Affiliation(s)
| | | | - Diego M. Lopez
- Telematics Engineering Research Group, Telematics Department, Universidad Del Cauca (Unicauca), Popayán 190002, Colombia; (C.A.M.); (N.A.G.)
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