1
|
Wu S, Guo J, Chen X, Wang J, Zhao G, Ma S, Hao T, Tan J, Li Y. Rapid weather changes are associated with daily hospital visitors for atrial fibrillation accompanied by abnormal ECG repolarization: a case-crossover study. Eur J Med Res 2024; 29:62. [PMID: 38245805 PMCID: PMC10799445 DOI: 10.1186/s40001-023-01632-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 12/30/2023] [Indexed: 01/22/2024] Open
Abstract
BACKGROUND Atrial fibrillation (AF) is highly prevalent in the population, yet the factors contributing to AF events in susceptible individuals remain partially understood. The potential relationship between meteorological factors and AF, particularly with abnormal electrocardiograph (ECG) repolarization, has not been adequately studied. This case-crossover study aims to investigate the association between meteorological factors and daily hospital visits for AF with abnormal ECG repolarization in Shanghai, China. METHODS The study cohort comprised 10,325 patients with ECG-confirmed AF who sought treatment at Shanghai Sixth People's Hospital between 2015 and 2018. Meteorological and air pollutant concentration data were matched with the patient records. Using a case-crossover design, we analyzed the association between meteorological factors and the daily count of hospital visitors for AF with abnormal ECG repolarization at our AF center. Lag analysis models were applied to examine the temporal relationship between meteorological factors and AF events. RESULTS The analysis revealed statistically significant associations between AF occurrence and specific meteorological factors. AF events were significantly associated with average atmospheric pressure (lag 0 day, OR 0.9901, 95% CI 0.9825-0.9977, P < 0.05), average temperature (lag 1 day, OR 0.9890, 95% CI 0.9789-0.9992, P < 0.05), daily pressure range (lag 7 days, OR 1.0195, 95% CI 1.0079-1.0312, P < 0.01), and daily temperature range (lag 5 days, OR 1.0208, 95% CI 1.0087-1.0331, P < 0.01). Moreover, a significant correlation was observed between daily pressure range and daily temperature range with AF patients, particularly those with abnormal ECG repolarization, as evident in the case-crossover analysis. CONCLUSION This study highlights a significant correlation between meteorological factors and daily hospital visits for AF accompanied by abnormal ECG repolarization in Shanghai, China. In addition, AF patients with abnormal ECG repolarization were found to be more vulnerable to rapid daily changes in pressure and temperature compared to AF patients without such repolarization abnormalities.
Collapse
Affiliation(s)
- Shanmei Wu
- Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
- Department of Cardiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, People's Republic of China
| | - Jingyi Guo
- Clinical Research Center, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
| | - Xin Chen
- Department of Cardiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, People's Republic of China
| | - Jie Wang
- Department of General Surgery, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
| | - Gang Zhao
- Department of Cardiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, People's Republic of China
| | - Shixin Ma
- Department of Cardiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, People's Republic of China
| | - Tianzheng Hao
- Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
| | - Jianguo Tan
- Shanghai Meteorological IT Support Center, Shanghai, People's Republic of China.
- Key Laboratory of Urban Meteorology, China Meteorological Administration, Beijing, People's Republic of China.
| | - Yongguang Li
- Department of Cardiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, People's Republic of China.
| |
Collapse
|
2
|
Kolasa K, Admassu B, Hołownia-Voloskova M, Kędzior KJ, Poirrier JE, Perni S. Systematic reviews of machine learning in healthcare: a literature review. Expert Rev Pharmacoecon Outcomes Res 2024; 24:63-115. [PMID: 37955147 DOI: 10.1080/14737167.2023.2279107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 10/31/2023] [Indexed: 11/14/2023]
Abstract
INTRODUCTION The increasing availability of data and computing power has made machine learning (ML) a viable approach to faster, more efficient healthcare delivery. METHODS A systematic literature review (SLR) of published SLRs evaluating ML applications in healthcare settings published between1 January 2010 and 27 March 2023 was conducted. RESULTS In total 220 SLRs covering 10,462 ML algorithms were reviewed. The main application of AI in medicine related to the clinical prediction and disease prognosis in oncology and neurology with the use of imaging data. Accuracy, specificity, and sensitivity were provided in 56%, 28%, and 25% SLRs respectively. Internal and external validation was reported in 53% and less than 1% of the cases respectively. The most common modeling approach was neural networks (2,454 ML algorithms), followed by support vector machine and random forest/decision trees (1,578 and 1,522 ML algorithms, respectively). EXPERT OPINION The review indicated considerable reporting gaps in terms of the ML's performance, both internal and external validation. Greater accessibility to healthcare data for developers can ensure the faster adoption of ML algorithms into clinical practice.
Collapse
Affiliation(s)
- Katarzyna Kolasa
- Division of Health Economics and Healthcare Management, Kozminski University, Warsaw, Poland
| | - Bisrat Admassu
- Division of Health Economics and Healthcare Management, Kozminski University, Warsaw, Poland
| | | | | | | | | |
Collapse
|
3
|
Baj G, Gandin I, Scagnetto A, Bortolussi L, Cappelletto C, Di Lenarda A, Barbati G. Comparison of discrimination and calibration performance of ECG-based machine learning models for prediction of new-onset atrial fibrillation. BMC Med Res Methodol 2023; 23:169. [PMID: 37481514 PMCID: PMC10363301 DOI: 10.1186/s12874-023-01989-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 07/13/2023] [Indexed: 07/24/2023] Open
Abstract
BACKGROUND Machine learning (ML) methods to build prediction models starting from electrocardiogram (ECG) signals are an emerging research field. The aim of the present study is to investigate the performances of two ML approaches based on ECGs for the prediction of new-onset atrial fibrillation (AF), in terms of discrimination, calibration and sample size dependence. METHODS We trained two models to predict new-onset AF: a convolutional neural network (CNN), that takes as input the raw ECG signals, and an eXtreme Gradient Boosting model (XGB), that uses the signal's extracted features. A penalized logistic regression model (LR) was used as a benchmark. Discrimination was evaluated with the area under the ROC curve, while calibration with the integrated calibration index. We investigated the dependence of models' performances on the sample size and on class imbalance corrections introduced with random under-sampling. RESULTS CNN's discrimination was the most affected by the sample size, outperforming XGB and LR only around n = 10.000 observations. Calibration showed only a small dependence on the sample size for all the models considered. Balancing the training set with random undersampling did not improve discrimination in any of the models. Instead, the main effect of imbalance corrections was to worsen the models' calibration (for CNN, integrated calibration index from 0.014 [0.01, 0.018] to 0.17 [0.16, 0.19]). The sample size emerged as a fundamental point for developing the CNN model, especially in terms of discrimination (AUC = 0.75 [0.73, 0.77] when n = 10.000, AUC = 0.80 [0.79, 0.81] when n = 150.000). The effect of the sample size on the other two models was weaker. Imbalance corrections led to poorly calibrated models, for all the approaches considered, reducing the clinical utility of the models. CONCLUSIONS Our results suggest that the choice of approach in the analysis of ECG should be based on the amount of data available, preferring more standard models for small datasets. Moreover, imbalance correction methods should be avoided when developing clinical prediction models, where calibration is crucial.
Collapse
Affiliation(s)
- Giovanni Baj
- Department of Mathematics and Geosciences, University of Trieste, Trieste, Italy.
| | - Ilaria Gandin
- Department of Medical Sciences, Biostatistics Unit, University of Trieste, Trieste, Italy
| | - Arjuna Scagnetto
- Cardiovascular Center, Territorial Specialistic Department, University Hospital and Health Services of Trieste, Trieste, Italy
| | - Luca Bortolussi
- Department of Mathematics and Geosciences, University of Trieste, Trieste, Italy
| | - Chiara Cappelletto
- Cardiovascular Center, Territorial Specialistic Department, University Hospital and Health Services of Trieste, Trieste, Italy
| | - Andrea Di Lenarda
- Cardiovascular Center, Territorial Specialistic Department, University Hospital and Health Services of Trieste, Trieste, Italy
| | - Giulia Barbati
- Department of Medical Sciences, Biostatistics Unit, University of Trieste, Trieste, Italy
| |
Collapse
|
4
|
Silva R, Fred A, Plácido da Silva H. Morphological Autoencoders for Beat-by-Beat Atrial Fibrillation Detection Using Single-Lead ECG. SENSORS (BASEL, SWITZERLAND) 2023; 23:2854. [PMID: 36905058 PMCID: PMC10007386 DOI: 10.3390/s23052854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 02/21/2023] [Accepted: 03/04/2023] [Indexed: 06/18/2023]
Abstract
Engineered feature extraction can compromise the ability of Atrial Fibrillation (AFib) detection algorithms to deliver near real-time results. Autoencoders (AEs) can be used as an automatic feature extraction tool, tailoring the resulting features to a specific classification task. By coupling an encoder to a classifier, it is possible to reduce the dimension of the Electrocardiogram (ECG) heartbeat waveforms and classify them. In this work we show that morphological features extracted using a Sparse AE are sufficient to distinguish AFib from Normal Sinus Rhythm (NSR) beats. In addition to the morphological features, rhythm information was included in the model using a proposed short-term feature called Local Change of Successive Differences (LCSD). Using single-lead ECG recordings from two referenced public databases, and with features from the AE, the model was able to achieve an F1-score of 88.8%. These results show that morphological features appear to be a distinct and sufficient factor for detecting AFib in ECG recordings, especially when designed for patient-specific applications. This is an advantage over state-of-the-art algorithms that need longer acquisition times to extract engineered rhythm features, which also requires careful preprocessing steps. To the best of our knowledge, this is the first work that presents a near real-time morphological approach for AFib detection under naturalistic ECG acquisition with a mobile device.
Collapse
Affiliation(s)
- Rafael Silva
- Department of Bioengineering (DBE), Instituto Superior Técnico (IST), Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal
- Instituto de Telecomunicações (IT), Av. Rovisco Pais 1, Torre Norte—Piso 10, 1049-001 Lisboa, Portugal
| | - Ana Fred
- Department of Bioengineering (DBE), Instituto Superior Técnico (IST), Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal
- Instituto de Telecomunicações (IT), Av. Rovisco Pais 1, Torre Norte—Piso 10, 1049-001 Lisboa, Portugal
| | - Hugo Plácido da Silva
- Department of Bioengineering (DBE), Instituto Superior Técnico (IST), Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal
- Instituto de Telecomunicações (IT), Av. Rovisco Pais 1, Torre Norte—Piso 10, 1049-001 Lisboa, Portugal
| |
Collapse
|
5
|
Zhang H, Liu C, Tang F, Li M, Zhang D, Xia L, Crozier S, Gan H, Zhao N, Xu W, Liu F. Atrial fibrillation classification based on the 2D representation of minimal subset ECG and a non-deep neural network. Front Physiol 2023; 14:1070621. [PMID: 36866172 PMCID: PMC9971936 DOI: 10.3389/fphys.2023.1070621] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Accepted: 01/30/2023] [Indexed: 02/16/2023] Open
Abstract
Atrial fibrillation (AF) is the most common cardiac arrhythmia, and its early detection is critical for preventing complications and optimizing treatment. In this study, a novel AF prediction method is proposed, which is based on investigating a subset of the 12-lead ECG data using a recurrent plot and ParNet-adv model. The minimal subset of ECG leads (II &V1) is determined via a forward stepwise selection procedure, and the selected 1D ECG data is transformed into 2D recurrence plot (RP) images as an input to train a shallow ParNet-adv Network for AF prediction. In this study, the proposed method achieved F1 score of 0.9763, Precision of 0.9654, Recall of 0.9875, Specificity of 0.9646, and Accuracy of 0.9760, which significantly outperformed solutions based on single leads and complete 12 leads. When studying several ECG datasets, including the CPSC and Georgia ECG databases of the PhysioNet/Computing in Cardiology Challenge 2020, the new method achieved F1 score of 0.9693 and 0.8660, respectively. The results suggested a good generalization of the proposed method. Compared with several state-of-art frameworks, the proposed model with a shallow network of only 12 depths and asymmetric convolutions achieved the highest average F1 score. Extensive experimental studies proved that the proposed method has a high potential for AF prediction in clinical and particularly wearable applications.
Collapse
Affiliation(s)
- Hua Zhang
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD, Australia
| | - Chengyu Liu
- School of Instrument Science and Engineering, Southeast University, Nanjing, Jiangsu, China
| | - Fangfang Tang
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD, Australia
| | - Mingyan Li
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD, Australia
| | - Dongxia Zhang
- Zhejiang Provincial Centre for Disease Control and Prevention CN, Hangzhou, Zhejiang, China
| | - Ling Xia
- Department of Biomedical Engineering, Zhejiang University, Hangzhou, Zhejiang, China
| | - Stuart Crozier
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD, Australia
| | - Hongping Gan
- School of Software, Northwestern Polytechnical University, Xi’an, China
| | - Nan Zhao
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD, Australia
| | - Wenlong Xu
- Department of Biomedical Engineering, China Jiliang University, Hangzhou, Zhejiang, China,*Correspondence: Wenlong Xu, ; Feng Liu,
| | - Feng Liu
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD, Australia,*Correspondence: Wenlong Xu, ; Feng Liu,
| |
Collapse
|
6
|
Wesselius FJ, van Schie MS, de Groot NMS, Hendriks RC. An accurate and efficient method to train classifiers for atrial fibrillation detection in ECGs: Learning by asking better questions. Comput Biol Med 2022; 143:105331. [PMID: 35231835 DOI: 10.1016/j.compbiomed.2022.105331] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 02/11/2022] [Accepted: 02/16/2022] [Indexed: 11/03/2022]
Abstract
BACKGROUND An increasing number of wearables are capable of measuring electrocardiograms (ECGs), which may help in early detection of atrial fibrillation (AF). Therefore, many studies focus on automated detection of AF in ECGs. A major obstacle is the required amount of manually labelled data. This study aimed to provide an efficient and reliable method to train a classifier for AF detection using large datasets of real-life ECGs. METHOD Human-controlled semi-supervised learning was applied, consisting of two phases: the pre-training phase and the semi-automated training phase. During pre-training, an initial classifier was trained, which was used to predict the classes of new ECG segments in the semi-automated training phase. Based on the degree of certainty, segments were added to the training dataset automatically or after human validation. Thereafter, the classifier was retrained and this procedure was repeated. To test the model performance, a real-life telemetry dataset containing 3,846,564 30-s ECG segments of hospitalized patients (n = 476) and the CinC Challenge 2017 database were used. RESULTS After pre-training, the average F1-score on a hidden testing dataset was 89.0%. Furthermore, after the pre-training phase 68.0% of all segments in the hidden test set could be classified with an estimated probability of successful classification of 99%, providing an F1-score of 97.9% for these segments. During the semi-automated training phase, this F1-score showed little variation (97.3%-97.9% in the hidden test set), whilst the number of segments which could be automatically classified increased from 68.0% to 75.8% due to the enhanced training dataset. At the same time, the overall F1-score increased from 89.0% to 91.4%. CONCLUSIONS Human-validated semi-supervised learning makes training a classifier more time efficient without compromising on accuracy, hence this method might be valuable in the automated detection of AF in real-life ECGs.
Collapse
Affiliation(s)
- Fons J Wesselius
- Department of Cardiology, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Mathijs S van Schie
- Department of Cardiology, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Natasja M S de Groot
- Department of Cardiology, Erasmus Medical Center, Rotterdam, the Netherlands; Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, the Netherlands.
| | - Richard C Hendriks
- Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, the Netherlands
| |
Collapse
|
7
|
Jeong H, Jeong YW, Park Y, Kim K, Park J, Kang DR. Applications of deep learning methods in digital biomarker research using noninvasive sensing data. Digit Health 2022; 8:20552076221136642. [PMID: 36353696 PMCID: PMC9638529 DOI: 10.1177/20552076221136642] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 10/04/2022] [Indexed: 07/02/2024] Open
Abstract
Introduction: Noninvasive digital biomarkers are critical elements in digital healthcare in terms of not only the ease of measurement but also their use of raw data. In recent years, deep learning methods have been put to use to analyze these diverse heterogeneous data; these methods include representation learning for feature extraction and supervised learning for the prediction of these biomarkers. Methods: We introduce clinical cases of digital biomarkers and various deep-learning methods applied according to each data type. In addition, deep learning methods for the integrated analysis of multidimensional heterogeneous data are introduced, and the utility of these data as an integrated digital biomarker is presented. The current status of digital biomarker research is examined by surveying research cases applied to various types of data as well as modeling methods. Results: We present a future research direction for using data from heterogeneous sources together by introducing deep learning methods for dimensionality reduction and mode integration from multimodal digital biomarker studies covering related domains. The integration of multimodality has led to advances in research through the improvement of performance and complementarity between modes. Discussion: The integrative digital biomarker will be more useful for research on diseases that require data from multiple sources to be treated together. Since delicate signals from patients are not missed and the interaction effects between signals are also considered, it will be helpful for immediate detection and more accurate prediction of symptoms.
Collapse
Affiliation(s)
- Hoyeon Jeong
- Department of Biostatistics, Yonsei University Wonju College of
Medicine, Wonju, Republic of Korea
| | - Yong W Jeong
- Department of Biostatistics, Yonsei University Wonju College of
Medicine, Wonju, Republic of Korea
| | - Yeonjae Park
- Department of Biostatistics, Yonsei University Wonju College of
Medicine, Wonju, Republic of Korea
| | - Kise Kim
- School of Health and Environmental Science, Korea University, Seoul, Republic of Korea
| | | | - Dae R Kang
- Department of Biostatistics, Yonsei University Wonju College of
Medicine, Wonju, Republic of Korea
- Department of Precision Medicine, Yonsei University Wonju College of
Medicine, Wonju, Republic of Korea
| |
Collapse
|