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Cross-modal multiscale multi-instance learning for long-term ECG classification
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The MIL approach also shows potential in integrating other types of cross-scale data.
“The MIL approach also shows potential in integrating other types of cross-scale data [175].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [175]
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References 40 · Cited by 23
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- Tsao, 2022, Heart disease and stroke statistics—2022 update: a report from the American Heart Association, Circulation, 145, 153, 10.1161/CIR.0000000000001052 · DOI · Paper page
- Serhani, 2020, ECG monitoring systems: review, architecture, processes, and key challenges, Sensors, 20, 1, 10.3390/s20061796 · DOI
- Ebrahimi, 2020, A review on deep learning methods for ECG arrhythmia classification, Expert Syst. Appl. X, 7, 1
- Kiranyaz, 2015, Real-time patient-specific ECG classification by 1-D convolutional neural networks, IEEE Trans. Biomed. Eng., 63, 664, 10.1109/TBME.2015.2468589 · DOI
- Xu, 2020, Interpretation of electrocardiogram (ECG) rhythm by combined CNN and BiLSTM, IEEE Access, 8, 125380, 10.1109/ACCESS.2020.3006707 · DOI
- Natarajan, 2020, A wide and deep transformer neural network for 12-lead ECG classification, 10.22489/CinC.2020.107 · DOI
- Sindi, 2021, An adaptive deep learning framework to classify unknown composite power quality event using known single power quality events, Expert Syst. Appl., 178, 1, 10.1016/j.eswa.2021.115023 · DOI
- Yiğit, 2021, Automatic detection of power quality disturbance using convolutional neural network structure with gated recurrent unit, Mob. Inf. Syst., 2021, 1
- Yıldırım, 2018, Arrhythmia detection using deep convolutional neural network with long duration ECG signals, Comput. Biol. Med., 102, 411, 10.1016/j.compbiomed.2018.09.009 · DOI
- Campanella, 2019, Clinical-grade computational pathology using weakly supervised deep learning on whole slide images, Nat. Med., 25, 1301, 10.1038/s41591-019-0508-1 · DOI · Paper page
- Alber, 2019, Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences, npj Digit. Med., 2, 1, 10.1038/s41746-019-0193-y · DOI
- Qi, 2021, Multiscale deep neural network for obstructive sleep apnea detection using RR interval from single-lead ECG signal, IEEE Trans. Instrum. Meas., 70, 1
- Wang, 2021, Multiscale residual network based on channel spatial attention mechanism for multilabel ECG classification, J. Healthc. Eng., 2021, 1
- Nour, 2021, A novel classification framework using multiple bandwidth method with optimized CNN for brain–computer interfaces with EEG-fNIRS signals, Neural Comput. Appl., 33, 15815, 10.1007/s00521-021-06202-4 · DOI
- Ji, 2019, Electrocardiogram classification based on faster regions with convolutional neural network, Sensors, 19, 1, 10.3390/s19112558 · DOI
- Feng, 2019, Myocardial infarction classification based on convolutional neural network and recurrent neural network, Appl. Sci., 9, 1, 10.3390/app9091879 · DOI
- Zhang, 2021, ECG-based multi-class arrhythmia detection using spatio-temporal attention-based convolutional recurrent neural network, Artif. Intell. Med., 106, 1
- Niu, 2019, Inter-patient ECG classification with symbolic representations and multi-perspective convolutional neural networks, IEEE J. Biomed. Health Inform., 24, 1321, 10.1109/JBHI.2019.2942938 · DOI
- Chu, 2019, ECG authentication method based on parallel multi-scale one-dimensional residual network with center and margin loss, IEEE Access, 7, 51598, 10.1109/ACCESS.2019.2912519 · DOI
- Kraus, 2016, Classifying and segmenting microscopy images with deep multiple instance learning, Bioinformatics, 32, 52, 10.1093/bioinformatics/btw252 · DOI
- Shao, 2021, Transmil: transformer based correlated multiple instance learning for whole slide image classification, Adv. Neural Inf. Process. Syst., 34, 2136
- Christ, 2018, Time series feature extraction on basis of scalable hypothesis tests (tsfresh–a python package), Neurocomputing, 307, 72, 10.1016/j.neucom.2018.03.067 · DOI
- Xu, 2020, ECG heartbeat classification using convolutional neural networks, IEEE Access, 8, 8614, 10.1109/ACCESS.2020.2964749 · DOI
- Novotna, 2020, Deep-learning premature contraction localization in 12-lead ECG from whole signal annotations, 1, 10.22489/CinC.2020.193 · DOI
- Vaswani, 2017, Attention is all you need, Adv. Neural Inf. Process. Syst., 30, 1
- Zhang, 2022, Automatic diagnosis of arrhythmia with electrocardiogram using multiple instance learning: from rhythm annotation to heartbeat prediction, Artif. Intell. Med., 132, 1, 10.1016/j.artmed.2022.102379 · DOI
Metadata
- Authors
- Chen, L., Lian, C., Zeng, Z., Xu, B., & Su, Y.
- Affiliations
- Wuhan University of Technology (CN)
- Huazhong University of Science and Technology (CN)
- Corresponding author
- Lian, Cheng
- Venue
- Information Sciences
- Publisher
- Elsevier BV
- Published
- 2023-09
- Volume
- 643
- Pages
- 119230
- ISSN
- 0020-0255 (ISSN-L)
- OpenAlex
- W4379196925
- Cited by
- 23
- References
- 40
- Citations by year
- 2026: 2 · 2025: 8 · 2024: 11 · 2023: 2
- FWCI
- 4.53
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Information Sciences (publishedVersion)
- Funders
- National Natural Science Foundation of China (62176193, 62206204)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac electrophysiology and arrhythmias
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Keywords
- electrocardiogram, multiple instance learning, cross-modal learning, multi-scale features, arrhythmia detection, abnormal heartbeats
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