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Augmentation Strate...
Augmentation Strategies for Self-Supervised Representation Learning from Electrocardiograms
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- Andersson, Matilda (författare)
- Neko Health
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- Nilsson, Mattias (författare)
- Neko Health
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- Flood, Gabrielle (författare)
- Lund University,Lunds universitet,Matematik LTH,Matematikcentrum,Institutioner vid LTH,Lunds Tekniska Högskola,LTH profilområde: AI och digitalisering,LTH profilområden,Mathematics (Faculty of Engineering),Centre for Mathematical Sciences,Departments at LTH,Faculty of Engineering, LTH,LTH Profile Area: AI and Digitalization,LTH Profile areas,Faculty of Engineering, LTH
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- Aström, Kalle (författare)
- Lund University,Lunds universitet,Mathematical Imaging Group,Forskargrupper vid Lunds universitet,Matematik LTH,Matematikcentrum,Institutioner vid LTH,Lunds Tekniska Högskola,LTH profilområde: AI och digitalisering,LTH profilområden,LTH profilområde: Teknik för hälsa,Lund University Research Groups,Mathematics (Faculty of Engineering),Centre for Mathematical Sciences,Departments at LTH,Faculty of Engineering, LTH,LTH Profile Area: AI and Digitalization,LTH Profile areas,Faculty of Engineering, LTH,LTH Profile Area: Engineering Health,Faculty of Engineering, LTH
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(creator_code:org_t)
- 2023
- 2023
- Engelska 5 s.
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Ingår i: 31st European Signal Processing Conference, EUSIPCO 2023 - Proceedings. - 2219-5491. - 9789464593600 ; , s. 1075-1079
- Relaterad länk:
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http://dx.doi.org/10...
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https://lup.lub.lu.s...
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https://doi.org/10.2...
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Abstract
Ämnesord
Stäng
- In this paper, we investigate the effects of different augmentation strategies in self-supervised representation learning from electrocardiograms. Our study examines the impact of random resized crop and time out on downstream performance. We also consider the importance of the signal length. Furthermore, instead of using two augmented copies of the sample as a positive pair, we suggest augmenting only one. The second signal is kept as the original signal. These different augmentation strategies are investigated in the context of pre-training and fine-tuning, following the different self-supervised learning frameworks BYOL, SimCLR, and VICReg. We formulate the downstream task as a multi-label classification task using a public dataset containing ECG recordings and annotations. In our experiments, we demonstrate that self-supervised learning can consistently outperform classical supervised learning when configured correctly. These findings are of particular importance in the medical domain, as the medical labeling process is particularly expensive, and clinical ground truth is often difficult to define. We are hopeful that our findings will be a catalyst for further research into augmentation strategies in self-supervised learning to improve performance in the detection of cardiovascular disease.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- augmentation
- ECG
- electrocardiogram
- pre-processing
- representation learning
- self-supervised
Publikations- och innehållstyp
- kon (ämneskategori)
- ref (ämneskategori)
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