IncepSE: Leveraging InceptionTime's performance with Squeeze and Excitaion mechanism in ECG analysis
Authors: 
Tue Cao, Nhat Tran, Le Nguyen, Hung Nguyen, Hieu PhamAuthors Info & Claims
Tue Cao, Nhat Tran, Le Nguyen, Hung Nguyen, Hieu PhamAuthors Info & ClaimsPages 578 - 584
Abstract
Our study focuses on the potential for modifications of Inception-like architecture within the electrocardiogram (ECG) domain. To this end, we introduce IncepSE, a novel network characterized by strategic architectural incorporation that leverages the strengths of both InceptionTime and channel attention mechanisms. Furthermore, we propose a training setup that employs stabilization techniques that are aimed at tackling the formidable challenges of severe imbalance dataset PTB-XL and gradient corruption. By this means, we manage to set a new height for deep learning model in a supervised learning manner across the majority of tasks. Our model consistently surpasses InceptionTime by substantial margins compared to other state-of-the-arts in this domain, noticeably 0.013 AUROC score improvement in the "all" task, while also mitigating the inherent dataset fluctuations during training.
References
[1]
Erick A Perez Alday, Annie Gu, Amit J Shah, Chad Robichaux, An-Kwok Ian Wong, Chengyu Liu, Feifei Liu, Ali Bahrami Rad, Andoni Elola, Salman Seyedi, Qiao Li, Ashish Sharma, Gari D Clifford, and Matthew A Reyna. 2020. Classification of 12-lead ECGs: the PhysioNet/Computing in Cardiology Challenge 2020. Physiological Measurement 41, 12 (dec 2020), 124003. https://doi.org/10.1088/1361-6579/abc960
[2]
Steven Allender, Peter Scarborough, Viv Peto, Mike Rayner, Jose Leal, Ramon Luengo-Fernandez, and Alastair Gray. 2008. European cardiovascular disease statistics. European Heart Network 3 (2008), 11–35.
[3]
Ghadir Ali Altuwaijri, Ghulam Muhammad, Hamdi Altaheri, and Mansour Alsulaiman. 2022. A Multi-Branch Convolutional Neural Network with Squeeze-and-Excitation Attention Blocks for EEG-Based Motor Imagery Signals Classification. Diagnostics 12, 4 (2022). https://doi.org/10.3390/diagnostics12040995
[4]
Atul Anand, Tushar Kadian, Manu Kumar Shetty, and Anubha Gupta. 2022. Explainable AI decision model for ECG data of cardiac disorders. Biomedical Signal Processing and Control 75 (2022), 103584.
[5]
Hassan Ismail Fawaz, Benjamin Lucas, Germain Forestier, Charlotte Pelletier, Daniel F. Schmidt, Jonathan Weber, Geoffrey I. Webb, Lhassane Idoumghar, Pierre-Alain Muller, and François Petitjean. 2019. InceptionTime: Finding AlexNet for Time Series Classification. CoRR abs/1909.04939 (2019). arXiv:1909.04939http://arxiv.org/abs/1909.04939
[6]
Rongjun Ge, Tengfei Shen, Ying Zhou, Chengyu Liu, Libo Zhang, Benqiang Yang, Ying Yan, Jean-Louis Coatrieux, and Yang Chen. 2021. Convolutional squeeze-and-excitation network for ECG arrhythmia detection. Artificial Intelligence in Medicine 121 (2021), 102181.
[7]
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. 770–778.
[8]
Tong He, Zhi Zhang, Hang Zhang, Zhongyue Zhang, Junyuan Xie, and Mu Li. 2018. Bag of Tricks for Image Classification with Convolutional Neural Networks. CoRR abs/1812.01187 (2018). arXiv:1812.01187http://arxiv.org/abs/1812.01187
[9]
Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation 9, 8 (1997), 1735–1780.
[10]
Shenda Hong, Yuxi Zhou, Junyuan Shang, Cao Xiao, and Jimeng Sun. 2020. Opportunities and challenges of deep learning methods for electrocardiogram data: A systematic review. Computers in biology and medicine 122 (2020), 103801.
[11]
Jie Hu, Li Shen, and Gang Sun. 2018. Squeeze-and-Excitation Networks. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 7132–7141. https://doi.org/10.1109/CVPR.2018.00745
[12]
Hassan Ismail Fawaz, Benjamin Lucas, Germain Forestier, Charlotte Pelletier, Daniel F Schmidt, Jonathan Weber, Geoffrey I Webb, Lhassane Idoumghar, Pierre-Alain Muller, and François Petitjean. 2020. Inceptiontime: Finding alexnet for time series classification. Data Mining and Knowledge Discovery 34, 6 (2020), 1936–1962.
[13]
Ard Kastrati, Martyna Beata Płomecka, Damián Pascual, Lukas Wolf, Victor Gillioz, Roger Wattenhofer, and Nicolas Langer. 2021. EEGEyeNet: a simultaneous electroencephalography and eye-tracking dataset and benchmark for eye movement prediction. arXiv preprint arXiv:2111.05100 (2021).
[14]
Feifei Liu, Chengyu Liu, Lina Zhao, Xiangyu Zhang, Xiaoling Wu, Xiaoyan Xu, Yulin Liu, Caiyun Ma, Shoushui Wei, Zhiqiang He, 2018. An open access database for evaluating the algorithms of electrocardiogram rhythm and morphology abnormality detection. Journal of Medical Imaging and Health Informatics 8, 7 (2018), 1368–1373.
[15]
Temesgen Mehari and Nils Strodthoff. 2022. Self-supervised representation learning from 12-lead ECG data. Computers in biology and medicine 141 (2022), 105114.
[16]
Petr Nejedly, Adam Ivora, Radovan Smisek, Ivo Viscor, Zuzana Koscova, Pavel Jurak, and Filip Plesinger. 2021. Classification of ECG Using Ensemble of Residual CNNs with Attention Mechanism. In 2021 Computing in Cardiology (CinC), Vol. 48. 1–4. https://doi.org/10.23919/CinC53138.2021.9662723
[17]
Matthew A Reyna, Nadi Sadr, Erick A Perez Alday, Annie Gu, Amit J Shah, Chad Robichaux, Ali Bahrami Rad, Andoni Elola, Salman Seyedi, Sardar Ansari, 2021. Will two do? Varying dimensions in electrocardiography: the PhysioNet/Computing in Cardiology Challenge 2021. In 2021 Computing in Cardiology (CinC), Vol. 48. IEEE, 1–4.
[18]
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen. 2019. MobileNetV2: Inverted Residuals and Linear Bottlenecks. arxiv:1801.04381 [cs.CV]
[19]
Iqbal H Sarker. 2021. Deep learning: a comprehensive overview on techniques, taxonomy, applications and research directions. SN Computer Science 2, 6 (2021), 420.
[20]
Robin Tibor Schirrmeister, Jost Tobias Springenberg, Lukas Dominique Josef Fiederer, Martin Glasstetter, Katharina Eggensperger, Michael Tangermann, Frank Hutter, Wolfram Burgard, and Tonio Ball. 2017. Deep learning with convolutional neural networks for EEG decoding and visualization. Human brain mapping 38, 11 (2017), 5391–5420.
[21]
Farzan Shenavarmasouleh, Farid Ghareh Mohammadi, Khaled M Rasheed, and Hamid R Arabnia. 2023. Deep Learning in Healthcare: An In-Depth Analysis. arXiv preprint arXiv:2302.10904 (2023).
[22]
Nils Strodthoff, Patrick Wagner, Tobias Schaeffter, and Wojciech Samek. 2021. Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL. IEEE Journal of Biomedical and Health Informatics 25, 5 (2021), 1519–1528. https://doi.org/10.1109/jbhi.2020.3022989
[23]
Patrick Wagner, Nils Strodthoff, Ralf-Dieter Bousseljot, Dieter Kreiseler, Fatima I Lunze, Wojciech Samek, and Tobias Schaeffter. 2020. PTB-XL, a large publicly available electrocardiography dataset. Scientific data 7, 1 (2020), 154.
[24]
Zhiguang Wang, Weizhong Yan, and Tim Oates. 2017. Time series classification from scratch with deep neural networks: A strong baseline. In 2017 International joint conference on neural networks (IJCNN). IEEE, 1578–1585.
[25]
Colin White, Mahmoud Safari, Rhea Sukthanker, Binxin Ru, Thomas Elsken, Arber Zela, Debadeepta Dey, and Frank Hutter. 2023. Neural architecture search: Insights from 1000 papers. arXiv preprint arXiv:2301.08727 (2023).
[26]
Zhuoyang Xu, Yangming Guo, Tingting Zhao, Yue Zhao, Zhuo Liu, Xingzhi Sun, Guotong Xie, and Yichong Li. 2022. Abnormality classification from electrocardiograms with various lead combinations. Physiological Measurement 43, 7 (jul 2022), 074002. https://doi.org/10.1088/1361-6579/ac70a4
[27]
Jing Zhang, Deng Liang, Aiping Liu, Min Gao, Xiang Chen, Xu Zhang, and Xun Chen. 2021. MLBF-Net: A multi-lead-branch fusion network for multi-class arrhythmia classification using 12-Lead ECG. IEEE journal of translational engineering in health and medicine 9 (2021), 1–11.
Index Terms
- IncepSE: Leveraging InceptionTime's performance with Squeeze and Excitaion mechanism in ECG analysis
Recommendations
Detecting ECG abnormalities via transductive transfer learning
BCB '12: Proceedings of the ACM Conference on Bioinformatics, Computational Biology and BiomedicineDetecting Electrocardiogram (ECG) abnormalities is the process of identifying irregular cardiac activities which may lead to severe heart damage or even sudden death. Due to the rapid development of cyberphysic systems and health informatics, embedding ...
Cancelable biometric authentication system based on ECG
Biometrics are widely deployed in various security systems; however, they have drawbacks in the form of leakage or stealing, therefore numerous solutions have been proposed to secure biometric template such as cancelable biometric, which is one of the ...
An efficient method for ECG beat classification and correction of ectopic beats
A method based on radial basis function system for cancelling out ectopic beat by classification between normal and abnormal beats is proposed.The proposed solution addresses for ECG recordings.Several experiments have demonstrated the enhancement of ...
Comments
Information & Contributors
Information
Published In
December 2023
1058 pages
ISBN:9798400708916
DOI:10.1145/3628797
Copyright © 2023 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 07 December 2023
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
SOICT 2023
SOICT 2023: The 12th International Symposium on Information and Communication Technology
December 7 - 8, 2023
Ho Chi Minh, Vietnam
Acceptance Rates
Overall Acceptance Rate 147 of 318 submissions, 46%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 40Total Downloads
- Downloads (Last 12 months)40
- Downloads (Last 6 weeks)4
Reflects downloads up to 02 Sep 2024
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format