Conceptual Explanations of ECG Classification using Large Language Models
Article No.: 59, Pages 1 - 3
Abstract
Conceptual-level explanations of ECG signals has been of great interest in order that arrhythmias can be automatically classified and explained. We leverage and evaluate the power of large language models (LLMs), specifically ChatGPT, to generate the main conceptual features extraction code from raw ECG data and perform classification. Thereafter, we use ChatGPT to generate meaningful conceptual level explanations for the outcomes. Results suggest that ChatGPT is able to provide accurate and consistent explanations for ECG classification. In addition to this, the explanations are simple and and meaningful for doctors.
References
[1]
U Rajendra Acharya, Hamido Fujita, Shu Lih Oh, Yuki Hagiwara, Jen Hong Tan, Muhammad Adam, and Ru San Tan. 2019. Deep Convolutional Neural Network for the Automated Diagnosis of Congestive Heart Failure Using ECG Signals. Applied Intelligence 49 (01 2019).
[2]
Abhijit Bhattacharyya, Lokesh Singh, and Ram Bilas Pachori. 2018. Fourier–Bessel series expansion based empirical wavelet transform for analysis of non-stationary signals. Digital Signal Processing 78 (2018), 185–196. https://doi.org/10.1016/j.dsp.2018.02.020
[3]
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the ACL: Human Language Technologies, Volume.
[4]
Nada Lavrac, Ivan Bratko, Igor Mozetič, Boian Čerček, Anton Grad, and Matija Horvat. 2007. KARDIO‐E‐an expert system for electrocardiographic diagnosis of cardiac arrhythmias. Expert Systems 2 (04 2007), 46 – 55. https://doi.org/10.1111/j.1468-0394.1985.tb00449.x
[5]
Jun Li, Che Liu, Sibo Cheng, Rossella Arcucci, and Shenda Hong. 2023. Frozen Language Model Helps ECG Zero-Shot Learning. arxiv:2303.12311 [cs.LG]
[6]
Wavelet Ot, Aeromagnet Dat, and Thomas Ridsdill-smith. 2000. The Application of the Wavelet Transform to the Processing of Aeromagnetic Data. (12 2000).
[7]
Boris Pyakillya, N Kazachenko, and N Mikhailovsky. 2017. Deep Learning for ECG Classification. Journal of Physics: Conference Series 913 (10 2017), 012004. https://doi.org/10.1088/1742-6596/913/1/012004
[8]
Wei Emma Zhang, Quan Z. Sheng, Ahoud Alhazmi, and Chenliang Li. 2020. Adversarial Attacks on Deep-Learning Models in Natural Language Processing: A Survey. ACM Trans. Intell. Syst. Technol. 11, 3, Article 24 (apr 2020), 41 pages. https://doi.org/10.1145/3374217
[9]
Zhenyu Zheng, Zhencheng Chen, Fangrong Hu, Jianming Zhu, Qunfeng Tang, and Yongbo Liang. 2020. An Automatic Diagnosis of Arrhythmias Using a Combination of CNN and LSTM Technology. Electronics 9, 1 (2020). https://doi.org/10.3390/electronics9010121
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- Conceptual Explanations of ECG Classification using Large Language Models
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Published In
October 2023
381 pages
ISBN:9798400716492
DOI:10.1145/3639856
Copyright © 2023 Owner/Author.
Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 17 May 2024
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- Demonstration
- Research
- Refereed limited
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AIMLSystems 2023
AIMLSystems 2023: The Third International Conference on Artificial Intelligence and Machine Learning Systems
October 25 - 28, 2023
Bangalore, India
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