AI-based 3-Lead to 12-Lead ECG Reconstruction: Towards Smartphone-based Public Healthcare
| Autor: | Mallick, Aditya, R, Rahul L, Shaiju, Albert, Neelapala, Satya Deepika, Giri, Lopamudra, Sarkar, Rahuldeb, Jana, Soumya |
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| Rok vydání: | 2024 |
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| Druh dokumentu: | Working Paper |
| Popis: | Clinicians generally diagnose cardiovascular diseases (CVDs) using standard 12-Lead electrocardiogram (ECG). However, for smartphone-based public healthcare systems, a reduced 3-lead system may be preferred because of (i) increased portability, and (ii) reduced requirement for power, storage and bandwidth. Subsequently, clinicians require accurate 3-lead to 12-Lead ECG reconstruction, which has so far been studied only in the personalized setting. When each device is dedicated to one individual, artificial intelligence (AI) methods such as temporal long short-term memory (LSTM) and a further improved spatio-temporal LSTM-UNet combine have proven effective. In contrast, in the current smartphone-based public health setting where a common device is shared by many, developing an AI lead-reconstruction model that caters to the extensive ECG signal variability in the general population appears a far greater challenge. In this direction, we take a first step, and observe that the performance improvement achieved by a generative model, specifically, 1D Pix2Pix GAN (generative adversarial network), over LSTM-UNet is encouraging. Comment: Accepted to IEEE Healthcom 2024 for presentation as a Main Conference Paper |
| Databáze: | arXiv |
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