arXiv Open Access 2022

Novel Blood Pressure Waveform Reconstruction from Photoplethysmography using Cycle Generative Adversarial Networks

Milad Asgari Mehrabadi Seyed Amir Hossein Aqajari Amir Hosein Afandizadeh Zargari Nikil Dutt Amir M. Rahmani

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Abstrak

Continuous monitoring of blood pressure (BP)can help individuals manage their chronic diseases such as hypertension, requiring non-invasive measurement methods in free-living conditions. Recent approaches fuse Photoplethysmograph (PPG) and electrocardiographic (ECG) signals using different machine and deep learning approaches to non-invasively estimate BP; however, they fail to reconstruct the complete signal, leading to less accurate models. In this paper, we propose a cycle generative adversarial network (CycleGAN) based approach to extract a BP signal known as ambulatory blood pressure (ABP) from a clean PPG signal. Our approach uses a cycle generative adversarial network that extends theGAN architecture for domain translation, and outperforms state-of-the-art approaches by up to 2x in BP estimation.

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cs.LG

Penulis (5)

Milad Asgari Mehrabadi

Seyed Amir Hossein Aqajari

Amir Hosein Afandizadeh Zargari

Nikil Dutt

Amir M. Rahmani

Format Sitasi

APA MLA BibTeX

Mehrabadi, M.A., Aqajari, S.A.H., Zargari, A.H.A., Dutt, N., Rahmani, A.M. (2022). Novel Blood Pressure Waveform Reconstruction from Photoplethysmography using Cycle Generative Adversarial Networks. https://arxiv.org/abs/2201.09976

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Informasi Jurnal

Tahun Terbit: 2022
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓