@inproceedings{90143cef73a94c3b8c36213b5ffd1c86,
title = "Discrete Hilbert Transform via Memristor Crossbars for Compact Biosignal Processing",
abstract = "The Hilbert transform is widely used in biomedical signal processing and requires efficient implementation. We propose the implementation of the discrete Hilbert transform based on emerging memristor devices. It uses two matrix multiplication layers using weights programmed in the memristor array and a linear Hadamard product calculation layer mappable to CMOS. The functionality was tested on a dataset of optical cardiac signals from the human heart. The results show negligible <1\% angle error between the proposed implementation and the MATLAB function. It also has robustness to non-idealities. This proposed solution can be applied to bio-signal processing at the edge.",
keywords = "Biomedical, Discrete Fourier Transform, Hilbert Transform, In- memory Computing, Memristor",
author = "Lei Zhang and Zhuolin Yang and Kedar Aras and Efimov, \{Igor R.\} and Adam, \{Gina C.\}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 16th IEEE International Conference on Application of Information and Communication Technologies, AICT 2022 ; Conference date: 12-10-2022 Through 14-10-2022",
year = "2022",
doi = "10.1109/AICT55583.2022.10013604",
language = "English",
series = "16th IEEE International Conference on Application of Information and Communication Technologies, AICT 2022 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "16th IEEE International Conference on Application of Information and Communication Technologies, AICT 2022 - Proceedings",
address = "United States",
}