Discrete Hilbert Transform via Memristor Crossbars for Compact Biosignal Processing

Lei Zhang; Zhuolin Yang; Kedar Aras; Igor R. Efimov; Gina C. Adam

doi:10.1109/AICT55583.2022.10013604

Discrete Hilbert Transform via Memristor Crossbars for Compact Biosignal Processing

Lei Zhang^*, Zhuolin Yang, Kedar Aras, Igor R. Efimov, Gina C. Adam

^*Corresponding author for this work

Biomedical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English (US)
Title of host publication	16th IEEE International Conference on Application of Information and Communication Technologies, AICT 2022 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665451628
DOIs	https://doi.org/10.1109/AICT55583.2022.10013604
State	Published - 2022
Event	16th IEEE International Conference on Application of Information and Communication Technologies, AICT 2022 - Washington, United States Duration: Oct 12 2022 → Oct 14 2022

Publication series

Name	16th IEEE International Conference on Application of Information and Communication Technologies, AICT 2022 - Proceedings

Conference

Conference	16th IEEE International Conference on Application of Information and Communication Technologies, AICT 2022
Country/Territory	United States
City	Washington
Period	10/12/22 → 10/14/22

Funding

ACKNOWLEDGMENT This work was supported by the NSF grant CRII: FET 1948127, NIH grant R01HL141470, NIH grant 1K99HL148523-01A1, Leducq Foundation grant RHYTHM, and the George Washington University Cross-Disciplinary Research Fund. All experiments were conducted on de-identified human heart tissue and approved by the Institutional Review Board (Office of Human Research) at George Washington University.

Keywords

Biomedical
Discrete Fourier Transform
Hilbert Transform
In- memory Computing
Memristor

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Computer Vision and Pattern Recognition
Information Systems
Health Informatics
Information Systems and Management

Access to Document

10.1109/AICT55583.2022.10013604

Cite this

Zhang, L., Yang, Z., Aras, K., Efimov, I. R., & Adam, G. C. (2022). Discrete Hilbert Transform via Memristor Crossbars for Compact Biosignal Processing. In 16th IEEE International Conference on Application of Information and Communication Technologies, AICT 2022 - Proceedings (16th IEEE International Conference on Application of Information and Communication Technologies, AICT 2022 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/AICT55583.2022.10013604

Zhang, Lei ; Yang, Zhuolin ; Aras, Kedar et al. / Discrete Hilbert Transform via Memristor Crossbars for Compact Biosignal Processing. 16th IEEE International Conference on Application of Information and Communication Technologies, AICT 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. (16th IEEE International Conference on Application of Information and Communication Technologies, AICT 2022 - Proceedings).

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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 (US)",

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",

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Zhang, L, Yang, Z, Aras, K, Efimov, IR & Adam, GC 2022, Discrete Hilbert Transform via Memristor Crossbars for Compact Biosignal Processing. in 16th IEEE International Conference on Application of Information and Communication Technologies, AICT 2022 - Proceedings. 16th IEEE International Conference on Application of Information and Communication Technologies, AICT 2022 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 16th IEEE International Conference on Application of Information and Communication Technologies, AICT 2022, Washington, United States, 10/12/22. https://doi.org/10.1109/AICT55583.2022.10013604

Discrete Hilbert Transform via Memristor Crossbars for Compact Biosignal Processing. / Zhang, Lei; Yang, Zhuolin; Aras, Kedar et al.
16th IEEE International Conference on Application of Information and Communication Technologies, AICT 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. (16th IEEE International Conference on Application of Information and Communication Technologies, AICT 2022 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Discrete Hilbert Transform via Memristor Crossbars for Compact Biosignal Processing

AU - Zhang, Lei

AU - Yang, Zhuolin

AU - Aras, Kedar

AU - Efimov, Igor R.

AU - Adam, Gina C.

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

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KW - Discrete Fourier Transform

KW - Hilbert Transform

KW - In- memory Computing

KW - Memristor

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M3 - Conference contribution

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T3 - 16th IEEE International Conference on Application of Information and Communication Technologies, AICT 2022 - Proceedings

BT - 16th IEEE International Conference on Application of Information and Communication Technologies, AICT 2022 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 12 October 2022 through 14 October 2022

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Zhang L, Yang Z, Aras K, Efimov IR, Adam GC. Discrete Hilbert Transform via Memristor Crossbars for Compact Biosignal Processing. In 16th IEEE International Conference on Application of Information and Communication Technologies, AICT 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2022. (16th IEEE International Conference on Application of Information and Communication Technologies, AICT 2022 - Proceedings). doi: 10.1109/AICT55583.2022.10013604

Discrete Hilbert Transform via Memristor Crossbars for Compact Biosignal Processing

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