Hybrid CNN-LSTM Network for ECG Classification and Its Software-Hardware Co-Design Approach

Song Nien Tang; Yuan Ho Chen; Yu Wei Chang; Yu Ting Chen; Shuo Hung Chou

doi:10.1109/ISOCC59558.2023.10396448

Hybrid CNN-LSTM Network for ECG Classification and Its Software-Hardware Co-Design Approach

Song Nien Tang^*, Yuan Ho Chen, Yu Wei Chang, Yu Ting Chen, Shuo Hung Chou

^*Corresponding author for this work

Department of Electronic Engineering

Chung Yuan Christian University

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

2 Scopus citations

Abstract

The hybrid architecture of a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) model has been progressively applied to the time-series data application. This paper developed a hybrid CNN-LSTM network to classify electrocardiography signals. Moreover, we proposed a software (SW)-hardware (HW) co-design approach using a system-on-chip (SoC) field-programmable gate array (FPGA) platform to implement the hybrid CNN-LSTM inference. In our SoC-FPGA design, the CNN model was completed using the SW program while the LSTM model that employs the block circulant weight matrix was realized using the FPGA HW. An experiment was made to achieve 98.63 % ECG detection accuracy within 208.2 ms using the proposed SW-HW co-design SoC-FPGA approach.

Original language	English
Title of host publication	Proceedings - International SoC Design Conference 2023, ISOCC 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	173-174
Number of pages	2
ISBN (Electronic)	9798350327038
DOIs	https://doi.org/10.1109/ISOCC59558.2023.10396448
State	Published - 2023
Event	20th International SoC Design Conference, ISOCC 2023 - Jeju, Korea, Republic of Duration: 25 10 2023 → 28 10 2023

Publication series

Name	Proceedings - International SoC Design Conference 2023, ISOCC 2023

Conference

Conference	20th International SoC Design Conference, ISOCC 2023
Country/Territory	Korea, Republic of
City	Jeju
Period	25/10/23 → 28/10/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

CNN
Convolution Neural Network
FPGA
hybrid
long short term memory
LSTM
software-hardware co-design

Access to Document

10.1109/ISOCC59558.2023.10396448

Cite this

Tang, S. N., Chen, Y. H., Chang, Y. W., Chen, Y. T., & Chou, S. H. (2023). Hybrid CNN-LSTM Network for ECG Classification and Its Software-Hardware Co-Design Approach. In Proceedings - International SoC Design Conference 2023, ISOCC 2023 (pp. 173-174). (Proceedings - International SoC Design Conference 2023, ISOCC 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISOCC59558.2023.10396448

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title = "Hybrid CNN-LSTM Network for ECG Classification and Its Software-Hardware Co-Design Approach",

abstract = "The hybrid architecture of a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) model has been progressively applied to the time-series data application. This paper developed a hybrid CNN-LSTM network to classify electrocardiography signals. Moreover, we proposed a software (SW)-hardware (HW) co-design approach using a system-on-chip (SoC) field-programmable gate array (FPGA) platform to implement the hybrid CNN-LSTM inference. In our SoC-FPGA design, the CNN model was completed using the SW program while the LSTM model that employs the block circulant weight matrix was realized using the FPGA HW. An experiment was made to achieve 98.63 % ECG detection accuracy within 208.2 ms using the proposed SW-HW co-design SoC-FPGA approach.",

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author = "Tang, {Song Nien} and Chen, {Yuan Ho} and Chang, {Yu Wei} and Chen, {Yu Ting} and Chou, {Shuo Hung}",

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year = "2023",

doi = "10.1109/ISOCC59558.2023.10396448",

language = "英语",

series = "Proceedings - International SoC Design Conference 2023, ISOCC 2023",

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Tang, SN, Chen, YH, Chang, YW, Chen, YT & Chou, SH 2023, Hybrid CNN-LSTM Network for ECG Classification and Its Software-Hardware Co-Design Approach. in Proceedings - International SoC Design Conference 2023, ISOCC 2023. Proceedings - International SoC Design Conference 2023, ISOCC 2023, Institute of Electrical and Electronics Engineers Inc., pp. 173-174, 20th International SoC Design Conference, ISOCC 2023, Jeju, Korea, Republic of, 25/10/23. https://doi.org/10.1109/ISOCC59558.2023.10396448

Hybrid CNN-LSTM Network for ECG Classification and Its Software-Hardware Co-Design Approach. / Tang, Song Nien; Chen, Yuan Ho; Chang, Yu Wei et al.
Proceedings - International SoC Design Conference 2023, ISOCC 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 173-174 (Proceedings - International SoC Design Conference 2023, ISOCC 2023).

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

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PY - 2023

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AB - The hybrid architecture of a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) model has been progressively applied to the time-series data application. This paper developed a hybrid CNN-LSTM network to classify electrocardiography signals. Moreover, we proposed a software (SW)-hardware (HW) co-design approach using a system-on-chip (SoC) field-programmable gate array (FPGA) platform to implement the hybrid CNN-LSTM inference. In our SoC-FPGA design, the CNN model was completed using the SW program while the LSTM model that employs the block circulant weight matrix was realized using the FPGA HW. An experiment was made to achieve 98.63 % ECG detection accuracy within 208.2 ms using the proposed SW-HW co-design SoC-FPGA approach.

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Tang SN, Chen YH, Chang YW, Chen YT, Chou SH. Hybrid CNN-LSTM Network for ECG Classification and Its Software-Hardware Co-Design Approach. In Proceedings - International SoC Design Conference 2023, ISOCC 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 173-174. (Proceedings - International SoC Design Conference 2023, ISOCC 2023). doi: 10.1109/ISOCC59558.2023.10396448

Hybrid CNN-LSTM Network for ECG Classification and Its Software-Hardware Co-Design Approach

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Keywords

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