HRV-Spark: Computing Heart Rate Variability Measures Using Apache Spark

Xufeng Qu; Yuanyuan Wu; Jinze Liu; Licong Cui

doi:10.1109/BIBM49941.2020.9313361

HRV-Spark: Computing Heart Rate Variability Measures Using Apache Spark

Xufeng Qu, Yuanyuan Wu, Jinze Liu, Licong Cui

Computer Science

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

1 Scopus citations

Abstract

Heart rate variability (HRV) analysis has been serving as a significant promising marker in clinical research over the last few decades. The rapidly growing heart rate data generated from various devices, particularly the electrocardiograph (ECG), need to be stored properly and processed timely. There is a pressing need to develop efficient approaches for performing HRV analyses based on ECG signals. In this paper, we introduce a cloud computing approach (called HRV-Spark) to compute HRV measures in parallel by leveraging Apache Spark and a QRS detection algorithm in [1]. We ran HRV-Spark on Amazon Web Services (AWS) clusters using large-scale datasets in the National Sleep Research Resource. We evaluated the performance and scalability of HRV-Spark in terms of the number of computing nodes in the AWS cluster, the size of the input datasets, and the hardware configuration of the computing nodes. The results show that HRV-Spark is an efficient and scalable approach for computing HRV measures.

Original language	English
Title of host publication	Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020
Editors	Taesung Park, Young-Rae Cho, Xiaohua Tony Hu, Illhoi Yoo, Hyun Goo Woo, Jianxin Wang, Julio Facelli, Seungyoon Nam, Mingon Kang
Pages	2235-2241
Number of pages	7
ISBN (Electronic)	9781728162157
DOIs	https://doi.org/10.1109/BIBM49941.2020.9313361
State	Published - Dec 16 2020
Event	2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020 - Virtual, Seoul, Korea, Republic of Duration: Dec 16 2020 → Dec 19 2020

Publication series

Name	Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020

Conference

Conference	2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020
Country/Territory	Korea, Republic of
City	Virtual, Seoul
Period	12/16/20 → 12/19/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Keywords

Amazon Web Services
Apache Spark
Cloud Computing
Heart Rate Variability

ASJC Scopus subject areas

Computer Science Applications
Information Systems and Management
Medicine (miscellaneous)
Health Informatics

Access to Document

10.1109/BIBM49941.2020.9313361

Cite this

Qu, X., Wu, Y., Liu, J., & Cui, L. (2020). HRV-Spark: Computing Heart Rate Variability Measures Using Apache Spark. In T. Park, Y.-R. Cho, X. T. Hu, I. Yoo, H. G. Woo, J. Wang, J. Facelli, S. Nam, & M. Kang (Eds.), Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020 (pp. 2235-2241). Article 9313361 (Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020). https://doi.org/10.1109/BIBM49941.2020.9313361

Qu, Xufeng ; Wu, Yuanyuan ; Liu, Jinze et al. / HRV-Spark : Computing Heart Rate Variability Measures Using Apache Spark. Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020. editor / Taesung Park ; Young-Rae Cho ; Xiaohua Tony Hu ; Illhoi Yoo ; Hyun Goo Woo ; Jianxin Wang ; Julio Facelli ; Seungyoon Nam ; Mingon Kang. 2020. pp. 2235-2241 (Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020).

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abstract = "Heart rate variability (HRV) analysis has been serving as a significant promising marker in clinical research over the last few decades. The rapidly growing heart rate data generated from various devices, particularly the electrocardiograph (ECG), need to be stored properly and processed timely. There is a pressing need to develop efficient approaches for performing HRV analyses based on ECG signals. In this paper, we introduce a cloud computing approach (called HRV-Spark) to compute HRV measures in parallel by leveraging Apache Spark and a QRS detection algorithm in [1]. We ran HRV-Spark on Amazon Web Services (AWS) clusters using large-scale datasets in the National Sleep Research Resource. We evaluated the performance and scalability of HRV-Spark in terms of the number of computing nodes in the AWS cluster, the size of the input datasets, and the hardware configuration of the computing nodes. The results show that HRV-Spark is an efficient and scalable approach for computing HRV measures.",

keywords = "Amazon Web Services, Apache Spark, Cloud Computing, Heart Rate Variability",

author = "Xufeng Qu and Yuanyuan Wu and Jinze Liu and Licong Cui",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020 ; Conference date: 16-12-2020 Through 19-12-2020",

year = "2020",

month = dec,

day = "16",

doi = "10.1109/BIBM49941.2020.9313361",

language = "English",

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Qu, X, Wu, Y, Liu, J & Cui, L 2020, HRV-Spark: Computing Heart Rate Variability Measures Using Apache Spark. in T Park, Y-R Cho, XT Hu, I Yoo, HG Woo, J Wang, J Facelli, S Nam & M Kang (eds), Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020., 9313361, Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020, pp. 2235-2241, 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020, Virtual, Seoul, Korea, Republic of, 12/16/20. https://doi.org/10.1109/BIBM49941.2020.9313361

HRV-Spark: Computing Heart Rate Variability Measures Using Apache Spark. / Qu, Xufeng; Wu, Yuanyuan; Liu, Jinze et al.
Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020. ed. / Taesung Park; Young-Rae Cho; Xiaohua Tony Hu; Illhoi Yoo; Hyun Goo Woo; Jianxin Wang; Julio Facelli; Seungyoon Nam; Mingon Kang. 2020. p. 2235-2241 9313361 (Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020).

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

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T2 - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020

AU - Qu, Xufeng

AU - Wu, Yuanyuan

AU - Liu, Jinze

AU - Cui, Licong

PY - 2020/12/16

Y1 - 2020/12/16

N2 - Heart rate variability (HRV) analysis has been serving as a significant promising marker in clinical research over the last few decades. The rapidly growing heart rate data generated from various devices, particularly the electrocardiograph (ECG), need to be stored properly and processed timely. There is a pressing need to develop efficient approaches for performing HRV analyses based on ECG signals. In this paper, we introduce a cloud computing approach (called HRV-Spark) to compute HRV measures in parallel by leveraging Apache Spark and a QRS detection algorithm in [1]. We ran HRV-Spark on Amazon Web Services (AWS) clusters using large-scale datasets in the National Sleep Research Resource. We evaluated the performance and scalability of HRV-Spark in terms of the number of computing nodes in the AWS cluster, the size of the input datasets, and the hardware configuration of the computing nodes. The results show that HRV-Spark is an efficient and scalable approach for computing HRV measures.

AB - Heart rate variability (HRV) analysis has been serving as a significant promising marker in clinical research over the last few decades. The rapidly growing heart rate data generated from various devices, particularly the electrocardiograph (ECG), need to be stored properly and processed timely. There is a pressing need to develop efficient approaches for performing HRV analyses based on ECG signals. In this paper, we introduce a cloud computing approach (called HRV-Spark) to compute HRV measures in parallel by leveraging Apache Spark and a QRS detection algorithm in [1]. We ran HRV-Spark on Amazon Web Services (AWS) clusters using large-scale datasets in the National Sleep Research Resource. We evaluated the performance and scalability of HRV-Spark in terms of the number of computing nodes in the AWS cluster, the size of the input datasets, and the hardware configuration of the computing nodes. The results show that HRV-Spark is an efficient and scalable approach for computing HRV measures.

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KW - Cloud Computing

KW - Heart Rate Variability

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BT - Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020

A2 - Park, Taesung

A2 - Cho, Young-Rae

A2 - Hu, Xiaohua Tony

A2 - Yoo, Illhoi

A2 - Woo, Hyun Goo

A2 - Wang, Jianxin

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Qu X, Wu Y, Liu J, Cui L. HRV-Spark: Computing Heart Rate Variability Measures Using Apache Spark. In Park T, Cho YR, Hu XT, Yoo I, Woo HG, Wang J, Facelli J, Nam S, Kang M, editors, Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020. 2020. p. 2235-2241. 9313361. (Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020). doi: 10.1109/BIBM49941.2020.9313361

HRV-Spark: Computing Heart Rate Variability Measures Using Apache Spark

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