Premature Ventricular Contraction Beat Classification via Hyperdimensional Computing

Brandon W. Ung; Lulu Ge; Keshab K. Parhi

doi:10.1109/IEEECONF56349.2022.10052044

Premature Ventricular Contraction Beat Classification via Hyperdimensional Computing

Brandon W. Ung, Lulu Ge, Keshab K. Parhi

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

1 Scopus citations

Abstract

Hyperdimensional computing (HD) is an emerging brain-inspired paradigm used for machine learning classification tasks. It manipulates ultra-long vectors-hypervectors- using simple operations, which allows for fast learning, energy efficiency, noise tolerance, and a highly parallel distributed framework. HD computing has shown a significant promise in the area of biological signal classification. This paper addresses group-specific premature ventricular contraction (PVC) beat detection with HD computing using the data from the MIT-BIH arrhythmia database. Temporal, heart rate variability (HRV), and spectral features are extracted, and minimal redundancy maximum relevance (mRMR) is used to rank and select features for classification. Three encoding approaches are explored for mapping the features into the HD space. The HD computing classifiers can achieve a PVC beat detection accuracy of 97.7 % accuracy, compared to 99.4% achieved by more computationally complex methods such as convolutional neural networks (CNNs).

Original language	English (US)
Title of host publication	56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022
Editors	Michael B. Matthews
Publisher	IEEE Computer Society
Pages	1306-1310
Number of pages	5
ISBN (Electronic)	9781665459068
DOIs	https://doi.org/10.1109/IEEECONF56349.2022.10052044
State	Published - 2022
Externally published	Yes
Event	56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022 - Virtual, Online, United States Duration: Oct 31 2022 → Nov 2 2022

Publication series

Name	Conference Record - Asilomar Conference on Signals, Systems and Computers
Volume	2022-October
ISSN (Print)	1058-6393

Conference

Conference	56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022
Country/Territory	United States
City	Virtual, Online
Period	10/31/22 → 11/2/22

Bibliographical note

Funding Information:
This paper was supported in part by grants from the National Science Foundation under grant number CCF-1814759 and CISCO Systems, Inc.

Publisher Copyright:
© 2022 IEEE.

Access

10.1109/IEEECONF56349.2022.10052044

OpenUrl availability

Full text

Cite this

Ung, B. W., Ge, L., & Parhi, K. K. (2022). Premature Ventricular Contraction Beat Classification via Hyperdimensional Computing. In M. B. Matthews (Ed.), 56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022 (pp. 1306-1310). (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2022-October). IEEE Computer Society. https://doi.org/10.1109/IEEECONF56349.2022.10052044

Premature Ventricular Contraction Beat Classification via Hyperdimensional Computing. / Ung, Brandon W.; Ge, Lulu; Parhi, Keshab K.
56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022. ed. / Michael B. Matthews. IEEE Computer Society, 2022. p. 1306-1310 (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2022-October).

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

Ung, BW, Ge, L & Parhi, KK 2022, Premature Ventricular Contraction Beat Classification via Hyperdimensional Computing. in MB Matthews (ed.), 56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022. Conference Record - Asilomar Conference on Signals, Systems and Computers, vol. 2022-October, IEEE Computer Society, pp. 1306-1310, 56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022, Virtual, Online, United States, 10/31/22. https://doi.org/10.1109/IEEECONF56349.2022.10052044

@inproceedings{b38368df54d84bdaa358de2d5786f713,

title = "Premature Ventricular Contraction Beat Classification via Hyperdimensional Computing",

abstract = "Hyperdimensional computing (HD) is an emerging brain-inspired paradigm used for machine learning classification tasks. It manipulates ultra-long vectors-hypervectors- using simple operations, which allows for fast learning, energy efficiency, noise tolerance, and a highly parallel distributed framework. HD computing has shown a significant promise in the area of biological signal classification. This paper addresses group-specific premature ventricular contraction (PVC) beat detection with HD computing using the data from the MIT-BIH arrhythmia database. Temporal, heart rate variability (HRV), and spectral features are extracted, and minimal redundancy maximum relevance (mRMR) is used to rank and select features for classification. Three encoding approaches are explored for mapping the features into the HD space. The HD computing classifiers can achieve a PVC beat detection accuracy of 97.7 % accuracy, compared to 99.4% achieved by more computationally complex methods such as convolutional neural networks (CNNs).",

author = "Ung, {Brandon W.} and Lulu Ge and Parhi, {Keshab K.}",

note = "Funding Information: This paper was supported in part by grants from the National Science Foundation under grant number CCF-1814759 and CISCO Systems, Inc. Publisher Copyright: {\textcopyright} 2022 IEEE.; 56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022 ; Conference date: 31-10-2022 Through 02-11-2022",

year = "2022",

doi = "10.1109/IEEECONF56349.2022.10052044",

language = "English (US)",

series = "Conference Record - Asilomar Conference on Signals, Systems and Computers",

publisher = "IEEE Computer Society",

pages = "1306--1310",

editor = "Matthews, {Michael B.}",

booktitle = "56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022",

}

TY - GEN

T1 - Premature Ventricular Contraction Beat Classification via Hyperdimensional Computing

AU - Ung, Brandon W.

AU - Ge, Lulu

AU - Parhi, Keshab K.

PY - 2022

Y1 - 2022

N2 - Hyperdimensional computing (HD) is an emerging brain-inspired paradigm used for machine learning classification tasks. It manipulates ultra-long vectors-hypervectors- using simple operations, which allows for fast learning, energy efficiency, noise tolerance, and a highly parallel distributed framework. HD computing has shown a significant promise in the area of biological signal classification. This paper addresses group-specific premature ventricular contraction (PVC) beat detection with HD computing using the data from the MIT-BIH arrhythmia database. Temporal, heart rate variability (HRV), and spectral features are extracted, and minimal redundancy maximum relevance (mRMR) is used to rank and select features for classification. Three encoding approaches are explored for mapping the features into the HD space. The HD computing classifiers can achieve a PVC beat detection accuracy of 97.7 % accuracy, compared to 99.4% achieved by more computationally complex methods such as convolutional neural networks (CNNs).

AB - Hyperdimensional computing (HD) is an emerging brain-inspired paradigm used for machine learning classification tasks. It manipulates ultra-long vectors-hypervectors- using simple operations, which allows for fast learning, energy efficiency, noise tolerance, and a highly parallel distributed framework. HD computing has shown a significant promise in the area of biological signal classification. This paper addresses group-specific premature ventricular contraction (PVC) beat detection with HD computing using the data from the MIT-BIH arrhythmia database. Temporal, heart rate variability (HRV), and spectral features are extracted, and minimal redundancy maximum relevance (mRMR) is used to rank and select features for classification. Three encoding approaches are explored for mapping the features into the HD space. The HD computing classifiers can achieve a PVC beat detection accuracy of 97.7 % accuracy, compared to 99.4% achieved by more computationally complex methods such as convolutional neural networks (CNNs).

UR - http://www.scopus.com/inward/record.url?scp=85150197281&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85150197281&partnerID=8YFLogxK

U2 - 10.1109/IEEECONF56349.2022.10052044

DO - 10.1109/IEEECONF56349.2022.10052044

M3 - Conference contribution

AN - SCOPUS:85150197281

T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers

SP - 1306

EP - 1310

BT - 56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022

A2 - Matthews, Michael B.

PB - IEEE Computer Society

T2 - 56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022

Y2 - 31 October 2022 through 2 November 2022

ER -

Premature Ventricular Contraction Beat Classification via Hyperdimensional Computing

Abstract

Publication series

Conference

Bibliographical note

Access

OpenUrl availability

Other files and links

Fingerprint

Cite this