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An Operative Analysis of Influence on Human Physical Activities Using Ubiquitous Ambulatory Electrocardiogram

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Islahuzzaman Nuryadin
Islahuzzaman Nuryadin
Islahuzzaman Nuryadin
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Haris Nugroho
Haris Nugroho
Haris Nugroho
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Sri Santoso Sabarini
Sri Santoso Sabarini
Sri Santoso Sabarini
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Rumi Doewes at Sebelas Maret University
Rumi Doewes
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Abstract and Figures

Reproductive-aged adults are increasingly likely to suffer from cardiovascular disease and cardiac abnormalities as a result of today's stressful lifestyles and competitive contests. A wearable ambulatory electrocardiogram recorder, often known as an A-ECG plotter, is one example of a device that is both physiologically useful and crucial. Due to the advances in telemedicine, modern A-ECG recorders can also send a notification to the doctor whenever an abnormal cardiac event or arrhythmia is detected, in addition to collecting the ECG signals and other pertinent physiological data. The focus of this study has been on the modifications to the A-ECG data that result from the use of a portable ambulatory ECG plotter. The primary goal of this study was to classify the various types of BMAs and analyse their impact on the A-ECG data. Motion artefacts in the A-ECG signal have been linked to a number of issues. Our key areas of focus were on motion artefact phantom reading, motion artefact abstraction beginning with electrocardiogram data, motion artefact topography extraction and the development of several methods for BMA classification. Recorded A-ECGs' residuals after principal component analysis (PCA) with 5, 10 and 15 main components are regarded as signals owing to motion artefacts. As soon as an electrocardiogram (ECG) is captured by a wearable device for the purpose of ambulatory cardiac monitoring, the signal will inevitably be tainted by motion artefacts from the user's own physical activity (PA) or body movement activities (BMAs). The impact of these BMAs on the A-ECG and the classification of BMAs have been accomplished after the motion artefacts were removed from the signal. The study concluded that by isolating the motion artefacts from the A-ECG signal, the effect of these BMAs is effectively perceptible.
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Vol.:(0123456789)
SN Computer Science (2023) 4:755
https://doi.org/10.1007/s42979-023-02180-7
SN Computer Science
ORIGINAL RESEARCH
An Operative Analysis ofInfluence onHuman Physical Activities Using
Ubiquitous Ambulatory Electrocardiogram
IslahuzzamanNuryadin1· HarisNugroho1· SriSantosoSabarini1· RumiIqbalDoewes1·
MohammadFurqonHidayatullah1
Received: 16 March 2023 / Accepted: 21 July 2023
© The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2023
Abstract
Reproductive-aged adults are increasingly likely to suffer from cardiovascular disease and cardiac abnormalities as a result of
today's stressful lifestyles and competitive contests. A wearable ambulatory electrocardiogram recorder, often known as an
A-ECG plotter, is one example of a device that is both physiologically useful and crucial. Due to the advances in telemedicine,
modern A-ECG recorders can also send a notification to the doctor whenever an abnormal cardiac event or arrhythmia is
detected, in addition to collecting the ECG signals and other pertinent physiological data. The focus of this study has been on
the modifications to the A-ECG data that result from the use of a portable ambulatory ECG plotter. The primary goal of this
study was to classify the various types of BMAs and analyse their impact on the A-ECG data. Motion artefacts in the A-ECG
signal have been linked to a number of issues. Our key areas of focus were on motion artefact phantom reading, motion arte-
fact abstraction beginning with electrocardiogram data, motion artefact topography extraction and the development of several
methods for BMA classification. Recorded A-ECGs' residuals after principal component analysis (PCA) with 5, 10 and 15
main components are regarded as signals owing to motion artefacts. As soon as an electrocardiogram (ECG) is captured by
a wearable device for the purpose of ambulatory cardiac monitoring, the signal will inevitably be tainted by motion artefacts
from the user's own physical activity (PA) or body movement activities (BMAs). The impact of these BMAs on the A-ECG
and the classification of BMAs have been accomplished after the motion artefacts were removed from the signal. The study
concluded that by isolating the motion artefacts from the A-ECG signal, the effect of these BMAs is effectively perceptible.
Keywords A-ECG· PCA· BMA· PA· DWT
Introduction
Cardiovascular diseases are increasingly widespread in
people's 30s and 40s because of the stress of modern life.
It has been noted that more and more resources are being
devoted to the introduction of cutting-edge technologies
with the aim of improving existing healthcare options and,
by extension, human health. Researchers from preeminent
technological institutions are persistently working toward
building cutting-edge, tech-enabled healthcare recording
and/or monitoring items in tandem with and with the sup-
port of preeminent medicinal experts. Researchers have
also achieved some measure of success in developing
small, durable, and largely accurate monitoring devices
for essential physiological parameters like blood com-
pression, physique heat, an ECG or any supplementary
biological data. The importance of an ECG signal as a
measurement of various key characteristics has led to a
This article is part of the topical collection “Machine Intelligence
and Smart Systems” guest edited by Manish Gupta and Shikha
Agrawal.
* Islahuzzaman Nuryadin
mase.ior2004@staff.uns.ac.id
Haris Nugroho
harisnugroho@staff.uns.ac.id
Sri Santoso Sabarini
srisantoso@staff.uns.ac.id
Rumi Iqbal Doewes
king.doewes@staff.uns.ac.id
Mohammad Furqon Hidayatullah
mohammadfurqon@staff.uns.ac.id
1 Faculty ofSport, Universitas Sebelas Maret, Jl. Ir. Sutami,
36A, Kentingan, Surakarta, Indonesia
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