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Paper Title
LOW-COST DEVICE FOR THE EARLY PREDICTION OF VASOVAGAL SYNCOPE USING ARDUINO UNO AND MACHINE LEARNING MODEL
Authors
Amana Aleemath , Noor Fathima KKT , Sona VP , Tinu A , Shibitha KP
Keywords
Vasovagal syncope, Isolation Forest, heart rate variability, Arduino UNO, embedded machine learning.
Abstract
Vasovagal syncope (VVS) is a brief loss of consciousness brought on by an imbalance in the autonomic nervous system that frequently results in secondary damage. This paper describes
a real-time embedded system for VVS prediction that uses an Isolation Forest classifier installed on an Arduino Uno and a web interface, along with ECG-derived heart rate variability
(HRV) parameters. The system's accuracy in identifying pre-syncopal patterns is 95.68%. According to a recent study by Lee et al. (2024), machine learning models utilising HRV alone
could predict syncope with 72% accuracy about three minutes before to onset. In order to achieve similar predictive capability without relying on external computing equipment, the current work expands on these findings by constructing a standalone embedded system. For continuous ambulatory monitoring, the suggested system provides an affordable solution.
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How To Cite
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IJCRT — journal style
"LOW-COST DEVICE FOR THE EARLY PREDICTION OF VASOVAGAL SYNCOPE USING ARDUINO UNO AND MACHINE LEARNING MODEL", IJCRT - International Journal of Creative Research Thoughts (www.ijcrt.org), ISSN:2320-2882, Vol.14, Issue 3, page no.h925-h930, March 2026, Available :https://ijcrt.org/papers/IJCRT2603952.pdf
APA — 7th edition
Aleemath, A., KKT, N. F., VP, S., A, T., & KP, S. (2026). LOW-COST DEVICE FOR THE EARLY PREDICTION OF VASOVAGAL SYNCOPE USING ARDUINO UNO AND MACHINE LEARNING MODEL. International Journal of Creative Research Thoughts, 14(3), h925-h930. https://ijcrt.org/viewpaperforall.php?paper=IJCRT2603952
MLA — 9th edition
Aleemath, Amana, et al. "LOW-COST DEVICE FOR THE EARLY PREDICTION OF VASOVAGAL SYNCOPE USING ARDUINO UNO AND MACHINE LEARNING MODEL." International Journal of Creative Research Thoughts, vol. 14, no. 3, 2026, pp. h925-h930, https://ijcrt.org/viewpaperforall.php?paper=IJCRT2603952.
Chicago — 17th, bibliography
Aleemath, Amana, et al. "LOW-COST DEVICE FOR THE EARLY PREDICTION OF VASOVAGAL SYNCOPE USING ARDUINO UNO AND MACHINE LEARNING MODEL." International Journal of Creative Research Thoughts 14, no. 3 (2026): h925-h930. https://ijcrt.org/viewpaperforall.php?paper=IJCRT2603952.
Harvard — author–date
Aleemath, A. et al. (2026) 'LOW-COST DEVICE FOR THE EARLY PREDICTION OF VASOVAGAL SYNCOPE USING ARDUINO UNO AND MACHINE LEARNING MODEL', International Journal of Creative Research Thoughts, 14(3), pp. h925-h930. Available at: https://ijcrt.org/viewpaperforall.php?paper=IJCRT2603952
IEEE — numbered reference
A. Aleemath, N. F. KKT, S. VP, T. A and S. KP, "LOW-COST DEVICE FOR THE EARLY PREDICTION OF VASOVAGAL SYNCOPE USING ARDUINO UNO AND MACHINE LEARNING MODEL," IJCRT, vol. 14, no. 3, pp. h925-h930, Mar. 2026.
Vancouver — biomedical
Aleemath A, KKT NF, VP S, A T, KP S. LOW-COST DEVICE FOR THE EARLY PREDICTION OF VASOVAGAL SYNCOPE USING ARDUINO UNO AND MACHINE LEARNING MODEL. IJCRT. 2026 Mar;14(3):h925-h930.
AMA — 11th edition
Aleemath A, KKT NF, VP S, A T, KP S. LOW-COST DEVICE FOR THE EARLY PREDICTION OF VASOVAGAL SYNCOPE USING ARDUINO UNO AND MACHINE LEARNING MODEL. IJCRT. 2026;14(3):h925-h930. Accessed at https://ijcrt.org/viewpaperforall.php?paper=IJCRT2603952
BibTeX — Zotero, Mendeley, LaTeX
@article{IJCRT2603952,
author = {Amana Aleemath and Noor Fathima KKT and Sona VP and Tinu A and Shibitha KP},
title = {LOW-COST DEVICE FOR THE EARLY PREDICTION OF VASOVAGAL SYNCOPE USING ARDUINO UNO AND MACHINE LEARNING MODEL},
journal = {International Journal of Creative Research Thoughts},
volume = {14},
number = {3},
pages = {h925-h930},
year = {2026},
month = {March},
issn = {2320-2882},
url = {https://ijcrt.org/viewpaperforall.php?paper=IJCRT2603952}
}
RIS — EndNote, RefWorks
TY - JOUR
AU - Aleemath, Amana
AU - KKT, Noor Fathima
AU - VP, Sona
AU - A, Tinu
AU - KP, Shibitha
TI - LOW-COST DEVICE FOR THE EARLY PREDICTION OF VASOVAGAL SYNCOPE USING ARDUINO UNO AND MACHINE LEARNING MODEL
T2 - International Journal of Creative Research Thoughts
JA - IJCRT
VL - 14
IS - 3
PY - 2026
SN - 2320-2882
UR - https://ijcrt.org/viewpaperforall.php?paper=IJCRT2603952
SP - h925-h930
ER -
Issue
Volume 14 Issue 3,
March-2026
Pages : h925-h930
Article Information
- Pages
- h925-h930
- Published
- March 2026
- Registration ID
- IJCRT_304002
- Published Paper ID
- IJCRT2603952
- Research Area
- Science and Technology
- Author Type
- Indian Author
- Location
- Kozhikode, Kerala, India
- Peer Review
- Request Peer Review Report
- Licence
- CC BY 4.0 · Open Access
- ISSN & Impact Factor
- 2320-2882 · IF 9.17
- Downloads
- 138
- Permanent Link
- https://ijcrt.org/viewpaperforall.php?paper=IJCRT2603952
- Published Paper PDF
- https://ijcrt.org/papers/IJCRT2603952.pdf
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