LinkedIn

 آمار

تعداد نشریات20
تعداد شماره‌ها1,212
تعداد مقالات11,056
تعداد مشاهده مقاله74,716,449
تعداد دریافت فایل اصل مقاله99,970,974
Dharma, Abdi, Sihombing, Poltak, Efendi, Syahril, Mawengkang, Herman, Turnip, Arjon. (1404). Portable Holter with Cloud-Based Learning Analytics for Real-Time Health Monitoring. سامانه مدیریت نشریات علمی, 15(4), 393-406. doi: 10.31661/jbpe.v0i0.2411-1856
Abdi Dharma; Poltak Sihombing; Syahril Efendi; Herman Mawengkang; Arjon Turnip. "Portable Holter with Cloud-Based Learning Analytics for Real-Time Health Monitoring". سامانه مدیریت نشریات علمی, 15, 4, 1404, 393-406. doi: 10.31661/jbpe.v0i0.2411-1856
Dharma, Abdi, Sihombing, Poltak, Efendi, Syahril, Mawengkang, Herman, Turnip, Arjon. (1404). 'Portable Holter with Cloud-Based Learning Analytics for Real-Time Health Monitoring', سامانه مدیریت نشریات علمی, 15(4), pp. 393-406. doi: 10.31661/jbpe.v0i0.2411-1856
Dharma, Abdi, Sihombing, Poltak, Efendi, Syahril, Mawengkang, Herman, Turnip, Arjon. Portable Holter with Cloud-Based Learning Analytics for Real-Time Health Monitoring. سامانه مدیریت نشریات علمی, 1404; 15(4): 393-406. doi: 10.31661/jbpe.v0i0.2411-1856

Portable Holter with Cloud-Based Learning Analytics for Real-Time Health Monitoring

Journal of Biomedical Physics and Engineering
مقاله 9، دوره 15، شماره 4 - شماره پیاپی 1، آبان 2025، صفحه 393-406    اصل مقاله (1.45 M)
نوع مقاله: Technical Note
شناسه دیجیتال (DOI): 10.31661/jbpe.v0i0.2411-1856
نویسندگان
Abdi Dharma* 1؛ Poltak Sihombing1؛ Syahril Efendi1؛ Herman Mawengkang2؛ Arjon Turnip3
1Department of Computer Science, Faculty of Computer Science and Information Technology, Universitas Sumatera Utara, Medan, Indonesia
2Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Sumatera Utara, Medan, Indonesia
3Department of Electrical Engineering, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Bandung, Indonesia
چکیده
The increasing prevalence of cardiovascular diseases underscores the need for efficient and user-friendly tools to monitor heart health. Traditional Holter monitors, while effective, are often bulky and inconvenient, limiting their use in real-world scenarios. This study introduces the Smart Portable Holter, a wireless device designed for real-time cardiac monitoring, enabling early detection of heart irregularities with enhanced accuracy and user convenience. The device captures continuous electrocardiogram signals and transmits them to a secure cloud platform for processing. Machine learning models, including Random Forest and Extreme Gradient Boosting (XGBoost), analyze the data to detect cardiac events. The system’s performance was evaluated using real-world datasets, emphasizing accuracy and reliability in identifying cardiac arrhythmias. The Smart Portable Holter delivers an impressive 98% accuracy in detecting cardiac events. Its compact and wireless design enhances user comfort, allowing for seamless wear throughout the day. Coupled with advanced analytics, it offers detailed, time-stamped records that empower both users and healthcare professionals. These features facilitated early diagnosis and supported personalized treatment planning for patients with varying cardiac conditions. The Smart Portable Holter represents a significant advancement in cardiac care, combining portability, real-time analytics, and high diagnostic accuracy. By empowering patients and healthcare providers with actionable insights, it fosters proactive heart health management and contributes to improved clinical outcomes.

تازه های تحقیق

Abdi Dharma (Google Scholar)

کلیدواژه‌ها
Electrocardiogram؛ Early Diagnosis؛ Cardiac Arrhythmias؛ Holter Monitoring؛ XGBoost؛ Arrhythmias, Cardiac؛ Machine Learning
مراجع
  1. World Health Organization. E-health. Available from: https://www.emro.who.int/health-topics/ehealth/ (World Health Organization website). 2024.
  2. Bansal M. Cardiovascular disease and COVID-19. Diabetes Metab Syndr. 2020;14(3):247-50. doi: 10.1016/j.dsx.2020.03.013. PubMed PMID: 32247212. PubMed PMCID: PMC7102662.
  3. Rahman MO, Kashem MA, Nayan AA, Akter M, Rabbi F, Ahmed M, Asaduzzaman M. Internet of things (IoT) based ECG system for rural health care. (IJACSA) International Journal of Advanced Computer Science and Applications. 2021;12(6):470-7. doi: 10.14569/IJACSA.2021.0120653.
  4. Kamble P, Birajdar A. IoT based portable ECG monitoring device for smart healthcare. In fifth international conference on science technology engineering and mathematics (ICONSTEM); Chennai, India: IEEE; 2019. p. 471-4.
  5. Turnip A, Taufik M, Kusumandari DE. Precision blood pressure prediction leveraging Photoplethysmograph signals using Support Vector Regression. Egyptian Informatics Journal. 2025;29:100599. doi: 10.1016/j.eij.2024.100599.
  6. Faruk N, Abdulkarim A, Emmanuel I, Folawiyo YY, Adewole KS, Mojeed HA, et al. A comprehensive survey on low-cost ECG acquisition systems: Advances on design specifications, challenges and future direction. Biocybernetics and Biomedical Engineering. 2021;41(2):474-502. doi: 10.1016/j.bbe.2021.02.007.
  7. Sladojevic S, Anderla A, Gutai A, Arsenovic M, Sladojevic M, Stevanov B, et al. Mobile ECG Devices Selection: A Comprehensive Review. Tehnički Glasnik. 2024;18(2):319-30. doi: 10.31803/tg-20230615143201.
  8. Lucas S, Desai M, Khot A, Harriet S, Jain K. Mobile Heart Sync: An IoT Based Portable ECG Monitor. In Advancements in Communication and Systems; India: SCRS; 2024. p. 315-24.
  9. Kang TW, Lee J, Kwon Y, Lee YJ, Yeo WH. Recent progress in the development of flexible wearable electrodes for electrocardiogram monitoring during exercise. NanoBiomed Res. 2024;4(8):2300169. doi: 10.1002/anbr.202300169.
  10. Martín Gómez R, Allevard E, Kamstra H, Cotter J, Lamb P. Validity and Reliability of Movesense HR+ ECG Measurements for High-Intensity Running and Cycling. Sensors (Basel). 2024;24(17):5713. doi: 10.3390/s24175713. PubMed PMID: 39275624. PubMed PMCID: PMC11397956.
  11. Singh D, Bhati S, Deshmukh A, Sen D. Future Prediction by Portable ECG Machine. Int J Worldwide Eng Res. 2024;2(4):7-9.
  12. Alamatsaz N, Tabatabaei L, Yazdchi M, Payan H, Alamatsaz N, Nasimi F. A lightweight hybrid CNN-LSTM explainable model for ECG-based arrhythmia detection. Biomedical Signal Processing and Control. 2024;90:105884. doi: 10.1016/j.bspc.2023.105884.
  13. Qtaish A, Al-Shrouf A. A Portable IoT-cloud ECG Monitoring System for Healthcare. International Journal of Computer Science & Network Security. 2022;22(1):269-75. doi: 10.22937/IJCSNS.2022.22.1.37.
  14. Wang Y, Bi X, Zhao Y. Design and Development of ECG Monitoring Cloud Platform Based on the Internet of Things Electrocardiograph. Chinese Journal of Medical Instrumentation. 2024;48(2):228-31. doi: 10.12455/j.issn.1671-7104.230247. PubMed PMID: 38605627.
  15. Gunda S, Akyeampong D, Gomez-Arroyo J, Jovin DG, Kowlgi NG, Kaszala K, et al. Consequences of chronic frequent premature atrial contractions: Association with cardiac arrhythmias and cardiac structural changes. J Cardiovasc Electrophysiol. 2019;30(10):1952-9. doi: 10.1111/jce.14067. PubMed PMID: 31310360. PubMed PMCID: PMC6786912.
  16. Przybylski R, Meziab O, Gauvreau K, Dionne A, DeWitt ES, Bezzerides VJ, Abrams DJ. Premature ventricular contractions in children and young adults: natural history and clinical implications. 2024;26(3):euae052. doi: 10.1093/europace/euae052. PubMed PMID: 38441283. PubMed PMCID: PMC10927167.
  17. Tiwari A, Chugh A, Sharma A. Ensemble framework for cardiovascular disease prediction. Comput Biol Med. 2022;146:105624. doi: 10.1016/j.compbiomed.2022.105624. PubMed PMID: 35598355.
  18. Gilgen-Ammann R, Schweizer T, Wyss T. RR interval signal quality of a heart rate monitor and an ECG Holter at rest and during exercise. Eur J Appl Physiol. 2019;119(7):1525-32. doi: 10.1007/s00421-019-04142-5. PubMed PMID: 31004219.
  19. Bertrand É, Caru M, Harvey A, Andelfinger G, Laverdiere C, Krajinovic M, et al. QTc intervals at rest and during exercise assessed by group correction formulas in survivors of childhood acute lymphoblastic leukemia. J Electrocardiol. 2024;83:80-94. doi: 10.1016/j.jelectrocard.2024.01.010. PubMed PMID: 38382343.
  20. Breiman L, Friedman J, Olshen RA, Stone CJ. Classification and regression trees. (1st ed). Chapman and Hall/CRC; 1984.
  21. Ho TK. Random decision forests. In Proceedings of 3rd international conference on document analysis and recognition; Montreal, QC, Canada: IEEE; 1995. p. 278-82.
  22. Zhang Y, Nie B, Du J, Chen J, Du Y, Jin H, et al. Feature selection based on neighborhood rough sets and Gini index. PeerJ Comput Sci. 2023;9:e1711. doi: 10.7717/peerj-cs.1711. PubMed PMID: 38192483. PubMed PMCID: PMC10773927.
  23. Chen T, Guestrin C. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; New York, NY, USA: Association for Computing Machinery; 2016. p. 785-94.
  24. Chen M, Liu Q, Chen S, Liu Y, Zhang CH, Liu R. XGBoost-based algorithm interpretation and application on post-fault transient stability status prediction of power system. IEEE Access. 2019;7:13149-58. doi: 10.1109/ACCESS.2019.2893448.
  25. Quer G, Radin JM, Gadaleta M, Baca-Motes K, Ariniello L, Ramos E, et al. Wearable sensor data and self-reported symptoms for COVID-19 detection. Nat Med. 2021;27(1):73-7. doi: 10.1038/s41591-020-1123-x. PubMed PMID: 33122860.
آمار
تعداد مشاهده مقاله: 196
تعداد دریافت فایل اصل مقاله: 79


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب