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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
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