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Performance Comparison of ECG Bio-Amplifier Between Single and Bi-Polar Supply Using Spectrum Analysis Based on Fast Fourier Transform Anita Miftahul Maghfiroh; Syevana Dita Musvika; Vugar Abdullayev
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 4 No 4 (2022): November
Publisher : Department of electromedical engineering, Health Polytechnic of Surabaya, Ministry of Health Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v4i4.248

Heart performance is one of the vital signs that cannot be ignored and must be monitored periodically. In this case, the measuring range of the human heart rate is between 60-100 BPM, in which the measurement unit is expressed as Beat per Minute (BPM). Therefore, it is very important to use Electrocardiograph equipment to tap the electrical signals of the heart with correct readings and minimal interference such as frequency of electric lines and noise. The purpose of this study was to compare the instrumentation amplifier using a single supply with a bi-polar supply in the ECG design to select the best instrumentation amplifier, which is expected to contribute to other researchers in choosing the right type of instrumentation amplifier that is efficient and qualified. In this case, the research was carried out by comparing two single supply instrumentation amplifiers using the AD623 IC and the bi-polar supply using the AD620 IC, continued by the use of Fast Fourier Transform (FFT) to determine the frequency spectrum of the ECG signal. The test results further showed that the use of single power instrumentation could reduce more noise compared to the Bi-Polar instrumentation amplifier by strengthening 60 dB Low pass filter circuit. Meanwhile, the FFT results in finding the frequency spectrum explained that the FFT results on the ECG signal provided information that the ECG signal had a frequency range between 0.05 Hz and 100 Hz. When the frequency is more than 100 Hz, the frequency started to be suppressed and when the frequency is less than 100 Hz, the frequency is passed. This research could be further used as a reference by other researchers to determine which type of instrumentation amplifier is better.

Performance Comparison of ECG Bio-Amplifier Between Single and Bi-Polar Supply Using Spectrum Analysis Based on Fast Fourier Transform Maghfiroh, Anita Miftahul; Musvika, Syevana Dita; Abdullayev, Vugar
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 4 No. 4 (2022): November
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v4i4.156

Heart performance is one of the vital signs that cannot be ignored and must be monitored periodically. In this case, the measuring range of the human heart rate is between 60-100 BPM, in which the measurement unit is expressed as Beat per Minute (BPM). Therefore, it is very important to use Electrocardiograph equipment to tap the electrical signals of the heart with correct readings and minimal interference such as frequency of electric lines and noise. The purpose of this study was to compare the instrumentation amplifier using a single supply with a bi-polar supply in the ECG design to select the best instrumentation amplifier, which is expected to contribute to other researchers in choosing the right type of instrumentation amplifier that is efficient and qualified. In this case, the research was carried out by comparing two single supply instrumentation amplifiers using the AD623 IC and the bi-polar supply using the AD620 IC, continued by the use of Fast Fourier Transform (FFT) to determine the frequency spectrum of the ECG signal. The test results further showed that the use of single power instrumentation could reduce more noise compared to the Bi-Polar instrumentation amplifier by strengthening 60 dB Low pass filter circuit. Meanwhile, the FFT results in finding the frequency spectrum explained that the FFT results on the ECG signal provided information that the ECG signal had a frequency range between 0.05 Hz and 100 Hz. When the frequency is more than 100 Hz, the frequency started to be suppressed and when the frequency is less than 100 Hz, the frequency is passed. This research could be further used as a reference by other researchers to determine which type of instrumentation amplifier is better.

Telemedicine-Enabled Bedside Monitoring System for Low-Birth-Weight Infants: Strengthening Primary Healthcare Resilience and Family-Centered Neonatal Care in Indonesia Sari Luthfiyah; Bambang Guruh Irianto; Lusiana Lusiana; Abdul Kholiq; Syevana Dita Musvika; Much Faiz Nafi'u Pradana; Rifan Ramandani; Muhamad Muflih Ridwan
Frontiers in Community Service and Empowerment Vol. 5 No. 2 (2026): June
Publisher : Forum Ilmiah Teknologi dan Ilmu Kesehatan (FORITIKES)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ficse.v5i2.142

Low Birth Weight (LBW) remains a critical neonatal health problem due to its strong association with increased morbidity and mortality risks, requiring continuous and accurate physiological monitoring. This community service program aimed to implement a telemedicine-based bedside monitoring system at Gedangan Community Health Center, Sidoarjo Regency, to improve neonatal care services and strengthen health worker capacity in managing LBW infants. The intervention addressed key challenges, including limited access to real-time monitoring data, insufficient technological integration in primary care settings, and the need for improved technical competence among health workers. The method employed a structured community engagement approach consisting of counseling, training, demonstration, re-demonstration, and continuous mentoring. Thirty health workers, including nurses, midwives, and electromedical personnel, participated in the program conducted over two days. Evaluation was performed through direct oral questioning and structured practical observation during device operation and simulation activities. The results indicated a significant improvement in participants’ knowledge and skills, with an increase in understanding of neonatal monitoring concepts and telemedicine application. Participants demonstrated improved ability to operate the bedside monitor, interpret vital sign parameters, and apply standard operating procedures. However, post-implementation evaluation revealed partial non-compliance with SOPs in device operation, maintenance, and repair, highlighting the need for continuous training and supervision. The telemedicine system successfully enabled real-time transmission of neonatal physiological data, improving accessibility for both health workers and families. In conclusion, the implementation of a telemedicine-based bedside monitoring system effectively enhanced the capacity of primary healthcare services for LBW infants. Continuous mentoring, infrastructure support, and periodic training are essential to ensure sustainability and optimal utilization of the technology in neonatal care.

Android-Assisted Cardiovascular Education and Risk Screening to Improve Coronary Heart Disease Literacy in Primary Care: A One-Group Pre–Post Community Intervention in Indonesia Sari Luthfiyah; Triwiyanto Triwiyanto; Syevana Dita Musvika; Yudha Aditya Fahriza; Alfred Rafu Neno; Rizky Dwi Sisantiara
Frontiers in Community Service and Empowerment Vol. 5 No. 2 (2026): June
Publisher : Forum Ilmiah Teknologi dan Ilmu Kesehatan (FORITIKES)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ficse.v5i2.145

Coronary heart disease (CHD) prevention in primary care is constrained by limited consultation time, uneven health literacy, and insufficient reinforcement after one-off education. This community service program evaluated a hybrid intervention combining clinician-led education, guided Android application use, and self-risk screening. The program was delivered at Candi Primary Health Center, Sidoarjo, Indonesia, on 9 June 2026 using a one-group pre-test–post-test design. Community members were recruited consecutively; paired analysis included participants who attended the intervention and completed matchable pre- and post-tests. The intervention comprised baseline assessment, structured CHD education, application demonstration, hands-on practice, and immediate post-test. Knowledge was assessed with an expert-reviewed 20-item true–false questionnaire scored from 0 to 100. Approximately 60 electronic entries were screened; after duplicate and incomplete records were removed, 13 valid paired observations remained. The mean knowledge score changed from 63.50 ± 12.05 (pre-test) to 97.00 ± 4.62 (post-test), with an absolute change of 33.50 points (95% CI: 30.09–36.91), p < 0.001, and a large effect size (Cohen’s dz = 2.54). Application access, screening completion, and assistance needs were 54 (90%), 60 (100%), and not formally assessed in the dataset, respectively. The hybrid intervention was feasible under supervised conditions and was associated with an immediate improvement in CHD literacy. However, the small uncontrolled sample, immediate outcome assessment, and absence of longitudinal usage data preclude conclusions about sustained behavior change. Integration into routine non-communicable disease education should be accompanied by cadre training, user support, and longer-term evaluation.

Automated Detection and Grading of Tuberculosis Bacilli in Ziehl Neelsen-Stained Sputum Using YOLO with IUATLD-Based Classification Syevana Dita Musvika; Riries Rulaningtyas; Khusnul Ain; Pepy Dwi Endraswari; Annie Anak Joseph
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 3 (2026): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i3.1111

Tuberculosis (TB) remains one of the most pressing global health challenges, particularly in low- and middle-income countries, where diagnostic capacity is often limited. Accurate and efficient detection of Mycobacterium tuberculosis bacilli in sputum smear samples stained with Ziehl-Neelsen remains the cornerstone of TB diagnosis. However, conventional microscopic examination is inherently labor-intensive, subject to interobserver variability and prone to human error, leading to inconsistent diagnostic outcomes. Addressing these limitations, this study proposes the development of an automated bacilli detection and quantification system utilizing the YOLO (You Only Look Once) object detection framework, specifically the YOLOv8 architecture, to improve diagnostic accuracy, consistency, and efficiency in TB identification. The research methodology encompasses image acquisition of Ziehl Neelsen-stained sputum samples from the Microbiology Laboratory of Universitas Airlangga Hospital (RSUA) and publicly available repositories, followed by meticulous annotation using Roboflow. The annotated dataset was employed to train the YOLOv8 model, and performance was evaluated through key metrics, including accuracy, precision, and error rate. The developed model achieved an overall accuracy of 73.33%, with class-wise accuracies of 100% for BTA 1+, 80% for BTA 2+, and 40% for BTA 3+ categories, conforming to IUATLD classification standards. The suboptimal performance observed in the BTA 3+ category was attributed to discrepancies in Field of View (FOV) alignment between the microscope’s ocular lens and the attached digital camera, affecting image consistency. Despite this limitation, the results demonstrate the potential of YOLO-based automated detection systems to reduce dependence on manual analysis, enhance diagnostic objectivity, and accelerate TB screening workflows. Future work should prioritize hardware calibration, particularly FOV synchronization, and dataset diversification to further refine model performance and clinical applicability. The proposed approach represents a significant step towards scalable, rapid, and reliable TB diagnosis, with implications for broader adoption in resource-constrained healthcare environments.

Embedded Machine Learning on ESP32 for Upper-Limb Exoskeletons Based on EMG Triwiyanto Triwiyanto; Anita Miftahul Maghfiroh; Levana Forra Wakidi; Syevana Dita Musvika; Bedjo Utomo; Sumber Sumber; Priyambada Cahya Nugraha; Wahyu Caesarendra
Jurnal Teknokes Vol. 18 No. 4 (2025): Desember
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jteknokes.v18i4.134

Stroke remains one of the primary causes of long-term disability worldwide and frequently results in persistent impairment of upper limb motor function. To support more effective and intensive rehabilitation, there is a need for wearable devices that can interpret muscle activity and autonomously assist limb movement without relying on an external computer. This study aims to design and implement an upper-limb rehabilitation exoskeleton that is driven by electromyography (EMG) signal classification using machine learning and by real-time elbow angle monitoring, with all models deployed directly on an ESP32 microcontroller. The proposed exoskeleton is built from lightweight, ergonomic 3D-printed components and operates in both unilateral and bilateral modes. Its main contributions include: (1) embedding real-time EMG classification models on the ESP32 so that the device can function independently, (2) integrating EMG-based motor control with elbow angle feedback from an MPU6050 inertial measurement unit, and (3) incorporating a load cell to estimate biceps force during training. EMG signals from the forearm flexor muscles are processed to extract statistical features such as variance (VAR), waveform length (WL), integrated EMG (IEMG), and root mean square (RMS). These features are used to train Random Forest, Decision Tree, Support Vector Machine (SVM), and XGBoost classifiers. The trained models are converted to C code using the micromlgen library for execution on the ESP32. System evaluation involved thirty male participants aged 20–25 years with body weights between 50–85 kg. All tested models achieved 100% accuracy in distinguishing relaxed versus grasping muscle contractions, while the correlation of elbow angles between unilateral and bilateral ESP32 systems reached 0.9469, indicating highly consistent motion detection. The Decision Tree model was selected for deployment due to its superior memory efficiency on the microcontroller. These results demonstrate that the developed ESP32-based exoskeleton provides a practical, efficient, and easily integrable solution for wearable stroke rehabilitation

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