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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Teknokes Indonesian Journal of electronics, electromedical engineering, and medical informatics Journal of Applied Engineering and Technological Science (JAETS) Indonesian Journal of Electrical Engineering and Computer Science International Journal of Advanced Health Science and Technology Gemakes: Jurnal Pengabdian Kepada Masyarakat Frontiers in Community Service and Empowerment Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Teknokes
Triwiyanto Triwiyanto
Poltekkes Kemenkes Surabaya
Humanities Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Nursing Public Health Other

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INCU Analyzer for Infant Incubator Based on Android Application Using Bluetooth Communication to Improve Calibration Monitoring Vijay Anant Athavale; Abhilash Pati; A K M Bellal Hossain; Sari Luthfiyah; Triwiyanto Triwiyanto
Jurnal Teknokes Vol 15 No 1 (2022): March
Publisher : Jurusan Teknik Elektromedik, POLTEKKES KEMENKES Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/teknokes.v15i1.1

Abstract

Worldwide, over 4 million babies die within a month of birth each year. Of these, 3.9 million are in developing countries. A proportion approximately 25% of these deaths are due to complications of premature birth, most commonly inadequate thermoregulation, water loss, and neonatal jaundice. An infant incubator provides stable temperature, relative humidity, and airflow values. A periodical calibration should be applied on infant incubator to monitor the functionality. The study aims to develop a calibration device that measures temperature, humidity, airflow, and noise in the baby incubator based on an Android application with Bluetooth communication to improve the calibration monitoring process. This is to achieve a better performance of the conventional INCU analyzer. The contribution of this research is that the values of the temperature, humidity, airflow, and noise can be displayed on both devices, the INCU analyzer machine, and mobile phone; thus, the user can monitor the measurement activities wirelessly. Furthermore, the statistical calculation for all measurements can be saved on a mobile phone device. The main design consists of temperature sensor LM35, humidity sensor DHT22, airflow sensor MPX5010DP, an analog signal conditioning circuit, an Arduino Mega microcontroller, Bluetooth module HC05, and Android mobile phone. The resulting design was compared to the standard or calibrator INCU analyzer machine (Fluke Biomedical INCU II). This study found that the smallest error is -1.72%°C, -0.106 % RH, -1.727% dB, and <0.1% m/s for temperature, humidity, noise, and airflow parameters, respectively. After the evaluation process, this device can be used as an INCU analyzer to calibrate the infant incubator.
Analysis of the Effectiveness of Using Digital Filters in Electronic Stethoscopes Andi Fathkur Rohman; Muhammad Ridha Mak'ruf; Triwiyanto Triwiyanto; Lamidi Lamidi; Phuoc-Hai Huynh
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 4 No 4 (2022): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA and IKATEMI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v4i4.256

Abstract

The heart sound produced in some cases of the disease shows a certain pattern. The purpose of this study was to design an electronic stethoscope for cardiac auscultation with the following display. The contribution in this study is being able to show certain patterns that can be diagnosed in the sound signal. So that the pattern can be known when there is a heart disease disorder, an electronic stethoscope will be made for auscultation of the next display, making it easier for users to diagnose heart disease. The heart sound is obtained from the mechanical activity of the heart which is tapped by a condenser mic. The heart sound will be held in a pre-amp circuit, then the filters used are High Pass Filters and Low Pass Filters with an interrupted frequency of 20-95 Hz. The output of the filter circuit will enter the booster circuit. Then it will be processed by the microcontroller. In processing the data that will be displayed on Nextion and Speaker, the author uses Arduino Mega. Based on the test, it can be seen that the digital filter has a slight error rate because it removes the most noise, while in the analog filter there is still a lot of noise. The results of the research that has been done can be implemented using a system that really supports the needs.
Enhancing the Electrocardiogram Signal Quality by Applying Butterworth Infinite Impulse Response Filter 8th Order Nindia Rena Saputri; Sari Luthfiyah; Dyah Titisari; Bedjo Utomo; Lusiana Lusiana; Triwiyanto Triwiyanto; Faheem Ahmad Reegu; Wahyu Caesarendra
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 4 No 4 (2022): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA and IKATEMI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v4i4.259

Abstract

The electrocardiogram (ECG) of the human body is an important basis in heart function as well as the diagnosis of cardiovascular diseases, which has a very vital role in clinical diagnosis. Obtaining high-quality ECG signals with a portable remote ECG acquisition system is a big challenge given limited resources. According to the World Health Organization (WHO), disorders of the cardiovascular system still rank high, causing about 31% of deaths globally. This is because the symptoms of cardiovascular disease cannot be seen directly, but rather by conducting an electrocardiograph (ECG) examination. The purpose of this research is to develop and analysis the ECG signal by comparing the 2nd order AD8232 module analogue filter with the 8th order Butterworth digital filter by applying infinite impulse response. This research uses a multiplexer circuit for switching leads, AD8232 ECG module, 50Hz notch filter circuit, non-inverting amplifier, adder, Arduino Mega 2560, USB module, and an application to display digital signals, namely Delphi 7. Signal acquisition is done by monitoring for one minute. Data collection was carried out with 5 respondents 5 times on each lead. The results of the data collection can be concluded that 80% of digital filters display smoother signals for ECG signals than analogue filters.
Effect of Muscle Fatigue on Heart Signal on Physical Activity with Electromyogram and Electrocardiogram (EMG Parameter ) Monitoring Signals Muhammad Fauzi; Endro Yulianto; Bambang Guruh Irianto; Sari Luthfiyah; Triwiyanto Triwiyanto; Vishwajeet Shankhwar; Bahaa Eddine ELBAGHAZAOUI
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 4 No 3 (2022): August
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.v4i3.240

Abstract

Physical activity is an activity of body movement by utilizing skeletal muscles that is carried out daily. One form of physical activity is an exercise that aims to improve health and fitness. Parameters related to health and fitness are heart and muscle activity. Strong and prolonged muscle contractions result in muscle fatigue. To measure muscle fatigue, the authors used electromyographic (EMG) signals through monitoring changes in muscle electrical activity. This study aims to make a tool to detect the effect of muscle fatigue on cardiac signals on physical activity. This research method uses Fast Fourier Transform (FFT) with one group pre-test-post-test research design. The independent variable is the EMG signal when doing plank activities, while the dependent variable is the result of monitoring the EMG signal. To get more detailed measurement results, the authors use MPF, MDF and MNF and perform a T-test. The test results showed a significant value (pValue <0.05) in the pre-test and post-test. The Pearson correlation test got a value of 0.628 which indicates there is a strong relationship between exercise frequency and plank duration. When the respondent experiences muscle fatigue, the heart signal is affected by noise movement artifacts that appear when doing the plank. It is concluded that the tools in this study can be used properly. To overcome noise in the EMG signal, it is recommended to use dry electrodes and high-quality components. To improve the ability to transmit data, it is recommended to use a Raspberry microcontroller.
Effect of Muscle Fatigue on EMG Signal and Maximum Heart Rate for Pre and Post Physical Activity Arifah Putri Caesaria; Endro Yulianto; Sari Luthfiyah; Triwiyanto Triwiyanto; Achmad Rizal
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 5 No 1 (2023): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA and IKATEMI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v5i1.278

Abstract

Sport is a physical activity that can optimize body development through muscle movement. Physical activity without rest with strong and prolonged muscle contractions results in muscle fatigue. Muscle fatigue that occurs causes a decrease in the work efficiency of muscles. Electrocardiography (ECG) is a recording of the heart's electrical activity on the body's surface. EMG is a technique for measuring electrical activity in muscles. This study aims to detect the effect of muscle fatigue on cardiac signals by monitoring ECG and EMG signals. This research method uses the Maximum Heart Rate with a research design of one group pre-test-post-test. The independent variable is the ECG signal when doing plank activities, while the dependent variable is the result of monitoring the ECG signal. To get the Maximum Heart Rate results, respondents use the Karnoven formula and perform the T-test. Test results show a significant value (pValue <0.05) in pre-exercise and post-exercise. When the respondent experiences muscle fatigue, it shows the effect of changes in the shape of the ECG signal which is marked by the presence of movement artifact noise. It concluded that the tools in this study can be used properly. This study has limitations including noise in the AD8232 module circuit and the display on telemetry where the width of the box cannot be adjusted according to the ECG paper.is It recommended for further research to use components with better quality and replace the display using the Delphi interface.
ECG and NIBP Simulators in One Device Display on TFT Nextion Cantika Melinda; I Dewa Gede Hari Wisana; Andjar Pudji; Triwiyanto Triwiyanto
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 5 No 3 (2023): August
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.v5i3.293

Abstract

Accurate monitoring of NIBP (Non-Invasive Blood Pressure) parameters using vital sign monitors is crucial for patient care. Therefore, calibration of vital sign monitors is essential to ensure their safety and reliability. The purpose of this study is a vital sign simulator was developed, integrating ECG and NIBP parameters with a TFT Nextion display, to calibrate ECG and NIBP readings on vital sign monitors. The system utilized the Arduino Mega 2560 as the central controller and the MPX5050GP sensor for NIBP measurement and motor pump control. The NIBP parameters were measured at two settings: 60/30 and 80/50. The results showed a maximum systolic error of 3.5% and a diastolic error of 5.6% for the NIBP setting of 80/50. The largest standard deviation value of 2.05 was observed at the NIBP setting of 60/30. The highest uncertainty value of 0.5 was also found in the NIBP 60/30 setting. The obtained data indicated stable module readings within the acceptable threshold for vital sign monitor calibration. The developed vital sign simulator offers a reliable means of calibrating NIBP parameters, enabling accurate blood pressure measurements. Further research and refinement can be conducted to enhance the system's precision and expand its capabilities for calibration of additional vital sign parameters. By ensuring accurate calibration, healthcare professionals can rely on vital sign monitors for effective patient monitoring and diagnosis.
QCML: Qualified Contrastive Machine Learning methodology for infectious disease diagnosis in CT images Chandrika, G.Naga; Karpagam, J.; Richard, Titus; Shadrach, Finney Daniel; Triwiyanto, T
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 2 (2024): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i2.391

Abstract

The COVID-19 pandemic has had a terrible effect on human health, and computer-aided diagnostic (CAD) systems for chest computed tomography have emerged as a potential alternative for COVID-19 diagnosis. Yet, since the cost of data annotation may be excessively costly in the medical area, there is a shortage of data that has been annotated. A considerable quantity of labelled data is required in order to train a CAD system to a high level of accuracy. The study aims to describe an automatic and precise COVID-19 diagnostic method that utilizes a restricted amount of labelled CT images to solve this problem. The framework of the system is known as Qualified Contrastive Machine Learning (QCML), and the improvements that we have made may be summed up as follows: 1) In order to make use of all of the image's characteristics, we combine features with a two-dimensional discrete wavelet transform. 2) We employ the COVID-Net encoder with a redesign that focuses on the efficiency of learning and the task specificity of the data. 3) In order to strengthen our capacity to generalize, we have implemented a novel pertaining technique that is based on Qualified Contrastive Machine Learning. 4) In order to get better categorization results, we have included an extra auxiliary work. The application of Qualified Contrastive Machine Learning methodology for infectious disease diagnosis in CT images offers an accuracy of 93.55%, a recall of 91.59%, a precision of 96.92%, and an F1-score of 94.18%, demonstrating the potential for accurate and efficient COVID-19 diagnosis with limited labelled data.
Development of a High Flow Oxygen Analyzer for Monitoring Oxygen Therapy in Adults Using High Flow Nasal Cannula (HFNC) Silvian, Fawaida; Dian Setioningsih, Endang Dian Setioningsih1; Triwiyanto , Triwiyanto; Caesarendra, Wahyu
Jurnal Teknokes Vol. 17 No. 2 (2024): June
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Side effects of using HFNC include gastric insufflation (air entry into the stomach) because HFNC increases positive airway. The next side effect of using HFNC is complications of pneumothorax and pneumomediastinum. This complication occurs in the case of children. In these cases, oxygen administration was reported to exceed the recommended protocol. Although the incidence of air leaks in the use of HFNC for adults has not been reported, similar events may also occur in adults, so close monitoring is needed, especially on oxygen flow. Making the design of the High Flow Oxygen Analyzer can be used for monitoring the flow and oxygen concentration in HFNC. This study uses an Arduino microcontroller to process the oxygen concentration output from the OOA101-1 oxygen concentration sensor, then the processed oxygen concentration will be displayed on the TFT LCD. The variable in this study is the oxygen concentration setting value, while the independent variable is the OOA101-1 oxygen concentration sensor. The concentration value was adjusted using an oxygen blender, while the comparison tool used was gas flow analysis (Citrex H3). In the testing phase, the measurement value is 50% to 100% with a time of 1 minute at each point. Based on the measurements that have been made, the largest error value is obtained at a concentration of 50%, which is 3.07% and the smallest error value is at 100%, which is 0.40%. Dataretrieval using a compressor and central oxygen is very influential on the results of the flow and oxygen concentration. The results obtained are more stable than without the use of a compressor and central oxygen. From these results, the calibrator module has an error (value) which is still within the relative limits of the conclusion, which is ±5%. And also the design of this tool is portable and low cost and made for use in hospital companies as maintenance of HFNC equipment.
Improving Heart Rate Measurement Accuracy by Reducing Artifact Noise from Finger Sensors Using Digital Filters Maghfiroh, Anita Miftahul; Soetjiatie, Liliek; Irianto, Bambang Guruh; Triwiyanto , Triwiyanto; Hidayanti, Nuril; Rizal, Achmad
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 4 No. 2 (2022): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v4i2.144

Abstract

Heart rate is an important indicator in the health sector that can be used as an effective and rapid evaluation to determine the health status of the body. Motion or noise artifacts, power line interference, low amplitude PPG, and signal noise are all issues that might arise when measuring heart rate. This study aims to develop a digital filter that reduces noise artifacts on the finger sensor to improve heart rate measurement accuracy. Adaptive LMS and Butterworth are the two types of digital filters used in this research. In this study, data were collected from the patient while he or she was calm and moving around. In this research, the Nellcor finger sensor was employed to assess the blood flow in the fingers. The heart rate sensor will detect any changes in heart rate, and the measurement results will be presented on a personal computer (PC) as signals and heart rate values. The results of this investigation showed that utilizing an adaptive LMS filter and a Butterworth low pass filter with a cut-off frequency of 6Hz, order 4, and a sampling frequency of 1000Hz, with the Butterworth filter producing the least error value of 7.57 and adaptive LMS maximum error value of 27.65 as predicted by the researcher to eliminate noise artifacts. This research could be applied to other healthcare equipment systems that are being monitored to increase patient measurement accuracy.
Developing a Nutritional Assessment Tool for Toddlers Using Anthropometry and IoT Technology ANGELA ROSARI; jusuf julianto; Alfrinscha Dinda Larasati; Lintang Ayu Pramesti; Triwiyanto; Sari Lutfiyah; Vugar Abudlayev
International Journal of Advanced Health Science and Technology Vol. 4 No. 2 (2024): April
Publisher : Forum Ilmiah Teknologi dan Ilmu Kesehatan (FORITIKES)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijahst.v4i2.319

Abstract

Data from the Indonesian Toddler Nutrition Status Survey (SSGBI) in 2021, the prevalence of stunting in Indonesia is still quite high, at 24.4%, which is equivalent to 5.33 million toddlers. This figure is still above the standard tolerated by WHO, which is below 20%. Therefore, efforts are needed to accelerate stunting reduction so that the prevalence of stunting in toddlers decreases to 19.4% by 2024. This research aims to develop a tool for measuring toddlers' height and weight to assess their nutritional status promptly, aiming to preemptively address any nutritional abnormalities and prevent exacerbation. Anthropometry serves as the primary method for assessing toddlers' nutritional status in this study. The tool's design incorporates the ESP32 as the main control unit, the HC-SR04 sensor for height measurements, and the HX711 module and loadcell sensor as weight sensors. Data from the sensor are transmitted from the ESP32 master to the ESP32 slave for processing, culminating in a nutritional status assessment. Notably, the tool boasts a minimal error rate of 0.18% for weight measurement with 99.82% accuracy and a 2.66% error rate for height measurement with 97.34% accuracy. Furthermore, the tool's integration with IoT technology offers additional advantages. It facilitates real-time data transmission and analysis, enabling healthcare professionals to promptly identify any nutritional issues in toddlers. This, in turn, allows for timely intervention and appropriate management strategies to prevent the development or exacerbation of stunting. Overall, the benefits of this research for the Anthropometry Stunting Monitor based on IoT are manifold. It enhances accuracy and efficiency in measuring toddlers' height and weight, enables early detection of stunting, and facilitates timely intervention to address nutritional abnormalities. This holds significant promise for improving pediatric healthcare outcomes and reducing the prevalence of stunting among children.
Co-Authors A K M Bellal Hossain Abhilash Pati Achmad Rizal Achmad Rizal Alfred Rafu Neno Alfrinscha Dinda Larasati Allahshukur Ahmadzada Amrinsani, Farid Andi Fathkur Rohman Andjar Pudji Andjar Pundji ANGELA ROSARI Anita Miftahul Maghfiroh Arifah Putri Caesaria Bahaa Eddine ELBAGHAZAOUI Bahar Asgarova Bambang Guruh Irianto Bedjo MKes Bima Maulana Raharjo Cantika Melinda Chandrika, G.Naga Dewi Sari Oswarida Dwi Herry Andayani Dyah Titisari, Dyah Elvin Jafarov Endro Yulianto Faheem Ahmad Reegu Fairooz Azzahra Darmawan Forra Wakidi, Levana Gita Aprilyana Hafizh Ammar Wahyudi Hidayanti, Nuril I Dewa Gede Hari Wisana Indrato, Tri Bowo jusuf julianto Karpagam, J. Kinanti Elok Putri Lamidi, Lamidi Liliek Soetjiatie Lintang Ayu Pramesti Lusiana Lusiana Luthfiyah, Sari M Shaib Moch Sahrul Triandi Putra Sahrul Muhammad Fauzi Muhammad Ridha Mak'ruf Musyahadah Arum Pertiwi Nicat Babayev Nindia Rena Saputri Nuril Hidayanti Pawana, I Putu Alit Phuoc-Hai Huynh Priyambada Cahya Nugraha Raden Duta Ikrar Abadi Richard, Titus Rizky Dwi Sisantiara Roichatun Nashichah Sandeep Kumar Gupta Sari Lutfiyah Sasivimon Sukaphat Setioningsih, Endang Dian Shadrach, Finney Daniel Shofiyah Shofiyah Silvian, Fawaida Sumber, Sumber Syarifatul Ainiyah Syevana Dita Musvika Tetrik Fa’altin Torib Hamzah Torib Hamzah Triana Rahmawati Utomo, Bedjo Vijay Anant Athavale Vijay Anant Athavale Vishwajeet Shankhwar Vugar Abdullayev Vugar Abdullayev Vugar Abudlayev Wahyu Caesarendra Wahyu Caesarendra Wahyu Caesarendra Yudha Aditya Fahriza