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All Journal Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Journal of Electronics, Electromedical Engineering, and Medical Informatics Frontiers in Community Service and Empowerment
Sari Luthfiyah
Department of Medical Electronics Technology, Poltekkes Kemenkes Surabaya
Humanities Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Nursing Other

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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.
Implementation of Supervised Machine Learning on Embedded Raspberry Pi System to Recognize Hand Motion as Preliminary Study for Smart Prosthetic Hand Triwiyanto Triwiyanto; Sari Luthfiyah; Wahyu Caesarendra; Abdussalam Ali Ahmed
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 11, No 3: September 2023
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v11i3.4397

Abstract

EMG signals have random, non-linear, and non-stationary characteristics that require the selection of the suitable feature extraction and classifier for application to prosthetic hands based on EMG pattern recognition. This research aims to implement EMG pattern recognition on an embedded Raspberry Pi system to recognize hand motion as a preliminary study for a smart prosthetic hand. The contribution of this research is that the time domain feature extraction model and classifier machine can be implemented into the Raspberry Pi embedded system. In addition, the machine learning training and evaluation process is carried out online on the Raspberry Pi system. The online training process is carried out by integrating EMG data acquisition hardware devices, time domain features, classifiers, and motor control on embedded machine learning using Python programming. This study involved ten respondents in good health. EMG signals are collected at two lead flexor carpi radialis and extensor digitorum muscles. EMG signals are extracted using time domain features (TDF) mean absolute value (MAV), root mean square (RMS), variance (VAR) using a window length of 100 ms. Supervised machine learning decision tree (DT), support vector machine (SVM), and k-nearest neighbor (KNN) are chosen because they have a simple algorithm structure and less computation. Finally, the TDF and classifier are embedded in the Raspberry Pi 3 Model B+ microcomputer. Experimental results show that the highest accuracy is obtained in the open class, 97.03%. Furthermore, the additional datasets show a significant difference in accuracy (p-value <0.05). Based on the evaluation results obtained, the embedded system can be implemented for prosthetic hands based on EMG pattern recognition.
Application of Bio-Electrical Instruments for Monitoring the Effect of Muscle Massage on Post-Stroke Patients Through Electromyography Signal Measurement Triwiyanto Triwiyanto; Torib Hamzah; Sari Luthfiyah; Bedjo Utomo; Urip Mudjiono
Frontiers in Community Service and Empowerment Vol. 1 No. 4 (2022): December
Publisher : Forum Ilmiah Teknologi dan Ilmu Kesehatan (FORITIKES)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ficse.v1i4.24

Abstract

The problems faced by partners are: in carrying out traditional massage massage practice activities, partners do not carry out the process of recording medical conditions before or after the massage process. Thus, this causes partners to be unable to remember the conditions before and after the provision of traditional massage therapy. In addition, when giving traditional massage to patients who have to return periodically, partners cannot know the impact after giving traditional massage. So, to know the result is to ask questions to the patient (e.g. "how is the result after the massage?"). Therefore, monitoring the massage process in post-stroke patients is qualitative and subjective. The implementation methods are: Measuring the physical and medical parameters of the patient such as weight, height and blood pressure, before and after the partner performs traditional massage to the patient, the bio-electrical muscle signal (EMG) is measured by attaching electrodes to the partially paralyzed limbs. Next, the patient contracted the muscle by pressing a rubber ball connected to an electronic pressure measuring module. Together with partners, they monitor the measured value of the muscle bio-electric signal (which is displayed on the computer panel), c) monitor the bio-electric signal in post-stroke patients who undergo traditional massage therapy in subsequent therapy activities (2-5 therapies). The output of PKM activities with the title "Implementation of Muscle Bio-electric Signal Measurement to Monitor the Healing Process of Post-Stroke Patients as an Effort to Support Traditional Massage Workers" is the device that can be used by the partners to monitor the effect of the massage to the patient. The targets and achievements expected in this PKM activity are that partners can monitor the effectiveness of traditional massage with the support of science and technology.
Co-Authors Abdussalam Ali Ahmed Dyah Titisari Faheem Ahmad Reegu Lusiana Lusiana Nindia Rena Saputri Torib Hamzah Triwiyanto Triwiyanto Urip Mudjiono Utomo, Bedjo Wahyu Caesarendra Wahyu Caesarendra