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Department of Electromedical Engineering, Poltekkes Kemenkes Surabaya Jl. Pucang Jajar Timur No. 10, Surabaya, Indonesia
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Journal of Electronics, Electromedical Engineering, and Medical Informatics
Published by Polilteknik Kesehatan Kemenkes Surabaya
ISSN : -     EISSN : 26568632     DOI : https://doi.org/10.35882/jeeemi
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering
The Journal of Electronics, Electromedical Engineering, and Medical Informatics (JEEEMI) is a peer-reviewed open-access journal. The journal invites scientists and engineers throughout the world to exchange and disseminate theoretical and practice-oriented topics which covers three (3) majors areas of research that includes 1) Electronics, 2) Biomedical Engineering, and 3)Medical Informatics (emphasize on hardware and software design). Submitted papers must be written in English for an initial review stage by editors and further review process by a minimum of two reviewers.
Arjuna Subject : Teknik (semua kategori) - Teknik Biomedis
Articles 287 Documents
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Development and Evaluation of Biochips Specialized for Cell Counting Sawatari, Masato; Inada, Shunko A.
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 1 (2024): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i1.339

Abstract

Cell counters, which are dedicated cell analyzers, can be used to analyze cellular status. Cell counters are smaller and less expensive (about $13,000) than other cell analysis devices such as flow cytometers (FACS), real-time PCR, and sequencers, and can discriminate between life and death of fluorescently stained cells. Cell death can be roughly divided into two types: apoptosis and necrosis, but Cell counters cannot distinguish between apoptosis and necrosis in cells. This study developed a biochip system for inexpensive, simple, and capable of distinguishing between live, apoptotic, and necrotic cells. This biochip system (70 x 150 x 80 mm) comprises a slide into which fluorescently stained cells are injected, an LED light source, and a camera system. When cells stained with a fluorescent reagent are irradiated at the excitation wavelength, they fluoresce. By changing the combination of fluorescent reagent and excitation wavelength, live, apoptotic, and necrotic cells can be photographed. Then they are processed by a cell counting program using existing methods to determine numbers of live, dead, and necrotic cells. To demonstrate the effectiveness of this system, we conducted live cell, apoptosis, and necrosis detection experiments using colon cancer cells. Results of each experiment using the biochip system were compared with visual cell counts made by an operator. The novel biochip system successfully distinguishes between live, apoptotic and necrotic cells. Detection time was <1 s, and the detection error was 9%, compared to visual inspection.
Foot Clearance Prediction using Wrist Acceleration and Gait Speed Kitagawa, Kodai; Wada, Chikamune; Toya, Nobuyuki
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 1 (2024): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i1.345

Abstract

Elderly individuals experience fall accidents due to tripping because recognizing foot clearance during walking is difficult for them. To prevent fall accidents, foot clearance should be measured and informed in daily life. Foot clearance is commonly measured using vision-based systems, such as optical motion capture systems. However, problem of these vision-based systems is that these systems cannot measure foot clearance in daily life because they have limitations due to obstacles and field of view. Based on this problem, we developed a wearable fall prevention system using smart devices, such as smartphones and smartwatches. This study aimed to evaluate the proposed prediction method for foot clearance using sensor data obtained from wearable smart devices which can be used in daily life. The proposed method will contribute to measure foot clearance in daily life. This method predicts foot clearance from wrist acceleration and gait speed using a machine learning-based regression model. The proposed method was tested in a computational simulation with a public gait dataset obtained using an optical motion capture system. The results showed that the correlations between the predicted and actual foot clearance were at least 0.65. In conclusion, this study indicates the possibility that the proposed method can be used to measure foot clearance and thus can be used in wearable fall prevention systems.
Analysis of Multimodal Biosignals during Surprise Conditions Correlates with Psychological Traits Setiawan, Hendra; Miladiyah, Isnatin; Nuryadi, Satyo; Sahroni, Alvin
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 1 (2024): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i1.346

Abstract

Surprise can simultaneously represent bad or good, pleasant or unpleasant, with the same experiences since understanding how humans' physiological qualities link with their emotional or mental health is required. We conducted quantitative research to concisely correlate mental stress and emotional issues by measuring brain activity, breathing, and heart rate in real time while executing specialized audio-visual stimulation to elicit a surprise event. We evaluated the frequency and temporal domain characteristics to determine if physiological measurements matched biochemical metrics and subjective stress assessments during the elicit surprise condition experiment. We discovered that the brain is still preferable to most in recognizing a human's psychological changes over a short period of time. The temporal (T3) (r = 0.544, p = 0.005) and frontal (Fz) (r = 0.519, p = 0.008) regions were shown to correlate with salivary amylase activity. In comparison to other channels, there was a negative association between stress perception and the occipital site (O1, r = -0.618, p = 0.001). We also found that heart rate variability activity correlates with arousal perception. By looking at specific multimodal biosignals, it is possible to understand human psychological traits by recording specific physiological signals for daily mental health monitoring.
Enhancing Pneumonia Disease Classification using Genetic Algorithm-Tuned DCGANs and VGG-16 Integration Putri, Kania Ardhani; Fawwaz Al Maki, Wikky
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 1 (2024): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i1.349

Abstract

Diagnostic complications arise from pneumonia, characterized by lung inflammation caused by alveolar fluid accumulation, particularly in regions with limited radiologists. To tackle this issue, a new method utilizes the VGG16 methodology for categorization, bolstered by genetic algorithms. In addition, Deep Convolutional Generative Adversarial Networks (DCGANs) improve the dataset by adding fake X-rays of pneumonia. Genetic algorithms are used to optimize hyperparameters in classification tasks. In contrast, DCGANs are employed to increase data augmentation techniques, boosting models' accuracy in identifying and categorizing pneumonia cases. The study partitioned a dataset into training, testing, and validation sets for pneumonia X-ray pictures. The training of GANs entails utilizing both generators and discriminators to produce increasingly realistic pictures gradually. The genetic algorithm enhances the hyperparameter tuning process, resulting in a substantial increase in accuracy. Initially, VGG16 achieved a success rate of 89.50% and a fitness score of 87.50%. Post-optimization and DCGAN augmentation, accuracy climbed to 95.50%, and F1-Score improved to 94.75%. This study combines genetic algorithms and DCGANs to create a model that can produce genuine pneumonia X-ray pictures and enhance categorization accuracy.
A Comparative Study of Machine Learning Methods for Baby Cry Detection Using MFCC Features Riadi, Putri Agustina; Faisal, Mohammad Reza; Kartini, Dwi; Nugroho, Radityo Adi; Nugrahadi, Dodon Turianto; Magfira, Dike Bayu
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 1 (2024): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i1.350

Abstract

The vocalization of infants, commonly known as baby crying, represents one of the primary means by which infants effectively communicate their needs and emotional states to adults. While the act of crying can yield crucial insights into the well-being and comfort of a baby, there exists a dearth of research specifically investigating the influence of the audio range within a baby cry on research outcomes. The core problem of research is the lack of research on the influence of audio range on baby cry classification on machine learning. The purpose of this study is to ascertain the impact of the duration of an infant’s cry on the outcomes of machine learning classification and to gain knowledge regarding the accuracy of results F1 score obtained through the utilization of the machine learning method. The contribution is to enrich an understanding of the application of classification and feature selection in audio datasets, particulary in the context of baby cry audio. The utilized dataset, known as donate-a-cry-corpus, encompasses five distinct data classes and possesses a duration of seven seconds. The employed methodology consists of the spectrogram technique, cross-validation for data partitioning, MFCC feature extraction with 10, 20, and 30 coefficients, as well as machine learning models including Support Vector Machine, Random Forest, and Naïve Bayes. The findings of this study reveal that the Random Forest model achieved an accuracy of 0.844 and an F1 score of 0.773 when 10 MFCC coefficients were utilized and the optimal audio range was set at six seconds. Furthermore, the Support Vector Machine model with an RBF kernel yielded an accuracy of 0.836 and an F1 score of 0.761, while the Naïve Bayes model achieved an accuracy 0.538 and F1 score of 0.539. Notably, no discernible differences were observed when evaluating the Support Vector Machine and Naïve Bayes methods across the 1-7 second time trial. The implication of this research is to establish a foundation for the advancement of premature illness identification techniques grounded in the vocalizations of infants, thereby facilitating swifter diagnostic processes for pediatric practitioners.
Advancements within Molecular Engineering for Regenerative Medicine and Biomedical Applications an Investigation Analysis towards A Computing Retrospective Akhtar, Zarif Bin; Gupta, Anik Das
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 1 (2024): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i1.351

Abstract

The field of molecular engineering in medicine has witnessed remarkable progress in recent years, revolutionizing healthcare, diagnostics, and therapy development. However, the pandemic showcased there is still more requirement for progress along with further detailed investigation which is paramount and also a necessity moving forward. This research investigation delves into the interdisciplinary realm of molecular engineering, exploring its impact on regenerative medicine, biomaterials, tissue engineering, and the innovation from various advanced biotechnologies which has accelerated health science. The main objective for this research aims at providing an in depth investigative exploration of biomaterial applications with their respective roles within regenerative medicine and its associated advancements along with, tissue engineering, organ-on-a-chip device peripheral mechanics functionality and how bioprinting is paving the way for the creation of functional tissues and organs with a case study analysis on drug discovery, immune engineering, to the field of precision medicine, gene editing with the insight towards drug discovery processing, design and screening pipelined for biologics and the how therapeutics and drugs will play out in future healthcare. This exploration also provides many meaningful and remarkable conclusions on the advanced technologies which are explored and investigated throughout the step-by-step systematic technical computing methods approached for the research.
A Comparative Study of Convolutional Neural Network in Detecting Blast Cells for Diagnose Acute Myeloid Leukemia Ahmad Badruzzaman; Aniati Murni Arymurhty
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 1 (2024): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i1.354

Abstract

Understanding blood plays a crucial role in obtaining information for monitoring health conditions and diagnosis of hematologic diseases such as acute myeloid leukemia. It is characterized by irregular expansion of immature white blood cells called blast cells in the blood and bone marrow. To diagnose acute myeloid leukemia, a sample of bone marrow is necessary to be examined under a microscope through bone marrow examination. As for minimizing human subjectivity and automating medical screening, this study performed image classification for detecting blast cells in leukocytes from microscopic images. We compared a well-established convolutional neural network architecture such as ResNet, ResNeXt, and EfficientNetV2. The model’s performance assessment was done by two evaluation levels which are at a macro level and per class level. The experiment results show ResNet architecture with 18 layers (ResNet 18) outperforms the remaining models at both levels. Furthermore, as the architecture utilizes residual learning, ResNet and ResNeXt models converge faster than EfficientNetV2 at the training phase. In addition, ResNet architecture with 50 layers (ResNet 50) outperforms the remaining models specifically at blast cell identification in case of medical screening. Therefore, this study concludes that ResNet 50 is the best model to detect blast cells under this condition. However, EfficientNetV2 shows a promising potential at a macro level to classify leukocytes in general. We expect this study to become a preliminary study to develop a convolution neural network architecture specifically to detect blast cells in leukocytes.
Predicting the Need for Cardiovascular Surgery: A Comparative Study of Machine Learning Models Ghavidel, Arman; Pazos, Pilar; Del Aguila Suarez, Rolando; Atashi, Alireza
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.v6i2.359

Abstract

This research examines the efficacy of ensemble Machine Learning (ML) models, mainly focusing on Deep Neural Networks (DNNs), in predicting the need for cardiovascular surgery, a critical aspect of clinical decision-making. It addresses key challenges such as class imbalance, which is pivotal in healthcare settings. The research involved a comprehensive comparison and evaluation of the performance of previously published ML methods against a new Deep Learning (DL) model. This comparison utilized a dataset encompassing 50,000 patient records from a large hospital between 2015-2022. The study proposes enhancing the efficacy of these models through feature selection and hyperparameter optimization, employing techniques like grid search. A novel aspect of this research was the comparison of a newly developed DNN model with existing ensemble models based on similar cardiovascular datasets. The results indicated the DNN model's superior predictive accuracy, demonstrating an Area Under the Curve (AUC) of 74%, alongside notable precision (68%) and recall (72%) for the minority class, which indicates patients requiring surgery. The model further achieved a 70% F1-Score and a balanced accuracy rate of 72%, significantly outperforming the existing ensemble models in every key performance metric. The study underscores the transformative potential of DNNs in predictive modeling for cardiovascular care and highlights the importance of integrating advanced ML techniques into clinical workflows. Future research should delve into the practical application and integration of these models.
An Approach to ECG-based Gender Recognition Using Random Forest Algorithm Arif, Nuuruddin Hamid; Faisal, Mohammad Reza; Farmadi, Andi; Nugrahadi, Dodon; Abadi, Friska; Ahmad, Umar Ali
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.v6i2.363

Abstract

Human-Computer Interaction (HCI) has witnessed rapid advancements in signal processing research within the health domain, particularly in signal analyses like electrocardiogram (ECG), electromyogram (EMG), and electroencephalogram (EEG). ECG, containing diverse information about medical history, identity, emotional state, age, and gender, has exhibited potential for biometric recognition. The Random Forest method proves essential to facilitate gender classification based on ECG. This research delves into applying the Random Forest method for gender classification, utilizing ECG data from the ECG ID Database. The primary aim is to assess the efficacy of the Random Forest algorithm in gender classification. The dataset employed in this study comprises 10,000 features, encompassing both raw and filtered datasets, evaluated through 10-fold cross-validation with Random Forest Classification. Results reveal the highest accuracy for raw data at 55.000%, with sensitivity at 46.452% and specificity at 63.548%. In contrast, the filtered data achieved the highest accuracy of 65.806%, with sensitivity and specificity at 67.097%. These findings conclude that the most significant impact on gender classification in this study lies in the low sensitivity value in raw data. The implications of this research contribute to knowledge by presenting the performance results of the Random Forest algorithm in ECG-based gender classification.
Comparative Study of Various Hyperparameter Tuning on Random Forest Classification With SMOTE and Feature Selection Using Genetic Algorithm in Software Defect Prediction Suryadi, Mulia Kevin; Herteno, Rudy; Saputro, Setyo Wahyu; Faisal, Mohammad Reza; Nugroho, Radityo Adi
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.v6i2.375

Abstract

Software defect prediction is necessary for desktop and mobile applications. Random Forest defect prediction performance can be significantly increased with the parameter optimization process compared to the default parameter. However, the parameter tuning step is commonly neglected. Random Forest has numerous parameters that can be tuned, as a result manually adjusting parameters would diminish the efficiency of Random Forest, yield suboptimal results and it will take a lot of time. This research aims to improve the performance of Random Forest classification by using SMOTE to balance the data, Genetic Algorithm as selection feature, and using hyperparameter tuning to optimize the performance. Apart from that, it is also to find out which hyperparameter tuning method produces the best improvement on the Random Forest classification method. The dataset used in this study is NASA MDP which included 13 datasets. The method used contains SMOTE to handle imbalance data, Genetic Algorithm feature selection, Random Forest classification, and hyperparameter tuning methods including Grid Search, Random Search, Optuna, Bayesian (with Hyperopt), Hyperband, TPE and Nevergrad. The results of this research were carried out by evaluating performance using accuracy and AUC values. In terms of accuracy improvement, the three best methods are Nevergrad, TPE, and Hyperband. In terms of AUC improvement, the three best methods are Hyperband, Optuna, and Random Search. Nevergrad on average improves accuracy by about 3.9% and Hyperband on average improves AUC by about 3.51%. This study indicates that the use of hyperparameter tuning improves Random Forest performance and among all the hyperparameter tuning methods used, Hyperband has the best hyperparameter tuning performance with the highest average increase in both accuracy and AUC. The implication of this research is to increase the use of hyperparameter tuning in software defect prediction and improve software defect prediction performance.

Page 13 of 29 | Total Record : 287

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