Pub Date : 2024-02-27DOI: 10.35882/jeeemi.v6i2.359
Arman Ghavidel, Pilar Pazos, Rolando Del Aguila Suarez, Alireza Atashi
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.
本研究探讨了集合机器学习（ML）模型（主要侧重于深度神经网络（DNN））在预测心血管手术需求（临床决策的一个重要方面）方面的功效。它解决了类不平衡等关键挑战，这在医疗保健环境中至关重要。这项研究包括对以前发布的 ML 方法与新的深度学习 (DL) 模型的性能进行全面比较和评估。该比较利用了一个数据集，其中包含一家大型医院在 2015-2022 年间的 50,000 份患者记录。研究建议通过特征选择和超参数优化，采用网格搜索等技术来提高这些模型的功效。这项研究的一个新颖之处在于将新开发的 DNN 模型与基于类似心血管数据集的现有集合模型进行了比较。结果表明，DNN 模型的预测准确性更胜一筹，其曲线下面积（AUC）达到了 74%，同时在少数类别（表示需要手术的患者）中的精确度（68%）和召回率（72%）也非常显著。该模型还取得了 70% 的 F1 分数和 72% 的平衡准确率，在每个关键性能指标上都明显优于现有的集合模型。这项研究强调了 DNN 在心血管护理预测建模中的变革潜力，并突出了将先进的 ML 技术整合到临床工作流程中的重要性。未来的研究应深入探讨这些模型的实际应用和整合。
{"title":"Predicting the Need for Cardiovascular Surgery: A Comparative Study of Machine Learning Models","authors":"Arman Ghavidel, Pilar Pazos, Rolando Del Aguila Suarez, Alireza Atashi","doi":"10.35882/jeeemi.v6i2.359","DOIUrl":"https://doi.org/10.35882/jeeemi.v6i2.359","url":null,"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.","PeriodicalId":369032,"journal":{"name":"Journal of Electronics, Electromedical Engineering, and Medical Informatics","volume":"48 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140424350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-30DOI: 10.35882/jeeemi.v6i1.354
Ahmad Badruzzaman, Aniati Murni Arymurhty
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.
{"title":"A Comparative Study of Convolutional Neural Network in Detecting Blast Cells for Diagnose Acute Myeloid Leukemia","authors":"Ahmad Badruzzaman, Aniati Murni Arymurhty","doi":"10.35882/jeeemi.v6i1.354","DOIUrl":"https://doi.org/10.35882/jeeemi.v6i1.354","url":null,"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.","PeriodicalId":369032,"journal":{"name":"Journal of Electronics, Electromedical Engineering, and Medical Informatics","volume":"649 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140482997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-12DOI: 10.35882/jeeemi.v6i1.350
Putri Agustina Riadi, M. Faisal, D. Kartini, Radityo Adi Nugroho, D. T. Nugrahadi, Dike Bayu Magfira
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.
{"title":"A Comparative Study of Machine Learning Methods for Baby Cry Detection Using MFCC Features","authors":"Putri Agustina Riadi, M. Faisal, D. Kartini, Radityo Adi Nugroho, D. T. Nugrahadi, Dike Bayu Magfira","doi":"10.35882/jeeemi.v6i1.350","DOIUrl":"https://doi.org/10.35882/jeeemi.v6i1.350","url":null,"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.","PeriodicalId":369032,"journal":{"name":"Journal of Electronics, Electromedical Engineering, and Medical Informatics","volume":" 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139624599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"Analysis of Multimodal Biosignals during Surprise Conditions Correlates with Psychological Traits","authors":"Hendra Setiawan, Isnatin Miladiyah, Satyo Nuryadi, Alvin Sahroni","doi":"10.35882/jeeemi.v6i1.346","DOIUrl":"https://doi.org/10.35882/jeeemi.v6i1.346","url":null,"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.","PeriodicalId":369032,"journal":{"name":"Journal of Electronics, Electromedical Engineering, and Medical Informatics","volume":"127 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139629582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper evaluates the effectiveness of decisions made on two single-mode biometric systems based on facial recognition and fingerprints for access control. To achieve this, we first implemented an embedded system under Arduino to allow us to open and close doors, then we programmed two biometric recognition systems, namely facial recognition and fingerprint recognition, and finally we exploited the Susceptible-Infected-Covered model without demographics to evaluate the efficiency of these two access control systems. The variables used in the analysis were the number of individuals enrolled in the biometric system to be subject to access control (Susceptible), the number of individuals enrolled in the biometric system and denied access by the system, as well as the number of individuals not enrolled in the biometric system and allowed access by the system (Infected), and the number of false acceptance rates and false rejection rates at time t in the systems (Retrieved). In a sample of 600 individuals, of which 300 were enrolled and 300 were not, our two simple modal access control systems each obtained the following results: 270 true positives, 30 false negatives, 48 false positives and 252 true negatives for the facial recognition system, compared to 288 true positives, 12 false negatives, 24 false positives and 276 true negatives for the fingerprint recognition system, which constitute our confusion matrix. Based on this confusion matrix, we were able to exploit the false rejection rates and false acceptance rates to correct for these inconveniences using the SIR model, i.e. 78 infected individuals for the facial recognition system, compared to 36 infected individuals for the fingerprint recognition system over a period of 216 days. The results show that the fingerprint recognition system is more efficient than the facial recognition system, according to the SIR model without demographic formulation.
{"title":"Evaluation of two biometric access control systems using the Susceptible-Infected-Recovered model","authors":"Bopatriciat BOLUMA MANGATA, Odette Sangupamba Mwilu, Patience Ryan Tebua Tene, Gilgen Mate Landry","doi":"10.35882/jeemi.v5i2.288","DOIUrl":"https://doi.org/10.35882/jeemi.v5i2.288","url":null,"abstract":"This paper evaluates the effectiveness of decisions made on two single-mode biometric systems based on facial recognition and fingerprints for access control. To achieve this, we first implemented an embedded system under Arduino to allow us to open and close doors, then we programmed two biometric recognition systems, namely facial recognition and fingerprint recognition, and finally we exploited the Susceptible-Infected-Covered model without demographics to evaluate the efficiency of these two access control systems. The variables used in the analysis were the number of individuals enrolled in the biometric system to be subject to access control (Susceptible), the number of individuals enrolled in the biometric system and denied access by the system, as well as the number of individuals not enrolled in the biometric system and allowed access by the system (Infected), and the number of false acceptance rates and false rejection rates at time t in the systems (Retrieved). In a sample of 600 individuals, of which 300 were enrolled and 300 were not, our two simple modal access control systems each obtained the following results: 270 true positives, 30 false negatives, 48 false positives and 252 true negatives for the facial recognition system, compared to 288 true positives, 12 false negatives, 24 false positives and 276 true negatives for the fingerprint recognition system, which constitute our confusion matrix. Based on this confusion matrix, we were able to exploit the false rejection rates and false acceptance rates to correct for these inconveniences using the SIR model, i.e. 78 infected individuals for the facial recognition system, compared to 36 infected individuals for the fingerprint recognition system over a period of 216 days. The results show that the fingerprint recognition system is more efficient than the facial recognition system, according to the SIR model without demographic formulation.","PeriodicalId":369032,"journal":{"name":"Journal of Electronics, Electromedical Engineering, and Medical Informatics","volume":"295 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133104443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dalam Kaum Sambas Public Health Center is one of the public health centers that carry out the main activities of health improvement efforts in the form of promotive and preventive efforts, namely health screening for elementary school children, which are carried out manually, causing problems such as the length of recording, drawing conclusions based on examination results and recapitulation that are at risk of errors. This study aimed to develop an Information System for Primary School Children's Health Screening (SIPEKASDA) at Dalam Kaum Sambas Public Health Center. This research method is Research and Development (R&D) with system design using the Waterfall model and system evaluation with the EUCS End User Computing Satisfaction (EUCS) model. The design of the Information System in this study includes a Data Flow Diagram, Flowchart, Entity Relationship Diagram and User Interface, which produces an Information System with several menus, namely processing student data, parents, student class mapping, health checks and recapitulation of school-based health check results. The results obtained from SIPEKASDA include; the presentation of examination results data and recapitulation of examination results per school, village and sub-district. SIPEKASDA is a website-based information system that can be accessed anywhere and anytime. The quality of the Primary School Children's Health Screening Information System assessed from the evaluation of information systems using the End User Computing Satisfaction (EUCS) model is very good, which reaches 83.58%, so the information system can be relied on if you need data at any time. One of the principles of using information systems is timelines or the speed of information systems in responding to what is done by its users, presenting data promptly and up to date so that the use of information systems can increase. Problems in recording inspection results, data processing, data recapitulation, report making, and ease of accessing the system can be adequately resolved after the system is made.
{"title":"Development of Health Screening Information System for Elementary School Children at Dalam Kaum Sambas Public Health Center, Sambas Regency - West Kalimantan","authors":"Saherman Mohammad, Farid Agushybana, Mursid Raharjo","doi":"10.35882/jeeemi.v5i2.291","DOIUrl":"https://doi.org/10.35882/jeeemi.v5i2.291","url":null,"abstract":"Dalam Kaum Sambas Public Health Center is one of the public health centers that carry out the main activities of health improvement efforts in the form of promotive and preventive efforts, namely health screening for elementary school children, which are carried out manually, causing problems such as the length of recording, drawing conclusions based on examination results and recapitulation that are at risk of errors. This study aimed to develop an Information System for Primary School Children's Health Screening (SIPEKASDA) at Dalam Kaum Sambas Public Health Center. This research method is Research and Development (R&D) with system design using the Waterfall model and system evaluation with the EUCS End User Computing Satisfaction (EUCS) model. The design of the Information System in this study includes a Data Flow Diagram, Flowchart, Entity Relationship Diagram and User Interface, which produces an Information System with several menus, namely processing student data, parents, student class mapping, health checks and recapitulation of school-based health check results. The results obtained from SIPEKASDA include; the presentation of examination results data and recapitulation of examination results per school, village and sub-district. SIPEKASDA is a website-based information system that can be accessed anywhere and anytime. The quality of the Primary School Children's Health Screening Information System assessed from the evaluation of information systems using the End User Computing Satisfaction (EUCS) model is very good, which reaches 83.58%, so the information system can be relied on if you need data at any time. One of the principles of using information systems is timelines or the speed of information systems in responding to what is done by its users, presenting data promptly and up to date so that the use of information systems can increase. Problems in recording inspection results, data processing, data recapitulation, report making, and ease of accessing the system can be adequately resolved after the system is made.","PeriodicalId":369032,"journal":{"name":"Journal of Electronics, Electromedical Engineering, and Medical Informatics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128783711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nur Rachmat, Atika Febri Anggriani, Ahmad Hisyam, Doddy Suprayogi
Physically disabled is someone who has a movement system disorder or has physical abnormalities such as amputation, withering, stiffness, and others. The self-confidence of someone who has an amputee can be improved by having a prosthesis made, because it can replace the anatomical and functional functions of the body. The socket on the prosthesis is the most important component, because the use of the socket on the prosthesis is directly related to the patient's stump. One of the materials for making socket prosthesis is fiberglass. However, the use of fiberglass has a negative effect, namely it produces gas and dust emissions that can irritate stumps, is not a local product, and is difficult to recycle. Alternative fiber materials are using fibers from nature, coco fiber is an option as an alternative to fiberglass. The tensile strength test is the most basic test. The tensile strength test was carried out to determine the stress, strain, and elastic modulus of the fibrous polymer composites. The purpose of this study was to analyze and compare the tensile strength between fiberglass and coconut coir fiber, so as to find out which fiber material is suitable as an alternative to fiberglass in the manufacture of socket prosthesis. Using experimental quantitative methods. The composite material uses coconut coir fiber which was previously treated with 5% NaOH for 1 hour and then dried for 2-3 days. In the fabrication process using the vacuum bag method. Standard specimen refers to ASTM D3039/3039M. Tensile testing showed that the average tensile strength value of the coco fiber composite was 16.2 MPa and the average tensile strength value of the fiberglass composite was 30.2 MPa. This means that the value of the tensile strength of coco fiber is still below fiberglass and cannot be used as a substitute for fiberglass. However, coconut coir fiber can be used as an alternative to fiberglass, judging from the average maximum force that the coco fiber composite can withstand, which is 2630 N or equivalent to a load of 263 kg, this value is sufficient for the average adult weight in Indonesia with an average weight of 60 kg body
{"title":"Tensile Strength of Coconut Coir Fiber Composite as an Alternative Material to Replace Fiberglass in Hard Socket","authors":"Nur Rachmat, Atika Febri Anggriani, Ahmad Hisyam, Doddy Suprayogi","doi":"10.35882/jeemi.v5i2.297","DOIUrl":"https://doi.org/10.35882/jeemi.v5i2.297","url":null,"abstract":"Physically disabled is someone who has a movement system disorder or has physical abnormalities such as amputation, withering, stiffness, and others. The self-confidence of someone who has an amputee can be improved by having a prosthesis made, because it can replace the anatomical and functional functions of the body. The socket on the prosthesis is the most important component, because the use of the socket on the prosthesis is directly related to the patient's stump. One of the materials for making socket prosthesis is fiberglass. However, the use of fiberglass has a negative effect, namely it produces gas and dust emissions that can irritate stumps, is not a local product, and is difficult to recycle. Alternative fiber materials are using fibers from nature, coco fiber is an option as an alternative to fiberglass. The tensile strength test is the most basic test. The tensile strength test was carried out to determine the stress, strain, and elastic modulus of the fibrous polymer composites. The purpose of this study was to analyze and compare the tensile strength between fiberglass and coconut coir fiber, so as to find out which fiber material is suitable as an alternative to fiberglass in the manufacture of socket prosthesis. Using experimental quantitative methods. The composite material uses coconut coir fiber which was previously treated with 5% NaOH for 1 hour and then dried for 2-3 days. In the fabrication process using the vacuum bag method. Standard specimen refers to ASTM D3039/3039M. Tensile testing showed that the average tensile strength value of the coco fiber composite was 16.2 MPa and the average tensile strength value of the fiberglass composite was 30.2 MPa. This means that the value of the tensile strength of coco fiber is still below fiberglass and cannot be used as a substitute for fiberglass. However, coconut coir fiber can be used as an alternative to fiberglass, judging from the average maximum force that the coco fiber composite can withstand, which is 2630 N or equivalent to a load of 263 kg, this value is sufficient for the average adult weight in Indonesia with an average weight of 60 kg body","PeriodicalId":369032,"journal":{"name":"Journal of Electronics, Electromedical Engineering, and Medical Informatics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133358366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Poor vision is one of the most common eye health issues worldwide. Low vision patients are typically treated with optical devices or by substituting hearing or touch for visual capabilities. Head-mounted displays are currently the most promising form of low-vision assistive technology since they utilize the user's remaining natural visual capabilities. In this work, a prototype head-mounted display-based low-vision tool in the form of electronic glasses was designed utilizing a Raspberry Pi computer. The prototype was created using a Raspberry Pi 4 B coupled with cameras to allow real-time video acquisition. The LCD on the electronic eyewear frame as the camera showed the video recording. The prototype also included software utilizing five image processing modes—magnification, brightness enhancement, adaptive contrast enhancement, edge enhancement, and text detection and recognition- to help persons with limited vision acquire visual information more effectively. OpenCV was used with Python to create the software system. Average framerate measurements of 30–40 FPS for brightness and contrast improvement modes, 20 FPS for zooming and edge enhancement modes, and 1.3 FPS for text identification modes showed that the concept of electronic spectacles was successfully implemented in this research.
{"title":"Design of Low Vision Electronic Glasses with Image Processing Capabilities Using Raspberry Pi","authors":"Rachmad Setiawan, Rayhan Akmal Fadlurahman, Nada Fitrieyatul Hikmah","doi":"10.35882/jeeemi.v5i2.294","DOIUrl":"https://doi.org/10.35882/jeeemi.v5i2.294","url":null,"abstract":"Poor vision is one of the most common eye health issues worldwide. Low vision patients are typically treated with optical devices or by substituting hearing or touch for visual capabilities. Head-mounted displays are currently the most promising form of low-vision assistive technology since they utilize the user's remaining natural visual capabilities. In this work, a prototype head-mounted display-based low-vision tool in the form of electronic glasses was designed utilizing a Raspberry Pi computer. The prototype was created using a Raspberry Pi 4 B coupled with cameras to allow real-time video acquisition. The LCD on the electronic eyewear frame as the camera showed the video recording. The prototype also included software utilizing five image processing modes—magnification, brightness enhancement, adaptive contrast enhancement, edge enhancement, and text detection and recognition- to help persons with limited vision acquire visual information more effectively. OpenCV was used with Python to create the software system. Average framerate measurements of 30–40 FPS for brightness and contrast improvement modes, 20 FPS for zooming and edge enhancement modes, and 1.3 FPS for text identification modes showed that the concept of electronic spectacles was successfully implemented in this research.","PeriodicalId":369032,"journal":{"name":"Journal of Electronics, Electromedical Engineering, and Medical Informatics","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132998853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-14DOI: 10.35882/jeeemi.v5i2.287
Hilman Fauzi, Achmad Rizal, Mazaya 'Aqila, Alvin Oktarianto, Ziani Said
Analysis of heart sound signals for automatic segmentation and classification has revealed in recent decades that it has the potential to detect pathology accurately in clinical applications. Various audio signal processing techniques have been used to reduce the subjectivity of heart sound analysis. This study aims to classify normal and abnormal heart sound signals. The feature extraction process was optimized by EMD and calculated using five first-order statistical parameters: mean, variance, kurtosis, skewness, and entropy. The classification system is optimized with a mutual information algorithm to select traits that can significantly improve system performance. In addition, the selection of the optimal system configuration also includes the k-fold cross-validation and kNN methods with k values and the proper distance type. Based on the test results, the highest accuracy of 98.2% was obtained when the value of k = 1 and the type of cosine distance on kNN with a five-fold cross-validation system evaluation model.
{"title":"Classification of Normal and Abnormal Heart Sounds Using Empirical Mode Decomposition and First Order Statistic","authors":"Hilman Fauzi, Achmad Rizal, Mazaya 'Aqila, Alvin Oktarianto, Ziani Said","doi":"10.35882/jeeemi.v5i2.287","DOIUrl":"https://doi.org/10.35882/jeeemi.v5i2.287","url":null,"abstract":"Analysis of heart sound signals for automatic segmentation and classification has revealed in recent decades that it has the potential to detect pathology accurately in clinical applications. Various audio signal processing techniques have been used to reduce the subjectivity of heart sound analysis. This study aims to classify normal and abnormal heart sound signals. The feature extraction process was optimized by EMD and calculated using five first-order statistical parameters: mean, variance, kurtosis, skewness, and entropy. The classification system is optimized with a mutual information algorithm to select traits that can significantly improve system performance. In addition, the selection of the optimal system configuration also includes the k-fold cross-validation and kNN methods with k values and the proper distance type. Based on the test results, the highest accuracy of 98.2% was obtained when the value of k = 1 and the type of cosine distance on kNN with a five-fold cross-validation system evaluation model.","PeriodicalId":369032,"journal":{"name":"Journal of Electronics, Electromedical Engineering, and Medical Informatics","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129184494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-05DOI: 10.35882/jeeemi.v5i2.286
Yuli Triyani, Wahyuni Khabzli, Wiwin Styorini
Breast cancer has the largest prevalence in the world in 2020, with 2,261,419 cases or 11.7%. It is also the leading cause of cancer death, accounting for 6.9% of all cancer deaths. Asia and Indonesia have the greatest prevalence and mortality rates. This is an urgent issue that must be addressed. Ultrasonography (USG) is advised for assessing the features of breast nodules. Breast nodules on ultrasound pictures are interpreted using the Breast Imaging, Reporting, and Data System (BIRADS) category, which has five features. Yet, the probability of a False Positive Result (FPR) on ultrasound imaging is relatively high. Computer Aided Diagnosis (CAD) was created to reduce FPR. However, CAD research based on many BIRADS traits is currently margined. As a result, based on three BIRADS characteristics, namely the margin, posterior, and orientation aspects, this study aims to proposed the methode for diagnosing breast nodule malignancy. The proposed method consists of 4 stages, namely, pre-processing, automatic segmentation, features extraction, and classification. Pre-processing adaptive median filter maximum window size is 11 pixels, linear histogram normalizing, and Reduction Anisotropic Diffusion (SRAD) filter were used to construct the method. The neutrosophic watershed method was used in the suggested automatic segmentation. Based on the nodule's margin, orientation, and posterior, 10 features were proposed: nodule width, gradient, slenderness, margin sharpness, shadow indicators, skewness, energy, entropy, dispersion, and solidity. MLP is a classification approach. The test used 94 nodule pictures and yielded an accuracy of 88.30%, a sensitivity of 82.35%, a specificity of 91.67%, a Kappa of 0.7449, and an AUC of 0.865. As a result, it is feasible to conclude that the proposed method is capable of detecting malignancy in breast nodules in ultrasound images. To make the proposed method more reliable in the future, automatic RoI can be developed.
{"title":"Malignant Detection of Breast Nodules On BIRADS-Based Ultrasound Images Margin, Orientation, And Posterior","authors":"Yuli Triyani, Wahyuni Khabzli, Wiwin Styorini","doi":"10.35882/jeeemi.v5i2.286","DOIUrl":"https://doi.org/10.35882/jeeemi.v5i2.286","url":null,"abstract":"Breast cancer has the largest prevalence in the world in 2020, with 2,261,419 cases or 11.7%. It is also the leading cause of cancer death, accounting for 6.9% of all cancer deaths. Asia and Indonesia have the greatest prevalence and mortality rates. This is an urgent issue that must be addressed. Ultrasonography (USG) is advised for assessing the features of breast nodules. Breast nodules on ultrasound pictures are interpreted using the Breast Imaging, Reporting, and Data System (BIRADS) category, which has five features. Yet, the probability of a False Positive Result (FPR) on ultrasound imaging is relatively high. Computer Aided Diagnosis (CAD) was created to reduce FPR. However, CAD research based on many BIRADS traits is currently margined. As a result, based on three BIRADS characteristics, namely the margin, posterior, and orientation aspects, this study aims to proposed the methode for diagnosing breast nodule malignancy. The proposed method consists of 4 stages, namely, pre-processing, automatic segmentation, features extraction, and classification. Pre-processing adaptive median filter maximum window size is 11 pixels, linear histogram normalizing, and Reduction Anisotropic Diffusion (SRAD) filter were used to construct the method. The neutrosophic watershed method was used in the suggested automatic segmentation. Based on the nodule's margin, orientation, and posterior, 10 features were proposed: nodule width, gradient, slenderness, margin sharpness, shadow indicators, skewness, energy, entropy, dispersion, and solidity. MLP is a classification approach. The test used 94 nodule pictures and yielded an accuracy of 88.30%, a sensitivity of 82.35%, a specificity of 91.67%, a Kappa of 0.7449, and an AUC of 0.865. As a result, it is feasible to conclude that the proposed method is capable of detecting malignancy in breast nodules in ultrasound images. To make the proposed method more reliable in the future, automatic RoI can be developed.","PeriodicalId":369032,"journal":{"name":"Journal of Electronics, Electromedical Engineering, and Medical Informatics","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131977706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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