2022 4th International Conference on Biomedical Engineering (IBIOMED)最新文献_第2页

A Prototype Low-Cost Pharyngeal Vibration Device for Voice Rehabilitation Following Laryngectomy 一种用于喉切除术后语音康复的低成本咽振动装置原型

2022 4th International Conference on Biomedical Engineering (IBIOMED) Pub Date : 2022-10-18 DOI: 10.1109/IBIOMED56408.2022.9988665

Ricky Hu, Justin KM Wyss, Prateek Mathur, Housssam El-Hariri, Pardiss Danaei, H. Parhar, Ameen Amanian, D. Anderson

{"title":"A Prototype Low-Cost Pharyngeal Vibration Device for Voice Rehabilitation Following Laryngectomy","authors":"Ricky Hu, Justin KM Wyss, Prateek Mathur, Housssam El-Hariri, Pardiss Danaei, H. Parhar, Ameen Amanian, D. Anderson","doi":"10.1109/IBIOMED56408.2022.9988665","DOIUrl":"https://doi.org/10.1109/IBIOMED56408.2022.9988665","url":null,"abstract":"Laryngeal cancer disproportionately affects socioeconomically disadvantaged patients and its incidence is increasing in low-resource environments. Surgical ablation with laryngectomy results in loss of voice necessitating rehabilitation, for which the current devices are prohibitively expensive or difficult to repair. In this study, we developed a prototype phonation device with accessible and affordable electronic and mechanical components. Material and vibration wave properties were chosen after modelling a modified longitudinal wave equation. The device consists of a 3D-printed cylinder with a tip-mounted oscillating solenoid controlled by a timer circuit to propagate longitudinal waves to the neck. Speech intelligibility and volume were assessed on healthy volunteers by measuring the accuracy of words heard by a listener after a brief introduction to device technique. This was repeated with a commercial electrolarynx device for comparison. The mean accuracy of the word recorded was 0.956 (IQR 0.940 - 1.000) with an audible frequency of 57Hz to 138Hz. The device demonstrated listener accuracy statistically similar to commercial devices with phonation frequencies that in range of average human voice. The device was more affordable than commercial devices (under 35 USD compared to 600 USD) with common electronic components obtainable from international retailers. The results provide motivate for further development with the goal of open-source distribution of a blueprint to be manufactured remotely in in low-resource settings.","PeriodicalId":250112,"journal":{"name":"2022 4th International Conference on Biomedical Engineering (IBIOMED)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122562152","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}

引用次数: 0

VaderLogRest Algorithm: An Ensemble Learning Approach for Sentiment Analysis on Vaccination Tweets VaderLogRest算法:疫苗推文情感分析的集成学习方法

2022 4th International Conference on Biomedical Engineering (IBIOMED) Pub Date : 2022-10-18 DOI: 10.1109/IBIOMED56408.2022.9988439

Vijayarajan Rajangam, Ojaswa Yadav, Faiz Khan, Mridul Shukla, N. Sangeetha

{"title":"VaderLogRest Algorithm: An Ensemble Learning Approach for Sentiment Analysis on Vaccination Tweets","authors":"Vijayarajan Rajangam, Ojaswa Yadav, Faiz Khan, Mridul Shukla, N. Sangeetha","doi":"10.1109/IBIOMED56408.2022.9988439","DOIUrl":"https://doi.org/10.1109/IBIOMED56408.2022.9988439","url":null,"abstract":"Analyzing the emotions about the vaccines and vaccination will help to successfully carry forward the vaccination trials and government policies towards epidemic control. The tweets featured information on the most common immunizations has recently been available all around the world. The method of natural language processing is the successful tool to investigate the reactions of the people to various immunizations. This paper proposes a ensemble learning model making use of the VADER lexicon, logistic regression, and random forest algorithm for sentiment analysis to understand and interpret the people's sentiments through the tweets. We utilize a collection of tweets in April to May 2021 to extract inferences about public views on vaccinations as they become more widely available during the COVID-19 pandemic. The classification output of the VADER algorithm is used as one more feature that helps to achieve better accuracy using the random forest algorithm. One more feature is added with the available features using logistic regression. Hence, the classification outputs of VADER and logistic regression improve the classification accuracy to 88% for positive-negative outputs and 84% for positive, neutral, and negative outputs.","PeriodicalId":250112,"journal":{"name":"2022 4th International Conference on Biomedical Engineering (IBIOMED)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114814814","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}

引用次数: 0

Detecting Pregnancy Risk Type Using LSTM Algorithm 利用LSTM算法检测妊娠风险类型

2022 4th International Conference on Biomedical Engineering (IBIOMED) Pub Date : 2022-10-18 DOI: 10.1109/IBIOMED56408.2022.9987932

G. M. Damaraji, A. E. Permanasari, Indriana Hidayah, Michael Stephen Moses Paknahan, Aiie Kusuma Wardhana

{"title":"Detecting Pregnancy Risk Type Using LSTM Algorithm","authors":"G. M. Damaraji, A. E. Permanasari, Indriana Hidayah, Michael Stephen Moses Paknahan, Aiie Kusuma Wardhana","doi":"10.1109/IBIOMED56408.2022.9987932","DOIUrl":"https://doi.org/10.1109/IBIOMED56408.2022.9987932","url":null,"abstract":"Pregnancy is the most important yet vulnerable phase for all mothers-to-be. Approximately nine months of pregnancy requires special attention from medical workers to monitor the health of the womb. Specifically early detection of risks and diseases that may happen during pregnancy. Risk detection requires understanding, experience, and precise calculations from available dataset. Current methodology of pregnancy risk is manual calculation using KSPR (Poedji Rochyati Score Card). However, manual calculation opens a lot of human error possibilities. Therefore, there is a need to develop a more accurate system using the available data. This study aims to classify the risk of pregnant women using multi-class classification using the LSTM method. Data used in this research are primarily collected dataset from Dinas Kesehatan Kabupaten Boyolali. To create an accurate model, we pre-processed dataset into trainable data for a deep learning model. These processes include balancing data and feature selection. Pre-processed data are then trained and tested. Model hyperparameter are then tuned to provide the best evaluation metric. Final prediction model evaluation metrics collected from the model are 94.63% accuracy, sensitivity 94.57%, precision 94.88%, and F1-Score 94.60%.","PeriodicalId":250112,"journal":{"name":"2022 4th International Conference on Biomedical Engineering (IBIOMED)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114500879","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}

引用次数: 0

A Prototype of IoT-based Real-time Respiratory Rate Monitoring Using an Accelerometer Sensor 基于物联网的加速传感器呼吸速率实时监测原型

2022 4th International Conference on Biomedical Engineering (IBIOMED) Pub Date : 2022-10-18 DOI: 10.1109/IBIOMED56408.2022.9988053

Muhammad Hanif Andarevi, A. A. Iskandar

{"title":"A Prototype of IoT-based Real-time Respiratory Rate Monitoring Using an Accelerometer Sensor","authors":"Muhammad Hanif Andarevi, A. A. Iskandar","doi":"10.1109/IBIOMED56408.2022.9988053","DOIUrl":"https://doi.org/10.1109/IBIOMED56408.2022.9988053","url":null,"abstract":"The emergence of the internet of things (IoT) has provided a dynamic change of healthcare system that enables interoperability and secure data transmission for a more effective healthcare delivery. This paper extends the implementation of IoT in combining medical devices and sensors with an IoT platform. Among various fundamental vital signs, respiratory rate is one of the most sensitive indicators of physiological conditions. Although it is deemed as a prognostic parameter that can predict miscellaneous diseases, respiratory rate monitoring remains frequently omitted and considered as time-consuming using manual counting methods. To address this problem, this paper presents a prototype of a respiratory rate monitoring device based on IoT and real-time systems. The device is equipped with an accelerometer sensor to measure abdominal movement generated by the inhale-exhale process. Butterworth filter and peak detection algorithm are applied to the signal to detect respiratory cycles. The proposed system uses the message queuing telemetry transport (MQTT) protocol that implements publish-subscribe systems. Respiratory rate results are featured on the Node-RED dashboard with motion and peak signals plotted during the test. Monitoring was carried out on five participants and the results were analyzed using paired t-test. Good performance was demonstrated by having no significant difference in respiratory rate using the proposed method compared to the actual values (p>0.05). Hence, this work provides a convenient technique for respiratory rate estimation and may further allow for the improvement of the effectiveness of remote patient monitoring.","PeriodicalId":250112,"journal":{"name":"2022 4th International Conference on Biomedical Engineering (IBIOMED)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131842457","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}

引用次数: 0

Image Enhancement Techniques on Chest X-Ray Images to Improve COVID-19 Detection 胸部x线图像增强技术提高COVID-19检测

2022 4th International Conference on Biomedical Engineering (IBIOMED) Pub Date : 2022-10-18 DOI: 10.1109/IBIOMED56408.2022.9987990

Tengku Muaz Abdussalam, H. A. Nugroho, I. Soesanti

{"title":"Image Enhancement Techniques on Chest X-Ray Images to Improve COVID-19 Detection","authors":"Tengku Muaz Abdussalam, H. A. Nugroho, I. Soesanti","doi":"10.1109/IBIOMED56408.2022.9987990","DOIUrl":"https://doi.org/10.1109/IBIOMED56408.2022.9987990","url":null,"abstract":"The COVID-19 pandemic has claimed many lives. The diagnosis is made to prevent the spread of COVID-19. One of the diagnostic methods that have now become the gold standard is RT-PCR, but this method still has shortcomings in terms of accuracy so it is at risk of causing inaccurate decision-making. The use of medical imaging techniques such as CXR and chest CT scans in the diagnosis of COVID-19 is considered to be able to increase the accuracy of COVID-19 detection so that the risk of making inappropriate decisions can be minimized. Compared to a chest CT scan, CXR is considered superior in terms of price and availability so with these advantages the use of CXR is more effective in diagnosing COVID-19. However, it should be noted that in terms of performance, the chest CT scan far outperformed CXR. For CXR to be better utilized, image enhancement techniques are applied and combined with several classification algorithms. The experiments on two datasets showed that applying BCET (Balance Contrast Enhancement Technique) prior to classifying consistently outperforms other classification methods without enhancement techniques on other compared methods. Moreover, the SVM algorithm achieved the best classification results for all image types in both datasets by scoring the highest AUC compared to other algorithms.","PeriodicalId":250112,"journal":{"name":"2022 4th International Conference on Biomedical Engineering (IBIOMED)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115516295","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}

引用次数: 0

Detection of COVID-19 using CNN's Deep Learning Method: Review 使用CNN的深度学习方法检测COVID-19:回顾

2022 4th International Conference on Biomedical Engineering (IBIOMED) Pub Date : 2022-10-18 DOI: 10.1109/IBIOMED56408.2022.9988533

Ade Sitti Nur Zainab, I. Soesanti, Dzuhri Radityo Utomo

{"title":"Detection of COVID-19 using CNN's Deep Learning Method: Review","authors":"Ade Sitti Nur Zainab, I. Soesanti, Dzuhri Radityo Utomo","doi":"10.1109/IBIOMED56408.2022.9988533","DOIUrl":"https://doi.org/10.1109/IBIOMED56408.2022.9988533","url":null,"abstract":"cOVID-19 is a global pandemic that occurred in March 2020. COVID-19 spreads very quickly because it is an infectious disease. COVID-19 has similar characteristics to Pneumonia. The X-Ray results of COVID-19 and Pneumonia can also be said to be similar, making it difficult to distinguish. The object of detection is beneficial to the medical community, especially radiologists, who utilize it to diagnose patients with COVID-19. COVID-19 can be found by using X-Ray images in the medical field. In detecting COVID-19, there are usually many methods that can be used, one of which is deep learning. Convolutional Neural Network (CNN) is a Deep Learning model that can be used to detect images. This research examines previous research on the detection of COVID-19 using CNN's Deep Learning Method, many existing models for COVID-19 detection studies, and some researchers-built models using CNN's Deep Learning Method. The study shows that CNN's Deep Learning accurately detects COVID-19, Negative COVID-19, and Pneumonia. The Multi-layered CNN model uses 3.990 X-Ray images and offers good accuracy, sensitivity, and specificity","PeriodicalId":250112,"journal":{"name":"2022 4th International Conference on Biomedical Engineering (IBIOMED)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128693922","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}

引用次数: 1

Review of Brain MRI Image Segmentation Based on Deep Learning Method 基于深度学习方法的脑MRI图像分割研究进展

2022 4th International Conference on Biomedical Engineering (IBIOMED) Pub Date : 2022-10-18 DOI: 10.1109/IBIOMED56408.2022.9988536

Sekar Sari, Wirawan Setyo Prakoso, I. Soesanti

{"title":"Review of Brain MRI Image Segmentation Based on Deep Learning Method","authors":"Sekar Sari, Wirawan Setyo Prakoso, I. Soesanti","doi":"10.1109/IBIOMED56408.2022.9988536","DOIUrl":"https://doi.org/10.1109/IBIOMED56408.2022.9988536","url":null,"abstract":"MRI images are the best brain imaging modalities for identifying tumors because they provide detailed information about regions, size, shape, and volume differences. Nevertheless, there are some issues in the brain's MRI imaging, such as low image contrast, high noise, and the boundary of objects with unclear backgrounds. As a result, sharpening anomaly object before detection and analysis on brain MRI images is critical. Currently, the segmentation of MRI brain images uses the deep learning method. It has the potential to achieve impressive results with high accuracy in the future. The clinical application of these methods remains an exciting task and a challenge. The accuracy and quality of segmentation on brain MRI image abnormalities have not been well resolved and still require improvement in segmentation performance. Public datasets, such as BRATS, are widely used for comparing and benchmarking results. It is critical to use datasets to improve. Our review focuses on segmenting brain images using deep learning methods sourced from the Science Direct and IEEE Xplore databases from 2019 to 2022, which provide better development-related knowledge on image recognition issues.","PeriodicalId":250112,"journal":{"name":"2022 4th International Conference on Biomedical Engineering (IBIOMED)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114517287","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}

引用次数: 1

Leveraging Machine Learning and Model-Agnostic Explanations to Understand Automated Diagnosis of Cardiovascular Disease 利用机器学习和模型不可知的解释来理解心血管疾病的自动诊断

2022 4th International Conference on Biomedical Engineering (IBIOMED) Pub Date : 2022-10-18 DOI: 10.1109/IBIOMED56408.2022.9988121

Christopher Sun, J. Sharma, Milind Maiti

{"title":"Leveraging Machine Learning and Model-Agnostic Explanations to Understand Automated Diagnosis of Cardiovascular Disease","authors":"Christopher Sun, J. Sharma, Milind Maiti","doi":"10.1109/IBIOMED56408.2022.9988121","DOIUrl":"https://doi.org/10.1109/IBIOMED56408.2022.9988121","url":null,"abstract":"The pervasiveness of cardiovascular disease and physician misdiagnosis creates the need for artificial intelligence models to improve diagnosis accuracy. The study trains machine learning models on publicly available data sets containing simple medical information of patients to diagnose cardiovascular disease. The Multilayer Perceptron (MLP) assembled for this task performed optimally with an F1 score of 0.8968. This prompts the creation of an automated open-source diagnosis tool powered by the MLP. Local Interpretable Model-Agnostic Explanations (LIME) are employed to understand the impact of different features on the model's diagnosis in the form of marginal probabilities. K-Means Clustering segments patients into ten clusters, after which each example is passed through LIME. The resulting histograms depict a complex relationship between feature, cluster, and impact on diagnosis. A series of P-values with contrasting orders of magnitude shows nuances in the MLP's understanding of patients from different clusters. LIME analysis reveals that the most important features for cardiovascular disease diagnosis are fasting blood sugar, type of chest pain, and ST segment slope. Future experiments should replicate this study's LIME methodology on data sets containing more specialized features in order to gain practical medical insights about the different types of cardiovascular disease represented by each cluster. Finally, feature engineering pathways should be explored with consideration of these results to create versatile diagnosis models adaptable to other diseases as well.","PeriodicalId":250112,"journal":{"name":"2022 4th International Conference on Biomedical Engineering (IBIOMED)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125114424","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}

引用次数: 0