2023 IEEE 5th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS)最新文献

Implementation of IoT-Based Healthcare Kit 基于物联网的医疗保健工具包的实施

2023 IEEE 5th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS) Pub Date : 2023-06-02 DOI: 10.1109/ECBIOS57802.2023.10218615

Tanya Chanchalani, Gaurav R, Bhushan Kiran Munoli, Sinchitha H V, P. U

引用次数: 0

Inclusion-Exclusion Knowledge Filtering Approach for Conversation-Based Preliminary Diagnosis 基于会话的初步诊断的包含-排除知识过滤方法

2023 IEEE 5th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS) Pub Date : 2023-06-02 DOI: 10.1109/ECBIOS57802.2023.10218646

Binghong Chen, Jenhui Chen

{"title":"Inclusion-Exclusion Knowledge Filtering Approach for Conversation-Based Preliminary Diagnosis","authors":"Binghong Chen, Jenhui Chen","doi":"10.1109/ECBIOS57802.2023.10218646","DOIUrl":"https://doi.org/10.1109/ECBIOS57802.2023.10218646","url":null,"abstract":"Using natural language processing (NLP) techniques, we conducted a preliminary diagnosis of the disease from the patient syndrome description. Because patients are not medical professionals, they cannot accurately describe all symptoms. To solve this issue, we build a medical knowledge graph (KG) by constructing symptom-disease relation triples for pre-processing the patient syndrome description. According to the medical KG, the descriptions were reconstructed into KG embedding representation. To avoid the knowledge noise issue, we investigate an inclusion-exclusion knowledge filtering approach (IKFA) for symptom-to-disease triples to load them to a pretrained language model (PLM), i.e., bidirectional encoder representations from Transformers (BERT). To train the IKFA, we built a medical diagnosis question-answer dataset (MDQA dataset), which contains large-scale and high-quality questions (patient symptom description) and answers (diagnosis) (Q&A) corpus with 1.63 million entries in the size of 213 MB. The KG was built based on 8,731 diseases with detailed syndrome descriptions in the size of 1.98 MB. The experimental results showed that the IKFA preliminarily diagnosed 8,731 different diseases based on the patient's initial symptom description with an accuracy of 0.9894.","PeriodicalId":334600,"journal":{"name":"2023 IEEE 5th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS)","volume":"268 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127375718","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

Experimental Teaching System of Human Physiology and Artificial Intelligence Application in Basic Medical Education 人体生理学与人工智能在基础医学教育中的应用实验教学体系

2023 IEEE 5th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS) Pub Date : 2023-06-02 DOI: 10.1109/ECBIOS57802.2023.10218415

Nan Zhou, Xin Li, Dexiu Wang, Chengwen Jin, Weihao Wang, Xiaodong Cui

{"title":"Experimental Teaching System of Human Physiology and Artificial Intelligence Application in Basic Medical Education","authors":"Nan Zhou, Xin Li, Dexiu Wang, Chengwen Jin, Weihao Wang, Xiaodong Cui","doi":"10.1109/ECBIOS57802.2023.10218415","DOIUrl":"https://doi.org/10.1109/ECBIOS57802.2023.10218415","url":null,"abstract":"Presently, animal experimentation is a main teaching tool in basic medical education. Animal experiments can improve medical students' knowledge and its application with practical operation ability. However, based on the premise of the 3R principle, more scholars believe that animal experiments do not meet the needs of medical education and so need to be improved. With the current emergence of big data applications in artificial intelligence (AI) and computer science and technology, human physiological experiments are gradually replacing animal experiments. Although human experiments have drawbacks such as limiting their application in basic medical education. However, the data derived from computer and intelligent tool technology, are more intuitive and widely used than animal experiments. Combined with the application of AI, human computer experiments are gradually replacing animal experiments in medical practice and teaching. We discussed the principles and applications of human physiology in medical education combined with the experience in basic medical teaching. The discussion provided a reference value for medical basic education, especially the new teaching model of basic medical science using artificial intelligence computer simulation.","PeriodicalId":334600,"journal":{"name":"2023 IEEE 5th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116149356","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

Denoising of Heart Sound Signal for Myocardial Infarction Detection Based on Adaptive Filtering 基于自适应滤波的心音信号去噪方法在心肌梗死检测中的应用

2023 IEEE 5th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS) Pub Date : 2023-06-02 DOI: 10.1109/ECBIOS57802.2023.10218715

Ira Puspasari, T. Mengko, A. W. Setiawan, T. Adiono, M. Pramudyo

{"title":"Denoising of Heart Sound Signal for Myocardial Infarction Detection Based on Adaptive Filtering","authors":"Ira Puspasari, T. Mengko, A. W. Setiawan, T. Adiono, M. Pramudyo","doi":"10.1109/ECBIOS57802.2023.10218715","DOIUrl":"https://doi.org/10.1109/ECBIOS57802.2023.10218715","url":null,"abstract":"Processing heart sound signals, especially myocardial infarction (MI) signals, is crucial to identify essential features. The environment strongly influences the results of recording heart sound using a stethoscope on a patient in the hospital, the patient's condition, and other unpredictable noises. A crucial processing step of this signal is filtering. Noise removal in myocardial infarction signals has always been challenging in biomedical signal processing. We compare CEEMDAN and hard thresholding filtering methods. The signal result with the lowest MSE becomes the reference signal in LMSAF. The average MSE value in myocardial infarction signal noise reduction using LMSAF is 0.10, with an average time processing is 1.91 s. The normal signal temporal features on the systolic phase, namely T11: 0.81 s, and on the diastolic phase, namely T12: 0.33 s. The time duration of coronary artery disease (CAD) signal T11: 1.00 s, and T12: 0.46 s, CAD ST-elevation myocardial infarction (CAD STEMI) T-11: 0.99 s, and T12: 0.49 s, CAD non-ST-elevation myocardial infarction (CAD NSTEMI) T-11: 0.98 s, and T12: 0.51 s.","PeriodicalId":334600,"journal":{"name":"2023 IEEE 5th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS)","volume":"194 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116464218","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

Tap and Swipe Smartphone Gestures Indicating Hand Tremor and Finger Joint Range in Motion 点击和滑动智能手机手势指示手震颤和手指关节范围的运动

2023 IEEE 5th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS) Pub Date : 2023-06-02 DOI: 10.1109/ECBIOS57802.2023.10218438

Wilson O. Torres, Hannah S. Stuart

{"title":"Tap and Swipe Smartphone Gestures Indicating Hand Tremor and Finger Joint Range in Motion","authors":"Wilson O. Torres, Hannah S. Stuart","doi":"10.1109/ECBIOS57802.2023.10218438","DOIUrl":"https://doi.org/10.1109/ECBIOS57802.2023.10218438","url":null,"abstract":"Conditions such as multiple sclerosis or arthritis impair normative hand function by diminishing motor control and limiting finger range of motion (ROM), respectively. Day-to-day variation of these symptoms makes it difficult for physicians to track clinically relevant changes in function. This is worsened in populations that lack general access to healthcare. A smartphone holds the potential to be used as a frequent self-screening platform for changes in hand function. We use a custom smartphone application for detecting deviations in force control due to tremors and differences in finger ROM through simple tapping and swiping gestures. We conduct a 17-participant cross-sectional study, which includes two participants with known hand tremors. From the smartphone data during tap-and-hold, we see that people with hand tremors demonstrate less touchscreen force control than normative subjects. During the swiping task, we find a statistically significant moderate correlation between the path length of the swiping gesture and the maximum proximal interphalangeal joint flexion angle of the index finger. We find that different processing methods for the swiping data can reveal additional correlations with metacarpophalangeal flexion. These results are a promising start for the smartphone as an accessible screening tool for tremors and changes in finger ROM.","PeriodicalId":334600,"journal":{"name":"2023 IEEE 5th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128227217","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

Blockchain-Based Privacy Preserved Physiological Data Sharing Platform 基于区块链的隐私保护生理数据共享平台

2023 IEEE 5th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS) Pub Date : 2023-06-02 DOI: 10.1109/ECBIOS57802.2023.10218560

Xueyu Guan, Yu-Heng Hsieh, Shyan-Ming Yuan

{"title":"Blockchain-Based Privacy Preserved Physiological Data Sharing Platform","authors":"Xueyu Guan, Yu-Heng Hsieh, Shyan-Ming Yuan","doi":"10.1109/ECBIOS57802.2023.10218560","DOIUrl":"https://doi.org/10.1109/ECBIOS57802.2023.10218560","url":null,"abstract":"Wearable devices have significantly impacted precision medicine by providing real-time physiological and behavioral data. This data is crucial for accurate disease diagnosis, monitoring, and improved treatment outcomes. Wearable devices enable personalized health management and prevention programs, particularly being beneficial for chronic disease management. Overall, these devices offer more data and technical support for precision medicine, leading to better-individualized health management and treatment and ultimately improving medical outcomes and quality of life. However, the current device binding requires direct identifiers and grants manufacturers ownership of the generated data, limiting user control and raising privacy concerns. To address this, we propose a blockchain-based platform with two distinct blockchains: decentralized identity and physiological data. Users register decentralized identities on the first blockchain, which are then used for device binding on the second blockchain, enabling de-identified data collection. The platform generates user-specific smart contracts on the Physiological Data blockchain and ensures complete user control over data access. This system enhances the privacy, security, and credibility of users' physiological data, instilling confidence in the use of wearable devices.","PeriodicalId":334600,"journal":{"name":"2023 IEEE 5th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124743186","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

Pedestrian Fall Detection Using Improved YOLOv5 基于改进YOLOv5的行人跌倒检测

2023 IEEE 5th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS) Pub Date : 2023-06-02 DOI: 10.1109/ECBIOS57802.2023.10218379

Yaochang Xi, Peijiang Chen, Chaochao Miao

引用次数: 0

Application of 3-D Path Planning and Obstacle Avoidance Algorithms on Obstacle-Overcoming Robots 三维路径规划与避障算法在超障机器人中的应用

2023 IEEE 5th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS) Pub Date : 2023-06-02 DOI: 10.1109/ECBIOS57802.2023.10218652

Y. Huang, Huang Shi, Wang Hao, Ruifeng Meng

引用次数: 0

Classify Brain Tumors from MRI Images: Deep Learning-Based Approach 从MRI图像中分类脑肿瘤:基于深度学习的方法

2023 IEEE 5th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS) Pub Date : 2023-06-02 DOI: 10.1109/ECBIOS57802.2023.10218603

Chun-Cheng Peng, Bo-Han Liao

{"title":"Classify Brain Tumors from MRI Images: Deep Learning-Based Approach","authors":"Chun-Cheng Peng, Bo-Han Liao","doi":"10.1109/ECBIOS57802.2023.10218603","DOIUrl":"https://doi.org/10.1109/ECBIOS57802.2023.10218603","url":null,"abstract":"Brain tumors pose a significant health threat and cause severe damage to the body and its physiological functions. Traditional diagnostic methods for brain tumors involve expensive medical imaging scans and invasive surgical procedures, resulting in prolonged waiting times and recovery periods. Thus, we explore the potential of deep learning techniques and magnetic resonance imaging (MRI) for the diagnosis of brain tumors. With these technologies, we develop a diagnostic method that is faster, more accurate, and more reliable than current approaches. The proposed model employs preprocessing techniques and convolutional neural network (CNN) methods with the Adam optimizer. An average accuracy reaches 99.8% on the training set and 94.4% on the testing set. These results indicate that the classification of brain MRI is stable and reliable with the proposed method. This proposed approach outperforms four previous methods, demonstrating its superiority and potential for various applications in medical image analysis. In the future, improving overall performance and developing more advanced deep-learning models enables the medical community to diagnose diseases faster and more accurately.","PeriodicalId":334600,"journal":{"name":"2023 IEEE 5th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127843796","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

Prediction of Elbow Joint Motion of Stroke Patients by Analyzing Biceps and Triceps Electromyography Signals 分析二头肌和三头肌肌电信号预测脑卒中患者肘关节运动

2023 IEEE 5th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS) Pub Date : 2023-06-02 DOI: 10.1109/ECBIOS57802.2023.10218631

Hassan M. Qassim, W. Z. W. Hasan, H. R. Ramli, H. Harith, Liyanatul Najwa, Inchi Mat, Msf Salim

{"title":"Prediction of Elbow Joint Motion of Stroke Patients by Analyzing Biceps and Triceps Electromyography Signals","authors":"Hassan M. Qassim, W. Z. W. Hasan, H. R. Ramli, H. Harith, Liyanatul Najwa, Inchi Mat, Msf Salim","doi":"10.1109/ECBIOS57802.2023.10218631","DOIUrl":"https://doi.org/10.1109/ECBIOS57802.2023.10218631","url":null,"abstract":"Elbow flexion and extension is a common rehabilitation routine that is widely performed by stroke patients to rehabilitate elbow joints. The biceps and triceps muscles are the responsible muscles for flexing and extending the elbow joint. Hence, analyzing the electrical activity of those muscles provides beneficial information on elbow motion intention and eventually can be used for controlling purposes of potential rehabilitation robots. We investigate the Electromyography (EMG) signals of the biceps and triceps of stroke patients and their roles in elbow flexion and extension. The investigation process involves collecting, processing, filtering, and segmenting the collected surface Electromyography (sEMG) signal to ultimately extract specific features. Then, the optimum feature for elbow motion prediction is identified to be later used for controlling purposes. Six time-domain features, specifically MAV, RMS, SD, SAV, SSC, and ZC, were chosen to evaluate their efficiency in predicting elbow joint motion. MAV, RMS, SD, and SAV are the features that showed similar behavior during elbow flexion and extension. However, SAV showed the highest variation in the magnitude when the muscle's state changed from contraction to relaxation and vice-versa. On the other hand, SSC and ZC features showed an arbitrary behavior, where no reliable results were achieved. Eight stroke patients participated in this study after obtaining the ethics approval and consent agreements. The clinical trials were conducted at the Department of Rehabilitation Medicine, Hospital Pengajar Universiti Putra Malaysia (HPUPM).","PeriodicalId":334600,"journal":{"name":"2023 IEEE 5th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114904490","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