Pub Date : 2023-06-02DOI: 10.1109/ECBIOS57802.2023.10218623
Yu-Sheng Lin, Chih-Chun Lin, Guan-Wei Huang, Jing-Peng Wu, L. Kuo, Fong-Chin Su, Yi-Ching Yang
Hypertension is highly prevalent among older adults, and its global incidence is increasing primarily due to the aging population. With the development of information and communication technology, smart blood pressure monitors have been developed to offer more personalized guidance to users. A smart blood pressure monitor must provide users with professional guidance, such as monitoring pregnancy-induced hypertension for pregnant women and brain health indicators for older adults. This increases user willingness and engagement. In this study, we developed a smart blood pressure monitor system called “BP-ExerGuide” to provide a personalized exercise safety guideline for older adults in the community's smart gym. In this scenario, all smart gym devices are equipped with RFID sensors, and members can use their personal RFID cards to start a blood pressure measurement. Only when the blood pressure value meets the safety criteria for exercise, can they use the exercise devices. If an individual's blood pressure is too high, BP-ExerGuide acts like a sports coach, advising seniors to take a rest first and then start exercising once they meet the safety guidelines. The system also integrates exercise logs and blood pressure logs into a smart healthcare platform for seniors to track their physiological changes. Overall, the BP-ExerGuide system provides older adults with a safer and more personalized exercise experience while monitoring their blood pressure levels. This system improves the willingness of seniors to exercise regularly and thus promotes better health and well-being.
{"title":"BP-ExerGuide: Smart Blood Pressure Monitor System for Personalized Exercise Safety Guidelines in Senior Communities","authors":"Yu-Sheng Lin, Chih-Chun Lin, Guan-Wei Huang, Jing-Peng Wu, L. Kuo, Fong-Chin Su, Yi-Ching Yang","doi":"10.1109/ECBIOS57802.2023.10218623","DOIUrl":"https://doi.org/10.1109/ECBIOS57802.2023.10218623","url":null,"abstract":"Hypertension is highly prevalent among older adults, and its global incidence is increasing primarily due to the aging population. With the development of information and communication technology, smart blood pressure monitors have been developed to offer more personalized guidance to users. A smart blood pressure monitor must provide users with professional guidance, such as monitoring pregnancy-induced hypertension for pregnant women and brain health indicators for older adults. This increases user willingness and engagement. In this study, we developed a smart blood pressure monitor system called “BP-ExerGuide” to provide a personalized exercise safety guideline for older adults in the community's smart gym. In this scenario, all smart gym devices are equipped with RFID sensors, and members can use their personal RFID cards to start a blood pressure measurement. Only when the blood pressure value meets the safety criteria for exercise, can they use the exercise devices. If an individual's blood pressure is too high, BP-ExerGuide acts like a sports coach, advising seniors to take a rest first and then start exercising once they meet the safety guidelines. The system also integrates exercise logs and blood pressure logs into a smart healthcare platform for seniors to track their physiological changes. Overall, the BP-ExerGuide system provides older adults with a safer and more personalized exercise experience while monitoring their blood pressure levels. This system improves the willingness of seniors to exercise regularly and thus promotes better health and well-being.","PeriodicalId":334600,"journal":{"name":"2023 IEEE 5th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS)","volume":"89 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":"121442836","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 : 2023-06-02DOI: 10.1109/ECBIOS57802.2023.10218482
Salman Md Sultan, Mubina Tarannum Mollika, Sharvi Ahmed Fahim, Tahira Alam, A. F. Y. Mohammed, Tanzina Islam
Cell counting refers to any of several techniques used in life sciences, including medical diagnosis and treatment, to count or quantify cells. This is vital for various disease detection, treatment, and other medical research purposes. In general, one can manually count the number of cells in a digital image. However, the manual counting method takes a long time and labor and is costly. Hence, we require an automated cell counting system to boost efficiency, reduce labor expenses, and reduce mistake rates in order to overcome the limitations of human counting. Over the decade, various machine learning and deep learning methods have been proposed for counting cells automatically. However, a handful of algorithms are robust enough to determine the cell area with accuracy due to the tremendous density distribution of the cell in any image. In order to solve the issue of inaccurate approximation, we suggest an enhanced version of U-net. Implicit activation (IA) block is added to the extended U-net to extract more characteristics than regular U-net and improve the accuracy of cell counting. In terms of cell counting accuracy, the simulation results show that our suggested IA-based U-net (IA-U-net) is much better than the original U-net architecture.
{"title":"Automated Cell Counting System Using Improved Implicit Activation Based U-Net (IA-U-Net)","authors":"Salman Md Sultan, Mubina Tarannum Mollika, Sharvi Ahmed Fahim, Tahira Alam, A. F. Y. Mohammed, Tanzina Islam","doi":"10.1109/ECBIOS57802.2023.10218482","DOIUrl":"https://doi.org/10.1109/ECBIOS57802.2023.10218482","url":null,"abstract":"Cell counting refers to any of several techniques used in life sciences, including medical diagnosis and treatment, to count or quantify cells. This is vital for various disease detection, treatment, and other medical research purposes. In general, one can manually count the number of cells in a digital image. However, the manual counting method takes a long time and labor and is costly. Hence, we require an automated cell counting system to boost efficiency, reduce labor expenses, and reduce mistake rates in order to overcome the limitations of human counting. Over the decade, various machine learning and deep learning methods have been proposed for counting cells automatically. However, a handful of algorithms are robust enough to determine the cell area with accuracy due to the tremendous density distribution of the cell in any image. In order to solve the issue of inaccurate approximation, we suggest an enhanced version of U-net. Implicit activation (IA) block is added to the extended U-net to extract more characteristics than regular U-net and improve the accuracy of cell counting. In terms of cell counting accuracy, the simulation results show that our suggested IA-based U-net (IA-U-net) is much better than the original U-net architecture.","PeriodicalId":334600,"journal":{"name":"2023 IEEE 5th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS)","volume":"52 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":"121344785","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 : 2023-06-02DOI: 10.1109/ECBIOS57802.2023.10218530
Yu-Ting Hung, Bo Liu, Yang-Cheng Lin
The world has gradually entered an aging society, and many older people die of falls every year with sarcopenia being one of the main reasons for the elderly to fall. Thus, we present a novel approach with an intelligent rehabilitation knee brace developed by a Taiwanese start-up company (Ai Free) which collected 755 data from 55–70 age older patients in a local Tainan community in Taiwan. EMG signals and six-axis sensor values were extracted from the patients. According to the root mean square (RMS) value for muscle strength, the mean frequency (MNF) of muscle fatigue, and the Y-direction acceleration of the six-axis sensor were used as training data. In this study, a band-pass filtering technique was used to intercept and filter the sEMG and six-axis signals. Subsequently, a 10-second dataset was extracted at a sampling rate of 30 Hz for further analysis and processing. A total of 10,048 data sets were compiled and used as a database. We succeeded in training the decision tree (DT) at 93.56%, support vector machine (SVM) at 81.56%, random forest (RF) at 96.37%, K-nearest neighbor (KNN) at 89.65%, and Naive Bayes at 75.52% accuracy.
{"title":"AI Classification System on Sarcopenia for Elderly","authors":"Yu-Ting Hung, Bo Liu, Yang-Cheng Lin","doi":"10.1109/ECBIOS57802.2023.10218530","DOIUrl":"https://doi.org/10.1109/ECBIOS57802.2023.10218530","url":null,"abstract":"The world has gradually entered an aging society, and many older people die of falls every year with sarcopenia being one of the main reasons for the elderly to fall. Thus, we present a novel approach with an intelligent rehabilitation knee brace developed by a Taiwanese start-up company (Ai Free) which collected 755 data from 55–70 age older patients in a local Tainan community in Taiwan. EMG signals and six-axis sensor values were extracted from the patients. According to the root mean square (RMS) value for muscle strength, the mean frequency (MNF) of muscle fatigue, and the Y-direction acceleration of the six-axis sensor were used as training data. In this study, a band-pass filtering technique was used to intercept and filter the sEMG and six-axis signals. Subsequently, a 10-second dataset was extracted at a sampling rate of 30 Hz for further analysis and processing. A total of 10,048 data sets were compiled and used as a database. We succeeded in training the decision tree (DT) at 93.56%, support vector machine (SVM) at 81.56%, random forest (RF) at 96.37%, K-nearest neighbor (KNN) at 89.65%, and Naive Bayes at 75.52% accuracy.","PeriodicalId":334600,"journal":{"name":"2023 IEEE 5th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS)","volume":"330 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":"122838188","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 : 2023-06-02DOI: 10.1109/ECBIOS57802.2023.10218442
Kaye Antoinette V. Avila, Beatrice Corine R. Cabrera, Rosula S. J. Reyes, C. Oppus
Chronic pulmonary diseases remain a prevalent threat globally. With the emergence of COVID-19 and its transmission, there has been a rapid increase in the number of deaths due to respiratory illnesses. In this study, lung sound classifications were performed using a Thinklabs One digital stethoscope and through the utilization of Long Short-Term Memory (LSTM) in the classification of a person's lung auscultation record into either the normal, crackle, wheeze, or stridor categories with a 92.50% accuracy. Performance evaluation of this system was also done to cross-check for the validity of the algorithm modeled through Edge Impulse, which provided a 92.77% accuracy. The integration of the system adopted an Android-based mobile application as the pulmonary monitoring platform that records a person's general respiratory health data. The inputs from the mobile application were anonymously stored in a centralized database system correspondingly for post-processing and analysis.
{"title":"Development of Android-Based Pulmonary Monitoring System for Automated Lung Auscultation Using Long Short-Term Memory (LSTM) Network with Post-Processing from Edge Impulse","authors":"Kaye Antoinette V. Avila, Beatrice Corine R. Cabrera, Rosula S. J. Reyes, C. Oppus","doi":"10.1109/ECBIOS57802.2023.10218442","DOIUrl":"https://doi.org/10.1109/ECBIOS57802.2023.10218442","url":null,"abstract":"Chronic pulmonary diseases remain a prevalent threat globally. With the emergence of COVID-19 and its transmission, there has been a rapid increase in the number of deaths due to respiratory illnesses. In this study, lung sound classifications were performed using a Thinklabs One digital stethoscope and through the utilization of Long Short-Term Memory (LSTM) in the classification of a person's lung auscultation record into either the normal, crackle, wheeze, or stridor categories with a 92.50% accuracy. Performance evaluation of this system was also done to cross-check for the validity of the algorithm modeled through Edge Impulse, which provided a 92.77% accuracy. The integration of the system adopted an Android-based mobile application as the pulmonary monitoring platform that records a person's general respiratory health data. The inputs from the mobile application were anonymously stored in a centralized database system correspondingly for post-processing and analysis.","PeriodicalId":334600,"journal":{"name":"2023 IEEE 5th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS)","volume":"34 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":"132540431","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}
With the rapid development of science and technology, information security issues have been attracting more attention. According to statistics, tens of millions of computers around the world are infected by malicious software (Malware) every year, causing losses of up to several USD billion. Malware uses various methods to invade computer systems, including viruses, worms, Trojan horses, and others and exploit network vulnerabilities for intrusion. Most intrusion detection approaches employ behavioral analysis techniques to analyze malware threats with packet collection and filtering, feature engineering, and attribute comparison. These approaches are difficult to differentiate malicious traffic from legitimate traffic. Malware detection and classification are conducted with deep learning and graph neural networks (GNNs) to learn the characteristics of malware. In this study, a GNN-based model is proposed for malware detection and classification on a renewable energy management platform. It uses GNN to analyze malware with Cuckoo Sandbox malware records for malware detection and classification. To evaluate the effectiveness of the GNN-based model, the CIC-AndMal2017 dataset is used to examine its accuracy, precision, recall, and ROC curve. Experimental results show that the GNN-based model can reach better results.
{"title":"Graph Neural Network for Malware Detection and Classification on Renewable Energy Management Platform","authors":"Hsiao-Chung Lin, Ping Wang, Wen-Hui Lin, Yu-Hsiang Lin, Jia-Hong Chen","doi":"10.1109/ECBIOS57802.2023.10218478","DOIUrl":"https://doi.org/10.1109/ECBIOS57802.2023.10218478","url":null,"abstract":"With the rapid development of science and technology, information security issues have been attracting more attention. According to statistics, tens of millions of computers around the world are infected by malicious software (Malware) every year, causing losses of up to several USD billion. Malware uses various methods to invade computer systems, including viruses, worms, Trojan horses, and others and exploit network vulnerabilities for intrusion. Most intrusion detection approaches employ behavioral analysis techniques to analyze malware threats with packet collection and filtering, feature engineering, and attribute comparison. These approaches are difficult to differentiate malicious traffic from legitimate traffic. Malware detection and classification are conducted with deep learning and graph neural networks (GNNs) to learn the characteristics of malware. In this study, a GNN-based model is proposed for malware detection and classification on a renewable energy management platform. It uses GNN to analyze malware with Cuckoo Sandbox malware records for malware detection and classification. To evaluate the effectiveness of the GNN-based model, the CIC-AndMal2017 dataset is used to examine its accuracy, precision, recall, and ROC curve. Experimental results show that the GNN-based model can reach better results.","PeriodicalId":334600,"journal":{"name":"2023 IEEE 5th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS)","volume":"118 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":"128150910","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 : 2023-06-02DOI: 10.1109/ECBIOS57802.2023.10218598
P. Volf, W. Hsu, J. Hejda, Yi-Jia Lin, P. Kutílek, T. Sugiarto, Marek Sokol, Lýdie Leová, Hsiao-Liang Tsai, Ssu-Yu Chang, Li-Xin Tang
Nonlinear analysis methods enable the evaluation of signal chaos parameters such as variability, persistence, and complexity. In order to assess the differences between individual bilateral jump landing tasks and between IMU estimated angle and the gold standard, data obtained from Qualysis optical Mocap (Qualisys AB, Göteborg, Sweden) and Delsys inertial measurement systems (Delsys Inc., Boston, MA, USA) were used. 24 subjects were recruited in this study (6 females and 18 males, age: 22.6 ± 2.6 years old; height: 172.6 ± 10.3 cm; weight: 72.2 ± 16.02 kg). All of them perform double-leg landing tasks from a 30 cm height platform and a distance of half of their body height while wearing 8 IMU sensors on the sternum, L5, bilateral thigh, shank, and foot. In addition to IMU sensors on 8 locations, the subject wore retroreflective markers on the upper and lower parts for 3D motion capture data analysis. Multiscale Poincaré plots and recurrent quantification analysis were employed for subjective visual evaluation. Nonlinear analysis methods are promising practical applications, particularly with the use of recurrent quantification analysis and multiscale Poincaré plots for easier interpretation. In the case of multiscale Poincaré analysis, a difference between the estimated angle from the IMU and the gold standard in terms of confidence ellipse parameters was found. Additionally, the quantification parameters of these methods are suitable for integration with other evaluation methods in the time domain, frequency domain, and time-frequency domain.
{"title":"Use of Nonlinear Analysis Methods for Visual Evaluation and Graphical Representation of Bilateral Jump Landing Tasks","authors":"P. Volf, W. Hsu, J. Hejda, Yi-Jia Lin, P. Kutílek, T. Sugiarto, Marek Sokol, Lýdie Leová, Hsiao-Liang Tsai, Ssu-Yu Chang, Li-Xin Tang","doi":"10.1109/ECBIOS57802.2023.10218598","DOIUrl":"https://doi.org/10.1109/ECBIOS57802.2023.10218598","url":null,"abstract":"Nonlinear analysis methods enable the evaluation of signal chaos parameters such as variability, persistence, and complexity. In order to assess the differences between individual bilateral jump landing tasks and between IMU estimated angle and the gold standard, data obtained from Qualysis optical Mocap (Qualisys AB, Göteborg, Sweden) and Delsys inertial measurement systems (Delsys Inc., Boston, MA, USA) were used. 24 subjects were recruited in this study (6 females and 18 males, age: 22.6 ± 2.6 years old; height: 172.6 ± 10.3 cm; weight: 72.2 ± 16.02 kg). All of them perform double-leg landing tasks from a 30 cm height platform and a distance of half of their body height while wearing 8 IMU sensors on the sternum, L5, bilateral thigh, shank, and foot. In addition to IMU sensors on 8 locations, the subject wore retroreflective markers on the upper and lower parts for 3D motion capture data analysis. Multiscale Poincaré plots and recurrent quantification analysis were employed for subjective visual evaluation. Nonlinear analysis methods are promising practical applications, particularly with the use of recurrent quantification analysis and multiscale Poincaré plots for easier interpretation. In the case of multiscale Poincaré analysis, a difference between the estimated angle from the IMU and the gold standard in terms of confidence ellipse parameters was found. Additionally, the quantification parameters of these methods are suitable for integration with other evaluation methods in the time domain, frequency domain, and time-frequency domain.","PeriodicalId":334600,"journal":{"name":"2023 IEEE 5th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS)","volume":"2 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":"115001608","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 : 2023-06-02DOI: 10.1109/ECBIOS57802.2023.10218420
C. Chang, Chi-Hung Wei, Min-Tien Lin, S. Hwang
Water resources are inevitable for human survival but untreated wastewater harms the environment. Thus, ongoing monitoring of water quality is necessary to identify pollution sources and prevent further damage. For such monitoring, an IoT water quality monitoring system was developed using Arduino technology to collect and transmit data to MQTT Brokers and store it in a database. The data is presented on a monitoring webpage. Three machine learning methods (Random Forest, ANN, and LightGBM) were used for backend analysis and prediction. LightGBM was found to have the highest prediction accuracy for NH3, pH, ORP, and temperature. The research contributes to reducing the need for frequent and costly data collection by using an IoT system for real-time monitoring and employing machine learning predictions to compensate for missing data. This approach provides a more efficient and effective method for analyzing and predicting water quality.
{"title":"Machine Learning Approach to IoT- Based Water Quality Monitoring","authors":"C. Chang, Chi-Hung Wei, Min-Tien Lin, S. Hwang","doi":"10.1109/ECBIOS57802.2023.10218420","DOIUrl":"https://doi.org/10.1109/ECBIOS57802.2023.10218420","url":null,"abstract":"Water resources are inevitable for human survival but untreated wastewater harms the environment. Thus, ongoing monitoring of water quality is necessary to identify pollution sources and prevent further damage. For such monitoring, an IoT water quality monitoring system was developed using Arduino technology to collect and transmit data to MQTT Brokers and store it in a database. The data is presented on a monitoring webpage. Three machine learning methods (Random Forest, ANN, and LightGBM) were used for backend analysis and prediction. LightGBM was found to have the highest prediction accuracy for NH3, pH, ORP, and temperature. The research contributes to reducing the need for frequent and costly data collection by using an IoT system for real-time monitoring and employing machine learning predictions to compensate for missing data. This approach provides a more efficient and effective method for analyzing and predicting water quality.","PeriodicalId":334600,"journal":{"name":"2023 IEEE 5th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS)","volume":"1 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":"116295443","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 : 2023-06-02DOI: 10.1109/ECBIOS57802.2023.10218525
Qingyuan Wu, Teng Li, Dongchao Yang, Zuchang Ma, Yining Sun
By investigating whether brachial-ankle pulse wave velocity (baPWV), thoracic aortic calcification (TAC) and carotid atherosclerosis (CA) are predicted. We evaluate the abilities of baPWV to predict TAC and CA. 272 subjects without cardiovascular disease underwent baPWV, carotid ultrasound, chest computed tomography scan, clinical measurements, and lifestyle questionnaire. The 'main determinants of baPWV, TAC, and CA were analyzed by binary logistic regression. The cut-off values of baPWV predicting TAC and CA were obtained by the receiver-operating characteristic (ROC) curve. 185 subject data was used for the analysis. Arterial stiffness, CA and TAC were present as $54.1%(mathrm{n}=100), 77.8%(mathrm{n}=144)$, and $49.2% (mathrm{n}=91)$, respectively. Arterial stiffness was present in 63.2% (92/144) of the subjects with CA and not present in 80.5% (33/41) of the subjects without CA (P<0.001). Similarly, arterial stiffness was present in 78% (71 of 91) of the subjects with TAC and not present in 69.2% (65 of 94) of the subjects without TAC (P<0.001). Age and hypertension were the main factors. The cut-off values of baPWV to predict CA and TAC were respectively 1605 cm/s (95%CI: 0.715-0.863, $text{AUC}=0.789,mathrm{P} < 0.001)$ and 1675 cm/s (95%CI: 0.703-0.841, $text{AUC}=0.772, mathrm{P} < 0.001)$. The results suggested that CA and TAC were predicted by baPWV with the values of 1605 cm/s and 1675cm/s. It is important to use baPWV screening CA and TAC to prevent cardiovascular events in areas with limited medical resources.
{"title":"Thoracic Aortic Calcification and Carotid Atherosclerosis Prediction by Brachial-Ankle Pulse Wave Velocity","authors":"Qingyuan Wu, Teng Li, Dongchao Yang, Zuchang Ma, Yining Sun","doi":"10.1109/ECBIOS57802.2023.10218525","DOIUrl":"https://doi.org/10.1109/ECBIOS57802.2023.10218525","url":null,"abstract":"By investigating whether brachial-ankle pulse wave velocity (baPWV), thoracic aortic calcification (TAC) and carotid atherosclerosis (CA) are predicted. We evaluate the abilities of baPWV to predict TAC and CA. 272 subjects without cardiovascular disease underwent baPWV, carotid ultrasound, chest computed tomography scan, clinical measurements, and lifestyle questionnaire. The 'main determinants of baPWV, TAC, and CA were analyzed by binary logistic regression. The cut-off values of baPWV predicting TAC and CA were obtained by the receiver-operating characteristic (ROC) curve. 185 subject data was used for the analysis. Arterial stiffness, CA and TAC were present as $54.1%(mathrm{n}=100), 77.8%(mathrm{n}=144)$, and $49.2% (mathrm{n}=91)$, respectively. Arterial stiffness was present in 63.2% (92/144) of the subjects with CA and not present in 80.5% (33/41) of the subjects without CA (P<0.001). Similarly, arterial stiffness was present in 78% (71 of 91) of the subjects with TAC and not present in 69.2% (65 of 94) of the subjects without TAC (P<0.001). Age and hypertension were the main factors. The cut-off values of baPWV to predict CA and TAC were respectively 1605 cm/s (95%CI: 0.715-0.863, $text{AUC}=0.789,mathrm{P} < 0.001)$ and 1675 cm/s (95%CI: 0.703-0.841, $text{AUC}=0.772, mathrm{P} < 0.001)$. The results suggested that CA and TAC were predicted by baPWV with the values of 1605 cm/s and 1675cm/s. It is important to use baPWV screening CA and TAC to prevent cardiovascular events in areas with limited medical resources.","PeriodicalId":334600,"journal":{"name":"2023 IEEE 5th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS)","volume":"30 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":"128191575","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 : 2023-06-02DOI: 10.1109/ECBIOS57802.2023.10218520
Yukako Ijima, Kriengsak Masnok, Juan J. Pérez, A. González-Suárez, E. Berjano, Nobuo Watanabe
The cardiac muscle is elastic and deformable. Pushing a catheter in contact with the cardiac muscle surface to conduct focal energy-based ablative therapies, such as RF ablation, requires an adequate electrode-tissue contact surface to transfer the energy to the target site. In this regard, the relationship between the contact force (CF) and the resulting mechanical response is still unclear, in particular, the insertion depth (ID) and the diameter of the surface deformation. The objective of this study was to quantify these relationships using an ex vivo model and a computational model. A rigid bar with a 2.3 mm diameter blunt tip (mimicking a 7Fr standard ablation catheter) was placed at a perpendicular orientation on a fragment of the porcine heart. CF values ranged from 10 to 80g. We used ANSYS to build a Mooney-Rivlig model of 3 parameters based on hyperelastic material and to simulate the same conditions as in the experiments. The experimental results showed a strong linear correlation between CF and insertion depth ID ($mathrm{R}^{2}=0.97, mathrm{P} < 0.001$), from $0.7 pm 0.3$ mm at 10 gto $6.9 pm 0.1$ mm at 80 g. We also found a strong linear correlation between CF and minor and major diameters of the surface deformation assessed, from $4.0 pm 0.4$ mm at 20 g to $10.3 pm 0.0$ mm at 80 g ($mathrm{R}^{2}=0.96$), and from $6.4 pm 0.7$ mm at 20 g to $16.7 pm 0.1$ mm at 80 g ($mathrm{R}^{2}=0.95$), respectively. A descent gradient algorithm was used to minimize the mean square error (MSE) between the experimental and computational results of ID for the 10 values of CF. After trying different combinations for the3 parameters of the Mooney-Rivlig model, an optimal fit was achieved after 5 iterations, with an error of less than 0.55 mm for ID. This same mode was then used to predict the diameter of the surface deformation, obtaining an error of less than 0.65 mm. The results confirm that a Mooney-Rivlig model of three parameters based on hyperelastic material predicts the mechanical behavior of cardiac muscle reasonably well when subjected to CFs between 10 and 80 g. This information has important implications in cardiac ablative therapies based on focal energy application using a catheter tip.
{"title":"Relationship Between Mechanical Deformation and Contact Force Applied by Catheter Tip on Cardiac Muscle: Experimentation and Computer Modeling","authors":"Yukako Ijima, Kriengsak Masnok, Juan J. Pérez, A. González-Suárez, E. Berjano, Nobuo Watanabe","doi":"10.1109/ECBIOS57802.2023.10218520","DOIUrl":"https://doi.org/10.1109/ECBIOS57802.2023.10218520","url":null,"abstract":"The cardiac muscle is elastic and deformable. Pushing a catheter in contact with the cardiac muscle surface to conduct focal energy-based ablative therapies, such as RF ablation, requires an adequate electrode-tissue contact surface to transfer the energy to the target site. In this regard, the relationship between the contact force (CF) and the resulting mechanical response is still unclear, in particular, the insertion depth (ID) and the diameter of the surface deformation. The objective of this study was to quantify these relationships using an ex vivo model and a computational model. A rigid bar with a 2.3 mm diameter blunt tip (mimicking a 7Fr standard ablation catheter) was placed at a perpendicular orientation on a fragment of the porcine heart. CF values ranged from 10 to 80g. We used ANSYS to build a Mooney-Rivlig model of 3 parameters based on hyperelastic material and to simulate the same conditions as in the experiments. The experimental results showed a strong linear correlation between CF and insertion depth ID ($mathrm{R}^{2}=0.97, mathrm{P} < 0.001$), from $0.7 pm 0.3$ mm at 10 gto $6.9 pm 0.1$ mm at 80 g. We also found a strong linear correlation between CF and minor and major diameters of the surface deformation assessed, from $4.0 pm 0.4$ mm at 20 g to $10.3 pm 0.0$ mm at 80 g ($mathrm{R}^{2}=0.96$), and from $6.4 pm 0.7$ mm at 20 g to $16.7 pm 0.1$ mm at 80 g ($mathrm{R}^{2}=0.95$), respectively. A descent gradient algorithm was used to minimize the mean square error (MSE) between the experimental and computational results of ID for the 10 values of CF. After trying different combinations for the3 parameters of the Mooney-Rivlig model, an optimal fit was achieved after 5 iterations, with an error of less than 0.55 mm for ID. This same mode was then used to predict the diameter of the surface deformation, obtaining an error of less than 0.65 mm. The results confirm that a Mooney-Rivlig model of three parameters based on hyperelastic material predicts the mechanical behavior of cardiac muscle reasonably well when subjected to CFs between 10 and 80 g. This information has important implications in cardiac ablative therapies based on focal energy application using a catheter tip.","PeriodicalId":334600,"journal":{"name":"2023 IEEE 5th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS)","volume":"3 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131874212","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 : 2023-06-02DOI: 10.1109/ECBIOS57802.2023.10218671
C. Chen
After the COVID-19 epidemic, economic activities around the world are gradually recovering. At the same time, enterprises are thinking about organizational reconstruction in the post-epidemic era so enterprise diagnosis is very important. However, the priority in enterprise diagnosis is to discuss whether the activities are value-creating and benefit the corporate management activities of production, including HR, MKT, R&D, and FM of the company's daily operations. In Taiwan where AI, 5G, and other advanced technologies are being developed, it is urgent to use IT tools to digitize, network, and make application forms online. Therefore, enterprise diagnosis application forms were created using this study using Google Information Technology. The enterprise diagnosis application forms of large companies were analyzed using the content analysis method in the past three years. Two major categories and 31 items of sub-categories were provided for the application of enterprise diagnostic units to improve the efficiency of operation and the timeliness of diagnosis. The internal consistency reached 0.95, which confirmed the validity of the suty result.
{"title":"Study of Application of Google IT to Build Corporate Diagnostic Application Forms","authors":"C. Chen","doi":"10.1109/ECBIOS57802.2023.10218671","DOIUrl":"https://doi.org/10.1109/ECBIOS57802.2023.10218671","url":null,"abstract":"After the COVID-19 epidemic, economic activities around the world are gradually recovering. At the same time, enterprises are thinking about organizational reconstruction in the post-epidemic era so enterprise diagnosis is very important. However, the priority in enterprise diagnosis is to discuss whether the activities are value-creating and benefit the corporate management activities of production, including HR, MKT, R&D, and FM of the company's daily operations. In Taiwan where AI, 5G, and other advanced technologies are being developed, it is urgent to use IT tools to digitize, network, and make application forms online. Therefore, enterprise diagnosis application forms were created using this study using Google Information Technology. The enterprise diagnosis application forms of large companies were analyzed using the content analysis method in the past three years. Two major categories and 31 items of sub-categories were provided for the application of enterprise diagnostic units to improve the efficiency of operation and the timeliness of diagnosis. The internal consistency reached 0.95, which confirmed the validity of the suty result.","PeriodicalId":334600,"journal":{"name":"2023 IEEE 5th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS)","volume":"29 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":"129222898","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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