Pub Date : 2022-11-10DOI: 10.1109/BMEiCON56653.2022.10012114
W. Chang, K. Liou, Yo-Tsen Liu, K. Wen
The Inertial Measurement Unit (IMU) has been widely used in precision movement analysis and evaluation and applied in the diagnosis and treatment of many diseases. Parkinson disease (PD) is the most common neurodegenerative movement disorder with rest tremor, bradykinesia, and rigidity as the cardinal motor manifestations. A novel algorithm system has been derived to detect all the motor examinations of the Unified Parkinson's Disease Rating Scale (UPDRS), of which the accuracy has been verified by high-speed camera system. This system includes three categories of detection parameters: the trajectory parameters, time-frequency parameters, angle parameters. Average accuracy for the detection with IMU can reach to 87%, 90% and 95%, respectively.With 17 patients’ trial tests, it’s observed that there do have certain differences of the movement parameters in between patients and age 20th youth controls. For 3.6 Pronation and Supination, the rotation speed of normal control can be twice of the patients and the deviation of the amplitude can reach to 45 degrees of patient in comparison to 5 degrees of normal control. Also, for low power and wearable requirements, this processing system have been designed with chip solution and be implemented with TSMC 0.18 mm CMOS process. The power consumption is 0.3713mW and the chip area is 4.2mm by 4.2mm which will be well suited to wearable applications.Our results showed that this processing system could precisely measure the temporal patterns of speed and amplitude decay of the movements, and successfully capture the severity and difference of bradykinesia and poor coordination of the patients of PD.
惯性测量单元(IMU)已广泛用于精密运动分析和评估，并在许多疾病的诊断和治疗中得到应用。帕金森病(PD)是最常见的神经退行性运动障碍，以静止性震颤、运动迟缓和僵硬为主要运动表现。本文推导了一种新的算法系统，用于检测统一帕金森病评定量表(UPDRS)的所有运动检查，并通过高速摄像系统验证了其准确性。该系统包括三大类检测参数:弹道参数、时频参数、角度参数。IMU检测的平均准确率分别可达87%、90%和95%。通过17例患者的试验测试，观察到患者的运动参数与20岁青年对照有一定的差异。对于3.6旋前和旋后，正常控制的旋转速度可以是患者的两倍，与正常控制的5度相比，振幅的偏差可以达到患者的45度。此外，为了满足低功耗和可穿戴的要求，该处理系统采用芯片解决方案设计，并采用台积电0.18 mm CMOS工艺实现。功耗为0.3713mW，芯片面积为4.2mm × 4.2mm，非常适合可穿戴应用。结果表明，该处理系统能够精确测量运动速度和振幅衰减的时间模式，并成功捕获PD患者运动迟缓和协调性差的严重程度和差异。
{"title":"Precise Motor Function Monitor for Parkinson Disease using Low Power and Wearable IMU Body Area Network","authors":"W. Chang, K. Liou, Yo-Tsen Liu, K. Wen","doi":"10.1109/BMEiCON56653.2022.10012114","DOIUrl":"https://doi.org/10.1109/BMEiCON56653.2022.10012114","url":null,"abstract":"The Inertial Measurement Unit (IMU) has been widely used in precision movement analysis and evaluation and applied in the diagnosis and treatment of many diseases. Parkinson disease (PD) is the most common neurodegenerative movement disorder with rest tremor, bradykinesia, and rigidity as the cardinal motor manifestations. A novel algorithm system has been derived to detect all the motor examinations of the Unified Parkinson's Disease Rating Scale (UPDRS), of which the accuracy has been verified by high-speed camera system. This system includes three categories of detection parameters: the trajectory parameters, time-frequency parameters, angle parameters. Average accuracy for the detection with IMU can reach to 87%, 90% and 95%, respectively.With 17 patients’ trial tests, it’s observed that there do have certain differences of the movement parameters in between patients and age 20th youth controls. For 3.6 Pronation and Supination, the rotation speed of normal control can be twice of the patients and the deviation of the amplitude can reach to 45 degrees of patient in comparison to 5 degrees of normal control. Also, for low power and wearable requirements, this processing system have been designed with chip solution and be implemented with TSMC 0.18 mm CMOS process. The power consumption is 0.3713mW and the chip area is 4.2mm by 4.2mm which will be well suited to wearable applications.Our results showed that this processing system could precisely measure the temporal patterns of speed and amplitude decay of the movements, and successfully capture the severity and difference of bradykinesia and poor coordination of the patients of PD.","PeriodicalId":177401,"journal":{"name":"2022 14th Biomedical Engineering International Conference (BMEiCON)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127093242","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 : 2022-11-10DOI: 10.1109/BMEiCON56653.2022.10012102
Phanomkorn Homsiang, T. Treebupachatsakul, Komsan Kiatrungrit, Suvit Poomrittigul
Due to many factors such as anxiety from contracting the disease and concern about the socioeconomic impacts, Thai people have accumulated stress and are at risk of depression. The diagnosis of depression can be primarily assessed by testing the assessments such as PHQ8, PHQ-9, and CES-D. The applied deep learning technology in medicine has received research interest and has been developing. In this research, we tried the classification of depression and non-depression audio datasets with the implementation of 4 model architectures: 1D CNN, 2D CNN, LSTM, and GRU. By converting wave audio format (WAV) of Daic-woz database to the Melfrequency cepstrum (MFC). We have done the training and evaluated the 4 model architectures and compared the results between non-augmented and augmented datasets. The highest accuracy was obtained from 1D CNN with a non-data augmentation of 95%, and a 2D CNN with a data augmentation of 75%. These results confirm that human voices can differentiate between depression and non-depression.
{"title":"Classification of Depression Audio Data by Deep Learning","authors":"Phanomkorn Homsiang, T. Treebupachatsakul, Komsan Kiatrungrit, Suvit Poomrittigul","doi":"10.1109/BMEiCON56653.2022.10012102","DOIUrl":"https://doi.org/10.1109/BMEiCON56653.2022.10012102","url":null,"abstract":"Due to many factors such as anxiety from contracting the disease and concern about the socioeconomic impacts, Thai people have accumulated stress and are at risk of depression. The diagnosis of depression can be primarily assessed by testing the assessments such as PHQ8, PHQ-9, and CES-D. The applied deep learning technology in medicine has received research interest and has been developing. In this research, we tried the classification of depression and non-depression audio datasets with the implementation of 4 model architectures: 1D CNN, 2D CNN, LSTM, and GRU. By converting wave audio format (WAV) of Daic-woz database to the Melfrequency cepstrum (MFC). We have done the training and evaluated the 4 model architectures and compared the results between non-augmented and augmented datasets. The highest accuracy was obtained from 1D CNN with a non-data augmentation of 95%, and a 2D CNN with a data augmentation of 75%. These results confirm that human voices can differentiate between depression and non-depression.","PeriodicalId":177401,"journal":{"name":"2022 14th Biomedical Engineering International Conference (BMEiCON)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133839020","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 : 2022-11-10DOI: 10.1109/BMEiCON56653.2022.10012117
K. Shabeeb Ahamed, K. Arunachalam
This paper presents a compact water-loaded coaxial balun configurations for targeted heat delivery for intracavitary hyperthermia treatment of cancer. Balun configurations of choke were analyzed using a 3$lambda$/8 monopole at 915 MHz. The surface current density and volume loss density characteristics were used to evaluate balun efficiency and were compared with a conventional monopole without balun. The antenna performance with and without the balun configurations was numerically assessed and compared in terms of specific absorption rate (SAR) and input power reflection coefficient. The numerical designs were experimentally validated in muscle mimicking liquid phantoms.
{"title":"A compact water loaded choke configurations for intracavitary microwave hyperthermia","authors":"K. Shabeeb Ahamed, K. Arunachalam","doi":"10.1109/BMEiCON56653.2022.10012117","DOIUrl":"https://doi.org/10.1109/BMEiCON56653.2022.10012117","url":null,"abstract":"This paper presents a compact water-loaded coaxial balun configurations for targeted heat delivery for intracavitary hyperthermia treatment of cancer. Balun configurations of choke were analyzed using a 3$lambda$/8 monopole at 915 MHz. The surface current density and volume loss density characteristics were used to evaluate balun efficiency and were compared with a conventional monopole without balun. The antenna performance with and without the balun configurations was numerically assessed and compared in terms of specific absorption rate (SAR) and input power reflection coefficient. The numerical designs were experimentally validated in muscle mimicking liquid phantoms.","PeriodicalId":177401,"journal":{"name":"2022 14th Biomedical Engineering International Conference (BMEiCON)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121387987","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 : 2022-11-10DOI: 10.1109/BMEiCON56653.2022.10012095
Navid Shaghaghi, Yash Kamdar, Ron Huang, A. Calle, Jaidev Mirchandani, Michael Castillo
Prediction of the spread of infectious diseases such as the seasonal Influenza is of utmost importance in the preparation for and mitigation of the severity of their impact. eVision (short for Epidemic Vision) is a machine learning time series forecaster under research and development by Santa Clara University’s EPIC (Ethical, Pragmatic, and Intelligent Computing) and BioInnovation & Design laboratories. Since eVision’s Long Short-Term Memory (LSTM) neural network makes use of Influenza related keywords in Google Trends as prediction features, it stands to reason that further feature selection from trending keywords relating to the flu in social media posts could enhance its prediction. After close examination, the only social media platforms that prove capable of supplying relevant data for time series analysis are the Twitter micro-blogging and Reddit social news aggregation and discussion forum platforms; as other social media platforms are either meant for sharing images and videos, or private multi-cast communication rather than public broadcasting and discourse. However, due to the burstiness of flu related Reddit posts, no useful feature for time series forecasting can be extracted from that platform; and Twitter, which has been examined for Influenza forecasting by numerous other researchers with successful results, poses a number of obstacles such as changes in policy as well as placing features behind expensive paywalls through the disabling of existing free APIs. Regardless however, the results obtained by the addition of Twitter data as another feature in eVision’s LSTM resulted in an almost negligible predictive improvement as delineated in this paper.
{"title":"Attempts at Enhancing eVision’s Influenza Forecasting Using Social Media","authors":"Navid Shaghaghi, Yash Kamdar, Ron Huang, A. Calle, Jaidev Mirchandani, Michael Castillo","doi":"10.1109/BMEiCON56653.2022.10012095","DOIUrl":"https://doi.org/10.1109/BMEiCON56653.2022.10012095","url":null,"abstract":"Prediction of the spread of infectious diseases such as the seasonal Influenza is of utmost importance in the preparation for and mitigation of the severity of their impact. eVision (short for Epidemic Vision) is a machine learning time series forecaster under research and development by Santa Clara University’s EPIC (Ethical, Pragmatic, and Intelligent Computing) and BioInnovation & Design laboratories. Since eVision’s Long Short-Term Memory (LSTM) neural network makes use of Influenza related keywords in Google Trends as prediction features, it stands to reason that further feature selection from trending keywords relating to the flu in social media posts could enhance its prediction. After close examination, the only social media platforms that prove capable of supplying relevant data for time series analysis are the Twitter micro-blogging and Reddit social news aggregation and discussion forum platforms; as other social media platforms are either meant for sharing images and videos, or private multi-cast communication rather than public broadcasting and discourse. However, due to the burstiness of flu related Reddit posts, no useful feature for time series forecasting can be extracted from that platform; and Twitter, which has been examined for Influenza forecasting by numerous other researchers with successful results, poses a number of obstacles such as changes in policy as well as placing features behind expensive paywalls through the disabling of existing free APIs. Regardless however, the results obtained by the addition of Twitter data as another feature in eVision’s LSTM resulted in an almost negligible predictive improvement as delineated in this paper.","PeriodicalId":177401,"journal":{"name":"2022 14th Biomedical Engineering International Conference (BMEiCON)","volume":"601 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123175827","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 : 2022-11-10DOI: 10.1109/BMEiCON56653.2022.10011580
A. Sanpanich, N. Komalawardhana, K. Petsarb
An automatic ventilator is used to treat patient who has abnormality in respiratory system or spontaneous ventilation is not enough to maintain blood oxygen and carbondioxide level in normal level. It functions to provide a fresh gas flow into patient lungs during an inspiration and remove exhaled gas from lungs during an expiration. Ventilator is known as the most complicated equipment in ICU due to a parameter setting, waveform understanding and variation of patient pathological variable under controlled ventilation which affect to ventilator operation. Then, new user always need time to practice and familiar with ventilator. In this paper, we present a simplified ventilator model by using an effective simulation tools in order to use as a simple tool in ventilation parameter study. The proposed ventilator simulation is basically based on volume control ventilation mode (VCV) with focusing on PEEP setting at expiratory module. We also simulated an operation of O2 concentrator in gas supply module which is designed by using parallel flow system of both air and oxygen. As a preliminary, all main ventilation waveforms $(mathrm{P}_{aw}, mathrm{V}_{T}$, $dot{V}, mathrm{T}_{P}$, PEEP, O2%) obtain from this modified model show an effective response and be able to use as a routine practice for new practitioner. For further study, another basic ventilation mode and setting as PCV, IMV even patient triggering setting will be added in the future.
{"title":"A ventilator circuit for volume control mode","authors":"A. Sanpanich, N. Komalawardhana, K. Petsarb","doi":"10.1109/BMEiCON56653.2022.10011580","DOIUrl":"https://doi.org/10.1109/BMEiCON56653.2022.10011580","url":null,"abstract":"An automatic ventilator is used to treat patient who has abnormality in respiratory system or spontaneous ventilation is not enough to maintain blood oxygen and carbondioxide level in normal level. It functions to provide a fresh gas flow into patient lungs during an inspiration and remove exhaled gas from lungs during an expiration. Ventilator is known as the most complicated equipment in ICU due to a parameter setting, waveform understanding and variation of patient pathological variable under controlled ventilation which affect to ventilator operation. Then, new user always need time to practice and familiar with ventilator. In this paper, we present a simplified ventilator model by using an effective simulation tools in order to use as a simple tool in ventilation parameter study. The proposed ventilator simulation is basically based on volume control ventilation mode (VCV) with focusing on PEEP setting at expiratory module. We also simulated an operation of O2 concentrator in gas supply module which is designed by using parallel flow system of both air and oxygen. As a preliminary, all main ventilation waveforms $(mathrm{P}_{aw}, mathrm{V}_{T}$, $dot{V}, mathrm{T}_{P}$, PEEP, O2%) obtain from this modified model show an effective response and be able to use as a routine practice for new practitioner. For further study, another basic ventilation mode and setting as PCV, IMV even patient triggering setting will be added in the future.","PeriodicalId":177401,"journal":{"name":"2022 14th Biomedical Engineering International Conference (BMEiCON)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125052810","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 : 2022-11-10DOI: 10.1109/BMEiCON56653.2022.10012092
Yafei Ou, P. Ambalathankandy, Ryunosuke Furuya, Seiya Kawada, Tamotsu Kamishima, M. Ikebe
Rheumatoid arthritis is a form of autoimmune disease characterized by synovitis that can ultimately cause joint deformities and impaired functioning. The cartilage destruction is one of the most important indicators for diagnosis and treatment of Rheumatoid arthritis, and it is radiographically manifested as joint space narrowing. In this study, we propose a joint location detection method and a sub-pixel accurate method for quantifying joint space narrowing progression with a joint angle correction. The proposed joint location detection method can detect the location of 14 joints from a given hand radiographic image, the error of 89.13% joints is less than 3 pixels (spatial resolution: 0.175 mm/pixel). In our previous works, we measured joint space narrowing progression between a baseline and its follow-up finger joint images by using partial image phase only correlation. We found that the inconsistency of joint angles may lead to characteristic mismatch and thus affect the accuracy of joint space narrowing quantification. In this work, we introduce rotation invariant phase only correlation in joint space narrowing quantification for joint angle correction. In our experiment, the improved quantification method can effectively manage the mismatch due to the inconsistency of joint angles.
{"title":"Joint space narrowing progression quantification with joint angle correction in rheumatoid arthritis","authors":"Yafei Ou, P. Ambalathankandy, Ryunosuke Furuya, Seiya Kawada, Tamotsu Kamishima, M. Ikebe","doi":"10.1109/BMEiCON56653.2022.10012092","DOIUrl":"https://doi.org/10.1109/BMEiCON56653.2022.10012092","url":null,"abstract":"Rheumatoid arthritis is a form of autoimmune disease characterized by synovitis that can ultimately cause joint deformities and impaired functioning. The cartilage destruction is one of the most important indicators for diagnosis and treatment of Rheumatoid arthritis, and it is radiographically manifested as joint space narrowing. In this study, we propose a joint location detection method and a sub-pixel accurate method for quantifying joint space narrowing progression with a joint angle correction. The proposed joint location detection method can detect the location of 14 joints from a given hand radiographic image, the error of 89.13% joints is less than 3 pixels (spatial resolution: 0.175 mm/pixel). In our previous works, we measured joint space narrowing progression between a baseline and its follow-up finger joint images by using partial image phase only correlation. We found that the inconsistency of joint angles may lead to characteristic mismatch and thus affect the accuracy of joint space narrowing quantification. In this work, we introduce rotation invariant phase only correlation in joint space narrowing quantification for joint angle correction. In our experiment, the improved quantification method can effectively manage the mismatch due to the inconsistency of joint angles.","PeriodicalId":177401,"journal":{"name":"2022 14th Biomedical Engineering International Conference (BMEiCON)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127389034","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 : 2022-11-10DOI: 10.1109/BMEiCON56653.2022.10012087
Atthasak Kiang-Ia, Sathid Rukkhong, T. Srivongsa, Kittipong Kasantikul, Chalinee Thanasupsombat, S. Aootaphao, S. Thongvigitmanee
Images data from the micro cone-beam computed tomography (CBCT) are acquired from a rotation of an object located between an X-ray generator and a flat panel detector; therefore, the rotational position of the motor is very important for image quality of 3D cross-section images. This study focuses on designing the position control of the stepping motor using the motion module, which enhances the control 4-axis motor’s efficiency to optimize and increase the accuracy of motor movement. We designed the stepping motor position control system to control the movement of the micro CBCT system to perform ten circular rotations in a single scanning process. A phantom was used to verify the rotational image accuracy by considering the image at the same position each round. Comparison of the motor movement with and without the motion module showed slight differences of projection images causing artifacts in cross-section images. Thus, the design of the rotation position control using the motion module circuit yielded good performance in terms of precision and rotational accuracy on the CBCT.
{"title":"A control system in a micro cone-beam CT machine","authors":"Atthasak Kiang-Ia, Sathid Rukkhong, T. Srivongsa, Kittipong Kasantikul, Chalinee Thanasupsombat, S. Aootaphao, S. Thongvigitmanee","doi":"10.1109/BMEiCON56653.2022.10012087","DOIUrl":"https://doi.org/10.1109/BMEiCON56653.2022.10012087","url":null,"abstract":"Images data from the micro cone-beam computed tomography (CBCT) are acquired from a rotation of an object located between an X-ray generator and a flat panel detector; therefore, the rotational position of the motor is very important for image quality of 3D cross-section images. This study focuses on designing the position control of the stepping motor using the motion module, which enhances the control 4-axis motor’s efficiency to optimize and increase the accuracy of motor movement. We designed the stepping motor position control system to control the movement of the micro CBCT system to perform ten circular rotations in a single scanning process. A phantom was used to verify the rotational image accuracy by considering the image at the same position each round. Comparison of the motor movement with and without the motion module showed slight differences of projection images causing artifacts in cross-section images. Thus, the design of the rotation position control using the motion module circuit yielded good performance in terms of precision and rotational accuracy on the CBCT.","PeriodicalId":177401,"journal":{"name":"2022 14th Biomedical Engineering International Conference (BMEiCON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130575840","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 : 2022-11-10DOI: 10.1109/BMEiCON56653.2022.10012088
E. Martinez-Ríos, L. Montesinos, Mariel Alfaro
Hypertension is a health issue whose late diagnosis could lead to renal, cerebral, and cardiac events. In this work, it is proposed to use the wavelet scattering transform (WST) as a feature extraction technique applying classical machine learning techniques using photoplethysmography (PPG) signals as input to detect elevated blood pressure and compare its performance with transfer learning applied through fine-tuned convolutional neural networks. The results show that the features obtained by applying the WST and training a logistic regression and support vector machine produced similar results in terms of accuracy compared to fine-tuned convolutional neural networks, with the advantage that the WST could be used to generate a white-box model, which is better suited for a potential medical diagnosis application.
{"title":"A Comparison Between Wavelet Scattering Transform and Transfer Learning for Elevated Blood Pressure Detection","authors":"E. Martinez-Ríos, L. Montesinos, Mariel Alfaro","doi":"10.1109/BMEiCON56653.2022.10012088","DOIUrl":"https://doi.org/10.1109/BMEiCON56653.2022.10012088","url":null,"abstract":"Hypertension is a health issue whose late diagnosis could lead to renal, cerebral, and cardiac events. In this work, it is proposed to use the wavelet scattering transform (WST) as a feature extraction technique applying classical machine learning techniques using photoplethysmography (PPG) signals as input to detect elevated blood pressure and compare its performance with transfer learning applied through fine-tuned convolutional neural networks. The results show that the features obtained by applying the WST and training a logistic regression and support vector machine produced similar results in terms of accuracy compared to fine-tuned convolutional neural networks, with the advantage that the WST could be used to generate a white-box model, which is better suited for a potential medical diagnosis application.","PeriodicalId":177401,"journal":{"name":"2022 14th Biomedical Engineering International Conference (BMEiCON)","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133108587","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 : 2022-11-10DOI: 10.1109/BMEiCON56653.2022.10012110
K. Chessadangkul, N. Damrongplasit, S. Morakul, T. Tharasanit, A. Pimpin
The cultured meat is the solution to reduce resources using in a traditional meat production. It helps produce meat without killing livestock and decrease residue products. The method could also integrate with scaffold’s material which does not derive from animal products. This study aims to investigate the effects of carboxymethyl cellulose (CMC) and apple powder on printability and cytotoxicity as additives in alginate/agarose-based hydrogel. 3D structures of them were printed to find a proper printing condition. From our experiments, the structure could maintain their shapes and uniform line sizes for carboxylmethyl cellulose, but not for apple powder at the 2% w/v alginate and 0.8% w/v agarose. However, the combination of them could be printed well. In parallel, 293FT cells were cultured with hydrogel drop to test cytotoxicity. It showed that the hydrogel with both additives does not harm cells during 8-day culturing.
{"title":"Printability and cytotoxicity of alginate/agarose hydrogel with carboxylmethyl cellulose and apple powder","authors":"K. Chessadangkul, N. Damrongplasit, S. Morakul, T. Tharasanit, A. Pimpin","doi":"10.1109/BMEiCON56653.2022.10012110","DOIUrl":"https://doi.org/10.1109/BMEiCON56653.2022.10012110","url":null,"abstract":"The cultured meat is the solution to reduce resources using in a traditional meat production. It helps produce meat without killing livestock and decrease residue products. The method could also integrate with scaffold’s material which does not derive from animal products. This study aims to investigate the effects of carboxymethyl cellulose (CMC) and apple powder on printability and cytotoxicity as additives in alginate/agarose-based hydrogel. 3D structures of them were printed to find a proper printing condition. From our experiments, the structure could maintain their shapes and uniform line sizes for carboxylmethyl cellulose, but not for apple powder at the 2% w/v alginate and 0.8% w/v agarose. However, the combination of them could be printed well. In parallel, 293FT cells were cultured with hydrogel drop to test cytotoxicity. It showed that the hydrogel with both additives does not harm cells during 8-day culturing.","PeriodicalId":177401,"journal":{"name":"2022 14th Biomedical Engineering International Conference (BMEiCON)","volume":"226 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116763688","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 : 2022-11-10DOI: 10.1109/BMEiCON56653.2022.10012112
Yu-Ju Su, K. Wen, M. Cheng, Chen-Nen Chang
In this work, we proposed a system that supplies real-time epilepsy detection system (RED system) with a single inertial measurement unit (IMU) and a low power processing unit. Since the accuracy can reach 99.81%, the specificity can reach 99.81%, and false positive rate of 0.19%, it not only ensures reliability but also provides a quantification analysis for diagnosis. The proposed method has been verified by 60 patients and the processing unit has been implemented into a chip using TSMC 0.18 μm process, which proves the feasibility of mobile device to the RED system.
{"title":"Real-Time Epilepsy Detection with IMU and Low Power Processor Design","authors":"Yu-Ju Su, K. Wen, M. Cheng, Chen-Nen Chang","doi":"10.1109/BMEiCON56653.2022.10012112","DOIUrl":"https://doi.org/10.1109/BMEiCON56653.2022.10012112","url":null,"abstract":"In this work, we proposed a system that supplies real-time epilepsy detection system (RED system) with a single inertial measurement unit (IMU) and a low power processing unit. Since the accuracy can reach 99.81%, the specificity can reach 99.81%, and false positive rate of 0.19%, it not only ensures reliability but also provides a quantification analysis for diagnosis. The proposed method has been verified by 60 patients and the processing unit has been implemented into a chip using TSMC 0.18 μm process, which proves the feasibility of mobile device to the RED system.","PeriodicalId":177401,"journal":{"name":"2022 14th Biomedical Engineering International Conference (BMEiCON)","volume":"48 16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129944609","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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