Pub Date : 2022-11-10DOI: 10.1109/BMEiCON56653.2022.10012080
Thandar Oo, P. Phukpattaranont
An electromyography (EMG) recognition system is essential for enabling a variety of applications. However, motion artifact contaminated with the EMG signal as it passes through or by various tissues may degrade the recognition performance. We present the algorithm for signal-to-noise ratio (SNR) estimation in EMG signals contaminated with motion artifact. Six features derived from the EMG signals are used as the neural network input: skewness (SKEW), kurtosis (KURT), mean absolute value (MAV), wavelength (WL), zero crossing (ZC), and mean frequency (MNF). The estimated SNR values are the neural network output. The best mean and standard deviations of the correlation coefficient (CC) between the actual and estimated SNR values are provided by the MNF $(0.9699 pm 0.0076)$. Future research may concentrate on determining SNR values using real EMG signals in their natural surroundings.
{"title":"SNR estimation in EMG signals contaminated with motion artifact","authors":"Thandar Oo, P. Phukpattaranont","doi":"10.1109/BMEiCON56653.2022.10012080","DOIUrl":"https://doi.org/10.1109/BMEiCON56653.2022.10012080","url":null,"abstract":"An electromyography (EMG) recognition system is essential for enabling a variety of applications. However, motion artifact contaminated with the EMG signal as it passes through or by various tissues may degrade the recognition performance. We present the algorithm for signal-to-noise ratio (SNR) estimation in EMG signals contaminated with motion artifact. Six features derived from the EMG signals are used as the neural network input: skewness (SKEW), kurtosis (KURT), mean absolute value (MAV), wavelength (WL), zero crossing (ZC), and mean frequency (MNF). The estimated SNR values are the neural network output. The best mean and standard deviations of the correlation coefficient (CC) between the actual and estimated SNR values are provided by the MNF $(0.9699 pm 0.0076)$. Future research may concentrate on determining SNR values using real EMG signals in their natural surroundings.","PeriodicalId":177401,"journal":{"name":"2022 14th Biomedical Engineering International Conference (BMEiCON)","volume":"17 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":"134235837","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/BMEiCON56653.2022.10011578
Teerachote Pitayataratorn, Wannisa Sukjee, C. Sangma, S. Visitsattapongse
An electrochemical potentiometric biosensor based on the molecularly imprinted polymer (MIP) technique has been fabricated for creatinine detection. The polymer consists of azobisisobutyronitrile (AIBN) as an initiator together with N, N’(1,2-Dihydroxyethelene) bisacrylamide (DHEBA) as a cross-linker and graphene oxide (GO) was prepared along with several functional monomers combination to compare each combination’s effectiveness in the detection of creatinine. An experiment was divided into imprint and non-imprint polymer for imprinting effectiveness evaluation. Creatinine anhydrous were used as template molecules for imprinting the polymer. The analyte was prepared in buffer solution (PBS) at a pH of 7.4 with a concentration range from 0.01 mg/dl to 100 mg/dl. N-hydroxy succinimide (NHS) and D-glucose were used for the specificity test. This study can conclude that polymers consisting of functional monomer methyl methacrylate (MMA) and acrylamide (AAM) with a 1:1 ratio show significant sensitivity to creatinine with the detection limit of 0.1 mg/dl along with remarkable selectivity to creatinine against other negative control compared to other conditions in this study and the sensor has a response linearly ranges from 0.01 to 100 mg/dl.
{"title":"Detection of Creatinine Using Molecularly Imprinted Polymers (MIP) Technique","authors":"Teerachote Pitayataratorn, Wannisa Sukjee, C. Sangma, S. Visitsattapongse","doi":"10.1109/BMEiCON56653.2022.10011578","DOIUrl":"https://doi.org/10.1109/BMEiCON56653.2022.10011578","url":null,"abstract":"An electrochemical potentiometric biosensor based on the molecularly imprinted polymer (MIP) technique has been fabricated for creatinine detection. The polymer consists of azobisisobutyronitrile (AIBN) as an initiator together with N, N’(1,2-Dihydroxyethelene) bisacrylamide (DHEBA) as a cross-linker and graphene oxide (GO) was prepared along with several functional monomers combination to compare each combination’s effectiveness in the detection of creatinine. An experiment was divided into imprint and non-imprint polymer for imprinting effectiveness evaluation. Creatinine anhydrous were used as template molecules for imprinting the polymer. The analyte was prepared in buffer solution (PBS) at a pH of 7.4 with a concentration range from 0.01 mg/dl to 100 mg/dl. N-hydroxy succinimide (NHS) and D-glucose were used for the specificity test. This study can conclude that polymers consisting of functional monomer methyl methacrylate (MMA) and acrylamide (AAM) with a 1:1 ratio show significant sensitivity to creatinine with the detection limit of 0.1 mg/dl along with remarkable selectivity to creatinine against other negative control compared to other conditions in this study and the sensor has a response linearly ranges from 0.01 to 100 mg/dl.","PeriodicalId":177401,"journal":{"name":"2022 14th Biomedical Engineering International Conference (BMEiCON)","volume":"32 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":"134255191","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/BMEiCON56653.2022.10012090
L. Szolga
The objective of this paper was to implement a portable, small, minimally invasive system for detecting blood mineral concentrations. The goal was to find a relationship between impedance changes in solutions with different iron and calcium concentrations to subsequently find a correlation between the impedance of a healthy patient’s blood and blood with a mineral deficiency or excess. The system was built around the AD5933 integrated circuit, which validated complex bioimpedance well. The prototype operates in the 10-100 kHz range and is suitable for a single frequency, multifrequency point, or spectroscopic measurements.
{"title":"Detection of Mineral (Ca/Fe) Concentration in Blood Using the Complex Bioimpedance","authors":"L. Szolga","doi":"10.1109/BMEiCON56653.2022.10012090","DOIUrl":"https://doi.org/10.1109/BMEiCON56653.2022.10012090","url":null,"abstract":"The objective of this paper was to implement a portable, small, minimally invasive system for detecting blood mineral concentrations. The goal was to find a relationship between impedance changes in solutions with different iron and calcium concentrations to subsequently find a correlation between the impedance of a healthy patient’s blood and blood with a mineral deficiency or excess. The system was built around the AD5933 integrated circuit, which validated complex bioimpedance well. The prototype operates in the 10-100 kHz range and is suitable for a single frequency, multifrequency point, or spectroscopic measurements.","PeriodicalId":177401,"journal":{"name":"2022 14th Biomedical Engineering International Conference (BMEiCON)","volume":"63 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":"123600972","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/BMEiCON56653.2022.10012094
Wannika Sonarra, Naphatsawan Vongmanee, Nutthanan Wanluk, C. Pintavirooj, S. Visitsattapongse
The Coronavirus disease (COVID-19) infection has become a pandemic, and this is the most critical problem that has occurred in Thailand and also expanded all over the world. As such, it is not astonishing to know that this virus has had a direct effect on hospitals with the delayed screening of patients because of the increasing number of daily cases and the shortage of medical personnel and restricted treatment space. Due to such restrictions, in this study, we used a clinical decision-making system with predictive algorithms. Predictive algorithms could potentially ease the strain on healthcare systems by identifying the diseases. Moreover, image classification is one interesting aspect of image processing. Convolutional neural network (CNN) is a widely used algorithm for image classification by separating the images of the COVID-19 disease, images with a lung infection, and normal images. To evaluate the predictive performance of our models, precision, F1-score, recall, receiver operating characteristic (ROC) curve (area under the ROC curve), and accuracy scores were used. It was observed that the predictive models trained on the laboratory findings could be used to predict the COVID-19 infection as well and could be helpful for medical experts to appropriately prioritize the resources. This could be employed to assist medical experts in validating their initial laboratory findings and could also be used for clinical prediction studies.
{"title":"Detection and Classification of COVID-19 Chest X-rays by the Deep Learning Technique","authors":"Wannika Sonarra, Naphatsawan Vongmanee, Nutthanan Wanluk, C. Pintavirooj, S. Visitsattapongse","doi":"10.1109/BMEiCON56653.2022.10012094","DOIUrl":"https://doi.org/10.1109/BMEiCON56653.2022.10012094","url":null,"abstract":"The Coronavirus disease (COVID-19) infection has become a pandemic, and this is the most critical problem that has occurred in Thailand and also expanded all over the world. As such, it is not astonishing to know that this virus has had a direct effect on hospitals with the delayed screening of patients because of the increasing number of daily cases and the shortage of medical personnel and restricted treatment space. Due to such restrictions, in this study, we used a clinical decision-making system with predictive algorithms. Predictive algorithms could potentially ease the strain on healthcare systems by identifying the diseases. Moreover, image classification is one interesting aspect of image processing. Convolutional neural network (CNN) is a widely used algorithm for image classification by separating the images of the COVID-19 disease, images with a lung infection, and normal images. To evaluate the predictive performance of our models, precision, F1-score, recall, receiver operating characteristic (ROC) curve (area under the ROC curve), and accuracy scores were used. It was observed that the predictive models trained on the laboratory findings could be used to predict the COVID-19 infection as well and could be helpful for medical experts to appropriately prioritize the resources. This could be employed to assist medical experts in validating their initial laboratory findings and could also be used for clinical prediction studies.","PeriodicalId":177401,"journal":{"name":"2022 14th Biomedical Engineering International Conference (BMEiCON)","volume":"177 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132193187","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/BMEiCON56653.2022.10012068
T. Igasaki, Aoi Takahi, Saori Nishikawa
We attempted to express the psychological quantity of executing workload through statistical analysis of indices extracted from an electroencephalogram (EEG) and a heart rate variability (HRV) score of subjects when they were asked to solve jigsaw puzzles. First, we conducted a regression analysis of the emotional score of the mood evaluation questionnaire after the completion of the workload and the indices of the EEG and HRV before and after the start and completion of the workload, and thereafter confirmed the strongest correlation before the completion of the workload. Next, we conducted a principal component analysis of the indices of the EEG and HRV before the completion of the workload, and thereafter confirmed that three principal components were obtained that correlated with “friendship,” “fatigue-inertia,” and “vigor-activity” in the mood evaluation questionnaire. Therefore, we demonstrated that the physiological quantities of the EEG and HRV indices could statistically express the psychological quantities of positive/negative emotions, even with small data.
{"title":"Statistical Representation of Emotions for Puzzle Workload using Electroencephalogram and Heart Rate Variability","authors":"T. Igasaki, Aoi Takahi, Saori Nishikawa","doi":"10.1109/BMEiCON56653.2022.10012068","DOIUrl":"https://doi.org/10.1109/BMEiCON56653.2022.10012068","url":null,"abstract":"We attempted to express the psychological quantity of executing workload through statistical analysis of indices extracted from an electroencephalogram (EEG) and a heart rate variability (HRV) score of subjects when they were asked to solve jigsaw puzzles. First, we conducted a regression analysis of the emotional score of the mood evaluation questionnaire after the completion of the workload and the indices of the EEG and HRV before and after the start and completion of the workload, and thereafter confirmed the strongest correlation before the completion of the workload. Next, we conducted a principal component analysis of the indices of the EEG and HRV before the completion of the workload, and thereafter confirmed that three principal components were obtained that correlated with “friendship,” “fatigue-inertia,” and “vigor-activity” in the mood evaluation questionnaire. Therefore, we demonstrated that the physiological quantities of the EEG and HRV indices could statistically express the psychological quantities of positive/negative emotions, even with small data.","PeriodicalId":177401,"journal":{"name":"2022 14th Biomedical Engineering International Conference (BMEiCON)","volume":"62 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":"129738924","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}
Methicillin-resistant Staphylococcus aureus (MRSA) presents a major threat to a broad range of healthcare and community associated infections. MRSA bacteria have rapidly developed resistance to multiple drugs throughout the antibiotic history. It is imperative to develop novel antimicrobial strategies to address the currently shrinking therapeutic options against MRSA. Herein, we developed a nanoliposome formulation of natural antimicrobial compound such as linolenic acid and evaluate its potential application for the treatment of MRSA infection as well as its safety. We found that Nano-liposomal linolenic acid (LipoLNA) was successfully synthesized. LipoLNA was able to inhibit MRSA growth and exhibited a good safety profile on normal mammalian cells.
{"title":"Nanoliposome of Linolenic acid for Methicillin-resistant Staphylococcus aureus treatment","authors":"Azmee Okriss, Julinthip Puttawong, Mingkwan Yingkajorn, Somyot Chirasatitsin, Soracha D. Thamphiwatana","doi":"10.1109/BMEiCON56653.2022.10012101","DOIUrl":"https://doi.org/10.1109/BMEiCON56653.2022.10012101","url":null,"abstract":"Methicillin-resistant Staphylococcus aureus (MRSA) presents a major threat to a broad range of healthcare and community associated infections. MRSA bacteria have rapidly developed resistance to multiple drugs throughout the antibiotic history. It is imperative to develop novel antimicrobial strategies to address the currently shrinking therapeutic options against MRSA. Herein, we developed a nanoliposome formulation of natural antimicrobial compound such as linolenic acid and evaluate its potential application for the treatment of MRSA infection as well as its safety. We found that Nano-liposomal linolenic acid (LipoLNA) was successfully synthesized. LipoLNA was able to inhibit MRSA growth and exhibited a good safety profile on normal mammalian cells.","PeriodicalId":177401,"journal":{"name":"2022 14th Biomedical Engineering International Conference (BMEiCON)","volume":"3 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":"130827483","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/BMEiCON56653.2022.10012109
Chanin Lochotinunt, Suejit Pechprasarn, T. Treebupachatsakul
Urinary tract diseases can occur in many organs of the urinary system, such as kidneys, urinary bladder, renal pelvis, ureters, and urethra. The most common disease in the urinary system is bladder inflammation, cystitis, and acute nephritis. In this research, the classification artificial intelligent model is applied to predict 2 symptoms of inflammation of the urinary bladder and acute nephritis of the renal pelvis from 6 parameters, including body temperature of patient, nausea, lumbar pain, urinary pushing, micturition pains, and burning of the urethra. Here, the principal components analysis or PCA are also applied to identify the critical parameters employed to train the machine learning model. Here, we propose to compare several machine learning classification models and show the proper model accurately diagnosing these two symptoms.
{"title":"Classification model for predicting inflammation of the urinary bladder and acute nephritis of the renal pelvis","authors":"Chanin Lochotinunt, Suejit Pechprasarn, T. Treebupachatsakul","doi":"10.1109/BMEiCON56653.2022.10012109","DOIUrl":"https://doi.org/10.1109/BMEiCON56653.2022.10012109","url":null,"abstract":"Urinary tract diseases can occur in many organs of the urinary system, such as kidneys, urinary bladder, renal pelvis, ureters, and urethra. The most common disease in the urinary system is bladder inflammation, cystitis, and acute nephritis. In this research, the classification artificial intelligent model is applied to predict 2 symptoms of inflammation of the urinary bladder and acute nephritis of the renal pelvis from 6 parameters, including body temperature of patient, nausea, lumbar pain, urinary pushing, micturition pains, and burning of the urethra. Here, the principal components analysis or PCA are also applied to identify the critical parameters employed to train the machine learning model. Here, we propose to compare several machine learning classification models and show the proper model accurately diagnosing these two symptoms.","PeriodicalId":177401,"journal":{"name":"2022 14th Biomedical Engineering International Conference (BMEiCON)","volume":"444 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":"126679396","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/BMEiCON56653.2022.10012093
M. Nouman, S. Chatpun
Diabetic foot complications tend to increase plantar pressure distribution increasing the chances of ulceration and re-ulceration. The knowledge about the factors associated with the causes of ulceration and effective offloading still lacks. Custom-made insole (CMI) is one of offloading techniques. This study aimed to investigate the plantar pressure distribution with shod gait and with CMI during stance phase of the gait cycle using a finite element (FE) approach. A subject-specific three-dimensional model without and with CMI was constructed. The ground reaction force from the experimental study was applied to the FE foot model during different phases of the gait cycle. The peak contact pressure was reduced with the use of CMI from all phases of the gait cycle. Moreover, frictional stress was reduced especially from the forefoot at midstance and terminal stance of the gait cycle. CMI redistributed the plantar pressure and tends to reduce the frictional stress during stance phase of the gait cycle. FE analysis provides better understanding of different parameters that are difficult to calculate with the conventional method. The FE analysis ease the investigation of various conditions including without and with CMI during different phases of the gait cycle.
{"title":"Finite element analysis of plantar pressure distribution in diabetic foot during stance phase of the gait cycle without and with custom-made insole","authors":"M. Nouman, S. Chatpun","doi":"10.1109/BMEiCON56653.2022.10012093","DOIUrl":"https://doi.org/10.1109/BMEiCON56653.2022.10012093","url":null,"abstract":"Diabetic foot complications tend to increase plantar pressure distribution increasing the chances of ulceration and re-ulceration. The knowledge about the factors associated with the causes of ulceration and effective offloading still lacks. Custom-made insole (CMI) is one of offloading techniques. This study aimed to investigate the plantar pressure distribution with shod gait and with CMI during stance phase of the gait cycle using a finite element (FE) approach. A subject-specific three-dimensional model without and with CMI was constructed. The ground reaction force from the experimental study was applied to the FE foot model during different phases of the gait cycle. The peak contact pressure was reduced with the use of CMI from all phases of the gait cycle. Moreover, frictional stress was reduced especially from the forefoot at midstance and terminal stance of the gait cycle. CMI redistributed the plantar pressure and tends to reduce the frictional stress during stance phase of the gait cycle. FE analysis provides better understanding of different parameters that are difficult to calculate with the conventional method. The FE analysis ease the investigation of various conditions including without and with CMI during different phases of the gait cycle.","PeriodicalId":177401,"journal":{"name":"2022 14th Biomedical Engineering International Conference (BMEiCON)","volume":"154 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":"126931370","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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