Tomoya Myojin, T. Noda, S. Kubo, Y. Nishioka, Tsuneyuki Higashino, T. Imamura
{"title":"Development of a New Method to Trace Patient Data Using the National Database in Japan","authors":"Tomoya Myojin, T. Noda, S. Kubo, Y. Nishioka, Tsuneyuki Higashino, T. Imamura","doi":"10.14326/abe.11.203","DOIUrl":"https://doi.org/10.14326/abe.11.203","url":null,"abstract":"","PeriodicalId":54017,"journal":{"name":"Advanced Biomedical Engineering","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66999665","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}
{"title":"Data Processing Model for Compliance with International Medical Research Data Processing Rules","authors":"Yuki Kuroda, Goshiro Yamamoto, T. Kuroda","doi":"10.14326/abe.11.48","DOIUrl":"https://doi.org/10.14326/abe.11.48","url":null,"abstract":"","PeriodicalId":54017,"journal":{"name":"Advanced Biomedical Engineering","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66999773","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}
Keisuke Shichitani, Sota Tanaka, Yuki Fujio, Shintaro Yamamoto, Jyuhyon Kim, K. Nakajima
{"title":"A Basic Study on Capacitive Sensor for Diaper Absorption Volume Estimate","authors":"Keisuke Shichitani, Sota Tanaka, Yuki Fujio, Shintaro Yamamoto, Jyuhyon Kim, K. Nakajima","doi":"10.14326/abe.11.136","DOIUrl":"https://doi.org/10.14326/abe.11.136","url":null,"abstract":"","PeriodicalId":54017,"journal":{"name":"Advanced Biomedical Engineering","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66998809","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}
Sho Ageno, Shuitsu Tanaka, Ryoya Okura, K. Iramina
Numerous studies have suggested that sleep spindle waves may play a role in the hippocam-pal-cortical transmission of information associated with memory enhancement. In previous research, the clustering coefficient increased significantly from wakefulness to sleep, indicating that the graph theory may be able to characterize brain network activity during sleep. However, previous studies have not investigated in de-tail the characteristics of the brain network in individual sleep stages; the brain network activity in the EEG at each sleep stage has not yet been clarified. In this study, we compared the characteristics of the network activity in various sleep stages by determining the functional connectivity from EEG in individual stages, construct-ing the networks and comparing the clustering coefficients and characteristic path lengths. We found a significant decrease in the characteristic path length in LowBeta band (13–15 Hz) from Stage 1 to later stages. However, there was no significant difference in the clustering coefficient. Our results are consistent with the concept that sleep spindles are related to memory consolidation. Therefore, the results suggest that the networks generated by the brain are more efficient in middle and deep sleep.
{"title":"Differences in EEG-based Brain Network Activity during Non-REM Sleep","authors":"Sho Ageno, Shuitsu Tanaka, Ryoya Okura, K. Iramina","doi":"10.14326/abe.11.109","DOIUrl":"https://doi.org/10.14326/abe.11.109","url":null,"abstract":"Numerous studies have suggested that sleep spindle waves may play a role in the hippocam-pal-cortical transmission of information associated with memory enhancement. In previous research, the clustering coefficient increased significantly from wakefulness to sleep, indicating that the graph theory may be able to characterize brain network activity during sleep. However, previous studies have not investigated in de-tail the characteristics of the brain network in individual sleep stages; the brain network activity in the EEG at each sleep stage has not yet been clarified. In this study, we compared the characteristics of the network activity in various sleep stages by determining the functional connectivity from EEG in individual stages, construct-ing the networks and comparing the clustering coefficients and characteristic path lengths. We found a significant decrease in the characteristic path length in LowBeta band (13–15 Hz) from Stage 1 to later stages. However, there was no significant difference in the clustering coefficient. Our results are consistent with the concept that sleep spindles are related to memory consolidation. Therefore, the results suggest that the networks generated by the brain are more efficient in middle and deep sleep.","PeriodicalId":54017,"journal":{"name":"Advanced Biomedical Engineering","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66999073","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}
Ryo Otsuki, Osamu Sugiyama, Yuki Mori, M. Miyake, S. Hiragi, Goshiro Yamamoto, L. Santos, Yuta Nakanishi, Yoshikatsu Hosoda, H. Tamura, S. Matsumoto, A. Tsujikawa, T. Kuroda
Designing a deep neural network model that integrates clinical images with other electronic medical records entails various preprocessing operations. Preprocessing of clinical images often requires trimming of parts of the lesions shown in the images, whereas preprocessing of other electronic medical records requires vectorization of these records; for example, patient age is often converted into a categorical vector of 10-year intervals. Although these preprocessing operations are critical to the performance of the classification model, there is no guarantee that the preprocessing step chosen is appropriate for model training. The ability to integrate these preprocessing operations into a deep neural network model and to train the model, including the pre-processing operations, can help design a multi-modal medical classification model. This study proposes integration layers of preprocessing, both for clinical images and electronic medical records, in deep neural network models. Preprocessing of clinical images is realized by a vision transformer layer that selectively adopts the parts of the images requiring attention. The preprocessing of other medical electrical records is performed by adopting full-connection layers and normalizing these layers. These proposed preprocessing-integrated layers were verified using a posttreatment visual acuity prediction task in ophthalmology as a case study. This prediction task requires clinical images as well as patient profile data corresponding to each patient ʼ s posttreatment logMAR visual acuity. The performance of a heuristically designed prediction model was compared with the performance of the prediction model that includes the proposed preprocessing integration layers. The mean square errors between predicted and correct results were 0.051 for the heuristic model and 0.054 for the proposed model. Experimental results showed that the proposed model utilizing preprocessing integration layers achieved nearly the same performance as the heuristically designed model.
{"title":"Integrating Preprocessing Operations into Deep Learning Model: Case Study of Posttreatment Visual Acuity Prediction","authors":"Ryo Otsuki, Osamu Sugiyama, Yuki Mori, M. Miyake, S. Hiragi, Goshiro Yamamoto, L. Santos, Yuta Nakanishi, Yoshikatsu Hosoda, H. Tamura, S. Matsumoto, A. Tsujikawa, T. Kuroda","doi":"10.14326/abe.11.16","DOIUrl":"https://doi.org/10.14326/abe.11.16","url":null,"abstract":"Designing a deep neural network model that integrates clinical images with other electronic medical records entails various preprocessing operations. Preprocessing of clinical images often requires trimming of parts of the lesions shown in the images, whereas preprocessing of other electronic medical records requires vectorization of these records; for example, patient age is often converted into a categorical vector of 10-year intervals. Although these preprocessing operations are critical to the performance of the classification model, there is no guarantee that the preprocessing step chosen is appropriate for model training. The ability to integrate these preprocessing operations into a deep neural network model and to train the model, including the pre-processing operations, can help design a multi-modal medical classification model. This study proposes integration layers of preprocessing, both for clinical images and electronic medical records, in deep neural network models. Preprocessing of clinical images is realized by a vision transformer layer that selectively adopts the parts of the images requiring attention. The preprocessing of other medical electrical records is performed by adopting full-connection layers and normalizing these layers. These proposed preprocessing-integrated layers were verified using a posttreatment visual acuity prediction task in ophthalmology as a case study. This prediction task requires clinical images as well as patient profile data corresponding to each patient ʼ s posttreatment logMAR visual acuity. The performance of a heuristically designed prediction model was compared with the performance of the prediction model that includes the proposed preprocessing integration layers. The mean square errors between predicted and correct results were 0.051 for the heuristic model and 0.054 for the proposed model. Experimental results showed that the proposed model utilizing preprocessing integration layers achieved nearly the same performance as the heuristically designed model.","PeriodicalId":54017,"journal":{"name":"Advanced Biomedical Engineering","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66999023","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}
Keizo Tominaga, Yanling Pei, Yuji Nishizawa, G. Obinata
{"title":"Model-based Analysis of Knee Joint Spasticity Based on Pendulum Testing of the Lower Extremities and Independent Component Analysis","authors":"Keizo Tominaga, Yanling Pei, Yuji Nishizawa, G. Obinata","doi":"10.14326/abe.11.218","DOIUrl":"https://doi.org/10.14326/abe.11.218","url":null,"abstract":"","PeriodicalId":54017,"journal":{"name":"Advanced Biomedical Engineering","volume":"20 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66999261","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}
K. Fukuyama, O. Sugiyama, Kazuo Chin, Susumu Satou, S. Matsumoto, Manabu Muto
{"title":"Identification of Respiratory Sounds Collected from Microphones Embedded in Mobile Phones","authors":"K. Fukuyama, O. Sugiyama, Kazuo Chin, Susumu Satou, S. Matsumoto, Manabu Muto","doi":"10.14326/abe.11.58","DOIUrl":"https://doi.org/10.14326/abe.11.58","url":null,"abstract":"","PeriodicalId":54017,"journal":{"name":"Advanced Biomedical Engineering","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66999847","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}
The creation of laser surgical systems with feedback, which allows performance of high-precision low-trauma operations, is the current trend of modern surgery. CO 2 lasers with pulse-periodic pumping which generate radiation at a wavelength of 10.6 µm and modulated at a frequency of 5–20 kHz are widely used in medical practice. This paper reports the possibility of creating feedback based on the autodyne effect that occurs in such surgical CO 2 lasers during laser dissection / evaporation of biotissues. The algorithm for extracting the information component (Doppler signal) of the autodyne signal for such CO 2 lasers has been developed. We showed that application of this algorithm permits extraction of the Doppler component spectrum in the autodyne signal that occurs when dissecting biotissues. Doppler signals were obtained when dissecting pig tissues in vitro, with a signal-to-noise ratio in the range of 5–15. The results obtained can be used in the development of smart laser surgical systems with feedback.
{"title":"Extraction of the Information Component of the Autodyne Signal in Pulsed-periodic CO2 Lasers for Doppler Diagnostics of the Surgical Process","authors":"A. Konovalov, V. A. Ul’yanov","doi":"10.14326/abe.10.129","DOIUrl":"https://doi.org/10.14326/abe.10.129","url":null,"abstract":"The creation of laser surgical systems with feedback, which allows performance of high-precision low-trauma operations, is the current trend of modern surgery. CO 2 lasers with pulse-periodic pumping which generate radiation at a wavelength of 10.6 µm and modulated at a frequency of 5–20 kHz are widely used in medical practice. This paper reports the possibility of creating feedback based on the autodyne effect that occurs in such surgical CO 2 lasers during laser dissection / evaporation of biotissues. The algorithm for extracting the information component (Doppler signal) of the autodyne signal for such CO 2 lasers has been developed. We showed that application of this algorithm permits extraction of the Doppler component spectrum in the autodyne signal that occurs when dissecting biotissues. Doppler signals were obtained when dissecting pig tissues in vitro, with a signal-to-noise ratio in the range of 5–15. The results obtained can be used in the development of smart laser surgical systems with feedback.","PeriodicalId":54017,"journal":{"name":"Advanced Biomedical Engineering","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66997972","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}
Takato Matsuzaki, Yutaka Suzuki, M. Tanimoto, Keisuke Masuyama, Masashi Osano, O. Sakata, M. Morisawa
To prevent aspiration pneumonia, a system for non-invasive and quantitative evaluation of the swallowing function is required. Therefore, we have previously proposed a method of using ultrasound videos to establish evaluation indicators of the swallowing function. The proposed method aims to automatically estimate the velocities of the esophageal wall region and the bolus region in the ultrasound video. In this method, estimation of the bolus region comprises two steps: estimating the esophageal region through which the bolus flows and extracting only the frame in which the bolus passes through the esophageal region. However, the step of extracting the frame in which the bolus passes is still performed manually. Therefore, to automate this step, the purpose of this study was to automatically determine the frame in which the bolus flowed into the screen. This method was tested five times on five healthy adult male subjects by recording a cervical ultrasound video while swallowing a bolus of water. We identified the different elements of the esophageal region in the video by first identifying the esophageal wall region with the maximally stable extremal regions (MSER). Then, we used the luminance histogram of each frame to establish the graph of the histogram similarity. This, in turn, was used to detect a change in the observed region, thus indicating the inflow of the bolus. Moreover, we could distinguish the change caused by the inflow from the change caused by the elevation of the esophageal wall using the velocity results obtained by optical flow estimation in the anterior esophageal wall region. Our results showed that in most cases, the proposed method was successful in recognizing the inflow of the bolus and distinguishing it from the elevation of the esophageal wall. Furthermore, an accuracy sufficient for estimation of the velocity of the bolus was achieved. optical flow, esophagus, bolus, maximally stable extremal regions.
{"title":"Bolus Inflow Detection Method by Ultrasound Video Processing for Evaluation of Swallowing","authors":"Takato Matsuzaki, Yutaka Suzuki, M. Tanimoto, Keisuke Masuyama, Masashi Osano, O. Sakata, M. Morisawa","doi":"10.14326/ABE.10.18","DOIUrl":"https://doi.org/10.14326/ABE.10.18","url":null,"abstract":"To prevent aspiration pneumonia, a system for non-invasive and quantitative evaluation of the swallowing function is required. Therefore, we have previously proposed a method of using ultrasound videos to establish evaluation indicators of the swallowing function. The proposed method aims to automatically estimate the velocities of the esophageal wall region and the bolus region in the ultrasound video. In this method, estimation of the bolus region comprises two steps: estimating the esophageal region through which the bolus flows and extracting only the frame in which the bolus passes through the esophageal region. However, the step of extracting the frame in which the bolus passes is still performed manually. Therefore, to automate this step, the purpose of this study was to automatically determine the frame in which the bolus flowed into the screen. This method was tested five times on five healthy adult male subjects by recording a cervical ultrasound video while swallowing a bolus of water. We identified the different elements of the esophageal region in the video by first identifying the esophageal wall region with the maximally stable extremal regions (MSER). Then, we used the luminance histogram of each frame to establish the graph of the histogram similarity. This, in turn, was used to detect a change in the observed region, thus indicating the inflow of the bolus. Moreover, we could distinguish the change caused by the inflow from the change caused by the elevation of the esophageal wall using the velocity results obtained by optical flow estimation in the anterior esophageal wall region. Our results showed that in most cases, the proposed method was successful in recognizing the inflow of the bolus and distinguishing it from the elevation of the esophageal wall. Furthermore, an accuracy sufficient for estimation of the velocity of the bolus was achieved. optical flow, esophagus, bolus, maximally stable extremal regions.","PeriodicalId":54017,"journal":{"name":"Advanced Biomedical Engineering","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66998206","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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