Georg Böttcher-Rebmann, Vera Lange, Viktor Schell, Jakob Cramer, Thomas Lenarz, Thomas S. Rau
Abstract Introduction: In cochlear implant surgery, the insertion of the electrode array (EA) into the cochlea is critical as its implementation can influence the preservation of residual hearing. Insertion tests are the primary method for basic research on parameters influencing the insertion and help to further improve the design of EAs and surgical techniques. With automated insertion devices close to clinical application, a consensus on optimal insertion parameters is needed, which requires reliable testing methods. Moreover, the limited availability of EAs needs to be considered. We propose a test setup that provides high repeatability and flexibility for various research questions. Methods: Design requirements for multiple types of experiments such as variability of insertion speed or trajectory as well as cochlear geometry guided the computer aided design of the test bench. Moreover, repeated insertions with the same EA were supposed to be possible. To evaluate its functionality, insertion tests into a 3D printed cochlea model were performed and recorded. Results: The central components of the test bench are a linear actuator driving the EA and a goniometer changing the orientation of the target - a cochlea model or a specimen. A force sensor can be mounted below the target to measure forces in its frame of reference. The experimental results show high reproducibility of insertion forces for recurring trajectories with a single EA. Conclusion: The test bench enables reproducible insertion tests with a high number of repetitions and reduced EA usage. This allows a more detailed investigation of broadly discussed influences on the insertion such as the insertion speed or trajectory as well as cochlear geometry and can thereby drive future EA development.
{"title":"Universal test bench for repeatable multiparametric cochlear implant insertion tests","authors":"Georg Böttcher-Rebmann, Vera Lange, Viktor Schell, Jakob Cramer, Thomas Lenarz, Thomas S. Rau","doi":"10.1515/cdbme-2023-1032","DOIUrl":"https://doi.org/10.1515/cdbme-2023-1032","url":null,"abstract":"Abstract Introduction: In cochlear implant surgery, the insertion of the electrode array (EA) into the cochlea is critical as its implementation can influence the preservation of residual hearing. Insertion tests are the primary method for basic research on parameters influencing the insertion and help to further improve the design of EAs and surgical techniques. With automated insertion devices close to clinical application, a consensus on optimal insertion parameters is needed, which requires reliable testing methods. Moreover, the limited availability of EAs needs to be considered. We propose a test setup that provides high repeatability and flexibility for various research questions. Methods: Design requirements for multiple types of experiments such as variability of insertion speed or trajectory as well as cochlear geometry guided the computer aided design of the test bench. Moreover, repeated insertions with the same EA were supposed to be possible. To evaluate its functionality, insertion tests into a 3D printed cochlea model were performed and recorded. Results: The central components of the test bench are a linear actuator driving the EA and a goniometer changing the orientation of the target - a cochlea model or a specimen. A force sensor can be mounted below the target to measure forces in its frame of reference. The experimental results show high reproducibility of insertion forces for recurring trajectories with a single EA. Conclusion: The test bench enables reproducible insertion tests with a high number of repetitions and reduced EA usage. This allows a more detailed investigation of broadly discussed influences on the insertion such as the insertion speed or trajectory as well as cochlear geometry and can thereby drive future EA development.","PeriodicalId":10739,"journal":{"name":"Current Directions in Biomedical Engineering","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135393631","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}
Fadil Al-Jaberi, Melanie Fachet, Christoph Hoeschen, Matthias Moeskes, Martin Skalej
Abstract Multimodal image registration is vital in Deep Brain Stimulation (DBS) surgery. DBS treats movement disorders by implanting a neurostimulator device in the brain to deliver electrical impulses. Image registration between computed tomography (CT) and cone beam computed tomography (CBCT) involves fusing images with a specific field of view (FOV) to visualize individual electrode contacts. This contains important information about the location of segmented contacts that can reduce the time required for electrode programming. We performed a semi-automated multimodal image registration with different FOV between CT and CBCT images due to the tiny structures of segmented electrode contacts that necessitate high accuracy in the registration. In this work, we present an optimization workflow for multi-modal image registration using a combination of different similarity metrics, interpolators, and optimizers. Optimization-based rigid image registration (RIR) is a common method for registering images. The selection of appropriate interpolators and similarity metrics is crucial for the success of this optimization-based image registration process.We rely on quantitative measures to compare their performance. Registration was performed on CT and CBCT images for DBS datasets with an image registration algorithm written in Python using the Insight Segmentation and Registration Toolkit (ITK). Several combinations of similarity metrics and interpolators were used, including mean square difference (MSD), mutual information (MI), correlation and nearest neighbors (NN), linear (LI), and B-Spline (SPI), respectively. The combination of a correlation as similarity metric, B-Spline interpolation, and GD optimizer performs the best in optimizing the 3D RIR algorithm, enhancing the visualization of segmented electrode contacts. Patients undergoing DBS therapy may ultimately benefit from this.
多模态图像配准在深部脑刺激(DBS)手术中至关重要。DBS通过在大脑中植入神经刺激装置来传递电脉冲，从而治疗运动障碍。计算机断层扫描(CT)和锥束计算机断层扫描(CBCT)之间的图像配准涉及融合具有特定视场(FOV)的图像以可视化单个电极接触。这包含了关于分段触点位置的重要信息，可以减少电极编程所需的时间。由于分割电极接触的微小结构需要高精度的配准，因此我们在CT和CBCT图像之间进行了不同视场的半自动多模态图像配准。在这项工作中，我们提出了一个多模态图像配准的优化工作流程，使用不同的相似性度量、插值器和优化器的组合。基于优化的刚性图像配准(RIR)是一种常用的图像配准方法。选择合适的插值器和相似度度量对于这种基于优化的图像配准过程的成功至关重要。我们依靠定量指标来比较他们的表现。使用Insight Segmentation and Registration Toolkit (ITK)用Python编写的图像配准算法对DBS数据集的CT和CBCT图像进行配准。使用了几种相似度量和插值器的组合，分别包括均方差(MSD)、互信息(MI)、相关和最近邻(NN)、线性(LI)和b样条(SPI)。结合相似度度量、b样条插值和GD优化器对三维RIR算法进行了优化，增强了分割电极接触的可视化效果。接受DBS治疗的患者可能最终从中受益。
{"title":"Optimization Techniques for Semi-Automated 3D Rigid Registration in Multimodal Image-Guided Deep Brain Stimulation","authors":"Fadil Al-Jaberi, Melanie Fachet, Christoph Hoeschen, Matthias Moeskes, Martin Skalej","doi":"10.1515/cdbme-2023-1089","DOIUrl":"https://doi.org/10.1515/cdbme-2023-1089","url":null,"abstract":"Abstract Multimodal image registration is vital in Deep Brain Stimulation (DBS) surgery. DBS treats movement disorders by implanting a neurostimulator device in the brain to deliver electrical impulses. Image registration between computed tomography (CT) and cone beam computed tomography (CBCT) involves fusing images with a specific field of view (FOV) to visualize individual electrode contacts. This contains important information about the location of segmented contacts that can reduce the time required for electrode programming. We performed a semi-automated multimodal image registration with different FOV between CT and CBCT images due to the tiny structures of segmented electrode contacts that necessitate high accuracy in the registration. In this work, we present an optimization workflow for multi-modal image registration using a combination of different similarity metrics, interpolators, and optimizers. Optimization-based rigid image registration (RIR) is a common method for registering images. The selection of appropriate interpolators and similarity metrics is crucial for the success of this optimization-based image registration process.We rely on quantitative measures to compare their performance. Registration was performed on CT and CBCT images for DBS datasets with an image registration algorithm written in Python using the Insight Segmentation and Registration Toolkit (ITK). Several combinations of similarity metrics and interpolators were used, including mean square difference (MSD), mutual information (MI), correlation and nearest neighbors (NN), linear (LI), and B-Spline (SPI), respectively. The combination of a correlation as similarity metric, B-Spline interpolation, and GD optimizer performs the best in optimizing the 3D RIR algorithm, enhancing the visualization of segmented electrode contacts. Patients undergoing DBS therapy may ultimately benefit from this.","PeriodicalId":10739,"journal":{"name":"Current Directions in Biomedical Engineering","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135393641","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}
Tamer Abdulbaki Alshirbaji, Nour Aldeen Jalal, Paul D. Docherty, Thomas Neumuth, Knut Moeller
Abstract Surgical tool detection is an important aspect for recognising surgical activities and understanding surgical workflow. Laparoscopic videos represent an information source that can be used for recognising surgical tools. However, manual labelling of tool incidence and location in such data is extremely time intensive. Therefore, weaklysupervised approaches have been developed to perform tool localisation. In this study, three types of spatial pooling methods were implemented to evaluate the influence of each method on the performance of weakly-supervised model. The best achieved performance was a mean average precision (mAP) of 94% for tool classification and a f1-score of 70% for tool localisation. Experimental results showed the importance of selecting an appropriate pooling function to enhance model performance.
{"title":"A comparative evaluation of spatial pooling methods for surgical tool detection","authors":"Tamer Abdulbaki Alshirbaji, Nour Aldeen Jalal, Paul D. Docherty, Thomas Neumuth, Knut Moeller","doi":"10.1515/cdbme-2023-1054","DOIUrl":"https://doi.org/10.1515/cdbme-2023-1054","url":null,"abstract":"Abstract Surgical tool detection is an important aspect for recognising surgical activities and understanding surgical workflow. Laparoscopic videos represent an information source that can be used for recognising surgical tools. However, manual labelling of tool incidence and location in such data is extremely time intensive. Therefore, weaklysupervised approaches have been developed to perform tool localisation. In this study, three types of spatial pooling methods were implemented to evaluate the influence of each method on the performance of weakly-supervised model. The best achieved performance was a mean average precision (mAP) of 94% for tool classification and a f1-score of 70% for tool localisation. Experimental results showed the importance of selecting an appropriate pooling function to enhance model performance.","PeriodicalId":10739,"journal":{"name":"Current Directions in Biomedical Engineering","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135393642","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}
Abstract In orthopaedic technology unilateral leg amputees are provided with prosthesis which are attached through a shaft to the remaining part of the leg. The prosthesis legs are built according to manufacturer’s specifications and get adjusted to the individual needs through motion analysis. If the prosthesis is not adjusted correctly to the individual needs of the user, the chance for development of arthritis in the remaining knee joint is greatly increased. The goal of this paper is to develop a procedure based on AEA to detect signs of arthritis in the remaining knee joint early and the base of a procedure for a future assessment of the quality of the care. The measurement system consists of the AEA - measurement device, a force plate to measure ground reaction forces, two strain gauges which are attached to handles on a test rig and two video cameras which record the movement from frontal and sagittal view. The amputee performs a standard movement in the test rig which consists of three knee bends in ten seconds while wearing the prosthesis legs. The test rig is provided so the amputee can support themselves using arms and hands as prosthesis’ support during the extension phase of the knee bend. The posture and load of the knee joint during the movement is analyzed with the video and force data. The ground reaction forces show the load distribution between the remaining leg and the prosthesis and its support during the movement. The hand forces give clues how well the amputee can balance and which arm is favored under load. By combining kinematic data and AEA the state of the load transfer zones can be evaluated. Thus, it’s possible to examine the remaining knee joint for defects and evaluate the quality of the prosthesis adjustment.
{"title":"Acoustic Emission Analysis (AEA) in unilateral leg amputees as a future quality tool in orthopaedic technology to evaluate the adjusted prosthesis in relation to gonarthritis","authors":"Joerg Subke, Adeline Keller, Hermawan Hermawan, Michael Schmeiler, Benedict Schneider, Hans-Joachim Schwalbe","doi":"10.1515/cdbme-2023-1179","DOIUrl":"https://doi.org/10.1515/cdbme-2023-1179","url":null,"abstract":"Abstract In orthopaedic technology unilateral leg amputees are provided with prosthesis which are attached through a shaft to the remaining part of the leg. The prosthesis legs are built according to manufacturer’s specifications and get adjusted to the individual needs through motion analysis. If the prosthesis is not adjusted correctly to the individual needs of the user, the chance for development of arthritis in the remaining knee joint is greatly increased. The goal of this paper is to develop a procedure based on AEA to detect signs of arthritis in the remaining knee joint early and the base of a procedure for a future assessment of the quality of the care. The measurement system consists of the AEA - measurement device, a force plate to measure ground reaction forces, two strain gauges which are attached to handles on a test rig and two video cameras which record the movement from frontal and sagittal view. The amputee performs a standard movement in the test rig which consists of three knee bends in ten seconds while wearing the prosthesis legs. The test rig is provided so the amputee can support themselves using arms and hands as prosthesis’ support during the extension phase of the knee bend. The posture and load of the knee joint during the movement is analyzed with the video and force data. The ground reaction forces show the load distribution between the remaining leg and the prosthesis and its support during the movement. The hand forces give clues how well the amputee can balance and which arm is favored under load. By combining kinematic data and AEA the state of the load transfer zones can be evaluated. Thus, it’s possible to examine the remaining knee joint for defects and evaluate the quality of the prosthesis adjustment.","PeriodicalId":10739,"journal":{"name":"Current Directions in Biomedical Engineering","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135393644","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}
Ali Pashazadeh, Forough Jafarian, Christoph Hoeschen, Kaveh Tanha
Abstract Positron emission tomography (PET) is a widely used imaging modality in nuclear medicine for a variety of applications. Amongst the methods used for the quantifying and interpretation of PET images, the standardized uptake value (SUV) is a widely-adopted semi-quantitative tool that supplements visual understanding with quantitative information. SUV is used in both clinical and preclinical practices to report the status of various normal organs and tumors under investigation using PET imaging. While the determination of SUVs is typically done manually, which can be tedious, artificial intelligence (AI) can be utilized to enhance the efficiency of the process. In this study, a U-Net-based approach was employed for semi-automated determination of SUV in FDG-PET scans of mice brains. First, a U-Net model was trained using 50 FDG-PET images of six mice to perform the automatic segmentation task. The trained model then delineated the brain of a mouse which was then processed by a short in-house code to extract data and calculate the SUV. The process was also replicated in a manual way for comparison purposes. The comparison of the results from the U-Net-based segmentation method and the conventional manual method at nine different time points revealed that there were errors of less than 4.5% in eight out of the nine-time points. Although our U-Net model’s performance needs improvement, adapting a well-trained AIbased approach for SUV determination, particularly in preclinical studies, can help reduce the workload of organ delineation and minimize associated errors, facilitating SUV determination.
{"title":"U-Net-based SUV calculation in FDG-PET imaging of mice brain for enhanced analysis","authors":"Ali Pashazadeh, Forough Jafarian, Christoph Hoeschen, Kaveh Tanha","doi":"10.1515/cdbme-2023-1073","DOIUrl":"https://doi.org/10.1515/cdbme-2023-1073","url":null,"abstract":"Abstract Positron emission tomography (PET) is a widely used imaging modality in nuclear medicine for a variety of applications. Amongst the methods used for the quantifying and interpretation of PET images, the standardized uptake value (SUV) is a widely-adopted semi-quantitative tool that supplements visual understanding with quantitative information. SUV is used in both clinical and preclinical practices to report the status of various normal organs and tumors under investigation using PET imaging. While the determination of SUVs is typically done manually, which can be tedious, artificial intelligence (AI) can be utilized to enhance the efficiency of the process. In this study, a U-Net-based approach was employed for semi-automated determination of SUV in FDG-PET scans of mice brains. First, a U-Net model was trained using 50 FDG-PET images of six mice to perform the automatic segmentation task. The trained model then delineated the brain of a mouse which was then processed by a short in-house code to extract data and calculate the SUV. The process was also replicated in a manual way for comparison purposes. The comparison of the results from the U-Net-based segmentation method and the conventional manual method at nine different time points revealed that there were errors of less than 4.5% in eight out of the nine-time points. Although our U-Net model’s performance needs improvement, adapting a well-trained AIbased approach for SUV determination, particularly in preclinical studies, can help reduce the workload of organ delineation and minimize associated errors, facilitating SUV determination.","PeriodicalId":10739,"journal":{"name":"Current Directions in Biomedical Engineering","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135393949","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}
Christoph Brandt-Wunderlich, Wolfram Schmidt, Alexander Thiesen, Klaus-Peter Schmitz, Falk Reinhardt, Michael Stiehm, Stefan Siewert
Abstract The radial strength of balloon expandable stents represents a key property for a successful recanalization of sclerotic blood vessels. This study focuses on the most commonly used method for investigation of radial strength and radial stiffness via segmented head test setup. A custom made user software was used for evaluation of the radial force curves considering requirements of international standards such as ASTM F3067. Contributing factors during measurement such as friction and test setup deformation as well as the single cycle and multi cycle approach were addressed and discussed.
{"title":"Test methods for evaluation of balloon expandable vascular stents – measurement of radial strength and stiffness","authors":"Christoph Brandt-Wunderlich, Wolfram Schmidt, Alexander Thiesen, Klaus-Peter Schmitz, Falk Reinhardt, Michael Stiehm, Stefan Siewert","doi":"10.1515/cdbme-2023-1104","DOIUrl":"https://doi.org/10.1515/cdbme-2023-1104","url":null,"abstract":"Abstract The radial strength of balloon expandable stents represents a key property for a successful recanalization of sclerotic blood vessels. This study focuses on the most commonly used method for investigation of radial strength and radial stiffness via segmented head test setup. A custom made user software was used for evaluation of the radial force curves considering requirements of international standards such as ASTM F3067. Contributing factors during measurement such as friction and test setup deformation as well as the single cycle and multi cycle approach were addressed and discussed.","PeriodicalId":10739,"journal":{"name":"Current Directions in Biomedical Engineering","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135394166","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}
Idoia Badiola, Chenglin Lyu, Arne Ferchland, Fabian Comes, Vladimir Blazek, Steffen Leonhardt, Markus Lueken
Abstract The body’s ability to balance oxygen supply and demand can be compromised in conditions such as shock, sepsis, and heart failure. Thus, measuring venous oxygen saturation (SvO2) simultaneously with the well-established peripheral arterial oxygen saturation can help in the clinical management of these conditions. Some authors have suggested a non-invasive SvO2 estimation method that acquires venous blood volume variations generated through the calf muscle pump using photoplethysmography (PPG): the Venous Muscle Pump Test (VMPT). However, the technique presents significant variability in the rhythm and speed of the foot dorsal flexions needed for the VMPT and cannot be performed on unconscious subjects and those with reduced mobility. This study proposes using functional electrical stimulation (FES) to stimulate the calf muscle and generate rhythmic and reproducible muscle contractions. A human proof-of-concept study was conducted with three healthy young male participants. The PPG signals achieved through the VMPT with conventionally active and FES-induced movements were compared. We found that FES-induced movement produced reproducible venous blood volume variations comparable to the ones induced by the active movement. However, it also leads to lower venous refilling time and lower muscle power. Although further individualized tuning of the stimulation parameters is needed to achieve more conclusive results, FES-induced movement proves to be a promising alternative to the conventional VMPT technique to measure venous oxygen saturation and assess venous insufficiency in specific clinical situations.
{"title":"Muscle stimulation for peripheral venous oxygen saturation estimation using photoplethysmography: a proof-of-concept","authors":"Idoia Badiola, Chenglin Lyu, Arne Ferchland, Fabian Comes, Vladimir Blazek, Steffen Leonhardt, Markus Lueken","doi":"10.1515/cdbme-2023-1037","DOIUrl":"https://doi.org/10.1515/cdbme-2023-1037","url":null,"abstract":"Abstract The body’s ability to balance oxygen supply and demand can be compromised in conditions such as shock, sepsis, and heart failure. Thus, measuring venous oxygen saturation (SvO2) simultaneously with the well-established peripheral arterial oxygen saturation can help in the clinical management of these conditions. Some authors have suggested a non-invasive SvO2 estimation method that acquires venous blood volume variations generated through the calf muscle pump using photoplethysmography (PPG): the Venous Muscle Pump Test (VMPT). However, the technique presents significant variability in the rhythm and speed of the foot dorsal flexions needed for the VMPT and cannot be performed on unconscious subjects and those with reduced mobility. This study proposes using functional electrical stimulation (FES) to stimulate the calf muscle and generate rhythmic and reproducible muscle contractions. A human proof-of-concept study was conducted with three healthy young male participants. The PPG signals achieved through the VMPT with conventionally active and FES-induced movements were compared. We found that FES-induced movement produced reproducible venous blood volume variations comparable to the ones induced by the active movement. However, it also leads to lower venous refilling time and lower muscle power. Although further individualized tuning of the stimulation parameters is needed to achieve more conclusive results, FES-induced movement proves to be a promising alternative to the conventional VMPT technique to measure venous oxygen saturation and assess venous insufficiency in specific clinical situations.","PeriodicalId":10739,"journal":{"name":"Current Directions in Biomedical Engineering","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135394428","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}
Hanna Frese, Andrea Kauth, Henner Koch, Jonas Ort, Sven Ingebrandt
Abstract A profound understanding of brain disorders is essential for developing more effective treatments, targeted therapies, and reliable diagnoses. Electrical in vitro long-term and repetitive measurements give insight to neural activity of more complex neural circuits and, thus, into malfunctioning caused by illness. Flexible microelectrode arrays (flexMEAs) enable the recording of neural activity with promising spatial and temporal resolution over longer durations. The present study aims to combine the long-term recording by flexMEAs with the cultivation of brain slices inside an incubator. An 8 μm-thin flexMEA with up to 256 electrodes with 30 μm in diameter and interelectrode spacing of 200 μm was fabricated. Characterization showed uniform and reproducible electronic characteristics. Coating with PEDOT:PSS by electropolymerization formed stable low-impedance spectra. The platform is ready for future experiments with brain slices inside an incubator.
{"title":"Fabrication and characterization of flexible microelectrode arrays for the long-term recording of mammalian brain slices","authors":"Hanna Frese, Andrea Kauth, Henner Koch, Jonas Ort, Sven Ingebrandt","doi":"10.1515/cdbme-2023-1094","DOIUrl":"https://doi.org/10.1515/cdbme-2023-1094","url":null,"abstract":"Abstract A profound understanding of brain disorders is essential for developing more effective treatments, targeted therapies, and reliable diagnoses. Electrical in vitro long-term and repetitive measurements give insight to neural activity of more complex neural circuits and, thus, into malfunctioning caused by illness. Flexible microelectrode arrays (flexMEAs) enable the recording of neural activity with promising spatial and temporal resolution over longer durations. The present study aims to combine the long-term recording by flexMEAs with the cultivation of brain slices inside an incubator. An 8 μm-thin flexMEA with up to 256 electrodes with 30 μm in diameter and interelectrode spacing of 200 μm was fabricated. Characterization showed uniform and reproducible electronic characteristics. Coating with PEDOT:PSS by electropolymerization formed stable low-impedance spectra. The platform is ready for future experiments with brain slices inside an incubator.","PeriodicalId":10739,"journal":{"name":"Current Directions in Biomedical Engineering","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135394434","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}
Abstract Real time physiological signals like Electrodermal Activity (EDA), measured using wearable devices, could be used for emotion recognition, thus enabling new paradigms of health monitoring, including mental health. This paper analyses EDA signals, a measure of sympathetic nervous system activity, which plays a significant role in emotional regulation. A public database was used to categorize emotions based on valence and arousal values. The Continuous Wavelet Transform (CWT) was used to obtain the scalograms of the signals. The scalograms were then fed into the deep learning models, VGG16 and ResNet50, to recognize emotions. VGG16 gives a Pearson Linear Correlation Coefficient (PLCC) value of 0.7199 for valence and 0.7593 for arousal, with Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) values of 0.7653 and 1.0284, respectively. ResNet50 performs better, with a PLCC value of 0.7763 for valence and 0.8207 for arousal, and lower MAE and RMSE values of 0.7158 and 0.8712, respectively. This study proposes an adapted ResNet50 model for emotion recognition which could be integrated into wearable devices as a prospective feature.
{"title":"Prediction of Arousal and Valence State from Electrodermal Activity using Wavelet based ResNet50 Model","authors":"Abhinav Anthiyur Aravindan, Sriram Kalyan Chappidi, Anirudh Thumma, Rohini Palanisamy","doi":"10.1515/cdbme-2023-1139","DOIUrl":"https://doi.org/10.1515/cdbme-2023-1139","url":null,"abstract":"Abstract Real time physiological signals like Electrodermal Activity (EDA), measured using wearable devices, could be used for emotion recognition, thus enabling new paradigms of health monitoring, including mental health. This paper analyses EDA signals, a measure of sympathetic nervous system activity, which plays a significant role in emotional regulation. A public database was used to categorize emotions based on valence and arousal values. The Continuous Wavelet Transform (CWT) was used to obtain the scalograms of the signals. The scalograms were then fed into the deep learning models, VGG16 and ResNet50, to recognize emotions. VGG16 gives a Pearson Linear Correlation Coefficient (PLCC) value of 0.7199 for valence and 0.7593 for arousal, with Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) values of 0.7653 and 1.0284, respectively. ResNet50 performs better, with a PLCC value of 0.7763 for valence and 0.8207 for arousal, and lower MAE and RMSE values of 0.7158 and 0.8712, respectively. This study proposes an adapted ResNet50 model for emotion recognition which could be integrated into wearable devices as a prospective feature.","PeriodicalId":10739,"journal":{"name":"Current Directions in Biomedical Engineering","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135394539","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}
Thomas Wittenberg, Dominik Seuß, Jaspar Pahl, Eike Binz, Yon-Dschun Ko
Abstract Background: Due to an aging society and changing health behaviors, emergency room crowding has become a major problem in western health care systems. Empowering patients and health care workers to assess necessary and relevant information is critical to streamline clinical workflows. Health kiosks, designed for services like self-check-in or (ideally contactless) health self-assessment may be instrumental in solving this issue. Based on the collected data, automated workflows such as flagging critical patients, inducing specific diagnostics or early symptomatic treatment could be implemented. Objective: Using an AI-supported software, which visually analyzes and categorizes facial expressions, the emotional status of hemato-oncologic patients in a German oncology outpatient clinic was examined. Additionally a survey was conducted, evaluating the acceptance of such a self-assessment solution. Results: 98% of the participants were not stressed by the real-time emotion analysis. However, the current set of registered emotion categories was found to be only partially sufficient to adequately describe the emotional status of the patients. More importantly, 88% of the participants found such a system to be meaningful. Also, 84% of the participants agreed that such a self-analysis could be of potential assistance. No relevant generation- or gender-specific differences could be observed. Discussion: Automated analysis of patients’ emotional status can be a first step toward a more comprehensive assessment of the respective health status. Patients, in particular the elderly, approve to the vision and development of such a system. Next steps are a further improvement of the AI-based emotion recognition software with respect to more emotional states as well as the definition, inclusion and ideally contactless acquisition of physical biomarkers (as e.g. heart rate or respiratory rate) determining physical and mental well-being.
{"title":"Machine-based emotion-assessment in waiting rooms – a feasibility and acceptance study","authors":"Thomas Wittenberg, Dominik Seuß, Jaspar Pahl, Eike Binz, Yon-Dschun Ko","doi":"10.1515/cdbme-2023-1029","DOIUrl":"https://doi.org/10.1515/cdbme-2023-1029","url":null,"abstract":"Abstract Background: Due to an aging society and changing health behaviors, emergency room crowding has become a major problem in western health care systems. Empowering patients and health care workers to assess necessary and relevant information is critical to streamline clinical workflows. Health kiosks, designed for services like self-check-in or (ideally contactless) health self-assessment may be instrumental in solving this issue. Based on the collected data, automated workflows such as flagging critical patients, inducing specific diagnostics or early symptomatic treatment could be implemented. Objective: Using an AI-supported software, which visually analyzes and categorizes facial expressions, the emotional status of hemato-oncologic patients in a German oncology outpatient clinic was examined. Additionally a survey was conducted, evaluating the acceptance of such a self-assessment solution. Results: 98% of the participants were not stressed by the real-time emotion analysis. However, the current set of registered emotion categories was found to be only partially sufficient to adequately describe the emotional status of the patients. More importantly, 88% of the participants found such a system to be meaningful. Also, 84% of the participants agreed that such a self-analysis could be of potential assistance. No relevant generation- or gender-specific differences could be observed. Discussion: Automated analysis of patients’ emotional status can be a first step toward a more comprehensive assessment of the respective health status. Patients, in particular the elderly, approve to the vision and development of such a system. Next steps are a further improvement of the AI-based emotion recognition software with respect to more emotional states as well as the definition, inclusion and ideally contactless acquisition of physical biomarkers (as e.g. heart rate or respiratory rate) determining physical and mental well-being.","PeriodicalId":10739,"journal":{"name":"Current Directions in Biomedical Engineering","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135394546","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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