Pub Date : 2024-09-05DOI: 10.1109/OJEMB.2024.3454958
Ting-Ruen Wei;Michele Hell;Aren Vierra;Ran Pang;Young Kang;Mahesh Patel;Yuling Yan
Goal: This study aims to enhance AI-assisted breast cancer diagnosis through dual-view sonography using a data-centric approach. Methods: We customize a DenseNet-based model on our exclusive dual-view breast ultrasound dataset to enhance the model's ability to differentiate between malignant and benign masses. Various assembly strategies are designed to integrate the dual views into the model input, contrasting with the use of single views alone, with a goal to maximize performance. Subsequently, we compare the model against the radiologist and quantify the improvement in key performance metrics. We further assess how the radiologist's diagnostic accuracy is enhanced with the assistance of the model. Results: Our experiments consistently found that optimal outcomes were achieved by using a channel-wise stacking approach incorporating both views, with one duplicated as the third channel. This configuration resulted in remarkable model performance with an area underthe receiver operating characteristic curve (AUC) of 0.9754, specificity of 0.96, and sensitivity of 0.9263, outperforming the radiologist by 50% in specificity. With the model's guidance, the radiologist's performance improved across key metrics: accuracy by 17%, precision by 26%, and specificity by 29%. Conclusions: Our customized model, withan optimal configuration for dual-view image input, surpassed both radiologists and existing model results in the literature. Integrating the model as a standalone tool or assistive aid for radiologists can greatly enhance specificity, reduce false positives, thereby minimizing unnecessary biopsies and alleviating radiologists' workload.
{"title":"Breast Cancer Detection on Dual-View Sonography via Data-Centric Deep Learning","authors":"Ting-Ruen Wei;Michele Hell;Aren Vierra;Ran Pang;Young Kang;Mahesh Patel;Yuling Yan","doi":"10.1109/OJEMB.2024.3454958","DOIUrl":"10.1109/OJEMB.2024.3454958","url":null,"abstract":"<italic>Goal:</i>\u0000 This study aims to enhance AI-assisted breast cancer diagnosis through dual-view sonography using a data-centric approach. \u0000<italic>Methods:</i>\u0000 We customize a DenseNet-based model on our exclusive dual-view breast ultrasound dataset to enhance the model's ability to differentiate between malignant and benign masses. Various assembly strategies are designed to integrate the dual views into the model input, contrasting with the use of single views alone, with a goal to maximize performance. Subsequently, we compare the model against the radiologist and quantify the improvement in key performance metrics. We further assess how the radiologist's diagnostic accuracy is enhanced with the assistance of the model. \u0000<italic>Results:</i>\u0000 Our experiments consistently found that optimal outcomes were achieved by using a channel-wise stacking approach incorporating both views, with one duplicated as the third channel. This configuration resulted in remarkable model performance with an area underthe receiver operating characteristic curve (AUC) of 0.9754, specificity of 0.96, and sensitivity of 0.9263, outperforming the radiologist by 50% in specificity. With the model's guidance, the radiologist's performance improved across key metrics: accuracy by 17%, precision by 26%, and specificity by 29%. \u0000<italic>Conclusions:</i>\u0000 Our customized model, withan optimal configuration for dual-view image input, surpassed both radiologists and existing model results in the literature. Integrating the model as a standalone tool or assistive aid for radiologists can greatly enhance specificity, reduce false positives, thereby minimizing unnecessary biopsies and alleviating radiologists' workload.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"100-106"},"PeriodicalIF":2.7,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10666269","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142200299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Goal: To accurately detect infections in Diabetic Foot Ulcers (DFUs) using photographs taken at the Point of Care (POC). Achieving high performance is critical for preventing complications and amputations, as well as minimizing unnecessary emergency department visits and referrals. Methods: This paper proposes the Guided Conditional Diffusion Classifier (ConDiff). This novel deep-learning framework combines guided image synthesis with a denoising diffusion model and distance-based classification. The process involves (1) generating guided conditional synthetic images by injecting Gaussian noise to a guide (input) image, followed by denoising the noise-perturbed image through a reverse diffusion process, conditioned on infection status and (2) classifying infections based on the minimum Euclidean distance between synthesized images and the original guide image in embedding space. Results: ConDiff demonstrated superior performance with an average accuracy of 81% that outperformed state-of-the-art (SOTA) models by at least 3%. It also achieved the highest sensitivity of 85.4%, which is crucial in clinical domains while significantly improving specificity to 74.4%, surpassing the best SOTA model. Conclusions: ConDiff not only improves the diagnosis of DFU infections but also pioneers the use of generative discriminative models for detailed medical image analysis, offering a promising approach for improving patient outcomes.
{"title":"Guided Conditional Diffusion Classifier (ConDiff) for Enhanced Prediction of Infection in Diabetic Foot Ulcers","authors":"Palawat Busaranuvong;Emmanuel Agu;Deepak Kumar;Shefalika Gautam;Reza Saadati Fard;Bengisu Tulu;Diane Strong","doi":"10.1109/OJEMB.2024.3453060","DOIUrl":"10.1109/OJEMB.2024.3453060","url":null,"abstract":"<italic>Goal:</i>\u0000 To accurately detect infections in Diabetic Foot Ulcers (DFUs) using photographs taken at the Point of Care (POC). Achieving high performance is critical for preventing complications and amputations, as well as minimizing unnecessary emergency department visits and referrals. \u0000<italic>Methods:</i>\u0000 This paper proposes the Guided Conditional Diffusion Classifier (ConDiff). This novel deep-learning framework combines guided image synthesis with a denoising diffusion model and distance-based classification. The process involves (1) generating guided conditional synthetic images by injecting Gaussian noise to a guide (input) image, followed by denoising the noise-perturbed image through a reverse diffusion process, conditioned on infection status and (2) classifying infections based on the minimum Euclidean distance between synthesized images and the original guide image in embedding space. \u0000<italic>Results:</i>\u0000 ConDiff demonstrated superior performance with an average accuracy of 81% that outperformed state-of-the-art (SOTA) models by at least 3%. It also achieved the highest sensitivity of 85.4%, which is crucial in clinical domains while significantly improving specificity to 74.4%, surpassing the best SOTA model. \u0000<italic>Conclusions:</i>\u0000 ConDiff not only improves the diagnosis of DFU infections but also pioneers the use of generative discriminative models for detailed medical image analysis, offering a promising approach for improving patient outcomes.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"20-27"},"PeriodicalIF":2.7,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10663215","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142225757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Esophageal cancer (EC) poses a significant health concern, particularly among the elderly, warranting effective treatment strategies. While immunotherapy holds promise in activating the immune response against tumors, its specific impact and associated reactions in EC patients remain uncertain. Precise prognosis prediction becomes crucial for guiding appropriate interventions. This study, based on data from the First Affiliated Hospital of Xiamen University (January 2017 to May 2021), focuses on 113 EC patients undergoing immunotherapy. The primary objectives are to elucidate the effectiveness of immunotherapy in EC treatment and to introduce a stacking ensemble learning method for predicting the survival of EC patients who have undergone immunotherapy, in the context of small sample sizes, addressing the imperative of supporting clinical decision-making for healthcare professionals. Our method incorporates five sub-learners and one meta-learner. Leveraging optimal features from the training dataset, this approach achieved compelling accuracy (89.13%) and AUC (88.83%) in predicting three-year survival status, surpassing conventional techniques. The model proves efficient in guiding clinical decisions, especially in scenarios with small-size follow-up data.
{"title":"Prediction of Survival in Patients With Esophageal Cancer After Immunotherapy Based on Small-Size Follow-Up Data","authors":"Yuhan Su;Chaofeng Huang;Chen Yang;Qin Lin;Zhong Chen","doi":"10.1109/OJEMB.2024.3452983","DOIUrl":"10.1109/OJEMB.2024.3452983","url":null,"abstract":"Esophageal cancer (EC) poses a significant health concern, particularly among the elderly, warranting effective treatment strategies. While immunotherapy holds promise in activating the immune response against tumors, its specific impact and associated reactions in EC patients remain uncertain. Precise prognosis prediction becomes crucial for guiding appropriate interventions. This study, based on data from the First Affiliated Hospital of Xiamen University (January 2017 to May 2021), focuses on 113 EC patients undergoing immunotherapy. The primary objectives are to elucidate the effectiveness of immunotherapy in EC treatment and to introduce a stacking ensemble learning method for predicting the survival of EC patients who have undergone immunotherapy, in the context of small sample sizes, addressing the imperative of supporting clinical decision-making for healthcare professionals. Our method incorporates five sub-learners and one meta-learner. Leveraging optimal features from the training dataset, this approach achieved compelling accuracy (89.13%) and AUC (88.83%) in predicting three-year survival status, surpassing conventional techniques. The model proves efficient in guiding clinical decisions, especially in scenarios with small-size follow-up data.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"5 ","pages":"769-782"},"PeriodicalIF":2.7,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10663258","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142200300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-26DOI: 10.1109/OJEMB.2024.3449548
Giovanni Corvini;Michail Arvanitidis;Deborah Falla;Silvia Conforto
Goal: This study introduces a novel approach to examine the temporal-spatial information derived from High-Density surface Electromyography (HD-sEMG). By integrating and adapting postural control parameters into a framework for the analysis of myoelectrical activity, new metrics to evaluate muscle fatigue progression were proposed, investigating their ability to predict endurance time. Methods: Nine subjects performed a fatiguing isometric contraction of the lumbar erector spinae. Topographical amplitude maps were generated from two HD-sEMG grids. Once identified the coordinates of the muscle activity, novel metrics for quantifying the muscle spatial distribution over time were calculated. Results: Spatial metrics showed significant differences from beginning to end of the contraction, highlighting their ability of characterizing the neuromuscular adaptations in presence of fatigue. Additionally, linear regression models revealed strong correlations between these spatial metrics and endurance time. Conclusions: These innovative metrics can characterize the spatial distribution of muscle activity and predict the time of task failure.
{"title":"Novel Metrics for High-Density sEMG Analysis in the Time–Space Domain During Sustained Isometric Contractions","authors":"Giovanni Corvini;Michail Arvanitidis;Deborah Falla;Silvia Conforto","doi":"10.1109/OJEMB.2024.3449548","DOIUrl":"https://doi.org/10.1109/OJEMB.2024.3449548","url":null,"abstract":"<italic>Goal:</i>\u0000 This study introduces a novel approach to examine the temporal-spatial information derived from High-Density surface Electromyography (HD-sEMG). By integrating and adapting postural control parameters into a framework for the analysis of myoelectrical activity, new metrics to evaluate muscle fatigue progression were proposed, investigating their ability to predict endurance time. \u0000<italic>Methods:</i>\u0000 Nine subjects performed a fatiguing isometric contraction of the lumbar erector spinae. Topographical amplitude maps were generated from two HD-sEMG grids. Once identified the coordinates of the muscle activity, novel metrics for quantifying the muscle spatial distribution over time were calculated. \u0000<italic>Results:</i>\u0000 Spatial metrics showed significant differences from beginning to end of the contraction, highlighting their ability of characterizing the neuromuscular adaptations in presence of fatigue. Additionally, linear regression models revealed strong correlations between these spatial metrics and endurance time. \u0000<italic>Conclusions:</i>\u0000 These innovative metrics can characterize the spatial distribution of muscle activity and predict the time of task failure.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"5 ","pages":"760-768"},"PeriodicalIF":2.7,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10646524","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-26DOI: 10.1109/OJEMB.2024.3444428
Samiul Alam;Md. Rafiul Amin;Rose T. Faghih
Presents corrections to the article “Sparse Multichannel Decomposition of Electrodermal Activity With Physiological Priors”.
提出对文章 "Sparse Multichannel Decomposition of Electrodermal Activity With Physiological Priors "的更正。
{"title":"Corrections to “Sparse Multichannel Decomposition of Electrodermal Activity With Physiological Priors”","authors":"Samiul Alam;Md. Rafiul Amin;Rose T. Faghih","doi":"10.1109/OJEMB.2024.3444428","DOIUrl":"https://doi.org/10.1109/OJEMB.2024.3444428","url":null,"abstract":"Presents corrections to the article “Sparse Multichannel Decomposition of Electrodermal Activity With Physiological Priors”.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"5 ","pages":"759-759"},"PeriodicalIF":2.7,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10646596","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142091002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-15DOI: 10.1109/OJEMB.2024.3428898
Marianna Laviola
{"title":"Introduction to the Special Section on Computational Modeling and Digital Twin Technology in Biomedical Engineering","authors":"Marianna Laviola","doi":"10.1109/OJEMB.2024.3428898","DOIUrl":"https://doi.org/10.1109/OJEMB.2024.3428898","url":null,"abstract":"","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"5 ","pages":"607-610"},"PeriodicalIF":2.7,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10637907","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141991455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-05DOI: 10.1109/OJEMB.2024.3438562
Enrico Mattana;Matteo Bruno Lodi;Marco Simone;Giuseppe Mazzarella;Alessandro Fanti
THz radiationeffectively probes biological tissue water content due to its high sensibility to polar molecules. Skin and basal cell carcinoma (BCC), both rich in water, have been extensively studied in the THz range. Typically, the Double Debye model is used to study their dielectric permittivity. This work focuses on the viability of the multipole Cole-Cole model as an alternative dielectric model. To determine the best fit parameters, we used a genetic algorithm-based approach, solving a least squares problem. Compared with the Double Debye model, a maximum reduction of the RMSE value up to more than 50% and maximum relative percentage errors of 2.8% have been measured for both second and third order Cole-Cole models. Since the errors of the second and third order Cole-Cole models are similar, a two-poles model is enough to describe the behaviour both tissues from 0.2 THz to 2 THz.
{"title":"Cole–Cole Model for the Dielectric Characterization of Healthy Skin and Basal Cell Carcinoma at THz Frequencies","authors":"Enrico Mattana;Matteo Bruno Lodi;Marco Simone;Giuseppe Mazzarella;Alessandro Fanti","doi":"10.1109/OJEMB.2024.3438562","DOIUrl":"10.1109/OJEMB.2024.3438562","url":null,"abstract":"THz radiationeffectively probes biological tissue water content due to its high sensibility to polar molecules. Skin and basal cell carcinoma (BCC), both rich in water, have been extensively studied in the THz range. Typically, the Double Debye model is used to study their dielectric permittivity. This work focuses on the viability of the multipole Cole-Cole model as an alternative dielectric model. To determine the best fit parameters, we used a genetic algorithm-based approach, solving a least squares problem. Compared with the Double Debye model, a maximum reduction of the RMSE value up to more than 50% and maximum relative percentage errors of 2.8% have been measured for both second and third order Cole-Cole models. Since the errors of the second and third order Cole-Cole models are similar, a two-poles model is enough to describe the behaviour both tissues from 0.2 THz to 2 THz.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"5 ","pages":"600-606"},"PeriodicalIF":2.7,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10623268","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141948749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-19DOI: 10.1109/OJEMB.2024.3431030
K. Chansaengsri;B. Tunhoo;K. Onlaor;T. Thiwawong
Merging electrophysiology signal monitoring technology with wearable devices offers interesting future health care options. This study presented carbon-based screen-printing inks produced by mixing a graphite composite with a polymer emulsion to bind with flexible fabric substrates and tested with 10,000 bending cycles. The prepared carbon-based ink performed well for electrical conduction and vital signal response. Adding calcium carbonate resulted in a microstructure of graphite that decreased the electrical sheet resistance and resistance to 11.61 Ω/◻ and 0.127 Ω. The signal-to-noise ratio of the electrocardiogram (ECG) was 31.02 dB with built-in front-end powering noise filtration. Noninvasive blood pressure (NIBP) was achieved by bio-impedance measurement and showed outstanding systolic and diastolic pressure values with a correlation coefficient of 0.799, and exhibited a similar interval time to define the same precise heart rate. The ECG data from the prepared electrode were applied to the machine learning models. The Random Forest (RF) model exhibited the optimized prediction value, with an F1 score of 99.9%. Equipment made from carbon screen-printing inks showed potential for health care monitoring with no excessive pressure, dry processing, and repeatability as a flexible wearable bio-electronic device.
{"title":"Developing a Vital Signal Detection Electrode for Fabric Substrate Using a High-Performance Conductive Carbon-Based Ink","authors":"K. Chansaengsri;B. Tunhoo;K. Onlaor;T. Thiwawong","doi":"10.1109/OJEMB.2024.3431030","DOIUrl":"10.1109/OJEMB.2024.3431030","url":null,"abstract":"Merging electrophysiology signal monitoring technology with wearable devices offers interesting future health care options. This study presented carbon-based screen-printing inks produced by mixing a graphite composite with a polymer emulsion to bind with flexible fabric substrates and tested with 10,000 bending cycles. The prepared carbon-based ink performed well for electrical conduction and vital signal response. Adding calcium carbonate resulted in a microstructure of graphite that decreased the electrical sheet resistance and resistance to 11.61 Ω/◻ and 0.127 Ω. The signal-to-noise ratio of the electrocardiogram (ECG) was 31.02 dB with built-in front-end powering noise filtration. Noninvasive blood pressure (NIBP) was achieved by bio-impedance measurement and showed outstanding systolic and diastolic pressure values with a correlation coefficient of 0.799, and exhibited a similar interval time to define the same precise heart rate. The ECG data from the prepared electrode were applied to the machine learning models. The Random Forest (RF) model exhibited the optimized prediction value, with an F1 score of 99.9%. Equipment made from carbon screen-printing inks showed potential for health care monitoring with no excessive pressure, dry processing, and repeatability as a flexible wearable bio-electronic device.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"390-399"},"PeriodicalIF":2.7,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10605601","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141746418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-18DOI: 10.1109/OJEMB.2024.3427318
Nelson V. Barnett;Alec Hoyland;Divya A. Chari;Benjamin Parrell;Adam C. Lammert
Goal: We validate a recent reverse correlation approach to tinnitus characterization by applying it to individuals with clinically-diagnosed tinnitus. Methods: Two tinnitus patients assessed the subjective similarity of their non-tonal tinnitus percepts and random auditory stimuli. Regression of the responses onto the stimuli yielded reconstructions which were evaluated qualitatively by playing back resynthesized waveforms to the subjects and quantitatively by response prediction analysis. Results: Subject 1 preferred their resynthesis to white noise; subject 2 did not. Response prediction balanced accuracies were significantly higher than chance across subjects: subject 1: 0.5963, subject 2: 0.6922. Conclusion: Reverse correlation can provide the foundation for reconstructing accurate representations of complex, non-tonal tinnitus in clinically diagnosed subjects. Further refinements may yield highly similar waveforms to individualized tinnitus percepts.
{"title":"Reverse Correlation Characterizes More Complete Tinnitus Spectra in Patients","authors":"Nelson V. Barnett;Alec Hoyland;Divya A. Chari;Benjamin Parrell;Adam C. Lammert","doi":"10.1109/OJEMB.2024.3427318","DOIUrl":"10.1109/OJEMB.2024.3427318","url":null,"abstract":"<italic>Goal:</i>\u0000 We validate a recent reverse correlation approach to tinnitus characterization by applying it to individuals with clinically-diagnosed tinnitus. \u0000<italic>Methods:</i>\u0000 Two tinnitus patients assessed the subjective similarity of their non-tonal tinnitus percepts and random auditory stimuli. Regression of the responses onto the stimuli yielded reconstructions which were evaluated qualitatively by playing back resynthesized waveforms to the subjects and quantitatively by response prediction analysis. \u0000<italic>Results:</i>\u0000 Subject 1 preferred their resynthesis to white noise; subject 2 did not. Response prediction balanced accuracies were significantly higher than chance across subjects: subject 1: 0.5963, subject 2: 0.6922. \u0000<italic>Conclusion:</i>\u0000 Reverse correlation can provide the foundation for reconstructing accurate representations of complex, non-tonal tinnitus in clinically diagnosed subjects. Further refinements may yield highly similar waveforms to individualized tinnitus percepts.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"5 ","pages":"589-592"},"PeriodicalIF":2.7,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10602769","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141745358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-17DOI: 10.1109/OJEMB.2024.3429294
Filippo Agnesi;Lucia Carlucci;Gia Burjanadze;Fabio Bernini;Khatia Gabisonia;John W Osborn;Silvestro Micera;Fabio A. Recchia
The objective of this study was to characterize hemodynamic changes during trans-vascular stimulation of the renal nerve and their dependence on stimulation parameters. We employed a stimulation catheter inserted in the right renal artery under fluoroscopic guidance, in pigs. Systolic, diastolic and pulse blood pressure and heart rate were recorded during stimulations delivered at different intravascular sites along the renal artery or while varying stimulation parameters (amplitude, frequency, and pulse width). Blood pressure changes during stimulation displayed a pattern more complex than previously described in literature, with a series of negative and positive peaks over the first two minutes, followed by a steady state elevation during the remainder of the stimulation. Pulse pressure and heart rate only showed transient responses, then they returned to baseline values despite constant stimulation. The amplitude of the evoked hemodynamic response was roughly linearly correlated with stimulation amplitude, frequency, and pulse width.
{"title":"Complex Hemodynamic Responses to Trans-Vascular Electrical Stimulation of the Renal Nerve in Anesthetized Pigs","authors":"Filippo Agnesi;Lucia Carlucci;Gia Burjanadze;Fabio Bernini;Khatia Gabisonia;John W Osborn;Silvestro Micera;Fabio A. Recchia","doi":"10.1109/OJEMB.2024.3429294","DOIUrl":"10.1109/OJEMB.2024.3429294","url":null,"abstract":"The objective of this study was to characterize hemodynamic changes during trans-vascular stimulation of the renal nerve and their dependence on stimulation parameters. We employed a stimulation catheter inserted in the right renal artery under fluoroscopic guidance, in pigs. Systolic, diastolic and pulse blood pressure and heart rate were recorded during stimulations delivered at different intravascular sites along the renal artery or while varying stimulation parameters (amplitude, frequency, and pulse width). Blood pressure changes during stimulation displayed a pattern more complex than previously described in literature, with a series of negative and positive peaks over the first two minutes, followed by a steady state elevation during the remainder of the stimulation. Pulse pressure and heart rate only showed transient responses, then they returned to baseline values despite constant stimulation. The amplitude of the evoked hemodynamic response was roughly linearly correlated with stimulation amplitude, frequency, and pulse width.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"5 ","pages":"750-758"},"PeriodicalIF":2.7,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10601543","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141745359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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