Pub Date : 2021-06-23DOI: 10.1109/MeMeA52024.2021.9478744
Saif Almhairat, Bruce Wallace, J. Larivière-Chartier, A. El-Haraki, R. Goubran, F. Knoefel
The use of residence-based well-being assessment through ambient and, in some cases, wearable sensors has led to many research projects for residential monitoring systems and supportive smart homes that are connected over the Internet to cloud processing. Many of these systems have been pilot tested and some early examples are entering commercial release. This report focuses on a key aspect for scaled use that has not been extensively considered which is the telecommunications provider network between the residence and the cloud. This work reports a 10-fold difference in network traffic generated between systems performing the same functionality and predominance of very small packets which must be routed. The project compares two smart home systems providing well-being monitoring and two smart bed sensors that assess vital signs and sleep. The results show how the design of sensing systems can vary greatly and that widespread deployment, such as many residences in a multi-tenant building, will force consideration of these effects within the telecommunication provider services.
{"title":"Supportive Smart Home Systems: Utilization Assessment for Internet Service Provider Networks","authors":"Saif Almhairat, Bruce Wallace, J. Larivière-Chartier, A. El-Haraki, R. Goubran, F. Knoefel","doi":"10.1109/MeMeA52024.2021.9478744","DOIUrl":"https://doi.org/10.1109/MeMeA52024.2021.9478744","url":null,"abstract":"The use of residence-based well-being assessment through ambient and, in some cases, wearable sensors has led to many research projects for residential monitoring systems and supportive smart homes that are connected over the Internet to cloud processing. Many of these systems have been pilot tested and some early examples are entering commercial release. This report focuses on a key aspect for scaled use that has not been extensively considered which is the telecommunications provider network between the residence and the cloud. This work reports a 10-fold difference in network traffic generated between systems performing the same functionality and predominance of very small packets which must be routed. The project compares two smart home systems providing well-being monitoring and two smart bed sensors that assess vital signs and sleep. The results show how the design of sensing systems can vary greatly and that widespread deployment, such as many residences in a multi-tenant building, will force consideration of these effects within the telecommunication provider services.","PeriodicalId":429222,"journal":{"name":"2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114676325","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 : 2021-06-23DOI: 10.1109/MeMeA52024.2021.9478754
A. Zubiaga, C. Kirsch, G. Boiger, M. Bonmarin
The evaluation of the skin thermal properties is relevant for many applications ranging from clinical dermatology to cosmetology. We introduce a simple passive device, capable of rapidly measuring skin thermal parameters using transient surface temperature measurements. Thanks to the development of an analytic thermodynamic skin model, tissue thermal diffusivity can be extracted from experimental data. For validation purposes, the thermal response of the apparatus has been modelled using a layered finite-element 3D model of the skin in thermal contact with a metallic measuring tip. Simplified 1D analytical and semi-analytical models have also been developed with the intent of modelling the thermal properties of the skin surface. The simplified models can be used to fit the thermal response measured by the device and to extract the thermal diffusivity in real time.
{"title":"A Simple Instrument to Measure the Thermal Transport Properties of the Human Skin","authors":"A. Zubiaga, C. Kirsch, G. Boiger, M. Bonmarin","doi":"10.1109/MeMeA52024.2021.9478754","DOIUrl":"https://doi.org/10.1109/MeMeA52024.2021.9478754","url":null,"abstract":"The evaluation of the skin thermal properties is relevant for many applications ranging from clinical dermatology to cosmetology. We introduce a simple passive device, capable of rapidly measuring skin thermal parameters using transient surface temperature measurements. Thanks to the development of an analytic thermodynamic skin model, tissue thermal diffusivity can be extracted from experimental data. For validation purposes, the thermal response of the apparatus has been modelled using a layered finite-element 3D model of the skin in thermal contact with a metallic measuring tip. Simplified 1D analytical and semi-analytical models have also been developed with the intent of modelling the thermal properties of the skin surface. The simplified models can be used to fit the thermal response measured by the device and to extract the thermal diffusivity in real time.","PeriodicalId":429222,"journal":{"name":"2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124257932","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 : 2021-06-23DOI: 10.1109/MeMeA52024.2021.9478740
Frederick Zhang, P. Juneau, Connor McGuirk, Albert Tu, Kevin Cheung, N. Baddour, E. Lemaire
Movement assessments are invaluable in clinical practice. However, the feasibility of in-person evaluation has been greatly affected due to the COVID-19 pandemic. To overcome this barrier, a virtual assessment system using artificial intelligence (AI) and patient provided videos is needed. AI models for pose inference have produced viable results for identifying a person’s joint centers. Identifying AI models for pose inference that provide clinically meaningful results is important for designing a virtual motion assessment tool. This study aims to evaluate the clinical usefulness of two popular pose inference models, OpenPose and HyperPose. Videos recorded by two physicians, who independently performed movements they deemed clinically relevant. Keypoint skeletons were generated and manually inspected frame-by-frame to determine which model produced higher-quality pose inferences. OpenPose produced significantly better scores than HyperPose when comparing within videos (p<0.001). Right ankle and right wrist had the poorest performances. Best-practices to be used in the future design of a virtual motion assessment tool are required to improve video "AI-friendliness".
{"title":"Comparison of OpenPose and HyperPose artificial intelligence models for analysis of hand-held smartphone videos","authors":"Frederick Zhang, P. Juneau, Connor McGuirk, Albert Tu, Kevin Cheung, N. Baddour, E. Lemaire","doi":"10.1109/MeMeA52024.2021.9478740","DOIUrl":"https://doi.org/10.1109/MeMeA52024.2021.9478740","url":null,"abstract":"Movement assessments are invaluable in clinical practice. However, the feasibility of in-person evaluation has been greatly affected due to the COVID-19 pandemic. To overcome this barrier, a virtual assessment system using artificial intelligence (AI) and patient provided videos is needed. AI models for pose inference have produced viable results for identifying a person’s joint centers. Identifying AI models for pose inference that provide clinically meaningful results is important for designing a virtual motion assessment tool. This study aims to evaluate the clinical usefulness of two popular pose inference models, OpenPose and HyperPose. Videos recorded by two physicians, who independently performed movements they deemed clinically relevant. Keypoint skeletons were generated and manually inspected frame-by-frame to determine which model produced higher-quality pose inferences. OpenPose produced significantly better scores than HyperPose when comparing within videos (p<0.001). Right ankle and right wrist had the poorest performances. Best-practices to be used in the future design of a virtual motion assessment tool are required to improve video \"AI-friendliness\".","PeriodicalId":429222,"journal":{"name":"2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"696 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116667416","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 : 2021-06-23DOI: 10.1109/MeMeA52024.2021.9478714
J. J. Valdés, Pengcheng Xi, Madison Cohen-McFarlane, Bruce Wallace, R. Goubran, F. Knoefel
Remote monitoring and measurement are valuable tools for medical applications and they are particularly important in the context of pandemic outbreaks, like the current COVID-19. This paper presents an analysis of sound measurements of cough events from the point of view of their predictive content with respect to identification of different types of cough, including positive COVID-19 cases. The data consisted of a collection of audio samples collected from different sources including dry, wet, whooping and COVID-19 coughs. Unsupervised and supervised machine learning techniques were used to reveal the underlying structure of the data, described by dissimilarity spaces constructed from pair-wise dynamic time warping measures derived from the original sound measurements. Intrinsic dimensionality, nonlinear mappings to low-dimensional spaces and visual cluster assessment techniques allowed a representation of the cough types distribution. Supervised classification techniques were used to obtain models identifying cough classes and high performance classifiers were obtained for most of them, including COVID-19. These results are preliminary and there is potential to improve, as they were obtained directly from a small dataset, without signal preprocessing (trimming, filtering, etc.), hyperparameter tuning, ensemble models, and class imbalance handling approaches.
{"title":"Analysis of cough sound measurements including COVID-19 positive cases: A machine learning characterization","authors":"J. J. Valdés, Pengcheng Xi, Madison Cohen-McFarlane, Bruce Wallace, R. Goubran, F. Knoefel","doi":"10.1109/MeMeA52024.2021.9478714","DOIUrl":"https://doi.org/10.1109/MeMeA52024.2021.9478714","url":null,"abstract":"Remote monitoring and measurement are valuable tools for medical applications and they are particularly important in the context of pandemic outbreaks, like the current COVID-19. This paper presents an analysis of sound measurements of cough events from the point of view of their predictive content with respect to identification of different types of cough, including positive COVID-19 cases. The data consisted of a collection of audio samples collected from different sources including dry, wet, whooping and COVID-19 coughs. Unsupervised and supervised machine learning techniques were used to reveal the underlying structure of the data, described by dissimilarity spaces constructed from pair-wise dynamic time warping measures derived from the original sound measurements. Intrinsic dimensionality, nonlinear mappings to low-dimensional spaces and visual cluster assessment techniques allowed a representation of the cough types distribution. Supervised classification techniques were used to obtain models identifying cough classes and high performance classifiers were obtained for most of them, including COVID-19. These results are preliminary and there is potential to improve, as they were obtained directly from a small dataset, without signal preprocessing (trimming, filtering, etc.), hyperparameter tuning, ensemble models, and class imbalance handling approaches.","PeriodicalId":429222,"journal":{"name":"2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125778724","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 : 2021-06-23DOI: 10.1109/MeMeA52024.2021.9478749
S. Korganbayev, A. Orrico, Leonard M. Bianchi, M. Landrø, A. Wolf, A. Dostovalov, P. Saccomandi
Accurate treatment monitoring and control are essential factors to ensure safe and effective outcomes of thermal ablation therapies. In this work, we report a temperature feedback-controlled laser ablation system based on fiber Bragg grating (FBG) array measurements. A highly spatially resolved array of FBGs spaced at a distance of 0.01 mm was adopted to achieve accurate measurement of high-gradient temperature profiles during laser ablation in biological tissue. The temperature feedback-control based on FBG array measurements and on the subsequent laser current regulation was implemented to maintain stable peak temperature during ablation. The laser current was controlled based on the selection of threshold values, set for the maximum temperature in the laser-irradiated area, i.e., 43 °C, 48 °C, 55 °C, and 60 °C. The feedback-controlled system was validated comparing measured temperature maps during ablation and ablation results on the tissue surface. Finally, results suggest that our feedback system allows controlling the spatial extension of the ablated zone and preserving the maximum tissue temperature within useful ranges for a desired and optimal thermal ablation outcome.
{"title":"Feedback-controlled thermal therapy of tissues based on fiber Bragg grating thermometers","authors":"S. Korganbayev, A. Orrico, Leonard M. Bianchi, M. Landrø, A. Wolf, A. Dostovalov, P. Saccomandi","doi":"10.1109/MeMeA52024.2021.9478749","DOIUrl":"https://doi.org/10.1109/MeMeA52024.2021.9478749","url":null,"abstract":"Accurate treatment monitoring and control are essential factors to ensure safe and effective outcomes of thermal ablation therapies. In this work, we report a temperature feedback-controlled laser ablation system based on fiber Bragg grating (FBG) array measurements. A highly spatially resolved array of FBGs spaced at a distance of 0.01 mm was adopted to achieve accurate measurement of high-gradient temperature profiles during laser ablation in biological tissue. The temperature feedback-control based on FBG array measurements and on the subsequent laser current regulation was implemented to maintain stable peak temperature during ablation. The laser current was controlled based on the selection of threshold values, set for the maximum temperature in the laser-irradiated area, i.e., 43 °C, 48 °C, 55 °C, and 60 °C. The feedback-controlled system was validated comparing measured temperature maps during ablation and ablation results on the tissue surface. Finally, results suggest that our feedback system allows controlling the spatial extension of the ablated zone and preserving the maximum tissue temperature within useful ranges for a desired and optimal thermal ablation outcome.","PeriodicalId":429222,"journal":{"name":"2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122658691","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 : 2021-06-23DOI: 10.1109/MeMeA52024.2021.9478751
F. Nardo, A. Cucchiarelli, C. Morbidoni, S. Fioretti
Measuring stride duration as a marker of regular walking is a relevant issue, also in the modern gait analysis. The present project was designed to test the hypothesis that an artificial-neural-network approach is able to provide a reliable prediction of stride, stance, and swing duration, based on the analysis of only EMG signals acquired during able-bodied walking. To this objective, surface EMG signals from ten leg muscles of 23 adult subjects are used to train a multi-layer perceptron model. Performance of classifiers is tested vs. gold standard, represented by foot-floor-contact signals measured by means of three footswitches positioned under each foot. Outcomes indicate an accurate prediction of stride duration (mean absolute value, MAE ± SD = 18.1 ± 6.2 ms), stance duration (MAE ± SD = 29.2 ± 10.3 ms), and swing duration (MAE ± SD = 28.8 ± 9.6 ms), at least comparable to those reported in IMU-based studies. A significant contribution of this approach is that only sEMG signals (and no further data) during patient walking are needed to get the gait durations, after training the neural network. This contributes to reduce the costs of the test, the clinical time-wasting, and the invasiveness of instrumentation worn by the patient, making this approach very suitable especially for the clinical analysis of neuromuscular disorders where the evaluation of muscular recruitment is recommended.
{"title":"Prediction of stride duration by neural-network interpretation of surface EMG signals","authors":"F. Nardo, A. Cucchiarelli, C. Morbidoni, S. Fioretti","doi":"10.1109/MeMeA52024.2021.9478751","DOIUrl":"https://doi.org/10.1109/MeMeA52024.2021.9478751","url":null,"abstract":"Measuring stride duration as a marker of regular walking is a relevant issue, also in the modern gait analysis. The present project was designed to test the hypothesis that an artificial-neural-network approach is able to provide a reliable prediction of stride, stance, and swing duration, based on the analysis of only EMG signals acquired during able-bodied walking. To this objective, surface EMG signals from ten leg muscles of 23 adult subjects are used to train a multi-layer perceptron model. Performance of classifiers is tested vs. gold standard, represented by foot-floor-contact signals measured by means of three footswitches positioned under each foot. Outcomes indicate an accurate prediction of stride duration (mean absolute value, MAE ± SD = 18.1 ± 6.2 ms), stance duration (MAE ± SD = 29.2 ± 10.3 ms), and swing duration (MAE ± SD = 28.8 ± 9.6 ms), at least comparable to those reported in IMU-based studies. A significant contribution of this approach is that only sEMG signals (and no further data) during patient walking are needed to get the gait durations, after training the neural network. This contributes to reduce the costs of the test, the clinical time-wasting, and the invasiveness of instrumentation worn by the patient, making this approach very suitable especially for the clinical analysis of neuromuscular disorders where the evaluation of muscular recruitment is recommended.","PeriodicalId":429222,"journal":{"name":"2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"437 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123613296","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 : 2021-06-23DOI: 10.1109/MeMeA52024.2021.9478766
P. Daponte, L. D. Vito, Grazia Iadarola, F. Picariello, S. Rapuano
This paper proposes the use of the Deterministic Binary Block Diagonal (DBBD) matrix as sensing matrix for compressed sensing of heart sound signals. The use of a deterministic matrix has the advantage of not requiring the generation of random numbers in the acquisition node. Moreover, the DBBD matrix has a very low computational complexity at the compression side, as it only requires a sum of the samples. In the paper, the DBBD sensing matrix is used in combination with the Discrete Cosine Transform and the Mexican Hat wavelet to compress and reconstruct heart sound signal obtained from the PhysioNet database. The results show a lower value of the Percent Root Mean Square Difference compared to that obtained by the random sensing matrix previously used in the literature for heart sound signals.
{"title":"Deterministic Compressed Sensing of heart sound signals","authors":"P. Daponte, L. D. Vito, Grazia Iadarola, F. Picariello, S. Rapuano","doi":"10.1109/MeMeA52024.2021.9478766","DOIUrl":"https://doi.org/10.1109/MeMeA52024.2021.9478766","url":null,"abstract":"This paper proposes the use of the Deterministic Binary Block Diagonal (DBBD) matrix as sensing matrix for compressed sensing of heart sound signals. The use of a deterministic matrix has the advantage of not requiring the generation of random numbers in the acquisition node. Moreover, the DBBD matrix has a very low computational complexity at the compression side, as it only requires a sum of the samples. In the paper, the DBBD sensing matrix is used in combination with the Discrete Cosine Transform and the Mexican Hat wavelet to compress and reconstruct heart sound signal obtained from the PhysioNet database. The results show a lower value of the Percent Root Mean Square Difference compared to that obtained by the random sensing matrix previously used in the literature for heart sound signals.","PeriodicalId":429222,"journal":{"name":"2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124281459","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 : 2021-06-23DOI: 10.1109/MeMeA52024.2021.9478690
Yaohua Zhang, Daryl Ma, S. Carrara, P. Georgiou
This paper presents the design of CMOS potentiostats using the gm/ID methodology. We investigate the gm/ID methodology as a systematic framework for optimal potentiostat design in terms of power dissipation, noise and area, the three most important potentiostat performance criteria. To this end, we select a reference potentiostat design and redesign this reference circuit using the gm/ID methodology in a 0.18 µm CMOS technology. Simulated results show that the power dissipation can be reduced by using the gm/ID methodology. For instance, the power dissipation of the folded cascode op-amp decreased from from 409.641 nW to 161.674 nW, indicating a 60.5% improvement. The total transistor occupation area of the folded cascode op-amp also decreased from 307 µm2 to 275 µm2, indicating a 10.4% improvement. We demonstrate that the gm/ID methodology is a good tool for analogue IC design as it can help the designer understand performance trade-offs as well as determine transistor dimensions, which can otherwise be very time-consuming.
{"title":"Design of Low-Power Highly Accurate CMOS Potentiostat Using the gm/ID Methodology","authors":"Yaohua Zhang, Daryl Ma, S. Carrara, P. Georgiou","doi":"10.1109/MeMeA52024.2021.9478690","DOIUrl":"https://doi.org/10.1109/MeMeA52024.2021.9478690","url":null,"abstract":"This paper presents the design of CMOS potentiostats using the g<inf>m</inf>/I<inf>D</inf> methodology. We investigate the g<inf>m</inf>/I<inf>D</inf> methodology as a systematic framework for optimal potentiostat design in terms of power dissipation, noise and area, the three most important potentiostat performance criteria. To this end, we select a reference potentiostat design and redesign this reference circuit using the g<inf>m</inf>/I<inf>D</inf> methodology in a 0.18 µm CMOS technology. Simulated results show that the power dissipation can be reduced by using the g<inf>m</inf>/I<inf>D</inf> methodology. For instance, the power dissipation of the folded cascode op-amp decreased from from 409.641 nW to 161.674 nW, indicating a 60.5% improvement. The total transistor occupation area of the folded cascode op-amp also decreased from 307 µm<sup>2</sup> to 275 µm<sup>2</sup>, indicating a 10.4% improvement. We demonstrate that the g<inf>m</inf>/I<inf>D</inf> methodology is a good tool for analogue IC design as it can help the designer understand performance trade-offs as well as determine transistor dimensions, which can otherwise be very time-consuming.","PeriodicalId":429222,"journal":{"name":"2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123508786","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 : 2021-06-23DOI: 10.1109/MeMeA52024.2021.9478692
V. R. Kiran, P. Nabeel, M. Sivaprakasam, J. Joseph
Ultrasound-based arterial wall recognition and tracking methods in the literature apply to two-dimensional ultrasound data, either in the form of B-mode images or M-lines radio-frequency (RF) data. We propose a robust dynamic time warping method that is applicable to just one-dimensional single scan-line RF signals. It uniquely analyses the time-varying effects of tissue dynamics on the amplitude and phase features of the RF signals. Its performance was investigated via systemic in-vitro experiments on a pulsatile flow phantom. The recording was performed by an ultrasound imaging system where the B-mode video clips and the raw RF data were saved simultaneously for direct comparison of the proposed method’s versus B-mode reference measurements. The noise of different levels was added to the RF signals to evaluate the method’s robustness. The method detected the arterial walls in 95% -100% of the frames (with SNRs ≥ 10 dB), and for ~100% of those detections, the method accurately localized the walls in the frames. Even when SNR levels were poor (0 dB < SNR < 5 dB) the detection and correct rates were greater than 80% and 90%. The performance figures were consistent for different pulsation rates (0.4 to 3 Hz) emulated. Further, the tracking errors were < 5% for frames with SNR ≥ 5 dB, which improved (errors < 3%) with an increase in SNR. The distension measurements resulting from tracking were repeatable over continuous pulsation cycles (CoV < 0.5%) and were accurate compared to B-mode measurement, with RMSE = 22 μm. The measured versus reference distensions strongly correlated (r = 0.99, p < 0.05) to each other and yielding insignificant (p = 0.17) difference of -6 μm. The method has the potential to facilitate an automated framework for A-mode-based structural and functional analysis of the blood vessels. Therefore, it allows the realization of advanced and cost-effective real-time A-mode systems.
{"title":"Phantom Evaluation of a Time Warping Based Automated Arterial Wall Recognition and Tracking Method","authors":"V. R. Kiran, P. Nabeel, M. Sivaprakasam, J. Joseph","doi":"10.1109/MeMeA52024.2021.9478692","DOIUrl":"https://doi.org/10.1109/MeMeA52024.2021.9478692","url":null,"abstract":"Ultrasound-based arterial wall recognition and tracking methods in the literature apply to two-dimensional ultrasound data, either in the form of B-mode images or M-lines radio-frequency (RF) data. We propose a robust dynamic time warping method that is applicable to just one-dimensional single scan-line RF signals. It uniquely analyses the time-varying effects of tissue dynamics on the amplitude and phase features of the RF signals. Its performance was investigated via systemic in-vitro experiments on a pulsatile flow phantom. The recording was performed by an ultrasound imaging system where the B-mode video clips and the raw RF data were saved simultaneously for direct comparison of the proposed method’s versus B-mode reference measurements. The noise of different levels was added to the RF signals to evaluate the method’s robustness. The method detected the arterial walls in 95% -100% of the frames (with SNRs ≥ 10 dB), and for ~100% of those detections, the method accurately localized the walls in the frames. Even when SNR levels were poor (0 dB < SNR < 5 dB) the detection and correct rates were greater than 80% and 90%. The performance figures were consistent for different pulsation rates (0.4 to 3 Hz) emulated. Further, the tracking errors were < 5% for frames with SNR ≥ 5 dB, which improved (errors < 3%) with an increase in SNR. The distension measurements resulting from tracking were repeatable over continuous pulsation cycles (CoV < 0.5%) and were accurate compared to B-mode measurement, with RMSE = 22 μm. The measured versus reference distensions strongly correlated (r = 0.99, p < 0.05) to each other and yielding insignificant (p = 0.17) difference of -6 μm. The method has the potential to facilitate an automated framework for A-mode-based structural and functional analysis of the blood vessels. Therefore, it allows the realization of advanced and cost-effective real-time A-mode systems.","PeriodicalId":429222,"journal":{"name":"2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120842708","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 : 2021-06-23DOI: 10.1109/MeMeA52024.2021.9478750
D. Presti, C. Massaroni, M. Caponero, D. Formica, E. Schena
A continuous monitoring of cardiorespiratory activity can play an essential role in the health prevention since the cardiovascular and ventilatory systems regulate several vital functions of the human body and adapt themselves in response to various stressors. Typically, early detection of cardiorespiratory irregularities is performed by monitoring respiratory and heart rate (RR and HR) at rest. Among several technological solutions, the most promising are based on mechanical and optical systems such as gyroscopes (GYRs) and accelerometers in inertial measurement units, and fiber Bragg gratings (FBGs) embedded into wearable and non-wearable items.In this work, we investigated the capability of a mechanical system (i.e., a GYR) and an optical system (i.e., a flexible sensor based on FBG) to perform the simultaneous RR and HR monitoring. The system placement varied according to the sensor type to ensure the best unobtrusive cardiorespiratory monitoring: the GYR was worn on the chest, and the FBG-based flexible sensor was placed on a chair in contact with the chest back. Results showed similar performances between the mechanical and optical systems when compared to a reference instrument (mean absolute percentage error -MAPE < 7.7% and 6.1% for HR and MAPE ≤ 0.23% and 1.7% for RR for the FBG and the GYR, respectively).
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