Pub Date : 2022-06-22DOI: 10.1109/MeMeA54994.2022.9856579
J. Kulpa, Emma Farago, A. Chan
In the research and development stages of biomedical signal quality analysis tools, testing and validation help to ensure they work as intended and are robust enough to be used in all sorts of environments. Large datasets of biomedical signals (e.g., electrocardiogram, electromyogram) and signal contaminants (e.g., motion artifact, power line interference) are required for rigorous testing; however, obtaining a large, diverse database of real-life signals and contaminants is a challenging process. By accurately simulating signals and contaminants, researchers are able to more easily create large amounts of data, with known levels of contamination, which can be used for testing and validation of signal quality analysis tools. The Motion Artifact Signal Generation Toolkit allows for the synthesis of motion artifacts using one of three models: 1) autoregressive, 2) Markov chain, and 3) recurrent neural network. Each of these has been prepared for three use-cases: 1) pre-simulated motion artifacts, 2) pre-trained models that can be used to simulate motion artifacts, and 3) training a model using a motion artifact sample and using that model to simulate motion artifacts. The three model types were tested on nonstationary data, exposing some current limitations; specifically, the models' ability to model real-world, non-cyclical data. The recurrent neural network does appears to produce reasonable simulated motion artifact that exhibit similarities, in both the time and frequency domains, to short time segments of real-world motion artifact.
{"title":"A Toolkit for Motion Artifact Signal Generation","authors":"J. Kulpa, Emma Farago, A. Chan","doi":"10.1109/MeMeA54994.2022.9856579","DOIUrl":"https://doi.org/10.1109/MeMeA54994.2022.9856579","url":null,"abstract":"In the research and development stages of biomedical signal quality analysis tools, testing and validation help to ensure they work as intended and are robust enough to be used in all sorts of environments. Large datasets of biomedical signals (e.g., electrocardiogram, electromyogram) and signal contaminants (e.g., motion artifact, power line interference) are required for rigorous testing; however, obtaining a large, diverse database of real-life signals and contaminants is a challenging process. By accurately simulating signals and contaminants, researchers are able to more easily create large amounts of data, with known levels of contamination, which can be used for testing and validation of signal quality analysis tools. The Motion Artifact Signal Generation Toolkit allows for the synthesis of motion artifacts using one of three models: 1) autoregressive, 2) Markov chain, and 3) recurrent neural network. Each of these has been prepared for three use-cases: 1) pre-simulated motion artifacts, 2) pre-trained models that can be used to simulate motion artifacts, and 3) training a model using a motion artifact sample and using that model to simulate motion artifacts. The three model types were tested on nonstationary data, exposing some current limitations; specifically, the models' ability to model real-world, non-cyclical data. The recurrent neural network does appears to produce reasonable simulated motion artifact that exhibit similarities, in both the time and frequency domains, to short time segments of real-world motion artifact.","PeriodicalId":106228,"journal":{"name":"2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115300067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-22DOI: 10.1109/MeMeA54994.2022.9856476
Priscilla Dinkar Moyya, Mythili Asaithambi, A. K. Ramaniharan
Hormone receptors play a key role in female breast cancers as predictive biomarkers. Breast cancer subtype with Progesterone receptor (PgR) expression is one of the important hormone receptors in predicting prognosis and evaluating the Neoadjuvant Chemotherapy (NAC) treatment response. PgR (-) breast cancers are associated with a higher response to NAC compared to PgR (+) breast cancer patients. Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) is the widely used imaging modality in assessing the NAC response in patients. However, evaluating the treatment response of PgR breast cancers is complicated and challenging since breast cancer with positive receptor statuses will respond differently to NAC. Therefore, in this work, an attempt has been made to differentiate the PgR (+) and PgR (-) breast cancer patients due to NAC using Gabor derived Anisotropy Index (AI). A total of 50 PgR (+) and 63 PgR (-) DCE-MR images at 4 time points of NAC treatment are considered from the openly available I-SPY1 of the TCIA database. AI is calculated within the PgR status groups from Gabor energies that are acquired after designing the Gabor filter bank with 5 scales and 7 orientations. Results demonstrate that the AI values can significantly differentiate PgR (+) and PgR (-) breast cancer patients $(mathrm{p}leq 0.05)$ due to NAC. The mean AI values are observed to be high in PgR (+) patients $(4.14mathrm{E}+10pm$ 1.17E+ 11) than PgR (-) patients $(1.95mathrm{E}+10pm 8.06mathrm{E}+10)$. AI could statistically differentiate visit 1 & visit 4 of NAC treatment in both PgR status patients with a p-value of 0.0246 and 0.0387 respectively. Further, the percentage difference in the mean value of AI is observed to be high in PgR (-) between visit 1 V s 4, visit 2 V s 4, visit 1 V s 3, and visit 2 Vs 3 compared to PgR (+) subjects. Hence, AI could be used as a single index value in assessing the treatment response in both PgR (+) and PgR (-) subjects.
{"title":"Progesterone Receptor Status Analysis in Breast Cancer Patients using DCE- MR Images and Gabor Derived Anisotropy Index","authors":"Priscilla Dinkar Moyya, Mythili Asaithambi, A. K. Ramaniharan","doi":"10.1109/MeMeA54994.2022.9856476","DOIUrl":"https://doi.org/10.1109/MeMeA54994.2022.9856476","url":null,"abstract":"Hormone receptors play a key role in female breast cancers as predictive biomarkers. Breast cancer subtype with Progesterone receptor (PgR) expression is one of the important hormone receptors in predicting prognosis and evaluating the Neoadjuvant Chemotherapy (NAC) treatment response. PgR (-) breast cancers are associated with a higher response to NAC compared to PgR (+) breast cancer patients. Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) is the widely used imaging modality in assessing the NAC response in patients. However, evaluating the treatment response of PgR breast cancers is complicated and challenging since breast cancer with positive receptor statuses will respond differently to NAC. Therefore, in this work, an attempt has been made to differentiate the PgR (+) and PgR (-) breast cancer patients due to NAC using Gabor derived Anisotropy Index (AI). A total of 50 PgR (+) and 63 PgR (-) DCE-MR images at 4 time points of NAC treatment are considered from the openly available I-SPY1 of the TCIA database. AI is calculated within the PgR status groups from Gabor energies that are acquired after designing the Gabor filter bank with 5 scales and 7 orientations. Results demonstrate that the AI values can significantly differentiate PgR (+) and PgR (-) breast cancer patients $(mathrm{p}leq 0.05)$ due to NAC. The mean AI values are observed to be high in PgR (+) patients $(4.14mathrm{E}+10pm$ 1.17E+ 11) than PgR (-) patients $(1.95mathrm{E}+10pm 8.06mathrm{E}+10)$. AI could statistically differentiate visit 1 & visit 4 of NAC treatment in both PgR status patients with a p-value of 0.0246 and 0.0387 respectively. Further, the percentage difference in the mean value of AI is observed to be high in PgR (-) between visit 1 V s 4, visit 2 V s 4, visit 1 V s 3, and visit 2 Vs 3 compared to PgR (+) subjects. Hence, AI could be used as a single index value in assessing the treatment response in both PgR (+) and PgR (-) subjects.","PeriodicalId":106228,"journal":{"name":"2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115707648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-22DOI: 10.1109/MeMeA54994.2022.9856417
Jiho Choi, Jun Seong Lee, Moonwook Ryu, Gyutae Hwang, Gyeongyeon Hwang, Sang Jun Lee
Recently, interest in well-being has been increasing rapidly, and one way to do this is to deal with stress wisely. In order to manage or relieve stress, it is necessary to identify the current stress status and respond appropriately. Many existing studies have been conducted to detect stress, and lately many deep learning-based stress detection methods have been proposed. However, there is a room for improving the accuracy, and this paper proposes a novel deep learning algorithm for stress detection. The proposed model is based on long-term recurrent convolutional networks (LRCN) and an attention module, and we named this as Attention-LRCN. We used WESAD dataset which provides photoplethysmography (PPG) signals with normal and stress statuses for 15 subjects. The proposed method classifies the PPG signal into stress and normal statuses using a combination of convolutional neural networks (CNN) and long short-term memory (LSTM) layers. Since the PPG signals contain human interference, we utilized an attention module to reduce the effects of noise on the PPG signal. We compare Attention-LRCN with the state-of-the-art method for stress detection, and experimental results demonstrate that our proposed method is more effective in the stress detection application. The proposed method achieved 97.11 % and 95.47% for the accuracy and F1-score, respectively, and these metrics are 0.61 % and 2.1 % higher than the state-of-the-art method.
{"title":"Attention-LRCN: Long-term Recurrent Convolutional Network for Stress Detection from Photoplethysmography","authors":"Jiho Choi, Jun Seong Lee, Moonwook Ryu, Gyutae Hwang, Gyeongyeon Hwang, Sang Jun Lee","doi":"10.1109/MeMeA54994.2022.9856417","DOIUrl":"https://doi.org/10.1109/MeMeA54994.2022.9856417","url":null,"abstract":"Recently, interest in well-being has been increasing rapidly, and one way to do this is to deal with stress wisely. In order to manage or relieve stress, it is necessary to identify the current stress status and respond appropriately. Many existing studies have been conducted to detect stress, and lately many deep learning-based stress detection methods have been proposed. However, there is a room for improving the accuracy, and this paper proposes a novel deep learning algorithm for stress detection. The proposed model is based on long-term recurrent convolutional networks (LRCN) and an attention module, and we named this as Attention-LRCN. We used WESAD dataset which provides photoplethysmography (PPG) signals with normal and stress statuses for 15 subjects. The proposed method classifies the PPG signal into stress and normal statuses using a combination of convolutional neural networks (CNN) and long short-term memory (LSTM) layers. Since the PPG signals contain human interference, we utilized an attention module to reduce the effects of noise on the PPG signal. We compare Attention-LRCN with the state-of-the-art method for stress detection, and experimental results demonstrate that our proposed method is more effective in the stress detection application. The proposed method achieved 97.11 % and 95.47% for the accuracy and F1-score, respectively, and these metrics are 0.61 % and 2.1 % higher than the state-of-the-art method.","PeriodicalId":106228,"journal":{"name":"2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127365912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-22DOI: 10.1109/MeMeA54994.2022.9856441
Andrea Apicella, P. Arpaia, A. Cataldo, E. D. Benedetto, N. Donato, Luigi Duraccio, Salvatore Giugliano, R. Prevete
This paper proposes the adoption of an innovative algorithm to enhance the performance of highly wearable, reactive Brain-Computer Interfaces (BCIs), which exploit the Steady-State Visually Evoked Potential (SSVEP) paradigm. In particular, a combined time-domain/frequency-domain processing is performed in order to reduce the number of features of the brain signals acquired. Successively, these features are classified by means of an Artificial Neural Network (ANN) with a learnable activation function. In this way, the user intention can be translated into commands for external devices. The proposed algorithm was initially tested on a benchmark data set, composed by 35 subjects and 40 simultaneous flickering stimuli, obtaining performance comparable with the state of the art. Successively, the algorithm was also applied to a data set realized with highly wearable BCI equipment. In particular, (i) Augmented Reality (AR) smart glasses were used to generate the flickering stimuli necessary to the SSVEPs elicitation, and (ii) a single-channel EEG acquisition was conducted for each volunteer. The obtained results showed that the proposed strategy provides a significant enhancement in SSVEPs classification with respect to other state-of-the-art algorithms. This can contribute to improve reliability and usability of brain computer interfaces, thus favoring the adoption of this technology also in daily-life applications.
{"title":"Adoption of Machine Learning Techniques to Enhance Classification Performance in Reactive Brain-Computer Interfaces","authors":"Andrea Apicella, P. Arpaia, A. Cataldo, E. D. Benedetto, N. Donato, Luigi Duraccio, Salvatore Giugliano, R. Prevete","doi":"10.1109/MeMeA54994.2022.9856441","DOIUrl":"https://doi.org/10.1109/MeMeA54994.2022.9856441","url":null,"abstract":"This paper proposes the adoption of an innovative algorithm to enhance the performance of highly wearable, reactive Brain-Computer Interfaces (BCIs), which exploit the Steady-State Visually Evoked Potential (SSVEP) paradigm. In particular, a combined time-domain/frequency-domain processing is performed in order to reduce the number of features of the brain signals acquired. Successively, these features are classified by means of an Artificial Neural Network (ANN) with a learnable activation function. In this way, the user intention can be translated into commands for external devices. The proposed algorithm was initially tested on a benchmark data set, composed by 35 subjects and 40 simultaneous flickering stimuli, obtaining performance comparable with the state of the art. Successively, the algorithm was also applied to a data set realized with highly wearable BCI equipment. In particular, (i) Augmented Reality (AR) smart glasses were used to generate the flickering stimuli necessary to the SSVEPs elicitation, and (ii) a single-channel EEG acquisition was conducted for each volunteer. The obtained results showed that the proposed strategy provides a significant enhancement in SSVEPs classification with respect to other state-of-the-art algorithms. This can contribute to improve reliability and usability of brain computer interfaces, thus favoring the adoption of this technology also in daily-life applications.","PeriodicalId":106228,"journal":{"name":"2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116144920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-22DOI: 10.1109/MeMeA54994.2022.9856537
S. Ishwarya, Rahul Manoj, V. RajKiran, P. Nabeel, J. Joseph
Arterial stiffness measured from central arteries is widely recognized as a prognostic marker for cardiovascular risk stratification. Measurements in sitting posture make stiffness assessment potentially more rapid and feasible for large-scale population-level field screening. However, the blood pressure (BP) required for stiffness evaluation must be compensated for any hydrostatic pressure offset while performing measurements in a sitting posture. In this work, we developed and validated a hydrostatic pressure compensation unit integrated with our A-mode ultrasound device for carotid artery stiffness. The system was characterized, and its design parameters were carefully considered for concurrence with a physiologically interesting range. The smallest change it could reliably measure was 2 mm, which corresponded to 0.3 mmHg of blood pressure. The device was validated on 20 human subjects (11 males and 9 females). The results demonstrated that the average carotid systolic and diastolic pressures compensated with the hydrostatic pressure were 29% and 22% lesser than those without compensation. The ANOVA showed a statistically significant difference ($mathrm{p} < 0.0001$) between the $beta$ obtained from compensated ($5.21 pm 0.43$) and uncompensated ($5.73 pm 0.22$) pressures. Whereas Ep, AC did not show a statistically significant difference as they rely on the pulse pressure, which was not affected by the hydrostatic pressure correction. Conclusively, hydrostatic pressure affects the stiffness markers that rely on the absolute pressure values.
{"title":"Hydrostatic Pressure Compensator for Evaluation of Carotid Stiffness using A-Mode Ultrasound: Design, Characterization, and In-Vivo Validation","authors":"S. Ishwarya, Rahul Manoj, V. RajKiran, P. Nabeel, J. Joseph","doi":"10.1109/MeMeA54994.2022.9856537","DOIUrl":"https://doi.org/10.1109/MeMeA54994.2022.9856537","url":null,"abstract":"Arterial stiffness measured from central arteries is widely recognized as a prognostic marker for cardiovascular risk stratification. Measurements in sitting posture make stiffness assessment potentially more rapid and feasible for large-scale population-level field screening. However, the blood pressure (BP) required for stiffness evaluation must be compensated for any hydrostatic pressure offset while performing measurements in a sitting posture. In this work, we developed and validated a hydrostatic pressure compensation unit integrated with our A-mode ultrasound device for carotid artery stiffness. The system was characterized, and its design parameters were carefully considered for concurrence with a physiologically interesting range. The smallest change it could reliably measure was 2 mm, which corresponded to 0.3 mmHg of blood pressure. The device was validated on 20 human subjects (11 males and 9 females). The results demonstrated that the average carotid systolic and diastolic pressures compensated with the hydrostatic pressure were 29% and 22% lesser than those without compensation. The ANOVA showed a statistically significant difference ($mathrm{p} < 0.0001$) between the $beta$ obtained from compensated ($5.21 pm 0.43$) and uncompensated ($5.73 pm 0.22$) pressures. Whereas Ep, AC did not show a statistically significant difference as they rely on the pulse pressure, which was not affected by the hydrostatic pressure correction. Conclusively, hydrostatic pressure affects the stiffness markers that rely on the absolute pressure values.","PeriodicalId":106228,"journal":{"name":"2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"156 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116229264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-22DOI: 10.1109/MeMeA54994.2022.9856404
Alberto López, F. Ferrero, S. Qaisar, O. Postolache
This paper reports a study conducted to model saccadic eye movements based on a combination of Gaussian basis functions. Eye movement signal was recorded employing the electrooculography technique using a commercial bio amplifier that records the electrical activity of the eyes through surface electrodes. The Gaussian Mixture Model algorithm was employed for this purpose and implemented using MATLAB software. Modeling these eye movements is essential for feature extraction, processing, compression, transmission, and prediction applications. The proposed technique succeeded in modeling saccade based on root mean squared error, mean absolute error, mean percentage absolute error, and coefficient of determination, $mathrm{R}^{2}$, parameters employing 10 Gaussian basis components.
{"title":"Gaussian Mixture Model of Saccadic Eye Movements","authors":"Alberto López, F. Ferrero, S. Qaisar, O. Postolache","doi":"10.1109/MeMeA54994.2022.9856404","DOIUrl":"https://doi.org/10.1109/MeMeA54994.2022.9856404","url":null,"abstract":"This paper reports a study conducted to model saccadic eye movements based on a combination of Gaussian basis functions. Eye movement signal was recorded employing the electrooculography technique using a commercial bio amplifier that records the electrical activity of the eyes through surface electrodes. The Gaussian Mixture Model algorithm was employed for this purpose and implemented using MATLAB software. Modeling these eye movements is essential for feature extraction, processing, compression, transmission, and prediction applications. The proposed technique succeeded in modeling saccade based on root mean squared error, mean absolute error, mean percentage absolute error, and coefficient of determination, $mathrm{R}^{2}$, parameters employing 10 Gaussian basis components.","PeriodicalId":106228,"journal":{"name":"2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122601112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-22DOI: 10.1109/MeMeA54994.2022.9856455
E. Panero, D. Borzelli, C. Artusi, G. Massazza
Pisa syndrome is defined as a postural deviation that could occur among patients with Parkinson's disease, and it is described by a lateral flexion of the trunk (greater than 10° respect to the vertical alignment). The pathophysiology of Pisa syndrome is still not clear but different hypothesis, based on the investigation of altered posture, have been proposed involving the hyperactivity of spinal and abdominal muscles and the description of the relationship between postural control and vertical perception deficit. Different clinical solutions have been adopted and tested with experimental studies. Among them, the treatment with botulinum toxin of paraspinal muscles contributed to the reduction of muscles hyperactivity, bending angles and subjective evaluation of pain. The current research deals with the analysis of botulinum toxin effects on 13 Pisa syndrome patients. A standardized botulinum toxin treatment protocol was applied to all subjects. Subjects performed standing posture in natural and self-corrected conditions before and 1 month after the treatment. Spine kinematics, body weight distribution and muscles activations have been considered as objective biomechanical parameters for the analysis. Two healthy subjects participated to the test as control group. Results highlighted significant differences in body weight distribution for both natural (p-value=0.02) and correct (p-value=0.008) posture, with an improved symmetry after the treatment. Moreover, a significant reduction (p-value=0.002) of the modification in the contralateral muscle pattern was pointed out when assuming a correct posture. Despite the differences in kinematic posture do not highlight significant results, the investigation of several biomechanical features indicated a positive effects of botulinum treatment with potential clinical implications.
{"title":"Biomechanical assessment of botulinum toxin effects in Pisa syndrome disease","authors":"E. Panero, D. Borzelli, C. Artusi, G. Massazza","doi":"10.1109/MeMeA54994.2022.9856455","DOIUrl":"https://doi.org/10.1109/MeMeA54994.2022.9856455","url":null,"abstract":"Pisa syndrome is defined as a postural deviation that could occur among patients with Parkinson's disease, and it is described by a lateral flexion of the trunk (greater than 10° respect to the vertical alignment). The pathophysiology of Pisa syndrome is still not clear but different hypothesis, based on the investigation of altered posture, have been proposed involving the hyperactivity of spinal and abdominal muscles and the description of the relationship between postural control and vertical perception deficit. Different clinical solutions have been adopted and tested with experimental studies. Among them, the treatment with botulinum toxin of paraspinal muscles contributed to the reduction of muscles hyperactivity, bending angles and subjective evaluation of pain. The current research deals with the analysis of botulinum toxin effects on 13 Pisa syndrome patients. A standardized botulinum toxin treatment protocol was applied to all subjects. Subjects performed standing posture in natural and self-corrected conditions before and 1 month after the treatment. Spine kinematics, body weight distribution and muscles activations have been considered as objective biomechanical parameters for the analysis. Two healthy subjects participated to the test as control group. Results highlighted significant differences in body weight distribution for both natural (p-value=0.02) and correct (p-value=0.008) posture, with an improved symmetry after the treatment. Moreover, a significant reduction (p-value=0.002) of the modification in the contralateral muscle pattern was pointed out when assuming a correct posture. Despite the differences in kinematic posture do not highlight significant results, the investigation of several biomechanical features indicated a positive effects of botulinum treatment with potential clinical implications.","PeriodicalId":106228,"journal":{"name":"2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"95 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129570693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-22DOI: 10.1109/MeMeA54994.2022.9856490
V. Lopresto, S. Pisa, E. Pittella, E. Piuzzi
The aim of this paper is to describe a system designed for measuring the dielectric properties of biological tissues at extremely-low frequencies (ELF) and ultra-low frequencies (ULF), in particular in the 0.1 Hz – 1 kHz range. In this frequency range, literature data are very limited or absent, since measurement techniques are strongly affected by systematic errors. In order to carry out the aforementioned measurements, the paper presents the system design and metrological tests for assessing accuracy in complex impedance measurements. The uncertainty of the measuring system was determined using reference R-C circuits, showing extremely low errors as compared to high-accuracy multimeters and LCR meters. In order to obtain the sample complex permittivity, the system was calibrated in saline solutions to determine the cell constant K. Then, experimental results on the bioimpedance and related complex permittivity of bovine liver are shown, performed with the 4-electrode measurement technique to limit the effect caused by the electrodes polarization. In particular, measurements were performed in 20 tissue samples, obtained from 5 different livers. Both conductivity and relative permittivity results have been compared with the few existing literature data, finding a satisfactory agreement between the values obtained from the literature and those achieved by measurements with the proposed system.
{"title":"Compact system for measuring the dielectric properties of biological tissues at extremely-low and ultra-low frequencies","authors":"V. Lopresto, S. Pisa, E. Pittella, E. Piuzzi","doi":"10.1109/MeMeA54994.2022.9856490","DOIUrl":"https://doi.org/10.1109/MeMeA54994.2022.9856490","url":null,"abstract":"The aim of this paper is to describe a system designed for measuring the dielectric properties of biological tissues at extremely-low frequencies (ELF) and ultra-low frequencies (ULF), in particular in the 0.1 Hz – 1 kHz range. In this frequency range, literature data are very limited or absent, since measurement techniques are strongly affected by systematic errors. In order to carry out the aforementioned measurements, the paper presents the system design and metrological tests for assessing accuracy in complex impedance measurements. The uncertainty of the measuring system was determined using reference R-C circuits, showing extremely low errors as compared to high-accuracy multimeters and LCR meters. In order to obtain the sample complex permittivity, the system was calibrated in saline solutions to determine the cell constant K. Then, experimental results on the bioimpedance and related complex permittivity of bovine liver are shown, performed with the 4-electrode measurement technique to limit the effect caused by the electrodes polarization. In particular, measurements were performed in 20 tissue samples, obtained from 5 different livers. Both conductivity and relative permittivity results have been compared with the few existing literature data, finding a satisfactory agreement between the values obtained from the literature and those achieved by measurements with the proposed system.","PeriodicalId":106228,"journal":{"name":"2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128277522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-22DOI: 10.1109/MeMeA54994.2022.9856520
Ankita Dey, S. Rajan
With an increase in the number of breast cancer cases worldwide, there is an immediate need to develop techniques for early detection. Thermography has the potential to detect and diagnose early breast tumours. A novel non-learning-based method is proposed to detect abnormalities from a breast thermogram using bilateral symmetries. A total of 25 thermograms from Database of Mastology Research (DMR) and Ann Arbor thermography, consisting of 18 abnormal cases and 7 normal cases, were analyzed. The red-plane from the thermal images of the breasts were extracted and the resulting breast images were segmented to separate breast tissue profile from the surrounding pectoral muscles using Otsu's thresholding technique and seeded region growing segmentation method. Abnormal breasts were detected from the segmented red-plane breast tissue profile using bilateral ratios of statistical parameters. These statistical parameters were obtained from the left and the right breast of the thermogram. The bilateral ratios suggest symmetry between the right and the left breast when the value is close to 1 and suggest asymmetry otherwise. Detection of abnormal breast was followed by extraction of the region of abnormality using the similar bilateral ratio analysis. Abnormal breasts were detected with an accuracy of 92%, specificity of 87.5% and sensitivity of 94.12%. The proposed method needed no prior training dataset.
随着世界范围内乳腺癌病例数量的增加，迫切需要开发早期检测技术。热成像技术具有发现和诊断早期乳腺肿瘤的潜力。提出了一种新的非基于学习的方法，利用双侧对称性检测乳房热像图的异常。本文对来自美国乳腺研究数据库(Database of Mastology Research, DMR)和Ann Arbor热像仪的25张热像图进行分析，其中异常病例18例，正常病例7例。利用Otsu阈值分割技术和种子区域生长分割法，提取乳房热图像中的红色平面，对得到的乳房图像进行分割，将乳房组织轮廓与周围的胸肌分离开来。利用统计参数的双侧比值从分割的红平面乳房组织剖面中检测异常乳房。这些统计参数是从热像图的左乳房和右乳房得到的。当双侧比值接近1时，表明左右乳房对称，否则表明不对称。检测乳房异常后，采用相似双侧比值法提取异常区域。检测异常乳房的准确率为92%，特异性为87.5%，敏感性为94.12%。该方法不需要预先训练数据集。
{"title":"Red-plane Asymmetry Analysis of Breast Thermograms for Cancer Detection","authors":"Ankita Dey, S. Rajan","doi":"10.1109/MeMeA54994.2022.9856520","DOIUrl":"https://doi.org/10.1109/MeMeA54994.2022.9856520","url":null,"abstract":"With an increase in the number of breast cancer cases worldwide, there is an immediate need to develop techniques for early detection. Thermography has the potential to detect and diagnose early breast tumours. A novel non-learning-based method is proposed to detect abnormalities from a breast thermogram using bilateral symmetries. A total of 25 thermograms from Database of Mastology Research (DMR) and Ann Arbor thermography, consisting of 18 abnormal cases and 7 normal cases, were analyzed. The red-plane from the thermal images of the breasts were extracted and the resulting breast images were segmented to separate breast tissue profile from the surrounding pectoral muscles using Otsu's thresholding technique and seeded region growing segmentation method. Abnormal breasts were detected from the segmented red-plane breast tissue profile using bilateral ratios of statistical parameters. These statistical parameters were obtained from the left and the right breast of the thermogram. The bilateral ratios suggest symmetry between the right and the left breast when the value is close to 1 and suggest asymmetry otherwise. Detection of abnormal breast was followed by extraction of the region of abnormality using the similar bilateral ratio analysis. Abnormal breasts were detected with an accuracy of 92%, specificity of 87.5% and sensitivity of 94.12%. The proposed method needed no prior training dataset.","PeriodicalId":106228,"journal":{"name":"2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"93 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120919161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-22DOI: 10.1109/MeMeA54994.2022.9856541
Phillippe Forster, Bruce Wallace, R. Goubran, F. Knoefel
The ongoing assessment of well-being can enable aging adults to remain independent and continue to age in place in their own home. This provides them with a better quality of life and reduces demand on the limited supply of supportive living spaces. The assessment of changes in activities of daily living (ADL) including hygiene and food preparation such as reduced frequency or absence is important to support aging in place and independence. The measurement of when water is used, the use duration and where it is used within the home are assessments of importance within many ADLs. It can provide an indication of the ongoing capability for the resident(s) for daily hygiene (washroom) and nutrition (kitchen). This paper presents the use of low-cost thermistors applied to the outside of the hot and cold water pipes at point of use. This is an example of sensor substitution to measure water use where this substitution is important as the installation of typical flow meters can be complex especially if they are added to the pipes at multiple points of use. When water flows through the pipe, the pipe's temperature deviates from a steady-state room temperature and the paper shows how this deviation is detected by the system to indicate water use, distinguish between hot and cold water use and lastly measure the duration of water use. The sensor and presented method allow for the independent detection of hot and cold water. This provides an additional assessment that can be used in supportive smart home well-being and ADL assessment systems.
{"title":"Assessing Activities of Daily Living by Measuring Residential Water Use with Low Cost Thermistors","authors":"Phillippe Forster, Bruce Wallace, R. Goubran, F. Knoefel","doi":"10.1109/MeMeA54994.2022.9856541","DOIUrl":"https://doi.org/10.1109/MeMeA54994.2022.9856541","url":null,"abstract":"The ongoing assessment of well-being can enable aging adults to remain independent and continue to age in place in their own home. This provides them with a better quality of life and reduces demand on the limited supply of supportive living spaces. The assessment of changes in activities of daily living (ADL) including hygiene and food preparation such as reduced frequency or absence is important to support aging in place and independence. The measurement of when water is used, the use duration and where it is used within the home are assessments of importance within many ADLs. It can provide an indication of the ongoing capability for the resident(s) for daily hygiene (washroom) and nutrition (kitchen). This paper presents the use of low-cost thermistors applied to the outside of the hot and cold water pipes at point of use. This is an example of sensor substitution to measure water use where this substitution is important as the installation of typical flow meters can be complex especially if they are added to the pipes at multiple points of use. When water flows through the pipe, the pipe's temperature deviates from a steady-state room temperature and the paper shows how this deviation is detected by the system to indicate water use, distinguish between hot and cold water use and lastly measure the duration of water use. The sensor and presented method allow for the independent detection of hot and cold water. This provides an additional assessment that can be used in supportive smart home well-being and ADL assessment systems.","PeriodicalId":106228,"journal":{"name":"2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"135 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121974612","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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