Pub Date : 2022-06-22DOI: 10.1109/MeMeA54994.2022.9856565
Vaishali Balakarthikeyan, S. Vijayarangan, S. Preejith, M. Sivaprakasam
Heart Rate Variability (HRV) is a widely accepted technique used to measure the stress level of individuals. The state of the art HRV features namely Hilbert spectral and Detrended Fluctuation Analysis (DFA) estimates have opened ways to measure mental state of the individual. The HRV spectral estimates Instantaneous Amplitude of Low Frequency band (LFiA) & Instantaneous Amplitude of High Frequency band (H FiA) derived from Hilbert Transform provides better categorization of mental stress states than conventional frequency parameters due to joint 2-D representation of the low and High frequency HRV bands. Another HRV based approach DFA, which is robust against non-linearity and non-stationarity of cardiac time series caused by complex interactions, helps in providing reliable HRV interpretation. Based on the research evidences for both Hilbert and DFA estimates, it was observed that the use of Hilbert spectral estimates in stress assessment was not validated under free living condition and the application of DFA for mental stress assessment of individuals was not studied. In this work the utility of Hilbert Transform and DFA in HRV based stress assessment was studied under two different settings (confined and free living). The first objective was to determine whether DFA can be used to delineate between two mental states (baseline and stress), under both confined and free living conditions, and to quantify its discriminatory power in the context of mental stress detection. The second objective was to examine the utility of Hilbert estimates in determining mental state under free living conditions. The third objective was to compare the discriminatory power of DFA and Hilbert Transform in stress state detection. From this study, it was observed that both Hilbert and DFA methods can be used to delineate between two mental states under both confined and free living conditions. From the comparative analysis, it was observed that Hilbert estimates showed better discriminatory power than DFA under both the settings.
{"title":"A Comparative Study of Heart Rate Variability Methods for Stress Detection","authors":"Vaishali Balakarthikeyan, S. Vijayarangan, S. Preejith, M. Sivaprakasam","doi":"10.1109/MeMeA54994.2022.9856565","DOIUrl":"https://doi.org/10.1109/MeMeA54994.2022.9856565","url":null,"abstract":"Heart Rate Variability (HRV) is a widely accepted technique used to measure the stress level of individuals. The state of the art HRV features namely Hilbert spectral and Detrended Fluctuation Analysis (DFA) estimates have opened ways to measure mental state of the individual. The HRV spectral estimates Instantaneous Amplitude of Low Frequency band (LFiA) & Instantaneous Amplitude of High Frequency band (H FiA) derived from Hilbert Transform provides better categorization of mental stress states than conventional frequency parameters due to joint 2-D representation of the low and High frequency HRV bands. Another HRV based approach DFA, which is robust against non-linearity and non-stationarity of cardiac time series caused by complex interactions, helps in providing reliable HRV interpretation. Based on the research evidences for both Hilbert and DFA estimates, it was observed that the use of Hilbert spectral estimates in stress assessment was not validated under free living condition and the application of DFA for mental stress assessment of individuals was not studied. In this work the utility of Hilbert Transform and DFA in HRV based stress assessment was studied under two different settings (confined and free living). The first objective was to determine whether DFA can be used to delineate between two mental states (baseline and stress), under both confined and free living conditions, and to quantify its discriminatory power in the context of mental stress detection. The second objective was to examine the utility of Hilbert estimates in determining mental state under free living conditions. The third objective was to compare the discriminatory power of DFA and Hilbert Transform in stress state detection. From this study, it was observed that both Hilbert and DFA methods can be used to delineate between two mental states under both confined and free living conditions. From the comparative analysis, it was observed that Hilbert estimates showed better discriminatory power than DFA under both the settings.","PeriodicalId":106228,"journal":{"name":"2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"14 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":"126538340","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/MeMeA54994.2022.9856558
Lili Zhu, P. Spachos, S. Gregori
Wearable technology is growing in popularity, and wearable devices, such as smartwatches, are used in many applications, from fitness tracking and activity recognition to health monitoring. As the affordability and popularity of such devices increase, so does the amount of personal and unique data that they provide. At the same time, advantages in microprocessor and memory technology enable multiple physiological signal sensors integrated into wearable devices to collect personal and unique data. After the data is extracted, machine learning classification algorithms can help investigate the insights of the data. In this work, we examine the performance of a real-time stress detection system based on physiological signals collected from wearable devices. Specifically, three physiological signals, electrodermal activity (EDA), electrocardiogram (ECG), and photoplethysmo-graph (PPG) that can be collected through smartwatches, are examined for stress classification. Six machine learning methods are used for the classification in a post-acquisition phase, at a computer, including Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest, Naive Bayes, Logistic Regression, and Stacking Ensemble Learning (SEL). Data from two publicly available datasets are used for training and testing. We examine the accuracy of each modality and the combination of all modalities. According to evaluation results, EDA has the best accuracy when SEL is used for classification. Also, the accuracy of EDA outperforms the other signals and combinations, in comparison with any of the other machine learning approaches, for both datasets. EDA collected from the wearable device has a great potential to be used for a real-time stress detection system.
{"title":"Multimodal Physiological Signals and Machine Learning for Stress Detection by Wearable Devices","authors":"Lili Zhu, P. Spachos, S. Gregori","doi":"10.1109/MeMeA54994.2022.9856558","DOIUrl":"https://doi.org/10.1109/MeMeA54994.2022.9856558","url":null,"abstract":"Wearable technology is growing in popularity, and wearable devices, such as smartwatches, are used in many applications, from fitness tracking and activity recognition to health monitoring. As the affordability and popularity of such devices increase, so does the amount of personal and unique data that they provide. At the same time, advantages in microprocessor and memory technology enable multiple physiological signal sensors integrated into wearable devices to collect personal and unique data. After the data is extracted, machine learning classification algorithms can help investigate the insights of the data. In this work, we examine the performance of a real-time stress detection system based on physiological signals collected from wearable devices. Specifically, three physiological signals, electrodermal activity (EDA), electrocardiogram (ECG), and photoplethysmo-graph (PPG) that can be collected through smartwatches, are examined for stress classification. Six machine learning methods are used for the classification in a post-acquisition phase, at a computer, including Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest, Naive Bayes, Logistic Regression, and Stacking Ensemble Learning (SEL). Data from two publicly available datasets are used for training and testing. We examine the accuracy of each modality and the combination of all modalities. According to evaluation results, EDA has the best accuracy when SEL is used for classification. Also, the accuracy of EDA outperforms the other signals and combinations, in comparison with any of the other machine learning approaches, for both datasets. EDA collected from the wearable device has a great potential to be used for a real-time stress detection system.","PeriodicalId":106228,"journal":{"name":"2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"27 Pt 2 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":"129761525","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/MeMeA54994.2022.9856444
Vinothini Selvaraju, P. Karthick, S. Ramakrishnan
Preterm birth (gestational age <37 weeks) is one of the most critical global concerns that causes maternal and fetal morbidity and mortality. Early detection of this condition allows for timely intervention to delay labor by providing tocolytic drugs and rest. The objective of this work is to explore the cyclostationary behavior in electrohysterography (EHG) signals and to predict preterm conditions. The signals recorded prior to the 26 weeks of pregnancy are considered in this work. It is pre-processed using Butterworth bandpass filters to remove artifacts. The fast Fourier transform accumulation method (FAM) is applied to the pre-processed signals to estimate the spectral correlation density (SCD). The degree of cyclostationarity (DCS) is calculated from SCD to evaluate the presence of cyclostationarity in the signals. Features, such as mean, variance, cyclic frequency spectral area (CFSA), and full width half maximum (FWHM), are extracted from the spectra and statistically analyzed. The results illustrate that SCD and DCS confirm the existence of cyclostationarity in EHG signals. All the extracted features are observed to decrease in preterm conditions. This might be due to the increased coordination that is reflected in the signal in terms of reduced frequency components. Further, extracted features are found to have statistical significance (p < 0.05) in discriminating both the conditions. Thus, it appears that cyclostationary features might be clinically beneficial in the early prediction of preterm birth.
{"title":"Spectral Correlation Density based Electrohysterography Signal Analysis for the Detection of Preterm Birth","authors":"Vinothini Selvaraju, P. Karthick, S. Ramakrishnan","doi":"10.1109/MeMeA54994.2022.9856444","DOIUrl":"https://doi.org/10.1109/MeMeA54994.2022.9856444","url":null,"abstract":"Preterm birth (gestational age <37 weeks) is one of the most critical global concerns that causes maternal and fetal morbidity and mortality. Early detection of this condition allows for timely intervention to delay labor by providing tocolytic drugs and rest. The objective of this work is to explore the cyclostationary behavior in electrohysterography (EHG) signals and to predict preterm conditions. The signals recorded prior to the 26 weeks of pregnancy are considered in this work. It is pre-processed using Butterworth bandpass filters to remove artifacts. The fast Fourier transform accumulation method (FAM) is applied to the pre-processed signals to estimate the spectral correlation density (SCD). The degree of cyclostationarity (DCS) is calculated from SCD to evaluate the presence of cyclostationarity in the signals. Features, such as mean, variance, cyclic frequency spectral area (CFSA), and full width half maximum (FWHM), are extracted from the spectra and statistically analyzed. The results illustrate that SCD and DCS confirm the existence of cyclostationarity in EHG signals. All the extracted features are observed to decrease in preterm conditions. This might be due to the increased coordination that is reflected in the signal in terms of reduced frequency components. Further, extracted features are found to have statistical significance (p < 0.05) in discriminating both the conditions. Thus, it appears that cyclostationary features might be clinically beneficial in the early prediction of preterm birth.","PeriodicalId":106228,"journal":{"name":"2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"13 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":"129900488","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/MeMeA54994.2022.9856498
B. Koteska, Hristina Mitrova, A. Bogdanova, F. Lehocki
Continuous monitoring of blood oxygen saturation level (SpO2) during the second triage in the high casualty event and determining the hemostability of a patient/victim until arrival to a medical facility, is essential in emergency situations. Using a SmartPatch device attached to a victim's chest that contains a Photoplethysmogram Waveforms (PPG) sensor, one can obtain the SpO2 parameter. Our interest in the process of the SmartPatch prototype development is to investigate the monitoring of a blood oxygen saturation level by using the embedded PPG sensor. We explore acquiring the Sp02 by extracting the set of features from the PPG signal utilizing two Python toolkits, HeartPy and Neurokit, in order to model the Machine learning predictors, using multiple regressors. The PPG signal is preprocessed by various filtering techniques to remove low/high frequency noise. The model was trained and tested using the clinical data collected from 52 subjects with SpO2 levels varying from 83 – 100%. The best experimental results - MAE (1.45), MSE (3.85), RMSE (1.96) and RMSLE (0.02) scores are achieved with the Random Forest regressor in the experiment with 7 features extracted from the both toolkits.
{"title":"Machine learning based SpO2 prediction from PPG signal's characteristics features","authors":"B. Koteska, Hristina Mitrova, A. Bogdanova, F. Lehocki","doi":"10.1109/MeMeA54994.2022.9856498","DOIUrl":"https://doi.org/10.1109/MeMeA54994.2022.9856498","url":null,"abstract":"Continuous monitoring of blood oxygen saturation level (SpO2) during the second triage in the high casualty event and determining the hemostability of a patient/victim until arrival to a medical facility, is essential in emergency situations. Using a SmartPatch device attached to a victim's chest that contains a Photoplethysmogram Waveforms (PPG) sensor, one can obtain the SpO2 parameter. Our interest in the process of the SmartPatch prototype development is to investigate the monitoring of a blood oxygen saturation level by using the embedded PPG sensor. We explore acquiring the Sp02 by extracting the set of features from the PPG signal utilizing two Python toolkits, HeartPy and Neurokit, in order to model the Machine learning predictors, using multiple regressors. The PPG signal is preprocessed by various filtering techniques to remove low/high frequency noise. The model was trained and tested using the clinical data collected from 52 subjects with SpO2 levels varying from 83 – 100%. The best experimental results - MAE (1.45), MSE (3.85), RMSE (1.96) and RMSLE (0.02) scores are achieved with the Random Forest regressor in the experiment with 7 features extracted from the both toolkits.","PeriodicalId":106228,"journal":{"name":"2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"86 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":"128860816","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/MeMeA54994.2022.9856407
Ardiana Carlucci, Marco Morisco, Francesco Dell’Olio
In recent years, the medical sector has made use of innovative and advanced electronic and robotic systems that offer enormous potential, allowing research, diagnosis, and treatments that had been considered unbelievable until today. In particular, medical robotics contributes to expanding and improving the possibilities of intervention in various sectors of medicine through the development of complex platforms integrating sensors, actuators, processing hardware, and software. The paper reports on the development, at prototype level, of an electronic device for vital sign detection. The device is integrated with Aphel, an artificial intelligence (AI) platform that includes humanoid robots, supporting patients and healthcare professionals in hospitals. The achieved results highlight the advantages of the convergence between electronic devices and robotic entities in a wide range of healthcare applications.
{"title":"Human Vital Sign Detection by a Microcontroller-Based Device Integrated into a Social Humanoid Robot","authors":"Ardiana Carlucci, Marco Morisco, Francesco Dell’Olio","doi":"10.1109/MeMeA54994.2022.9856407","DOIUrl":"https://doi.org/10.1109/MeMeA54994.2022.9856407","url":null,"abstract":"In recent years, the medical sector has made use of innovative and advanced electronic and robotic systems that offer enormous potential, allowing research, diagnosis, and treatments that had been considered unbelievable until today. In particular, medical robotics contributes to expanding and improving the possibilities of intervention in various sectors of medicine through the development of complex platforms integrating sensors, actuators, processing hardware, and software. The paper reports on the development, at prototype level, of an electronic device for vital sign detection. The device is integrated with Aphel, an artificial intelligence (AI) platform that includes humanoid robots, supporting patients and healthcare professionals in hospitals. The achieved results highlight the advantages of the convergence between electronic devices and robotic entities in a wide range of healthcare applications.","PeriodicalId":106228,"journal":{"name":"2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"13 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":"127693523","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/MeMeA54994.2022.9856413
E. Donati, Christos Chousidis
Voice-to-MIDI real-time conversion is a challenging task that presents a series of obstacles and complications. The main issue is the tracking of the pitch. The frequency tracking of human voice can be inaccurate and computationally expensive due to spectral complexity of voice sounds. Moreover, with microphone-based systems, the presence of environmental noise and neighbouring sounds further affect the accuracy of the frequency tracking. Another issue with the conversion of voice into MIDI, is the presence of non-singing phonemes. As every sound picked up by the microphone would go through the conversion system, any voice or sounded phonemes produced by the user will result in a MIDI output. This research addresses such issues by applying a novel experimental method which employs electroglottography, known to the medical community as EGG, as a source for the pitch tracking operation. Electroglottography improves both the accuracy of the tracking and the ease of processing as it delivers a direct evaluation of the vocal folds operation whilst bypassing any contamination from other sound sources. Furthermore, to address the issue of non-singing phonemes, the proposed method employs the use of neural networks for a real-time classification of the vocal act produced by the user.
{"title":"Electroglottography based voice-to-MIDI real time converter with AI voice act classification","authors":"E. Donati, Christos Chousidis","doi":"10.1109/MeMeA54994.2022.9856413","DOIUrl":"https://doi.org/10.1109/MeMeA54994.2022.9856413","url":null,"abstract":"Voice-to-MIDI real-time conversion is a challenging task that presents a series of obstacles and complications. The main issue is the tracking of the pitch. The frequency tracking of human voice can be inaccurate and computationally expensive due to spectral complexity of voice sounds. Moreover, with microphone-based systems, the presence of environmental noise and neighbouring sounds further affect the accuracy of the frequency tracking. Another issue with the conversion of voice into MIDI, is the presence of non-singing phonemes. As every sound picked up by the microphone would go through the conversion system, any voice or sounded phonemes produced by the user will result in a MIDI output. This research addresses such issues by applying a novel experimental method which employs electroglottography, known to the medical community as EGG, as a source for the pitch tracking operation. Electroglottography improves both the accuracy of the tracking and the ease of processing as it delivers a direct evaluation of the vocal folds operation whilst bypassing any contamination from other sound sources. Furthermore, to address the issue of non-singing phonemes, the proposed method employs the use of neural networks for a real-time classification of the vocal act produced by the user.","PeriodicalId":106228,"journal":{"name":"2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"93 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":"122412585","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/MeMeA54994.2022.9856519
Madison Cohen-McFarlane, Bruce Wallace, P. Xi, R. Goubran, F. Knoefel
The field of remote health monitoring is a growing field, which is being driven by the rapid advances in sensors and sensor measurement systems. The respiratory system can be affected by a variety of underlying conditions and respiratory event monitoring can provide medical professionals with information that would otherwise be unavailable. A key area of concern is respiration over the course of a night, changes in which can be indicative of breathing and sleep related disorders. Previous work has proposed the use of pressure sensitive mats (PSM) or audio measurement to independently detect these changes. However, neither the PSM measurement nor the audio measurement is able to capture all respiratory events and there are privacy concerns associated with continuous monitoring (especially when recording audio). This paper presents the feasibility of a system that would utilize both PSM and audio measurements. Here, a single participant was asked to lay down on a PSM and to perform a series of respiratory events (normal breathing, fast breathing, slow breathing, gasping, mimicking central sleep apnea, wheezing, snoring, and coughing) while a microphone was recording. Signal processing was applied to both measurements in order to investigate both breathing rate and uncommon respiratory events. The resulting signals were then compared. The advantages and disadvantages of both measurements are discussed and a sample scenario of the fusion of audio and PSM measurements is presented in order to capture obstructive sleep apnea events.
{"title":"Feasibility analysis of the fusion of pressure sensors and audio measurements for respiratory evaluations","authors":"Madison Cohen-McFarlane, Bruce Wallace, P. Xi, R. Goubran, F. Knoefel","doi":"10.1109/MeMeA54994.2022.9856519","DOIUrl":"https://doi.org/10.1109/MeMeA54994.2022.9856519","url":null,"abstract":"The field of remote health monitoring is a growing field, which is being driven by the rapid advances in sensors and sensor measurement systems. The respiratory system can be affected by a variety of underlying conditions and respiratory event monitoring can provide medical professionals with information that would otherwise be unavailable. A key area of concern is respiration over the course of a night, changes in which can be indicative of breathing and sleep related disorders. Previous work has proposed the use of pressure sensitive mats (PSM) or audio measurement to independently detect these changes. However, neither the PSM measurement nor the audio measurement is able to capture all respiratory events and there are privacy concerns associated with continuous monitoring (especially when recording audio). This paper presents the feasibility of a system that would utilize both PSM and audio measurements. Here, a single participant was asked to lay down on a PSM and to perform a series of respiratory events (normal breathing, fast breathing, slow breathing, gasping, mimicking central sleep apnea, wheezing, snoring, and coughing) while a microphone was recording. Signal processing was applied to both measurements in order to investigate both breathing rate and uncommon respiratory events. The resulting signals were then compared. The advantages and disadvantages of both measurements are discussed and a sample scenario of the fusion of audio and PSM measurements is presented in order to capture obstructive sleep apnea events.","PeriodicalId":106228,"journal":{"name":"2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"36 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":"121383923","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/MeMeA54994.2022.9856428
A. Golparvar, Assim Boukhayma, C. Enz, S. Carrara
Optical blood glucose sensing offers pain-free, non-invasive, continuous monitoring with minimum risk of infection since it does not require breaking the skin barrier. Among various optical detection and spectroscopic techniques, only Raman scattering offers both high-accuracy and chemical-specific acquisition along with label-free sensing. However, spontaneous Raman scattering is a feeble process. The integration time is long and high laser intensities are demanded to achieve acceptable sensitivity in detecting physiologically relevant glucose levels. This hinders the inherent advantages of Raman scattering-based technologies as a wearable medical point-of-care device. Therefore, this study applies stimulated Raman scattering (SRS) to glucose sensing, which overcomes the limitations of spontaneous Raman spectroscopy. This is the first study demonstrating the application of SRS in glucose concentration monitoring. Herein, by enhancing the Raman effect using stimulating excitation, we have recorded a linear calibration curve for concentrations below 100 mol/m3 with a theoretical limit of detection (LOD) of 3.5 mol/m3 in a phenomenal 0.6 s integration time merely by employing univariate data analysis. In addition, we have assessed the optimum required averaged laser power and sensing mechanism's feasibility in complete human serum glucose measurement and established a highly selective detection mechanism by solely identifying the characteristic Raman shift peak of glucose around 1130 cm−1.
{"title":"Rapid, Sensitive and Selective Optical Glucose Sensing with Stimulated Raman Scattering (SRS)","authors":"A. Golparvar, Assim Boukhayma, C. Enz, S. Carrara","doi":"10.1109/MeMeA54994.2022.9856428","DOIUrl":"https://doi.org/10.1109/MeMeA54994.2022.9856428","url":null,"abstract":"Optical blood glucose sensing offers pain-free, non-invasive, continuous monitoring with minimum risk of infection since it does not require breaking the skin barrier. Among various optical detection and spectroscopic techniques, only Raman scattering offers both high-accuracy and chemical-specific acquisition along with label-free sensing. However, spontaneous Raman scattering is a feeble process. The integration time is long and high laser intensities are demanded to achieve acceptable sensitivity in detecting physiologically relevant glucose levels. This hinders the inherent advantages of Raman scattering-based technologies as a wearable medical point-of-care device. Therefore, this study applies stimulated Raman scattering (SRS) to glucose sensing, which overcomes the limitations of spontaneous Raman spectroscopy. This is the first study demonstrating the application of SRS in glucose concentration monitoring. Herein, by enhancing the Raman effect using stimulating excitation, we have recorded a linear calibration curve for concentrations below 100 mol/m3 with a theoretical limit of detection (LOD) of 3.5 mol/m3 in a phenomenal 0.6 s integration time merely by employing univariate data analysis. In addition, we have assessed the optimum required averaged laser power and sensing mechanism's feasibility in complete human serum glucose measurement and established a highly selective detection mechanism by solely identifying the characteristic Raman shift peak of glucose around 1130 cm−1.","PeriodicalId":106228,"journal":{"name":"2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"88 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":"125698821","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/MeMeA54994.2022.9856451
E. D. Vita, Francesca De Tommasi, C. Altomare, Sofia Ialongo, C. Massaroni, D. Presti, E. Faiella, F. Andresciani, G. Pacella, Andrea Palermo, M. Carassiti, A. Iadicicco, R. Grasso, E. Schena, S. Campopiano
Traditional methods to treat thyroid nodules like thyroidectomy and radioiodine therapy can involve a multitude of risks, such as damages to parathyroid glands and aftercare hypothyroidism. Minimally invasive surgery (MIS) can represent an alternative solution, avoiding general anesthesia or radioactive substances. In the framework of MIS, thermal ablation therapies (TATs) are gaining momentum to treat both benign and malign tumors by inducing a significant temperature variation inside the treated tissue. Among TATs, microwave ablation (MWA) is a newly emerging technique which has proved to be an effective and safe method in treating tumors in different organs like liver and kidney, more recently including thyroid nodules. However, an experimental analysis of the temperature reached within the thyroid tissue during the treatment has not been performed yet. Temperature monitoring during TATs can be beneficial to ensure the complete tumor eradication, especially in case of new challenging organs like thyroid. In this regard, this work aims to assess the spatial and temporal distribution of the temperature increment during MWA by performing ex vivo tests on swine thyroid. Temperature variations have been recorded by means of different arrays of fiber optic Bragg grating sensors (FBGs), each of those embedding ten sensing points in parallel to the MW applicator inside the tissue. These trials could provide the first stage in the further investigation of thyroid MWA, towards a better understanding of the most suitable treatment settings for this kind of tumor to improve the treatment outcomes.
{"title":"Fiber Bragg Gratings for Temperature Monitoring during Thyroid Microwave Ablation: a Preliminary Analysis","authors":"E. D. Vita, Francesca De Tommasi, C. Altomare, Sofia Ialongo, C. Massaroni, D. Presti, E. Faiella, F. Andresciani, G. Pacella, Andrea Palermo, M. Carassiti, A. Iadicicco, R. Grasso, E. Schena, S. Campopiano","doi":"10.1109/MeMeA54994.2022.9856451","DOIUrl":"https://doi.org/10.1109/MeMeA54994.2022.9856451","url":null,"abstract":"Traditional methods to treat thyroid nodules like thyroidectomy and radioiodine therapy can involve a multitude of risks, such as damages to parathyroid glands and aftercare hypothyroidism. Minimally invasive surgery (MIS) can represent an alternative solution, avoiding general anesthesia or radioactive substances. In the framework of MIS, thermal ablation therapies (TATs) are gaining momentum to treat both benign and malign tumors by inducing a significant temperature variation inside the treated tissue. Among TATs, microwave ablation (MWA) is a newly emerging technique which has proved to be an effective and safe method in treating tumors in different organs like liver and kidney, more recently including thyroid nodules. However, an experimental analysis of the temperature reached within the thyroid tissue during the treatment has not been performed yet. Temperature monitoring during TATs can be beneficial to ensure the complete tumor eradication, especially in case of new challenging organs like thyroid. In this regard, this work aims to assess the spatial and temporal distribution of the temperature increment during MWA by performing ex vivo tests on swine thyroid. Temperature variations have been recorded by means of different arrays of fiber optic Bragg grating sensors (FBGs), each of those embedding ten sensing points in parallel to the MW applicator inside the tissue. These trials could provide the first stage in the further investigation of thyroid MWA, towards a better understanding of the most suitable treatment settings for this kind of tumor to improve the treatment outcomes.","PeriodicalId":106228,"journal":{"name":"2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"64 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":"125509897","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/MeMeA54994.2022.9856564
Andrei Krivošei, M. Min, P. Annus, Olev Märtens, M. Metshein, Kristina Lotamõis, M. Rist
In the paper we proposed a new method for the cardiac pulse wave base lines and peak lines estimation and correction. The proposed method is mainly directed, but not limited, to the Electrical Bio-Impedance (EBI) and Central Aortic Pressure (CAP) signals. However, the method can be extended to other signal kinds and application fields. Definitely, the proposed method can be applied to the PPG signals and blood pressure waveforms measured from different body locations, not only central aortic pressure. The base line correction approach, instead of filtering, is selected due to the physiological peculiarities of the cardiac cycle. The minimum value of a cardiac signal, which is the diastolic blood pressure (minimum pressure in the cardiac cycle), varies much less than the systolic peak value. Thus, in our research work we use the base line correction (subtraction) instead of mean value subtraction (filtering) to get cardiac signal's component. The proposed method is based on combination of the mathematical morphology and on the Hankel matrix. The method does not need separate estimates of peaks and valleys of the waveforms. Moreover, for correctly estimated signal frequency, the proposed method estimates the base line and the peak line as a piecewise lines between signal's minima or maxima. The result is a corrected cardiac signal that does not need additional processing, based on piecewise estimates of the base and peak lines.
{"title":"Hankel Matrix Based Algorithm for Cardiac Pulse Wave Base and Peak Lines Correction","authors":"Andrei Krivošei, M. Min, P. Annus, Olev Märtens, M. Metshein, Kristina Lotamõis, M. Rist","doi":"10.1109/MeMeA54994.2022.9856564","DOIUrl":"https://doi.org/10.1109/MeMeA54994.2022.9856564","url":null,"abstract":"In the paper we proposed a new method for the cardiac pulse wave base lines and peak lines estimation and correction. The proposed method is mainly directed, but not limited, to the Electrical Bio-Impedance (EBI) and Central Aortic Pressure (CAP) signals. However, the method can be extended to other signal kinds and application fields. Definitely, the proposed method can be applied to the PPG signals and blood pressure waveforms measured from different body locations, not only central aortic pressure. The base line correction approach, instead of filtering, is selected due to the physiological peculiarities of the cardiac cycle. The minimum value of a cardiac signal, which is the diastolic blood pressure (minimum pressure in the cardiac cycle), varies much less than the systolic peak value. Thus, in our research work we use the base line correction (subtraction) instead of mean value subtraction (filtering) to get cardiac signal's component. The proposed method is based on combination of the mathematical morphology and on the Hankel matrix. The method does not need separate estimates of peaks and valleys of the waveforms. Moreover, for correctly estimated signal frequency, the proposed method estimates the base line and the peak line as a piecewise lines between signal's minima or maxima. The result is a corrected cardiac signal that does not need additional processing, based on piecewise estimates of the base and peak lines.","PeriodicalId":106228,"journal":{"name":"2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"4 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":"126852427","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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