Pub Date : 2022-12-03DOI: 10.1109/SPMB55497.2022.10014708
E. Shachar, A. Lev, O. Rosen
Eye-blinks in electroencephalogram (EEG) signals can be regarded either as unwanted noise or as a source of information. In both cases, a reliable and accurate detector is needed. As many applications require detection and processing of eye-blinks in real-time, detectors are required to be fast and simple. In this work, we have developed a non-learning algorithm for the detection and extraction of eye-blink segments from EEG signals. The signals were recorded by Muse™, a portable EEG device for recreational use. The proposed algorithm detects eye-blinks via several deterministic processing steps. The algorithm extracts peaks occurring in the EEG signal during the two main eye-blink phases, via extraction of unique features of the EEG eye-blink signal. The proposed algorithm applies various pre-processing steps to ensure robust detection, as well as several sanity-checks to prevent the detection of false peaks and partial eye-blinks. A dataset with recordings of the length of approximately 20 seconds each, taken from few different subjects has been created. The eye-blink annotations were made manually. The proposed algorithm obtains an accuracy rate of 100% on the obtained dataset, while employing a set of deterministic operations which renders it usable in low-resource, real-time applications.
{"title":"MED: Muse™-based Eye-blink Detection Algorithm Using a Single EEG Channel","authors":"E. Shachar, A. Lev, O. Rosen","doi":"10.1109/SPMB55497.2022.10014708","DOIUrl":"https://doi.org/10.1109/SPMB55497.2022.10014708","url":null,"abstract":"Eye-blinks in electroencephalogram (EEG) signals can be regarded either as unwanted noise or as a source of information. In both cases, a reliable and accurate detector is needed. As many applications require detection and processing of eye-blinks in real-time, detectors are required to be fast and simple. In this work, we have developed a non-learning algorithm for the detection and extraction of eye-blink segments from EEG signals. The signals were recorded by Muse™, a portable EEG device for recreational use. The proposed algorithm detects eye-blinks via several deterministic processing steps. The algorithm extracts peaks occurring in the EEG signal during the two main eye-blink phases, via extraction of unique features of the EEG eye-blink signal. The proposed algorithm applies various pre-processing steps to ensure robust detection, as well as several sanity-checks to prevent the detection of false peaks and partial eye-blinks. A dataset with recordings of the length of approximately 20 seconds each, taken from few different subjects has been created. The eye-blink annotations were made manually. The proposed algorithm obtains an accuracy rate of 100% on the obtained dataset, while employing a set of deterministic operations which renders it usable in low-resource, real-time applications.","PeriodicalId":261445,"journal":{"name":"2022 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125555175","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 : 2022-12-03DOI: 10.1109/SPMB55497.2022.10014867
X. Li, P. Wang, Q. Li
In recent years, because of safety and timeliness of the ultrasound imaging, this technology has been widely used in the field of medical diagnosis [1]. In the process of ultrasound imaging, the beamforming process is the most important part, which directly determines the imaging quality [2]. At present, the most widely used algorithm is the traditional delay-and-sum (DAS), but DAS has some inherent disadvantages in low resolution and obviously artifacts [3]. For the purpose of solving these deficiencies, many advanced imaging methods have been proposed. Among them, the minimum variance (MV) designed by Capon is a kind of very potential algorithm due to its high resolution [4]. However, the effect of MV algorithm is mainly depended on the accuracy of the preset desired directional vector and the calculation of covariance matrix. Therefore, the MV has the problem of insufficient robustness [5]. In subsequent studies, many innovative methods had been used to overcome the shortcomings of MV algorithm [6], such as eigenspace-based MV (ESBMV).
{"title":"Frequency Domain Eigenspace-based Projection Minimum Variance for Ultrasound Imaging","authors":"X. Li, P. Wang, Q. Li","doi":"10.1109/SPMB55497.2022.10014867","DOIUrl":"https://doi.org/10.1109/SPMB55497.2022.10014867","url":null,"abstract":"In recent years, because of safety and timeliness of the ultrasound imaging, this technology has been widely used in the field of medical diagnosis [1]. In the process of ultrasound imaging, the beamforming process is the most important part, which directly determines the imaging quality [2]. At present, the most widely used algorithm is the traditional delay-and-sum (DAS), but DAS has some inherent disadvantages in low resolution and obviously artifacts [3]. For the purpose of solving these deficiencies, many advanced imaging methods have been proposed. Among them, the minimum variance (MV) designed by Capon is a kind of very potential algorithm due to its high resolution [4]. However, the effect of MV algorithm is mainly depended on the accuracy of the preset desired directional vector and the calculation of covariance matrix. Therefore, the MV has the problem of insufficient robustness [5]. In subsequent studies, many innovative methods had been used to overcome the shortcomings of MV algorithm [6], such as eigenspace-based MV (ESBMV).","PeriodicalId":261445,"journal":{"name":"2022 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129463995","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 : 2022-12-03DOI: 10.1109/SPMB55497.2022.10014965
M. Iglesias, A. Favaro, C. Motley, E. Oh, R. Stevens, A. Butala, L. Moro-Velázquez, N. Dehak
In the last decade, improvements in automated speech processing, powered by signal processing and machine learning, has led to new approaches for medical assessment. Additionally, previous research in clinical speech has identified interpretable measures that are sensitive to changes in the cognitive, linguistic, affective, and motoric domains. In order to include speech-based automatic approaches in clinical applications, factors such as robustness, specificity, and interpretability of speech features are crucial. We focused on the analysis of a multi-modal array of interpretable features obtained from the spoken responses of participants with Neurodegenerative Diseases (ND) and control participants (CN) to neuropsychological tests. ND participants have Alzheimer's disease (AD), Parkinson's disease (PD), or Parkinson's disease mimics (PDM). We first collected spoken responses to three tests, a modified version of the Stroop test (MST), a verb naming task (VNT), and a noun naming task (NNT). Then, we arranged two complementary sets of cognitive and acoustic features and analyzed their statistical significance between the groups studied. Our results suggested that AD participants had significantly greater reaction times and significantly lower response accuracy with respect to the other groups across tests. In addition, PDM participants, compared to CN and PD participants, took a significantly longer time to complete the MST and NNT, while all the groups of participants with NDs showed significantly lower confidence during the MST. Since the analyzed features provided good differentiation results, they can be used in diagnostic tools for the assessment of NDs.
{"title":"Cognitive and Acoustic Speech and Language Patterns Occurring in Different Neurodegenerative Disorders while Performing Neuropsychological Tests","authors":"M. Iglesias, A. Favaro, C. Motley, E. Oh, R. Stevens, A. Butala, L. Moro-Velázquez, N. Dehak","doi":"10.1109/SPMB55497.2022.10014965","DOIUrl":"https://doi.org/10.1109/SPMB55497.2022.10014965","url":null,"abstract":"In the last decade, improvements in automated speech processing, powered by signal processing and machine learning, has led to new approaches for medical assessment. Additionally, previous research in clinical speech has identified interpretable measures that are sensitive to changes in the cognitive, linguistic, affective, and motoric domains. In order to include speech-based automatic approaches in clinical applications, factors such as robustness, specificity, and interpretability of speech features are crucial. We focused on the analysis of a multi-modal array of interpretable features obtained from the spoken responses of participants with Neurodegenerative Diseases (ND) and control participants (CN) to neuropsychological tests. ND participants have Alzheimer's disease (AD), Parkinson's disease (PD), or Parkinson's disease mimics (PDM). We first collected spoken responses to three tests, a modified version of the Stroop test (MST), a verb naming task (VNT), and a noun naming task (NNT). Then, we arranged two complementary sets of cognitive and acoustic features and analyzed their statistical significance between the groups studied. Our results suggested that AD participants had significantly greater reaction times and significantly lower response accuracy with respect to the other groups across tests. In addition, PDM participants, compared to CN and PD participants, took a significantly longer time to complete the MST and NNT, while all the groups of participants with NDs showed significantly lower confidence during the MST. Since the analyzed features provided good differentiation results, they can be used in diagnostic tools for the assessment of NDs.","PeriodicalId":261445,"journal":{"name":"2022 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127832649","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 : 2022-12-03DOI: 10.1109/SPMB55497.2022.10014902
C. Chen, L. Moro-Velázquez, A. Ožbolt, A. Butala, A. Pantelyat, N. Dehak
Persons with Parkinson's Disease (PD) frequently have speech and voice disorders. Regular speech therapy with a speech-language pathologist is essential to mitigate progressive symptom deterioration. Speech-related therapies, such choral singing groups are alternative approaches designed to be more naturalistic and enhance participant enjoyment. It is important to measure and quantify the effects of these therapies on the vocal features of PD patients to determine efficacy. We performed a prospective crossover study of 25 PD patients attending discussion or choral-singing groups for 12 weeks each (Parkinsonics NCT02753621). Every six weeks, each participant produced several recordings of the sustained vowels /a:/ and /e:/ at ‘normal’ and ‘maximum’ loudness. The goal was to identify if there are signal-processing-based features that can help track changes in the voice of PD patients over time. Voice features were extracted from these recordings using the Automatic Voice Condition Analysis (AVCA) library and were compared using non-parametric statistical tests. Results suggest that neither therapy caused any significant improvements in the analyzed phonatory aspects of the patients' voices. Future work should require use of connected speech to analyze articulation and comparison with a control group of participants with PD not attending any therapy to evaluate if therapy can mitigate the progressive effects of PD on the voice of patients.
{"title":"Phonatory Analysis on Parkinson's Disease Patients Attending Singing and Discussion Therapy (Parkinsonics) using Signal Processing Techniques","authors":"C. Chen, L. Moro-Velázquez, A. Ožbolt, A. Butala, A. Pantelyat, N. Dehak","doi":"10.1109/SPMB55497.2022.10014902","DOIUrl":"https://doi.org/10.1109/SPMB55497.2022.10014902","url":null,"abstract":"Persons with Parkinson's Disease (PD) frequently have speech and voice disorders. Regular speech therapy with a speech-language pathologist is essential to mitigate progressive symptom deterioration. Speech-related therapies, such choral singing groups are alternative approaches designed to be more naturalistic and enhance participant enjoyment. It is important to measure and quantify the effects of these therapies on the vocal features of PD patients to determine efficacy. We performed a prospective crossover study of 25 PD patients attending discussion or choral-singing groups for 12 weeks each (Parkinsonics NCT02753621). Every six weeks, each participant produced several recordings of the sustained vowels /a:/ and /e:/ at ‘normal’ and ‘maximum’ loudness. The goal was to identify if there are signal-processing-based features that can help track changes in the voice of PD patients over time. Voice features were extracted from these recordings using the Automatic Voice Condition Analysis (AVCA) library and were compared using non-parametric statistical tests. Results suggest that neither therapy caused any significant improvements in the analyzed phonatory aspects of the patients' voices. Future work should require use of connected speech to analyze articulation and comparison with a control group of participants with PD not attending any therapy to evaluate if therapy can mitigate the progressive effects of PD on the voice of patients.","PeriodicalId":261445,"journal":{"name":"2022 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125248896","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 : 2022-12-03DOI: 10.1109/spmb55497.2022.10014955
M. Desai
In this talk, we will discuss the use of electroencephalograms (EEG) in Intensive Care Units (ICU). We will review the use of EEGs as a multi-dimensional biomarker. We will review applications of artificial intelligence (AI) and machine learning (ML) for each type of biomarker. We will review cases highlighting biomarker usage in clinical management. Continuous EEG (CEEG) is an invaluable tool in the ICU since it yields multi-multi-dimensional biomarkers. AI can overcome or ameliorate limitations of CEEG applications in the ICU. Real-time analysis and interpretation of CEEG data is essential to influence clinical decision-making and clinical outcomes. ML models and AI integration into the decision-making process provides standardization and automation. Opportunities exist for the integration of real-time annotation and AI-based decision-support to achieve better patient outcomes.
{"title":"Artificial Intelligence, EEG and Clinical Outcomes in Intensive Care Units","authors":"M. Desai","doi":"10.1109/spmb55497.2022.10014955","DOIUrl":"https://doi.org/10.1109/spmb55497.2022.10014955","url":null,"abstract":"In this talk, we will discuss the use of electroencephalograms (EEG) in Intensive Care Units (ICU). We will review the use of EEGs as a multi-dimensional biomarker. We will review applications of artificial intelligence (AI) and machine learning (ML) for each type of biomarker. We will review cases highlighting biomarker usage in clinical management. Continuous EEG (CEEG) is an invaluable tool in the ICU since it yields multi-multi-dimensional biomarkers. AI can overcome or ameliorate limitations of CEEG applications in the ICU. Real-time analysis and interpretation of CEEG data is essential to influence clinical decision-making and clinical outcomes. ML models and AI integration into the decision-making process provides standardization and automation. Opportunities exist for the integration of real-time annotation and AI-based decision-support to achieve better patient outcomes.","PeriodicalId":261445,"journal":{"name":"2022 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)","volume":"192 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126110680","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 : 2022-12-03DOI: 10.1109/SPMB55497.2022.10014874
R. Dhar, S. Darwish, S. Darwish, R. Sandler, H. Mansy
Heart diseases are a leading cause of mortality globally with cardiovascular disease (CVD) accounting for around 17.9 million deaths as of 2019 [1]. Limited access to healthcare services in low- and middle-income countries may be a main reason of the high mortality. The financial burden associated with CVD is also high and may reach $70 billion in USA by 2030 [2]. Early detection of heart diseases can reduce adverse health events and lower related costs. Regular monitoring of these conditions can alert patients and healthcare providers about life-threatening abnormalities, which can reduce hospitalization rates. Use of a reliable, simple-to-use and cost-effective non-invasive techniques to detect heart conditions can expedite the diagnosis and treatment processes, improving patient management and quality of life.
{"title":"Effects of Exercise on SCG Signals in Healthy Subjects","authors":"R. Dhar, S. Darwish, S. Darwish, R. Sandler, H. Mansy","doi":"10.1109/SPMB55497.2022.10014874","DOIUrl":"https://doi.org/10.1109/SPMB55497.2022.10014874","url":null,"abstract":"Heart diseases are a leading cause of mortality globally with cardiovascular disease (CVD) accounting for around 17.9 million deaths as of 2019 [1]. Limited access to healthcare services in low- and middle-income countries may be a main reason of the high mortality. The financial burden associated with CVD is also high and may reach $70 billion in USA by 2030 [2]. Early detection of heart diseases can reduce adverse health events and lower related costs. Regular monitoring of these conditions can alert patients and healthcare providers about life-threatening abnormalities, which can reduce hospitalization rates. Use of a reliable, simple-to-use and cost-effective non-invasive techniques to detect heart conditions can expedite the diagnosis and treatment processes, improving patient management and quality of life.","PeriodicalId":261445,"journal":{"name":"2022 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134555771","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 : 2022-12-03DOI: 10.1109/SPMB55497.2022.10014969
R. Gunawardena, P. Sarrigiannis, D. Blackburn, F. He
Dynamical, causal and cross-frequency coupling analysis using the EEG has received significant interest for the analysis and diagnosis of neurological disorders [1]–[3]. Due to the high computational requirements needed for some of these methods, EEG channel selection is crucial [4]. Functional connectivity (FC) between EEG channels is often used for channel selection and connectivity analysis [4, S, 6]. Ideally, in the case of selecting channels for dynamical and causal analysis, FC methods should be able to account for linear and nonlinear spatial and temporal interactions between EEG channels. In neuroscience, FC is quantified using different measures of (dis) similarity to assess the statistical dependence between two signals [5]. However, the interpretation of FC measures can differ significantly from one measure to another[5, 7]. In the early diagnosis of AD, [7] showed correlations among various (dis)similarity measures, and therefore these measures can be grouped. Thus, one from each is sufficient to extract information from the data [7]. Therefore, the development of a generic measure of (dis)similarity is important in FC analysis.
{"title":"Kernel-based Nonlinear Manifold Learning for EEG Functional Connectivity Analysis with Application to Alzheimer's Disease","authors":"R. Gunawardena, P. Sarrigiannis, D. Blackburn, F. He","doi":"10.1109/SPMB55497.2022.10014969","DOIUrl":"https://doi.org/10.1109/SPMB55497.2022.10014969","url":null,"abstract":"Dynamical, causal and cross-frequency coupling analysis using the EEG has received significant interest for the analysis and diagnosis of neurological disorders [1]–[3]. Due to the high computational requirements needed for some of these methods, EEG channel selection is crucial [4]. Functional connectivity (FC) between EEG channels is often used for channel selection and connectivity analysis [4, S, 6]. Ideally, in the case of selecting channels for dynamical and causal analysis, FC methods should be able to account for linear and nonlinear spatial and temporal interactions between EEG channels. In neuroscience, FC is quantified using different measures of (dis) similarity to assess the statistical dependence between two signals [5]. However, the interpretation of FC measures can differ significantly from one measure to another[5, 7]. In the early diagnosis of AD, [7] showed correlations among various (dis)similarity measures, and therefore these measures can be grouped. Thus, one from each is sufficient to extract information from the data [7]. Therefore, the development of a generic measure of (dis)similarity is important in FC analysis.","PeriodicalId":261445,"journal":{"name":"2022 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132672879","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 : 2022-12-03DOI: 10.1109/SPMB55497.2022.10014709
E. Feldman, Y. Chen, R. Schachar, P. Cosman
Accommodation is the eye's ability to focus up close by changing the shape of the lens. Accommodation affects the development of myopia and glaucoma and its age-related decline results in presbyopia. Presbyopia affects 100% of the population in the fifth decade of life. An understanding of accommodation is required to develop the best treatments for these maladies, but how the lens changes shape is still in dispute after more than 165 years. The fundamental issue is whether the change in lens shape results from all zonules (circumferential suspensory ligaments that connect the lens of the eye to the ciliary body) relaxing, which causes central and peripheral lens surface steepening, or whether instead just the anterior and posterior zonules relax while the equatorial zonules are under increased tension, which causes the lens surface to peripherally flatten and centrally steepen. The alternatives are illustrated in Figure 1.
{"title":"Detecting Human Posterior Lens Surface Topographical Changes During Accommodation","authors":"E. Feldman, Y. Chen, R. Schachar, P. Cosman","doi":"10.1109/SPMB55497.2022.10014709","DOIUrl":"https://doi.org/10.1109/SPMB55497.2022.10014709","url":null,"abstract":"Accommodation is the eye's ability to focus up close by changing the shape of the lens. Accommodation affects the development of myopia and glaucoma and its age-related decline results in presbyopia. Presbyopia affects 100% of the population in the fifth decade of life. An understanding of accommodation is required to develop the best treatments for these maladies, but how the lens changes shape is still in dispute after more than 165 years. The fundamental issue is whether the change in lens shape results from all zonules (circumferential suspensory ligaments that connect the lens of the eye to the ciliary body) relaxing, which causes central and peripheral lens surface steepening, or whether instead just the anterior and posterior zonules relax while the equatorial zonules are under increased tension, which causes the lens surface to peripherally flatten and centrally steepen. The alternatives are illustrated in Figure 1.","PeriodicalId":261445,"journal":{"name":"2022 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114372846","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 : 2022-12-03DOI: 10.1109/SPMB55497.2022.10014843
M. Abdelhady, J. Han, S. Majerus, L. Brody, M. Damaser
Cystometry measures the behavior of the bladder and is frequently used to evaluate lower urinary tract abnormalities. Cystometry is conducted using two catheters, one in the bladder and the other in the vagina or rectum, which increases discomfort and complexity of the test. In this work we evaluated a method to estimate detrusor pressure (PDET), the pressure generated by a bladder contraction, from only a single catheter measuring vesical pressure (PVES). Using twenty urodynamic studies, we used statistical inference and wavelet multiresolution analysis to maximize the correlation coefficient $(R)$ between estimated PDET and calculated PDET after detecting and eliminating artifacts. Moreover, the estimator design considered a prospective real-time implementation. Root main square (RMS) error and correlation coefficient were used to evaluate algorithm accuracy in estimating $mathbf{P}_{text{DET}}$, while a statistical F -score evaluated the accuracy of artifact detection. The output of the proposed estimator compared with calculated PDET, and overall estimation performance showed that $RMS=10.7pm 2.1 text{cmH}_{2}mathrm{O}$ and $R=0.88pm$ 0.6 $(mathrm{N}=20)$. Moreover, detection accuracy for cough and Valsalva events were 99.5% and 84.30/0, respectively. We conclude that estimating PDET from PVES only is feasible making single channel cystometry a possibility.
{"title":"Detrusor Pressure Estimation from Single Channel Bladder Pressure Recordings","authors":"M. Abdelhady, J. Han, S. Majerus, L. Brody, M. Damaser","doi":"10.1109/SPMB55497.2022.10014843","DOIUrl":"https://doi.org/10.1109/SPMB55497.2022.10014843","url":null,"abstract":"Cystometry measures the behavior of the bladder and is frequently used to evaluate lower urinary tract abnormalities. Cystometry is conducted using two catheters, one in the bladder and the other in the vagina or rectum, which increases discomfort and complexity of the test. In this work we evaluated a method to estimate detrusor pressure (PDET), the pressure generated by a bladder contraction, from only a single catheter measuring vesical pressure (PVES). Using twenty urodynamic studies, we used statistical inference and wavelet multiresolution analysis to maximize the correlation coefficient $(R)$ between estimated PDET and calculated PDET after detecting and eliminating artifacts. Moreover, the estimator design considered a prospective real-time implementation. Root main square (RMS) error and correlation coefficient were used to evaluate algorithm accuracy in estimating $mathbf{P}_{text{DET}}$, while a statistical F -score evaluated the accuracy of artifact detection. The output of the proposed estimator compared with calculated PDET, and overall estimation performance showed that $RMS=10.7pm 2.1 text{cmH}_{2}mathrm{O}$ and $R=0.88pm$ 0.6 $(mathrm{N}=20)$. Moreover, detection accuracy for cough and Valsalva events were 99.5% and 84.30/0, respectively. We conclude that estimating PDET from PVES only is feasible making single channel cystometry a possibility.","PeriodicalId":261445,"journal":{"name":"2022 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122370254","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 : 2022-12-03DOI: 10.1109/SPMB55497.2022.10014953
V. Pedapati, K. Du, A. Mina, A. Bradley, J. Espino, K. Batmanghelich, P. Thirumala, S. Visweswaran
Continuous intraoperative monitoring with electroencephalography (EEG) is routinely used in carotid endarterectomy (CEA) to detect cerebral ischemia [1]. Visually observed changes in EEG related to ischemia include an ipsilateral decrease in amplitude of faster frequencies or an ipsilateral increase in activity of slower frequencies. In the literature, significant EEG change has been defined as a decrease in the amplitude in the alpha frequency band by 50% or greater or an increase in activity in the theta or delta frequency band by 50% or greater [2], [3]. Compared to raw EEG, quantitative EEG (QEEG) parameters can enhance visual EEG review. QEEG parameters are derived by applying Fourier transformation to raw EEG signals to generate power spectra [4]. Examples of QEEG parameters include delta, theta, alpha, beta, gamma power values, alpha power to delta power ratio (ADR), beta power to delta power ratio (BDR), alpha-plus-beta power to delta-plus-theta power ratio (ABDTR), spectral edge frequency 90% (SEF90) and amplitude-integrated EEG (aEEG). QEEG parameters have been previously investigated in detecting ischemia in a relatively small number of patients undergoing CEA [5]. In this study, we report on the analyses of QEEG parameters in a large population of patients who underwent CEA with EEG monitoring.
{"title":"Quantitative EEG Changes in Carotid Endarterectomy Correlated with Ischemia","authors":"V. Pedapati, K. Du, A. Mina, A. Bradley, J. Espino, K. Batmanghelich, P. Thirumala, S. Visweswaran","doi":"10.1109/SPMB55497.2022.10014953","DOIUrl":"https://doi.org/10.1109/SPMB55497.2022.10014953","url":null,"abstract":"Continuous intraoperative monitoring with electroencephalography (EEG) is routinely used in carotid endarterectomy (CEA) to detect cerebral ischemia [1]. Visually observed changes in EEG related to ischemia include an ipsilateral decrease in amplitude of faster frequencies or an ipsilateral increase in activity of slower frequencies. In the literature, significant EEG change has been defined as a decrease in the amplitude in the alpha frequency band by 50% or greater or an increase in activity in the theta or delta frequency band by 50% or greater [2], [3]. Compared to raw EEG, quantitative EEG (QEEG) parameters can enhance visual EEG review. QEEG parameters are derived by applying Fourier transformation to raw EEG signals to generate power spectra [4]. Examples of QEEG parameters include delta, theta, alpha, beta, gamma power values, alpha power to delta power ratio (ADR), beta power to delta power ratio (BDR), alpha-plus-beta power to delta-plus-theta power ratio (ABDTR), spectral edge frequency 90% (SEF90) and amplitude-integrated EEG (aEEG). QEEG parameters have been previously investigated in detecting ischemia in a relatively small number of patients undergoing CEA [5]. In this study, we report on the analyses of QEEG parameters in a large population of patients who underwent CEA with EEG monitoring.","PeriodicalId":261445,"journal":{"name":"2022 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130761823","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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