Pub Date : 2022-12-03DOI: 10.1109/SPMB55497.2022.10014835
S. Ganeshamoorthy, L. Roden, D. Klepl, F. He
Gene Regulatory Networks (GRNs) depict the causal regulatory interactions between transcription factors (TFs) and their target genes [2], where TFs are proteins that regulate gene transcription. GRN plays a vital role in explaining gene function, which helps to identify and prioritize the candidate genes for functional analysis [3]. Currently, high-dimensional transcriptome datasets are produced from high-throughput sequencing techniques, such as microarray and RNA-Seq. These techniques can capture the differences in the expression of thousands of genes at once. Through these wet-lab experiments, studying the interconnections among a large number of genes or TFs at a network level is challenging [4]. Therefore, one of the important topics in computational biology is the inference of GRNs from high-dimensional gene expression data through statistical and machine learning approaches [2].
{"title":"Gene Regulatory Network Inference through Link Prediction using Graph Neural Network","authors":"S. Ganeshamoorthy, L. Roden, D. Klepl, F. He","doi":"10.1109/SPMB55497.2022.10014835","DOIUrl":"https://doi.org/10.1109/SPMB55497.2022.10014835","url":null,"abstract":"Gene Regulatory Networks (GRNs) depict the causal regulatory interactions between transcription factors (TFs) and their target genes [2], where TFs are proteins that regulate gene transcription. GRN plays a vital role in explaining gene function, which helps to identify and prioritize the candidate genes for functional analysis [3]. Currently, high-dimensional transcriptome datasets are produced from high-throughput sequencing techniques, such as microarray and RNA-Seq. These techniques can capture the differences in the expression of thousands of genes at once. Through these wet-lab experiments, studying the interconnections among a large number of genes or TFs at a network level is challenging [4]. Therefore, one of the important topics in computational biology is the inference of GRNs from high-dimensional gene expression data through statistical and machine learning approaches [2].","PeriodicalId":261445,"journal":{"name":"2022 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)","volume":"63 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":"114726199","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/SPMB55497.2022.10014890
A. Maratova, P. Lencastre, A. Yazidi, P. Lind
Analysis of functional connectivity helps to determine how brain regions interact with one another and to understand neurological diseases better. In this study, we compare functional connectivity networks derived from electroencephalogram (EEG) data using Pearson's correlation and mutual information. The TUH EEG Epilepsy Corpus (TUEP) dataset is analysed with methods from Graph Theory, Statistics and Machine Learning. Our findings can be used to develop features for predictive models. Specifically, we show that with just 19 channels, a convolutional neural network model achieves 94% and 95% area under the receiver operating characteristic (ROC) curve (AUC) for correlation and mutual information, respectively. Thus, we provide evidence that application of Machine Learning methods to EEG data not containing seizures can help to accurately identify individuals with epilepsy. This may have considerable implications on diagnosing the pathology.
{"title":"Comparative Analysis of Functional Connectivity Metrics in EEG Datasets","authors":"A. Maratova, P. Lencastre, A. Yazidi, P. Lind","doi":"10.1109/SPMB55497.2022.10014890","DOIUrl":"https://doi.org/10.1109/SPMB55497.2022.10014890","url":null,"abstract":"Analysis of functional connectivity helps to determine how brain regions interact with one another and to understand neurological diseases better. In this study, we compare functional connectivity networks derived from electroencephalogram (EEG) data using Pearson's correlation and mutual information. The TUH EEG Epilepsy Corpus (TUEP) dataset is analysed with methods from Graph Theory, Statistics and Machine Learning. Our findings can be used to develop features for predictive models. Specifically, we show that with just 19 channels, a convolutional neural network model achieves 94% and 95% area under the receiver operating characteristic (ROC) curve (AUC) for correlation and mutual information, respectively. Thus, we provide evidence that application of Machine Learning methods to EEG data not containing seizures can help to accurately identify individuals with epilepsy. This may have considerable implications on diagnosing the pathology.","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":"129815661","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/SPMB55497.2022.10014816
K. Du, V. Pedapati, A. Mina, A. Bradley, J. Espino, K. Batmanghelich, P. Thirumala, S. Visweswaran
In surgical procedures that are at high risk for intraoperative cerebral ischemia, such as carotid endarterectomy (CEA), continuous intraoperative monitoring (IONM) with electroencephalography (EEG) is routinely performed [1], [2]. In IONM, a neurophysiologist visually monitors the EEG and alerts the surgical team when the risk of ischemia is present. During CEA, the risk of ischemia is high in the period immediately after the clamping of the carotid artery. Typical changes reflective of cerebral ischemia that are visually observed on the EEG include an ipsilateral decrease in amplitude of faster frequencies or ipsilateral increase in activity of slower frequencies. Human visual monitoring of the EEG can be tedious and error-prone, and quantitative EEG (QEEG) parameters can enhance visual EEG review. However, it is not known if sex affects QEEG parameters. Thus, in this study, we focus on evaluating the difference in QEEG parameters between females and males, correcting for age and side of surgery.
{"title":"EEG Changes Correlated with Ischemia Across the Sexes in Carotid Endarterectomy","authors":"K. Du, V. Pedapati, A. Mina, A. Bradley, J. Espino, K. Batmanghelich, P. Thirumala, S. Visweswaran","doi":"10.1109/SPMB55497.2022.10014816","DOIUrl":"https://doi.org/10.1109/SPMB55497.2022.10014816","url":null,"abstract":"In surgical procedures that are at high risk for intraoperative cerebral ischemia, such as carotid endarterectomy (CEA), continuous intraoperative monitoring (IONM) with electroencephalography (EEG) is routinely performed [1], [2]. In IONM, a neurophysiologist visually monitors the EEG and alerts the surgical team when the risk of ischemia is present. During CEA, the risk of ischemia is high in the period immediately after the clamping of the carotid artery. Typical changes reflective of cerebral ischemia that are visually observed on the EEG include an ipsilateral decrease in amplitude of faster frequencies or ipsilateral increase in activity of slower frequencies. Human visual monitoring of the EEG can be tedious and error-prone, and quantitative EEG (QEEG) parameters can enhance visual EEG review. However, it is not known if sex affects QEEG parameters. Thus, in this study, we focus on evaluating the difference in QEEG parameters between females and males, correcting for age and side of surgery.","PeriodicalId":261445,"journal":{"name":"2022 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)","volume":"55 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":"126856638","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/SPMB55497.2022.10014848
L. Tong, Y. Wan
Circulating tumor cells (CTCs) shed from the primary tumor, intravasate into blood, and translocate to distant tissues via circulation [1]. CTC enumeration allows cancer detection, treatment monitoring, and survival prediction [2], [3]. In the clinical setting immunofluorescence-based CTC enumeration is primarily used by expert cytopathologists. Manual enumeration requires cytopathologists with rich experience to read hundreds to thousands of images in hours. Despite the seemingly high number, this poor efficiency hinders the relevant clinical implementation. Therefore, high-automation enumeration is missing but highly desired [4]. Here, we proposed an automatic CTC segmentation and enumeration method in digital pathology by using deep learning techniques. To prepare for enumeration, peripheral blood mononuclear cells (PBMC) were extracted from cancer patient blood followed by infection with a reengineered adenovirus, i.e., rAdCTC, which is a CD46-targeting, DF3 promoter-selective, and GFP-overexpression adenovirus. The rAdCTC ensures detection specificity and efficiency of expression for CTCs. Subsequently, PBMCs were stained with anti-CD45 fluorescence-labeled antibody and DNA staining dye DAPI. Finally, the acquired fluorescence images were used for automatic segmentation and enumeration [5]. It is noteworthy that the fluorescence images used in this study contain three channels. The green, red, and blue signals respectively represent overexpressed GFP in infected cells, CD45 staining on leukocyte membranes, and nuclear staining.
{"title":"Automatic Circulating Tumor Cell Segmentation and Enumeration in Digital Pathology by Using Deep Learning and Ball-scale Based Filtering Techniques","authors":"L. Tong, Y. Wan","doi":"10.1109/SPMB55497.2022.10014848","DOIUrl":"https://doi.org/10.1109/SPMB55497.2022.10014848","url":null,"abstract":"Circulating tumor cells (CTCs) shed from the primary tumor, intravasate into blood, and translocate to distant tissues via circulation [1]. CTC enumeration allows cancer detection, treatment monitoring, and survival prediction [2], [3]. In the clinical setting immunofluorescence-based CTC enumeration is primarily used by expert cytopathologists. Manual enumeration requires cytopathologists with rich experience to read hundreds to thousands of images in hours. Despite the seemingly high number, this poor efficiency hinders the relevant clinical implementation. Therefore, high-automation enumeration is missing but highly desired [4]. Here, we proposed an automatic CTC segmentation and enumeration method in digital pathology by using deep learning techniques. To prepare for enumeration, peripheral blood mononuclear cells (PBMC) were extracted from cancer patient blood followed by infection with a reengineered adenovirus, i.e., rAdCTC, which is a CD46-targeting, DF3 promoter-selective, and GFP-overexpression adenovirus. The rAdCTC ensures detection specificity and efficiency of expression for CTCs. Subsequently, PBMCs were stained with anti-CD45 fluorescence-labeled antibody and DNA staining dye DAPI. Finally, the acquired fluorescence images were used for automatic segmentation and enumeration [5]. It is noteworthy that the fluorescence images used in this study contain three channels. The green, red, and blue signals respectively represent overexpressed GFP in infected cells, CD45 staining on leukocyte membranes, and nuclear staining.","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":"122512497","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/SPMB55497.2022.10014769
S. Davidson, N. McCallan, K. Y. Ng, P. Biglarbeigi, D. Finlay, B.L. Lan, J. Mclaughlin
Epilepsy is one of the most common neurological disorders in the world [1], affecting about 50 million people worldwide [2]. Epileptic seizures occur when millions of neurons are synchronously excited, resulting in a wave of electrical activity in the cerebral cortex [3]. Electroencephalography (EEG) is a noninvasive tool that measures cortical activity with millisecond temporal resolution. EEGs record the electrical potentials generated by the cerebral cortex nerve cells [4]. Therefore, this tool is commonly used for the analysis and detection of seizures [5]. Epilepsy causes many difficulties in relation to the quality of life of the patient. It is therefore vital that automatic detection algorithms exist to aid neurologists to accurately classify the different types of seizures. Roy et al. [10] used different machine learning techniques to achieve an average F1-score of 0.561 using 2 s windows whilst Vanabelle et al. [11] used 1 s windows and achieved an accuracy of 51.33%, which shows that reducing the time window would also decrease the accuracy of classification. This paper aims to show that an NLP can be used for hierarchical classification, following upon an earlier work on combining simple partial and complex partial seizures [9]. The second aim is to show a pipeline that can be used to separate the seizures back into their original labels using neural networks. This method is quick, effective, and requires less training.
{"title":"Seizure Classification Using BERT NLP and a Comparison of Source Isolation Techniques with Two Different Time-Frequency Analysis","authors":"S. Davidson, N. McCallan, K. Y. Ng, P. Biglarbeigi, D. Finlay, B.L. Lan, J. Mclaughlin","doi":"10.1109/SPMB55497.2022.10014769","DOIUrl":"https://doi.org/10.1109/SPMB55497.2022.10014769","url":null,"abstract":"Epilepsy is one of the most common neurological disorders in the world [1], affecting about 50 million people worldwide [2]. Epileptic seizures occur when millions of neurons are synchronously excited, resulting in a wave of electrical activity in the cerebral cortex [3]. Electroencephalography (EEG) is a noninvasive tool that measures cortical activity with millisecond temporal resolution. EEGs record the electrical potentials generated by the cerebral cortex nerve cells [4]. Therefore, this tool is commonly used for the analysis and detection of seizures [5]. Epilepsy causes many difficulties in relation to the quality of life of the patient. It is therefore vital that automatic detection algorithms exist to aid neurologists to accurately classify the different types of seizures. Roy et al. [10] used different machine learning techniques to achieve an average F1-score of 0.561 using 2 s windows whilst Vanabelle et al. [11] used 1 s windows and achieved an accuracy of 51.33%, which shows that reducing the time window would also decrease the accuracy of classification. This paper aims to show that an NLP can be used for hierarchical classification, following upon an earlier work on combining simple partial and complex partial seizures [9]. The second aim is to show a pipeline that can be used to separate the seizures back into their original labels using neural networks. This method is quick, effective, and requires less training.","PeriodicalId":261445,"journal":{"name":"2022 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)","volume":"56 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":"127903404","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/SPMB55497.2022.10014721
A. Benwali, Q.R. Ferguson, S. Farahat, R. Sandler, E. Hill, H. Mansy
Muscle fatigue is defined as a decline in the ability to maintain a desired force against a load. Muscle fatigue may also be described as a decline in the muscle's maximum force during contraction. In contrast to muscle damage or weakness, characterized by a compromise in the ability of well-rested muscles to generate force, muscle fatigue is generally reversible with rest [2]. In a muscle experiencing fatigue, the nerves cannot sustain the high frequency signal necessary to reach the Maximum Contraction (MC) for a long time, resulting in a decline in muscle force during a sustained contraction. Due to its utility in providing information about nerve signaling and muscle's electrical activity, surface electromyography (sEMG) is currently the dominant method to detect muscle fatigue [2]. Mechanomyography (MMG) can reveal unique information that cannot be derived from the sEMG signal alone about the physiological behavior of muscles during contraction. However, more information may be needed about the ability of MMG to measure changes in muscle's activation patterns and mechanical properties that occur with muscle fatigue. Additionally, investigating the force-dependent characteristics of the MMG signal can provide information about physiological properties such as muscle activation strategies and fiber type distribution, which can be used to explore factors contributing to fatigue responses [1]. The purpose of this study is to examine and analyze the electrical and mechanical muscle responses to submaximal isometric contractions, as well as force-varying trapezoidal contractions in the rectus femoris muscle.
{"title":"Non-invasive Evaluation of Muscle Fatigue Using Mechanomyography and Surface Electromyography - A Pilot Study","authors":"A. Benwali, Q.R. Ferguson, S. Farahat, R. Sandler, E. Hill, H. Mansy","doi":"10.1109/SPMB55497.2022.10014721","DOIUrl":"https://doi.org/10.1109/SPMB55497.2022.10014721","url":null,"abstract":"Muscle fatigue is defined as a decline in the ability to maintain a desired force against a load. Muscle fatigue may also be described as a decline in the muscle's maximum force during contraction. In contrast to muscle damage or weakness, characterized by a compromise in the ability of well-rested muscles to generate force, muscle fatigue is generally reversible with rest [2]. In a muscle experiencing fatigue, the nerves cannot sustain the high frequency signal necessary to reach the Maximum Contraction (MC) for a long time, resulting in a decline in muscle force during a sustained contraction. Due to its utility in providing information about nerve signaling and muscle's electrical activity, surface electromyography (sEMG) is currently the dominant method to detect muscle fatigue [2]. Mechanomyography (MMG) can reveal unique information that cannot be derived from the sEMG signal alone about the physiological behavior of muscles during contraction. However, more information may be needed about the ability of MMG to measure changes in muscle's activation patterns and mechanical properties that occur with muscle fatigue. Additionally, investigating the force-dependent characteristics of the MMG signal can provide information about physiological properties such as muscle activation strategies and fiber type distribution, which can be used to explore factors contributing to fatigue responses [1]. The purpose of this study is to examine and analyze the electrical and mechanical muscle responses to submaximal isometric contractions, as well as force-varying trapezoidal contractions in the rectus femoris muscle.","PeriodicalId":261445,"journal":{"name":"2022 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)","volume":"38 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":"123247259","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/SPMB55497.2022.10014773
S. Ahdy, R. Sandler, M. Monduy, E. Baker, J. Wells, H. Mansy
Duchenne muscular dystrophy (DMD) is a lethal muscle degenerative disease affecting 1: 3500 male births [1]. It is caused by genetic mutations resulting in dystrophin protein deficiency. Dystrophin maintains membrane integrity; its deficiency causes myofiber damage under mechanical loading [1]. The resulting DMD muscle membrane tears impact its permeability which increases calcium concentration inside the cell and promotes inflammatory reactions and muscle degeneration [2]. Eventually, DMD muscle suffers a loss of mass, and becomes less functional due to inflammation and fibrosis [3]. Current therapies aim to slow disease progression, promote muscle regeneration and growth, and maintain muscle mass [2].
{"title":"DMD Muscle Characteristics in the Time and Frequency Domain","authors":"S. Ahdy, R. Sandler, M. Monduy, E. Baker, J. Wells, H. Mansy","doi":"10.1109/SPMB55497.2022.10014773","DOIUrl":"https://doi.org/10.1109/SPMB55497.2022.10014773","url":null,"abstract":"Duchenne muscular dystrophy (DMD) is a lethal muscle degenerative disease affecting 1: 3500 male births [1]. It is caused by genetic mutations resulting in dystrophin protein deficiency. Dystrophin maintains membrane integrity; its deficiency causes myofiber damage under mechanical loading [1]. The resulting DMD muscle membrane tears impact its permeability which increases calcium concentration inside the cell and promotes inflammatory reactions and muscle degeneration [2]. Eventually, DMD muscle suffers a loss of mass, and becomes less functional due to inflammation and fibrosis [3]. Current therapies aim to slow disease progression, promote muscle regeneration and growth, and maintain muscle mass [2].","PeriodicalId":261445,"journal":{"name":"2022 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)","volume":"10 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":"125890097","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/SPMB55497.2022.10014911
L. Wei, C. Mooney
Approximately 1 in every 150 children is diagnosed with epilepsy during the first ten years of life [1]. These children experience seizures, which disrupt their lives and directly harm the developing brain. Electroencephalography (EEG) is the main tool used clinically to diagnose seizures and epilepsy. However, the interpretation of EEGs requires time-consuming expert analysis [2]. Automated detection systems are a powerful tool that can help address the issue by reducing expert annotation time. Research on the automatic detection of seizures in pediatric EEG has been limited. Most seizure detection methods have been developed and tested using larger numbers of adult EEG [3], [4]. However, research has shown that brain events in EEG change with ageing [5], [6]. Therefore, model trained on EEGs from adults may not be be suitable for children. To test this hypothesis, we trained a seizure detection model on adult EEG and tested on adult and pediatric EEG recordings.
{"title":"Investigating the Need for Pediatric-Specific Automatic Seizure Detection","authors":"L. Wei, C. Mooney","doi":"10.1109/SPMB55497.2022.10014911","DOIUrl":"https://doi.org/10.1109/SPMB55497.2022.10014911","url":null,"abstract":"Approximately 1 in every 150 children is diagnosed with epilepsy during the first ten years of life [1]. These children experience seizures, which disrupt their lives and directly harm the developing brain. Electroencephalography (EEG) is the main tool used clinically to diagnose seizures and epilepsy. However, the interpretation of EEGs requires time-consuming expert analysis [2]. Automated detection systems are a powerful tool that can help address the issue by reducing expert annotation time. Research on the automatic detection of seizures in pediatric EEG has been limited. Most seizure detection methods have been developed and tested using larger numbers of adult EEG [3], [4]. However, research has shown that brain events in EEG change with ageing [5], [6]. Therefore, model trained on EEGs from adults may not be be suitable for children. To test this hypothesis, we trained a seizure detection model on adult EEG and tested on adult and pediatric EEG recordings.","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":"129888743","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/SPMB55497.2022.10014777
D. King, A. Voyatzoglou, R. Dhar, R. Sandler, H. Mansy
Cardiovascular disease (CVD) has been a pressing medical issue in the United States for over a century and has been a leading cause of death [1], [2] with a great impact on mortality, morbidity, and healthcare cost. The Centers for Disease Control and Prevention (CDC) [3] reported that CVD is responsible for one death every 34 seconds and approximately 697,000 deaths in 2020 alone. Additionally, between 2017 to 2018, CVD directly and indirectly cost the United States economy approximately 378 billion dollars [2].
{"title":"Audible and Subaudible Components of the First and Second Heart Sounds","authors":"D. King, A. Voyatzoglou, R. Dhar, R. Sandler, H. Mansy","doi":"10.1109/SPMB55497.2022.10014777","DOIUrl":"https://doi.org/10.1109/SPMB55497.2022.10014777","url":null,"abstract":"Cardiovascular disease (CVD) has been a pressing medical issue in the United States for over a century and has been a leading cause of death [1], [2] with a great impact on mortality, morbidity, and healthcare cost. The Centers for Disease Control and Prevention (CDC) [3] reported that CVD is responsible for one death every 34 seconds and approximately 697,000 deaths in 2020 alone. Additionally, between 2017 to 2018, CVD directly and indirectly cost the United States economy approximately 378 billion dollars [2].","PeriodicalId":261445,"journal":{"name":"2022 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)","volume":"18 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":"124943018","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/SPMB55497.2022.10014897
C. McCausland, P. Biglarbeigi, R. Bond, G. Yadollahikhales, D. Finlay
The development of automated sleep apnea detection algorithms is an emerging topic of interest [1], [2]. The main aim of automation is to reduce the time and cost associated with manually scoring polysomnogram (PSG) tests [3]. To automate the process, traditional algorithms attempt to mimic the human observer by implementing a series of predefined rules, such as the American Academy of Sleep Medicine's (AASM) scoring guidelines [4]. Recently, data driven methods have emerged [5]. Electroencephalogram (EEG) frequency is known to be an important feature for both the human observer and data driven methods for sleep staging classification. This study presents the initial findings for a novel approach to sleep stage analysis. EEG time-frequency analysis is used to characterise the dominant frequency with respect to time, specifically at the point of sleep stage transition. Poor inter-scorer agreement at sleep stage transitions is a noted limitation of current manual and automated methods as the point of transition is poorly defined [6]. The goal of this study is to further discuss on the topic of sleep staging automation and explore alternative and novel features to improve the inter-scorer reliability of sleep staging.
{"title":"Time-Frequency Ridge Analysis of Sleep Stage Transitions","authors":"C. McCausland, P. Biglarbeigi, R. Bond, G. Yadollahikhales, D. Finlay","doi":"10.1109/SPMB55497.2022.10014897","DOIUrl":"https://doi.org/10.1109/SPMB55497.2022.10014897","url":null,"abstract":"The development of automated sleep apnea detection algorithms is an emerging topic of interest [1], [2]. The main aim of automation is to reduce the time and cost associated with manually scoring polysomnogram (PSG) tests [3]. To automate the process, traditional algorithms attempt to mimic the human observer by implementing a series of predefined rules, such as the American Academy of Sleep Medicine's (AASM) scoring guidelines [4]. Recently, data driven methods have emerged [5]. Electroencephalogram (EEG) frequency is known to be an important feature for both the human observer and data driven methods for sleep staging classification. This study presents the initial findings for a novel approach to sleep stage analysis. EEG time-frequency analysis is used to characterise the dominant frequency with respect to time, specifically at the point of sleep stage transition. Poor inter-scorer agreement at sleep stage transitions is a noted limitation of current manual and automated methods as the point of transition is poorly defined [6]. The goal of this study is to further discuss on the topic of sleep staging automation and explore alternative and novel features to improve the inter-scorer reliability of sleep staging.","PeriodicalId":261445,"journal":{"name":"2022 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)","volume":"63 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":"127030955","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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