... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics最新文献
Pub Date : 2021-01-01DOI: 10.1109/BHI50953.2021.9508559
M. Hostin, Augustin C. Ogier, N. Pirró, Marc-Emmanuel Bellemare
{"title":"Combining loss functions for deep learning bladder segmentation on dynamic MRI","authors":"M. Hostin, Augustin C. Ogier, N. Pirró, Marc-Emmanuel Bellemare","doi":"10.1109/BHI50953.2021.9508559","DOIUrl":"https://doi.org/10.1109/BHI50953.2021.9508559","url":null,"abstract":"","PeriodicalId":72024,"journal":{"name":"... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76308653","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 : 2019-05-01Epub Date: 2019-09-12DOI: 10.1109/bhi.2019.8834504
Boshen Wang, Wei Tian, Xue Lei, Alan Perez-Rathke, Yan Yuan Tseng, Jie Liang
Missense SNPs are key factors contributing towards many Mendelian disorders and complex diseases. Identifying whether a single amino acid substitution will lead to pathological effects is important for interpreting personal genome and for precision medicine. In this study, we describe a novel method for predicting whether a missense SNP likely brings about pathological effects. Our approach integrates sequence information, biophysical properties, and topological properties of protein structures. In our test dataset consisting of 500 deleterious variants and 500 neutral, our method achieves an accuracy of 0.823. The ROC curve of model has an AUC of 0.910. Our methods outperforms two well known methods, and is comparable with the widely used Polyphen-2 method, while requiring a much smaller amount (approximately 25%) of training data. Our method can be used to aid in distinguishing driver and passenger mutations in cancer and in assessing missense mutations assocaited with rare diseases. It can also be used to identifying mutations in rare disease where only limited patient exome data exsit.
{"title":"Structure-based Method for Predicting Deleterious Missense SNPs.","authors":"Boshen Wang, Wei Tian, Xue Lei, Alan Perez-Rathke, Yan Yuan Tseng, Jie Liang","doi":"10.1109/bhi.2019.8834504","DOIUrl":"10.1109/bhi.2019.8834504","url":null,"abstract":"<p><p>Missense SNPs are key factors contributing towards many Mendelian disorders and complex diseases. Identifying whether a single amino acid substitution will lead to pathological effects is important for interpreting personal genome and for precision medicine. In this study, we describe a novel method for predicting whether a missense SNP likely brings about pathological effects. Our approach integrates sequence information, biophysical properties, and topological properties of protein structures. In our test dataset consisting of 500 deleterious variants and 500 neutral, our method achieves an accuracy of 0.823. The ROC curve of model has an AUC of 0.910. Our methods outperforms two well known methods, and is comparable with the widely used Polyphen-2 method, while requiring a much smaller amount (approximately 25%) of training data. Our method can be used to aid in distinguishing driver and passenger mutations in cancer and in assessing missense mutations assocaited with rare diseases. It can also be used to identifying mutations in rare disease where only limited patient exome data exsit.</p>","PeriodicalId":72024,"journal":{"name":"... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8204715/pdf/nihms-1707462.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39240733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-01Epub Date: 2019-09-12DOI: 10.1109/BHI.2019.8834652
John H L Hansen, Hussnain Ali, Juliana N Saba, Charan M C Ram, Nursadul Mamun, Ria Ghosh, Avamarie Brueggeman
Hearing loss is an increasingly prevalent condition resulting from damage to the inner ear which causes a reduction in speech intelligibility. The societal need for assistive hearing devices has increased exponentially over the past two decades; however, actual human performance with such devices has only seen modest gains relative to advancements in digital signal processing (DSP) technology. A major challenge with clinical hearing technologies is the limited ability to run complex signal processing algorithms requiring high computation power. The CCi-MOBILE platform, developed at UT-Dallas, provides the research community with an open-source, flexible, easy-to-use, software-mediated, powerful computing research interface to conduct a wide variety of listening experiments. The platform supports cochlear implants (CIs) and hearing aids (HAs) independently, as well as bimodal hearing (i.e., a CI in one ear and HA in the contralateral ear). The platform is ideally suited to address hearing research for: both quiet and naturalistic noisy conditions, sound localization, and lateralization. The platform uses commercially available smartphone/tablet devices as portable sound processors and can provide bilateral electric and acoustic stimulation. The hardware components, firmware, and software suite are presented to demonstrate safety to the speech scientist and CI/HA user, highlight user-specificity, and outline various applications of the platform for research.
{"title":"CCi-MOBILE: Design and Evaluation of a Cochlear Implant and Hearing Aid Research Platform for Speech Scientists and Engineers.","authors":"John H L Hansen, Hussnain Ali, Juliana N Saba, Charan M C Ram, Nursadul Mamun, Ria Ghosh, Avamarie Brueggeman","doi":"10.1109/BHI.2019.8834652","DOIUrl":"10.1109/BHI.2019.8834652","url":null,"abstract":"<p><p>Hearing loss is an increasingly prevalent condition resulting from damage to the inner ear which causes a reduction in speech intelligibility. The societal need for assistive hearing devices has increased exponentially over the past two decades; however, actual human performance with such devices has only seen modest gains relative to advancements in digital signal processing (DSP) technology. A major challenge with clinical hearing technologies is the limited ability to run complex signal processing algorithms requiring high computation power. The CCi-MOBILE platform, developed at UT-Dallas, provides the research community with an open-source, flexible, easy-to-use, software-mediated, powerful computing research interface to conduct a wide variety of listening experiments. The platform supports cochlear implants (CIs) and hearing aids (HAs) independently, as well as bimodal hearing (i.e., a CI in one ear and HA in the contralateral ear). The platform is ideally suited to address hearing research for: both quiet and naturalistic noisy conditions, sound localization, and lateralization. The platform uses commercially available smartphone/tablet devices as portable sound processors and can provide bilateral electric and acoustic stimulation. The hardware components, firmware, and software suite are presented to demonstrate safety to the speech scientist and CI/HA user, highlight user-specificity, and outline various applications of the platform for research.</p>","PeriodicalId":72024,"journal":{"name":"... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6874365/pdf/nihms-1019990.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49685542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-01DOI: 10.1109/BHI.2019.8834508
Cihan Berk Gungor, H. Toreyin
Noninvasive ubiquitous health-monitoring applications necessitate real-time, accurate, and energy-efficient computation of health-related parameters. R-waves are critical features for cardiac health assessment using ECG. In this paper, an energy-efficient application specific integrated circuit (ASIC) processor for real-time R-wave detection based on the Pan-Tompkins algorithm is presented. R-wave detection through processing in the analog domain is demonstrated using simulation results. The processor is designed in a 65 nm CMOS technology and consumes 0.5 nW from a 1 V supply. Based on simulation results using the MIT-BIH arrhythmia database, the processor achieves average R-wave detection sensitivity and positive predictive values of 98.98% and 98.9%, respectively.
{"title":"A 0.5 nW Analog ECG Processor for Real Time R-wave Detection Based on Pan-Tompkins Algorithm","authors":"Cihan Berk Gungor, H. Toreyin","doi":"10.1109/BHI.2019.8834508","DOIUrl":"https://doi.org/10.1109/BHI.2019.8834508","url":null,"abstract":"Noninvasive ubiquitous health-monitoring applications necessitate real-time, accurate, and energy-efficient computation of health-related parameters. R-waves are critical features for cardiac health assessment using ECG. In this paper, an energy-efficient application specific integrated circuit (ASIC) processor for real-time R-wave detection based on the Pan-Tompkins algorithm is presented. R-wave detection through processing in the analog domain is demonstrated using simulation results. The processor is designed in a 65 nm CMOS technology and consumes 0.5 nW from a 1 V supply. Based on simulation results using the MIT-BIH arrhythmia database, the processor achieves average R-wave detection sensitivity and positive predictive values of 98.98% and 98.9%, respectively.","PeriodicalId":72024,"journal":{"name":"... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85266254","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 : 2019-05-01Epub Date: 2019-09-12DOI: 10.1109/bhi.2019.8834586
Mohammed Alawad, Shang Gao, John Qiu, Noah Schaefferkoetter, Jacob D Hinkle, Hong-Jun Yoon, J Blair Christian, Xiao-Cheng Wu, Eric B Durbin, Jong Cheol Jeong, Isaac Hands, David Rust, Georgia Tourassi
Automated text information extraction from cancer pathology reports is an active area of research to support national cancer surveillance. A well-known challenge is how to develop information extraction tools with robust performance across cancer registries. In this study we investigated whether transfer learning (TL) with a convolutional neural network (CNN) can facilitate cross-registry knowledge sharing. Specifically, we performed a series of experiments to determine whether a CNN trained with single-registry data is capable of transferring knowledge to another registry or whether developing a cross-registry knowledge database produces a more effective and generalizable model. Using data from two cancer registries and primary tumor site and topography as the information extraction task of interest, our study showed that TL results in 6.90% and 17.22% improvement of classification macro F-score over the baseline single-registry models. Detailed analysis illustrated that the observed improvement is evident in the low prevalence classes.
{"title":"Deep Transfer Learning Across Cancer Registries for Information Extraction from Pathology Reports.","authors":"Mohammed Alawad, Shang Gao, John Qiu, Noah Schaefferkoetter, Jacob D Hinkle, Hong-Jun Yoon, J Blair Christian, Xiao-Cheng Wu, Eric B Durbin, Jong Cheol Jeong, Isaac Hands, David Rust, Georgia Tourassi","doi":"10.1109/bhi.2019.8834586","DOIUrl":"https://doi.org/10.1109/bhi.2019.8834586","url":null,"abstract":"Automated text information extraction from cancer pathology reports is an active area of research to support national cancer surveillance. A well-known challenge is how to develop information extraction tools with robust performance across cancer registries. In this study we investigated whether transfer learning (TL) with a convolutional neural network (CNN) can facilitate cross-registry knowledge sharing. Specifically, we performed a series of experiments to determine whether a CNN trained with single-registry data is capable of transferring knowledge to another registry or whether developing a cross-registry knowledge database produces a more effective and generalizable model. Using data from two cancer registries and primary tumor site and topography as the information extraction task of interest, our study showed that TL results in 6.90% and 17.22% improvement of classification macro F-score over the baseline single-registry models. Detailed analysis illustrated that the observed improvement is evident in the low prevalence classes.","PeriodicalId":72024,"journal":{"name":"... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/bhi.2019.8834586","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33458011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-01Epub Date: 2019-09-12DOI: 10.1109/bhi.2019.8834617
Jiaming Chen, Ali Valehi, Fatemeh Afghah, Abolfazl Razi
Current automated heart monitoring tools use supervised learning methods to recognize heart disorders based on ECG signal morphology. We develop a new ECG processing algorithm that enables early prediction of disorders through a novel deviation analysis. The idea is developing a patient-specific ECG baseline and characterizing the deviation of signal morphology towards any of the abnormality classes with specific morphological features. To enable this feature, a novel controlled non-linear transformation is designed to achieve maximal symme- try in the feature space. Our results using benchmark MIT-BIH database show that the proposed method achieves a classification accuracy of 96% and can be used to trigger yellow alarms to warn patients from increased risk of upcoming heart abnormalities (5% to 10% increase with respect to normal conditions). This feature can be used in health monitoring devices to advise patients to take preventive and precaution actions before critical situations.
{"title":"A Deviation Analysis Framework for ECG Signals Using Controlled Spatial Transformation.","authors":"Jiaming Chen, Ali Valehi, Fatemeh Afghah, Abolfazl Razi","doi":"10.1109/bhi.2019.8834617","DOIUrl":"10.1109/bhi.2019.8834617","url":null,"abstract":"<p><p>Current automated heart monitoring tools use supervised learning methods to recognize heart disorders based on ECG signal morphology. We develop a new ECG processing algorithm that enables early prediction of disorders through a novel deviation analysis. The idea is developing a patient-specific ECG baseline and characterizing the deviation of signal morphology towards any of the abnormality classes with specific morphological features. To enable this feature, a novel controlled non-linear transformation is designed to achieve maximal symme- try in the feature space. Our results using benchmark MIT-BIH database show that the proposed method achieves a classification accuracy of 96% and can be used to trigger yellow alarms to warn patients from increased risk of upcoming heart abnormalities (5% to 10% increase with respect to normal conditions). This feature can be used in health monitoring devices to advise patients to take preventive and precaution actions before critical situations.</p>","PeriodicalId":72024,"journal":{"name":"... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7552434/pdf/nihms-1634684.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38499539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-01Epub Date: 2019-09-12DOI: 10.1109/bhi.2019.8834638
Yuanda Zhu, Hang Wu, May D Wang
Approximately 0.5-1% of the global population is afflicted with epilepsy, a neurological disorder characterized by repeated seizures. Sudden Unexpected Death in Epilepsy (SUDEP) is a poorly understood complication that claims the lives of nearly 1-in-1000 epilepsy patients every year. This paper aims to explore diagnosis codes, demographic and payment features on mortality of epilepsy patients. We design a mortality prediction model with diagnosis codes and non-diagnosis features extracted from US commercial insurance claims data. We present classification accuracy of 0.91 and 0.85 by using different feature vectors. After analyzing the aforementioned features in prediction model, we extend the work to causal inference between modified diagnosis codes and selected non-diagnosis features. The uplift test of causal inference using three algorithms indicates that a patient is more likely to survive if upgrading from a low-coverage healthcare plan into a high-coverage plan.
{"title":"Feature Exploration and Causal Inference on Mortality of Epilepsy Patients Using Insurance Claims Data.","authors":"Yuanda Zhu, Hang Wu, May D Wang","doi":"10.1109/bhi.2019.8834638","DOIUrl":"https://doi.org/10.1109/bhi.2019.8834638","url":null,"abstract":"<p><p>Approximately 0.5-1% of the global population is afflicted with epilepsy, a neurological disorder characterized by repeated seizures. Sudden Unexpected Death in Epilepsy (SUDEP) is a poorly understood complication that claims the lives of nearly 1-in-1000 epilepsy patients every year. This paper aims to explore diagnosis codes, demographic and payment features on mortality of epilepsy patients. We design a mortality prediction model with diagnosis codes and non-diagnosis features extracted from US commercial insurance claims data. We present classification accuracy of 0.91 and 0.85 by using different feature vectors. After analyzing the aforementioned features in prediction model, we extend the work to causal inference between modified diagnosis codes and selected non-diagnosis features. The uplift test of causal inference using three algorithms indicates that a patient is more likely to survive if upgrading from a low-coverage healthcare plan into a high-coverage plan.</p>","PeriodicalId":72024,"journal":{"name":"... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/bhi.2019.8834638","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38078197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-01Epub Date: 2019-09-12DOI: 10.1109/bhi.2019.8834565
Alan Perez-Rathke, Samira Mali, Lin Du, Jie Liang
In this study, we focus on the following question: do genomic regions enriched in cancer variant mutations have significantly different chromatin folding patterns? We utilize publicly available Hi-C data to characterize chromatin folding patterns in healthy (GM12878) and cancer (K562) cells based on status of A/B compartmentalization and random vs non-random chromatin physical interactions. We then perform statistical testing to assess if chromatin folding patterns in cancer variant-enriched loci are significantly different from non-enriched loci. Our results indicate that loci with cancer variant status have significantly altered (FDR < 0.05) chromatin folding patterns.
{"title":"Alterations in Chromatin Folding Patterns in Cancer Variant-Enriched Loci.","authors":"Alan Perez-Rathke, Samira Mali, Lin Du, Jie Liang","doi":"10.1109/bhi.2019.8834565","DOIUrl":"https://doi.org/10.1109/bhi.2019.8834565","url":null,"abstract":"<p><p>In this study, we focus on the following question: do genomic regions enriched in cancer variant mutations have significantly different chromatin folding patterns? We utilize publicly available Hi-C data to characterize chromatin folding patterns in healthy (GM12878) and cancer (K562) cells based on status of A/B compartmentalization and random vs non-random chromatin physical interactions. We then perform statistical testing to assess if chromatin folding patterns in cancer variant-enriched loci are significantly different from non-enriched loci. Our results indicate that loci with cancer variant status have significantly altered (FDR < 0.05) chromatin folding patterns.</p>","PeriodicalId":72024,"journal":{"name":"... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/bhi.2019.8834565","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39060602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-01Epub Date: 2019-09-12DOI: 10.1109/bhi.2019.8834632
Yuanda Zhu, May D Wang, Li Tong, Shriprasad R Deshpande
Heart transplant rejection is one major threat for the survival of patients with a heart transplant. Endomyocardial biopsies are effective in showing signs of heart transplant rejection even before patients have any symptoms. Manually examining the tissue samples is costly, time-consuming and error-prone. With recent advances in deep learning (DL) based image processing methods, automatic training and prediction on heart transplant rejection using whole-slide images expect to be promising. This paper develops an advanced pipeline for quality control, feature extraction, clustering and classification. We first implement a stacked convolutional autoencoder to extract feature maps for each tile; we then incorporate multiple instance learning (MIL) with dimensionality reduction and unsupervised clustering prior to classification. Our results show that utilizing unsupervised clustering after feature extraction can achieve higher classification results while preserving the capability for multi-class classification.
{"title":"Improved Prediction on Heart Transplant Rejection Using Convolutional Autoencoder and Multiple Instance Learning on Whole-Slide Imaging.","authors":"Yuanda Zhu, May D Wang, Li Tong, Shriprasad R Deshpande","doi":"10.1109/bhi.2019.8834632","DOIUrl":"10.1109/bhi.2019.8834632","url":null,"abstract":"<p><p>Heart transplant rejection is one major threat for the survival of patients with a heart transplant. Endomyocardial biopsies are effective in showing signs of heart transplant rejection even before patients have any symptoms. Manually examining the tissue samples is costly, time-consuming and error-prone. With recent advances in deep learning (DL) based image processing methods, automatic training and prediction on heart transplant rejection using whole-slide images expect to be promising. This paper develops an advanced pipeline for quality control, feature extraction, clustering and classification. We first implement a stacked convolutional autoencoder to extract feature maps for each tile; we then incorporate multiple instance learning (MIL) with dimensionality reduction and unsupervised clustering prior to classification. Our results show that utilizing unsupervised clustering after feature extraction can achieve higher classification results while preserving the capability for multi-class classification.</p>","PeriodicalId":72024,"journal":{"name":"... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7310716/pdf/nihms-1595294.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38078196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-01Epub Date: 2019-09-12DOI: 10.1109/BHI.2019.8834637
Seyed Sajad Mousavi, Fatemah Afghah, Abolfazl Razi, U Rajendra Acharya
The complexity of the patterns associated with atrial fibrillation (AF) and the high level of noise affecting these patterns have significantly limited the application of current signal processing and shallow machine learning approaches to accurately detect this condition. Deep neural networks have shown to be very powerful to learn the non-linear patterns in various problems such as computer vision tasks. While deep learning approaches have been utilized to learn complex patterns related to the presence of AF in electrocardiogram (ECG) signals, they can considerably benefit from knowing which parts of the signal is more important to focus on during learning. In this paper, we introduce a two-channel deep neural network to more accurately detect the presence of AF in the ECG signals. The first channel takes in an ECG signal and automatically learns where to attend for detection of AF. The second channel simultaneously takes in the same ECG signal to consider all features of the entire signal. Besides improving detection accuracy, this model can guide the physicians via visualization that what parts of the given ECG signal are important to attend while trying to detect atrial fibrillation. The experimental results confirm that the proposed model significantly improves the performance of AF detection on well-known MIT-BIH AF database with 5-s ECG segments (achieved a sensitivity of 99.53%, specificity of 99.26% and accuracy of 99.40%).
{"title":"ECGNET: Learning where to attend for detection of atrial fibrillation with deep visual attention.","authors":"Seyed Sajad Mousavi, Fatemah Afghah, Abolfazl Razi, U Rajendra Acharya","doi":"10.1109/BHI.2019.8834637","DOIUrl":"10.1109/BHI.2019.8834637","url":null,"abstract":"<p><p>The complexity of the patterns associated with atrial fibrillation (AF) and the high level of noise affecting these patterns have significantly limited the application of current signal processing and shallow machine learning approaches to accurately detect this condition. Deep neural networks have shown to be very powerful to learn the non-linear patterns in various problems such as computer vision tasks. While deep learning approaches have been utilized to learn complex patterns related to the presence of AF in electrocardiogram (ECG) signals, they can considerably benefit from knowing which parts of the signal is more important to focus on during learning. In this paper, we introduce a two-channel deep neural network to more accurately detect the presence of AF in the ECG signals. The first channel takes in an ECG signal and automatically learns where to attend for detection of AF. The second channel simultaneously takes in the same ECG signal to consider all features of the entire signal. Besides improving detection accuracy, this model can guide the physicians via visualization that what parts of the given ECG signal are important to attend while trying to detect atrial fibrillation. The experimental results confirm that the proposed model significantly improves the performance of AF detection on well-known MIT-BIH AF database with 5-s ECG segments (achieved a sensitivity of 99.53%, specificity of 99.26% and accuracy of 99.40%).</p>","PeriodicalId":72024,"journal":{"name":"... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7570949/pdf/nihms-1634679.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38614878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}