Pub Date : 2019-09-01DOI: 10.23919/CinC49843.2019.9005878
Shailesh Nirgudkar, Tianyu Ding
This paper describes a methodology to detect sepsis ahead of time by analyzing hourly patient records. The Physionet 2019 challenge consists of medical records of over 40,000 patients. Using imputation and weak ensem- bler technique to analyze these medical records and 3-fold validation, a model is created and validated internally. On a hidden test data set maintained by the organizers, the model obtained a utility score of 0.192. The utility score as defined by the organizers takes into account true positives, negatives and false alarms. Our team was Team Tesseract and our overall ranking was 49 out of 79 officially ranked entries.
{"title":"Early Detection of Sepsis Using Ensemblers","authors":"Shailesh Nirgudkar, Tianyu Ding","doi":"10.23919/CinC49843.2019.9005878","DOIUrl":"https://doi.org/10.23919/CinC49843.2019.9005878","url":null,"abstract":"This paper describes a methodology to detect sepsis ahead of time by analyzing hourly patient records. The Physionet 2019 challenge consists of medical records of over 40,000 patients. Using imputation and weak ensem- bler technique to analyze these medical records and 3-fold validation, a model is created and validated internally. On a hidden test data set maintained by the organizers, the model obtained a utility score of 0.192. The utility score as defined by the organizers takes into account true positives, negatives and false alarms. Our team was Team Tesseract and our overall ranking was 49 out of 79 officially ranked entries.","PeriodicalId":6697,"journal":{"name":"2019 Computing in Cardiology (CinC)","volume":"38 1","pages":"Page 1-Page 4"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80744531","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 : 2019-09-01DOI: 10.23919/CinC49843.2019.9005875
S. Palacios, E. Caiani, E. Pueyo, J. P. Martínez
Head-Down Bed Rest (HDBR) microgravity simulation induces cardiovascular deconditioning, including effects on ventricular repolarization. The index of Periodic Repolarization Dynamics (PRD) was developed to quantify low-frequency oscillations of cardiac repolarization. In this study, PRD was quantified by Phase Rectified Signal Averaging (PRDPRSA) and Continuous Wavelet Transform (PRDCWT) methods. PRD was analyzed in ECGs from 22 volunteers at rest and during orthostatic Tilt-Table Test (TTT) performed before and after -6° 60-days HDBR. Significant correlation was found between PRD measured by PRSA and CWT (Pearson’s ρ = 0.93, p < 10-54 and Kendall’s τ = 0.79 p < 10-38). A highly significant increase was found when PRDPRSA values were measured at POST-HDBR with respect to PRE-HDBR in the tilt phase: 1.40 [1.10] deg and 0.97 [0.90] deg (median [IQR]), p = 0.008, respectively. PRDPRSA also increased significantly in the tilt phase with respect to baseline, both at POST-HDBR (0.90 [0.57] deg, p = 0.003) and at PRE-HDBR (0.75 [0.45] deg, p = 0.011). PRD, either measured with PRSA or with CWT, is able to measure changes in ventricular repolarization induced by microgravity simulation as well as following sympathetic provocation.
头下床休息(HDBR)微重力模拟诱导心血管降功能，包括对心室复极的影响。建立了周期复极化动力学指标(PRD)来量化心脏复极化的低频振荡。在本研究中，PRD通过相位校正信号平均(PRDPRSA)和连续小波变换(PRDCWT)方法进行量化。研究人员分析了22名志愿者在-6°60天HDBR前后休息时和直立倾斜台试验(TTT)期间的心电图中的PRD。PRSA测量的PRD与CWT之间存在显著相关(Pearson’s ρ = 0.93, p < 10-54, Kendall’s τ = 0.79 p < 10-38)。相对于倾斜阶段的PRE-HDBR，在hdbr后测量PRDPRSA值时发现了非常显著的增加:分别为1.40[1.10]度和0.97[0.90]度(中位数[IQR])， p = 0.008。相对于基线，在hdbr后(0.90[0.57]度，p = 0.003)和hdbr前(0.75[0.45]度，p = 0.011)， PRDPRSA在倾斜期也显著增加。PRD，无论是用PRSA还是CWT测量，都能够测量微重力模拟和交感刺激引起的心室复极的变化。
{"title":"Response of Ventricular Repolarization to Simulated Microgravity Measured by Periodic Repolarization Dynamics Using Phase-Rectified Signal Averaging","authors":"S. Palacios, E. Caiani, E. Pueyo, J. P. Martínez","doi":"10.23919/CinC49843.2019.9005875","DOIUrl":"https://doi.org/10.23919/CinC49843.2019.9005875","url":null,"abstract":"Head-Down Bed Rest (HDBR) microgravity simulation induces cardiovascular deconditioning, including effects on ventricular repolarization. The index of Periodic Repolarization Dynamics (PRD) was developed to quantify low-frequency oscillations of cardiac repolarization. In this study, PRD was quantified by Phase Rectified Signal Averaging (PRDPRSA) and Continuous Wavelet Transform (PRDCWT) methods. PRD was analyzed in ECGs from 22 volunteers at rest and during orthostatic Tilt-Table Test (TTT) performed before and after -6° 60-days HDBR. Significant correlation was found between PRD measured by PRSA and CWT (Pearson’s ρ = 0.93, p < 10-54 and Kendall’s τ = 0.79 p < 10-38). A highly significant increase was found when PRDPRSA values were measured at POST-HDBR with respect to PRE-HDBR in the tilt phase: 1.40 [1.10] deg and 0.97 [0.90] deg (median [IQR]), p = 0.008, respectively. PRDPRSA also increased significantly in the tilt phase with respect to baseline, both at POST-HDBR (0.90 [0.57] deg, p = 0.003) and at PRE-HDBR (0.75 [0.45] deg, p = 0.011). PRD, either measured with PRSA or with CWT, is able to measure changes in ventricular repolarization induced by microgravity simulation as well as following sympathetic provocation.","PeriodicalId":6697,"journal":{"name":"2019 Computing in Cardiology (CinC)","volume":"45 1","pages":"Page 1-Page 4"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87323504","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 : 2019-09-01DOI: 10.23919/CinC49843.2019.9005766
D. Ge, Alfredo I. Hernández
Robust, real-time apnea and hypopnea detection for monitoring patients suffering from sleep apnea syndrome (SAS) still represents an open problem due to the effect of noise artifacts, the complexity of respiratory patterns and inter-subject variability. We propose in this study the application of an original multi-feature probabilistic detector (MFPD) for SAS event detection during long-term monitoring recordings on three SAS patients. The nasal pressure signal is used as input to derive a set of respiratory features (variance, peak-to-peak amplitude and total respiration cycle) which are statistically characterized during time and used to provide a mono-feature detection probability in realtime. A centralized fusion approach based on the Kullback-Leibler divergence (KLD), optimally combines these mono-feature distributions in order to produce a final detection. While the optimal feature set selection lies beyond the scope of our study, we illustrate the ability to adapt each feature’s weight dynamically to make centralized fusion decisions. The method can be directly applied to data acquired from multiple sensors as long as features are synchronized. Our proposed fusion method achieves a very high sensitivity (94%) as compared with reference thresholding based methods in the literature.
{"title":"Multi-Feature Probabilistic Detector Applied to Apnea/Hypopnea Monitoring","authors":"D. Ge, Alfredo I. Hernández","doi":"10.23919/CinC49843.2019.9005766","DOIUrl":"https://doi.org/10.23919/CinC49843.2019.9005766","url":null,"abstract":"Robust, real-time apnea and hypopnea detection for monitoring patients suffering from sleep apnea syndrome (SAS) still represents an open problem due to the effect of noise artifacts, the complexity of respiratory patterns and inter-subject variability. We propose in this study the application of an original multi-feature probabilistic detector (MFPD) for SAS event detection during long-term monitoring recordings on three SAS patients. The nasal pressure signal is used as input to derive a set of respiratory features (variance, peak-to-peak amplitude and total respiration cycle) which are statistically characterized during time and used to provide a mono-feature detection probability in realtime. A centralized fusion approach based on the Kullback-Leibler divergence (KLD), optimally combines these mono-feature distributions in order to produce a final detection. While the optimal feature set selection lies beyond the scope of our study, we illustrate the ability to adapt each feature’s weight dynamically to make centralized fusion decisions. The method can be directly applied to data acquired from multiple sensors as long as features are synchronized. Our proposed fusion method achieves a very high sensitivity (94%) as compared with reference thresholding based methods in the literature.","PeriodicalId":6697,"journal":{"name":"2019 Computing in Cardiology (CinC)","volume":"67 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75435560","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 : 2019-09-01DOI: 10.23919/CinC49843.2019.9005457
S. Schellenberger, Kilin Shi, J. P. Wiedemann, F. Lurz, R. Weigel, A. Koelpin
Sepsis is a life-threatening condition that has to be treated at an early stage. Doctors use the Sequential Organ Failure Assessment score for the earliest possible recognition. In addition, the practitioner’s many years of experience help in order to facilitate an immediate response. Mortality decreases with every hour that sepsis is detected and treated with antibiotics. In this years PhysioNet/Computing in Cardiology Challenge the objective is to automatically detect sepsis six hours before the clinical prediction. This paper describes the implementation of an Long Short-Term Memory network for an early detection of sepsis in provided hourly physiological data. An utility score of 0.29 was achieved when testing on the full hidden test set. All entries were submitted using the team name "404: Sepsis not found".
败血症是一种危及生命的疾病，必须在早期治疗。医生使用序贯器官衰竭评估评分来尽早识别。此外，从业人员多年的经验有助于迅速作出反应。发现败血症并使用抗生素治疗的时间越长，死亡率就越低。在今年的PhysioNet/Computing In Cardiology挑战赛中，目标是在临床预测前6小时自动检测败血症。这篇论文描述了一个长短期记忆网络的实现，在提供的每小时生理数据中早期检测败血症。在对完整隐藏测试集进行测试时，实现了0.29的效用得分。所有参赛作品都以“404:败血症未找到”的团队名称提交。
{"title":"An Ensemble LSTM Architecture for Clinical Sepsis Detection","authors":"S. Schellenberger, Kilin Shi, J. P. Wiedemann, F. Lurz, R. Weigel, A. Koelpin","doi":"10.23919/CinC49843.2019.9005457","DOIUrl":"https://doi.org/10.23919/CinC49843.2019.9005457","url":null,"abstract":"Sepsis is a life-threatening condition that has to be treated at an early stage. Doctors use the Sequential Organ Failure Assessment score for the earliest possible recognition. In addition, the practitioner’s many years of experience help in order to facilitate an immediate response. Mortality decreases with every hour that sepsis is detected and treated with antibiotics. In this years PhysioNet/Computing in Cardiology Challenge the objective is to automatically detect sepsis six hours before the clinical prediction. This paper describes the implementation of an Long Short-Term Memory network for an early detection of sepsis in provided hourly physiological data. An utility score of 0.29 was achieved when testing on the full hidden test set. All entries were submitted using the team name \"404: Sepsis not found\".","PeriodicalId":6697,"journal":{"name":"2019 Computing in Cardiology (CinC)","volume":"8 1","pages":"Page 1-Page 4"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90835087","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 : 2019-09-01DOI: 10.23919/CinC49843.2019.9005867
Victor Gonçalves Marques, M. Rodrigo, M. Guillem, J. Salinet
Atrial tachycardia (AT), atrial flutter (AFL) and atrial fibrillation (AF) are among the most common cardiac arrhythmias and are driven by localized sources (ectopic focus in AT, macro-reentrant circuit in AFL and rotors in AF) that can be targeted for ablation. We aimed to characterize such mechanisms from the non-invasive perspective of body surface potential mapping (BSPM), using realistic computer models. Dominant frequency (DF) maps were studied to estimate the frequency of the driving mechanism and to analyze its spatio-temporal distribution of this frequency. Singularity points (SPs) were detected in phase maps and their distribution and rotational patterns were compared between arrhythmias. The driver frequencies were more expressed on the anterior portion of the torso for the right atrium and on the posterior portion for the left atrium. Rotational activity was detected in all arrhythmias, with increasing levels of spatial-temporal stability (AF, AT and AFL, respectively). These results can be used to identify the driving mechanisms and, in the future, to locate them in the atria.
{"title":"Non-Invasive Characterization of Atrial Arrhythmic Driving Mechanisms in Computer Models","authors":"Victor Gonçalves Marques, M. Rodrigo, M. Guillem, J. Salinet","doi":"10.23919/CinC49843.2019.9005867","DOIUrl":"https://doi.org/10.23919/CinC49843.2019.9005867","url":null,"abstract":"Atrial tachycardia (AT), atrial flutter (AFL) and atrial fibrillation (AF) are among the most common cardiac arrhythmias and are driven by localized sources (ectopic focus in AT, macro-reentrant circuit in AFL and rotors in AF) that can be targeted for ablation. We aimed to characterize such mechanisms from the non-invasive perspective of body surface potential mapping (BSPM), using realistic computer models. Dominant frequency (DF) maps were studied to estimate the frequency of the driving mechanism and to analyze its spatio-temporal distribution of this frequency. Singularity points (SPs) were detected in phase maps and their distribution and rotational patterns were compared between arrhythmias. The driver frequencies were more expressed on the anterior portion of the torso for the right atrium and on the posterior portion for the left atrium. Rotational activity was detected in all arrhythmias, with increasing levels of spatial-temporal stability (AF, AT and AFL, respectively). These results can be used to identify the driving mechanisms and, in the future, to locate them in the atria.","PeriodicalId":6697,"journal":{"name":"2019 Computing in Cardiology (CinC)","volume":"23 1","pages":"Page 1-Page 4"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90331913","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 : 2019-09-01DOI: 10.23919/CinC49843.2019.9005739
Kim Munck, J. Struijk, Kasper Sørensen, S. Schmidt
The multichannel seismocardiography (mchSCG) project aims to develop the technology and knowledge-base to understand the distribution of vibration waves on the chest wall caused by cardio-mechanic events. This study focuses on the developing visualization methods for the vibration waves based on the multi-dimensional map obtained with the mchSCG equipment. We investigated four visualization methods. Vibration signals were collected in a four by four grid with 16 three-axis accelerometers placed on the chest of 11 healthy males. The visualization methods were investigated for their abilities to show temporal, spatial, and directional information. Of the four methods the SCG chart shows best temporal and small amplitude sensitivity information. Color plots provides an improved spatial overview. Tracking maps provide good directionality. The seismic mesh method is good at showing spatial and directionality information. Dependent on which signal aspects are of interest, the four visualization methods have their specific suited purposes. These visualization methods can assist further investigation of the vibration waves behavior and its relation to cardio-mechanic events.
{"title":"Visualization of the Multichannel Seismocardiogram","authors":"Kim Munck, J. Struijk, Kasper Sørensen, S. Schmidt","doi":"10.23919/CinC49843.2019.9005739","DOIUrl":"https://doi.org/10.23919/CinC49843.2019.9005739","url":null,"abstract":"The multichannel seismocardiography (mchSCG) project aims to develop the technology and knowledge-base to understand the distribution of vibration waves on the chest wall caused by cardio-mechanic events. This study focuses on the developing visualization methods for the vibration waves based on the multi-dimensional map obtained with the mchSCG equipment. We investigated four visualization methods. Vibration signals were collected in a four by four grid with 16 three-axis accelerometers placed on the chest of 11 healthy males. The visualization methods were investigated for their abilities to show temporal, spatial, and directional information. Of the four methods the SCG chart shows best temporal and small amplitude sensitivity information. Color plots provides an improved spatial overview. Tracking maps provide good directionality. The seismic mesh method is good at showing spatial and directionality information. Dependent on which signal aspects are of interest, the four visualization methods have their specific suited purposes. These visualization methods can assist further investigation of the vibration waves behavior and its relation to cardio-mechanic events.","PeriodicalId":6697,"journal":{"name":"2019 Computing in Cardiology (CinC)","volume":"33 1","pages":"Page 1-Page 4"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78245430","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 : 2019-09-01DOI: 10.23919/CinC49843.2019.9005928
A. Verkerk, R. Wilders
Heterozygous carriers of the A414G mutation in the HCN4 gene, which encodes the HCN4 protein, show moderate to severe sinus bradycardia. Tetramers of HCN4 subunits constitute the ion channels that conduct the cardiac hyperpolarization-activated ‘funny current’ (If), which plays an important modulating role in the pacemaker activity of sinus node cells.We assessed the mechanism by which the A414G mutation in HCN4 causes sinus bradycardia. We carried out voltage clamp experiments on HCN4 channels expressed in Chinese hamster ovary (CHO) cells and incorporated the experimentally observed mutation-induced changes in If into the Fabbri-Severi model of a single human sinus node cell.In the Fabbri-Severi model, the experimentally observed effects on If increased the cycle length from 813 to 1004 ms, corresponding with a 19% decrease in beating rate from 74 to 60 beats/min. These mutation effects became more prominent at 10 nM ACh (vagal tone) and in the presence of a hyperpolarizing atrial load.We conclude that the experimentally identified mutation-induced changes in If can explain the clinically observed sinus bradycardia in carriers of the A414G mutation in the HCN4 gene.
{"title":"Mechanism of Sinus Bradycardia in Carriers of the A414G Mutation in the HCN4 Gene","authors":"A. Verkerk, R. Wilders","doi":"10.23919/CinC49843.2019.9005928","DOIUrl":"https://doi.org/10.23919/CinC49843.2019.9005928","url":null,"abstract":"Heterozygous carriers of the A414G mutation in the HCN4 gene, which encodes the HCN4 protein, show moderate to severe sinus bradycardia. Tetramers of HCN4 subunits constitute the ion channels that conduct the cardiac hyperpolarization-activated ‘funny current’ (If), which plays an important modulating role in the pacemaker activity of sinus node cells.We assessed the mechanism by which the A414G mutation in HCN4 causes sinus bradycardia. We carried out voltage clamp experiments on HCN4 channels expressed in Chinese hamster ovary (CHO) cells and incorporated the experimentally observed mutation-induced changes in If into the Fabbri-Severi model of a single human sinus node cell.In the Fabbri-Severi model, the experimentally observed effects on If increased the cycle length from 813 to 1004 ms, corresponding with a 19% decrease in beating rate from 74 to 60 beats/min. These mutation effects became more prominent at 10 nM ACh (vagal tone) and in the presence of a hyperpolarizing atrial load.We conclude that the experimentally identified mutation-induced changes in If can explain the clinically observed sinus bradycardia in carriers of the A414G mutation in the HCN4 gene.","PeriodicalId":6697,"journal":{"name":"2019 Computing in Cardiology (CinC)","volume":"18 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75215605","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 : 2019-09-01DOI: 10.23919/CinC49843.2019.9005854
Sasan Yazdani, Alexandre Cherqui, N. Bourdillon, G. Millet, J. Vesin
The aim of this work is to investigate the relation between a phenomenon called "U-patterns" and their possible correlation to movement events in the context of sleep deprivation. U-patterns take place in the RR-interval time series during sleep. As their name suggests, these patterns present a U-shaped decrease-increase in RR-intervals, with a duration lasting from 20 to 40 seconds together with a minimum decrease of 15% in the local RR mean value.Over a span of 17 days, 15 healthy subjects (7males, 22.1 ± 1.7 yrs.) participated in a study of three subsequent stages. First, a baseline phase of seven days, during which the subjects slept normally. Immediately after, a sleep deprivation phase with a duration of three days, during which participants slept only three hours per night. Finally, in a 7-day recovery phase subjects went back to their normal baseline sleeping routine. Subjects underwent polysomnography (PSG) data acquisition while sleeping. U-patterns were extracted from RR-intervals while movement events were extracted from different PSG channels. Their relative temporal layout was studied to determine whether U-patterns are caused due to subject movement during sleep or vice versa. Results show that U-pattern/movement events are correlated, always initiated by U-patterns with movement events terminating before the termination of their respective U-patterns.
{"title":"Sleep RR-Interval U-Patterns and Their Correlation to Movement Events","authors":"Sasan Yazdani, Alexandre Cherqui, N. Bourdillon, G. Millet, J. Vesin","doi":"10.23919/CinC49843.2019.9005854","DOIUrl":"https://doi.org/10.23919/CinC49843.2019.9005854","url":null,"abstract":"The aim of this work is to investigate the relation between a phenomenon called \"U-patterns\" and their possible correlation to movement events in the context of sleep deprivation. U-patterns take place in the RR-interval time series during sleep. As their name suggests, these patterns present a U-shaped decrease-increase in RR-intervals, with a duration lasting from 20 to 40 seconds together with a minimum decrease of 15% in the local RR mean value.Over a span of 17 days, 15 healthy subjects (7males, 22.1 ± 1.7 yrs.) participated in a study of three subsequent stages. First, a baseline phase of seven days, during which the subjects slept normally. Immediately after, a sleep deprivation phase with a duration of three days, during which participants slept only three hours per night. Finally, in a 7-day recovery phase subjects went back to their normal baseline sleeping routine. Subjects underwent polysomnography (PSG) data acquisition while sleeping. U-patterns were extracted from RR-intervals while movement events were extracted from different PSG channels. Their relative temporal layout was studied to determine whether U-patterns are caused due to subject movement during sleep or vice versa. Results show that U-pattern/movement events are correlated, always initiated by U-patterns with movement events terminating before the termination of their respective U-patterns.","PeriodicalId":6697,"journal":{"name":"2019 Computing in Cardiology (CinC)","volume":"54 1","pages":"Page 1-Page 4"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85458012","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 : 2019-09-01DOI: 10.23919/CinC49843.2019.9005884
W. Good, K. Gillette, J. Bergquist, B. Zenger, J. Tate, Lindsay C. Rupp, Devan Anderson, G. Plank, R. Macleod
Introduction: Changes in conduction velocity are indicative of a wide variety of cardiac abnormalities yet measuring conduction velocity is challenging, especially within the myocardial volume. In this study we investigated a novel technique to reconstruct activation fronts and estimate three-dimensional (3D) conduction velocity (CV) from experimental intramural recordings.Methods: From the intermittently sampled electrograms we both reconstruct the activation profile and compute the reciprocal of the gradient of activation times and a series of streamlines that allows for the CV estimation. Results: The reconstructed activation times agreed closely with simulated values, with 50% to 70% of the nodes ≤ 1ms of absolute error. We found close agreement between the CVs calculated using reconstructed versus simulated activation times. Across the reconstructed stimulation sites we saw that the reconstructed CV was on average 3.8% different than the ground truth CV. Discussion: This study used simulated datasets to validate our methods for reconstructing 3D activation fronts and estimating conduction velocities. Our results indicate that our method allows accurate reconstructions from sparse measurements, thus allowing us to examine changes in activation induced by experimental interventions such as acute ischemia, ectopic pacing, or drugs.
{"title":"Validation of Intramural Wavefront Reconstruction and Estimation of 3D Conduction Velocity","authors":"W. Good, K. Gillette, J. Bergquist, B. Zenger, J. Tate, Lindsay C. Rupp, Devan Anderson, G. Plank, R. Macleod","doi":"10.23919/CinC49843.2019.9005884","DOIUrl":"https://doi.org/10.23919/CinC49843.2019.9005884","url":null,"abstract":"Introduction: Changes in conduction velocity are indicative of a wide variety of cardiac abnormalities yet measuring conduction velocity is challenging, especially within the myocardial volume. In this study we investigated a novel technique to reconstruct activation fronts and estimate three-dimensional (3D) conduction velocity (CV) from experimental intramural recordings.Methods: From the intermittently sampled electrograms we both reconstruct the activation profile and compute the reciprocal of the gradient of activation times and a series of streamlines that allows for the CV estimation. Results: The reconstructed activation times agreed closely with simulated values, with 50% to 70% of the nodes ≤ 1ms of absolute error. We found close agreement between the CVs calculated using reconstructed versus simulated activation times. Across the reconstructed stimulation sites we saw that the reconstructed CV was on average 3.8% different than the ground truth CV. Discussion: This study used simulated datasets to validate our methods for reconstructing 3D activation fronts and estimating conduction velocities. Our results indicate that our method allows accurate reconstructions from sparse measurements, thus allowing us to examine changes in activation induced by experimental interventions such as acute ischemia, ectopic pacing, or drugs.","PeriodicalId":6697,"journal":{"name":"2019 Computing in Cardiology (CinC)","volume":"51 1","pages":"Page 1-Page 4"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85756615","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}
The automated 3D echocardiography segmentation on left ventricle (LV) is very important for clinical evaluation of LV function. However, the segmentation is difficult due to the 3D echocardiography’s challenges, such as the low signal-to-noise ratio, indistinguishable boundaries between LV and other heart substructures, and limited annotation data. This paper aims to propose a novel method to achieve accurate 3D echocardiography segmentation on LV, based on a weakly supervised deformable network. The deformation network was optimized by generative adversarial constraint and volume similarity constraint. The proposed framework was trained and validated on 3D echocardiography datasets which including 70 patients (35 train patients and 35 test patients). The results demonstrated the proposed method is relatively accurate and has potential for further research and application.
{"title":"Weakly Supervised Deformation Network for 3D Echocardiography Segmentation on Left Ventricle","authors":"Suyu Dong, Gongning Luo, Naren Wulan, Shaodong Cao, Kuanquan Wang, Henggui Zhang","doi":"10.23919/CinC49843.2019.9005792","DOIUrl":"https://doi.org/10.23919/CinC49843.2019.9005792","url":null,"abstract":"The automated 3D echocardiography segmentation on left ventricle (LV) is very important for clinical evaluation of LV function. However, the segmentation is difficult due to the 3D echocardiography’s challenges, such as the low signal-to-noise ratio, indistinguishable boundaries between LV and other heart substructures, and limited annotation data. This paper aims to propose a novel method to achieve accurate 3D echocardiography segmentation on LV, based on a weakly supervised deformable network. The deformation network was optimized by generative adversarial constraint and volume similarity constraint. The proposed framework was trained and validated on 3D echocardiography datasets which including 70 patients (35 train patients and 35 test patients). The results demonstrated the proposed method is relatively accurate and has potential for further research and application.","PeriodicalId":6697,"journal":{"name":"2019 Computing in Cardiology (CinC)","volume":"27 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82791111","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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