2020 Computing in Cardiology最新文献_第6页

Application of Deep Learning for Quality Assessment of Atrial Fibrillation ECG Recordings 深度学习在房颤心电图记录质量评估中的应用

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.367

Á. Huerta, A. Martínez-Rodrigo, M. A. Arias, P. Langley, J. J. Rieta, R. Alcaraz

{"title":"Application of Deep Learning for Quality Assessment of Atrial Fibrillation ECG Recordings","authors":"Á. Huerta, A. Martínez-Rodrigo, M. A. Arias, P. Langley, J. J. Rieta, R. Alcaraz","doi":"10.22489/CinC.2020.367","DOIUrl":"https://doi.org/10.22489/CinC.2020.367","url":null,"abstract":"In the last years, atrial fibrillation (AF) has become one of the most remarkable health problems in the developed world. This arrhythmia is associated with an increased risk of cardiovascular events, being its early detection an unresolved challenge. To palliate this issue, long-term wearable electrocardiogram (ECG) recording systems are used, because most of AF episodes are asymptomatic and very short in their initial stages. Unfortunately, portable equipments are very susceptible to be contaminated with different kind of noises, since they work in highly dynamics and ever-changing environments. Within this scenario, the correct identification of free-noise ECG segments results critical for an accurate and robust AF detection. Hence, this work presents a deep learning-based algorithm to identify high-quality intervals in single-lead ECG recordings obtained from patients with paroxysmal AF. The obtained results have provided a remarkable ability to classify between high- and low-quality ECG segments about 92%, only misclassifying around 7% of clean AF intervals as noisy segments. These outcomes have overcome most previous ECG quality assessment algorithms also dealing with AF signals by more than 20%.","PeriodicalId":407282,"journal":{"name":"2020 Computing in Cardiology","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132994604","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}

引用次数: 1

ECGi Metrics in Atrial Fibrillation Dependency on Epicardium Segmentation 心外膜分割依赖性心房颤动的ECGi指标

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.156

Ana González-Ascaso, R. Molero, A. Climent, M. Guillem

引用次数: 1

On the Application of Convolutional Neural Networks for 12-lead ECG Multi-label Classification Using Datasets From Multiple Centers 卷积神经网络在多中心数据集12导联心电多标签分类中的应用

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.349

D. Borra, A. Andalò, S. Severi, C. Corsi

{"title":"On the Application of Convolutional Neural Networks for 12-lead ECG Multi-label Classification Using Datasets From Multiple Centers","authors":"D. Borra, A. Andalò, S. Severi, C. Corsi","doi":"10.22489/CinC.2020.349","DOIUrl":"https://doi.org/10.22489/CinC.2020.349","url":null,"abstract":"Cardiac arrhythmia is a group of conditions in which falls changes in the heartbeat. Electrocardiography (ECG) is the most common tool used to identify a pathology in the cardiac electrical conduction system. ECG analysis is usually manually performed by an expert physician. However, manual interpretation is time-consuming and challenging even for cardiologists. Many automatic algorithms relying on handcrafted features and traditional machine learning classifiers were developed to recognize cardiac diseases. However, a large a priori knowledge about ECG signals is exploited. To overcome this main limitation and provide higher performance, recently, deep neural networks were designed and applied for 12-lead ECG classification. In this study, we designed decoding workflows based on three state-of-the-art architectures for time series classification. These were InceptionTime, ResNet and XResNet. Experiments were conducted using the training datasets provided during the PhysioNet/Computing in Cardiology Challenge 2020. The best-performing algorithm was based on InceptionTime, scoring a training 5-fold cross-validation challenge metric of 0.5183±0.0016, while using a low number of parameters (510491 in total). Thus, this algorithm provided the best compromise between performance and complexity.","PeriodicalId":407282,"journal":{"name":"2020 Computing in Cardiology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130229382","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}

引用次数: 4

Rule-Based Method and Deep Learning Networks for Automatic Classification of ECG 基于规则的心电自动分类方法和深度学习网络

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.116

G. Bortolan, I. Christov, I. Simova

{"title":"Rule-Based Method and Deep Learning Networks for Automatic Classification of ECG","authors":"G. Bortolan, I. Christov, I. Simova","doi":"10.22489/CinC.2020.116","DOIUrl":"https://doi.org/10.22489/CinC.2020.116","url":null,"abstract":"The objective of the study is to explore the potentiality of combining a classical rule-based method with a Deep Learning method for automatic classification of ECG for participation in PhysicNet/Computing in Cardiology Challenge 2020. Six databases are considered for training set. They consist 43101 12 -leads ECG recording, lasting from 6 to 60 seconds considering 24 diagnostic classes. The rule-based method is using morphological and time-frequency ECG descriptors, characterizing each diagnostic labels. These rules have been extracted from the knowledge-base of a physician, with no direct learning procedure in the first phase, while a refinement have been tested in the second phase. The Deep Learning method consider both raw ECG signals and median beat signals. These data are processed by continuous wavelet transform analysis obtaining a time-frequency domain representtation, with the generation of specific images. These images are used for training Convolutional Neural Networks for ECG diagnostic classification. Official result of the classification accuracy of the ECGs Test set of our team named ‘Gio_Ivo’ produced a challenge validation score of 0.325 for the rule based method, and a 0.426 for the Deep learning methodology with GoogleNet, which was chosen for the final score, obtaining a full test score of 0.298, placing us 12th out of 41 in the official ranking.","PeriodicalId":407282,"journal":{"name":"2020 Computing in Cardiology","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125380728","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}

引用次数: 3

NeuroSky Mindwave Mobile Headset 2 as an Intervention for Reduction of Stress and Anxiety Measured With Pulse Rate Variability NeuroSky Mindwave移动耳机2作为减少压力和焦虑的干预措施，测量脉搏变异性

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.350

Habshi Al-Kaf, A. Khandoker, K. Khalaf, H. Jelinek

{"title":"NeuroSky Mindwave Mobile Headset 2 as an Intervention for Reduction of Stress and Anxiety Measured With Pulse Rate Variability","authors":"Habshi Al-Kaf, A. Khandoker, K. Khalaf, H. Jelinek","doi":"10.22489/CinC.2020.350","DOIUrl":"https://doi.org/10.22489/CinC.2020.350","url":null,"abstract":"NeuroSky Mindwave Mobile headset 2 (MMH2) is an EEG-based biofeedback device that can be used to assist in relaxation by providing users feedback on their level of relaxation. In this study, we aimed to assess the effect of using MMH2 on pulse rate variability (PRV) as a measure of relaxation in addition to the in-App real-time data. Six participants were recruited and provided information about the study once they contacted the university. Participants were required to use the Brainwave Visualizer application as part of the NeuroSky suite for relaxation for 10 minutes. PRV of each participant was determined before and after use of the MMH2. Biosignals were initially preprocessed to remove artifacts and resampled at 8Hz for time and frequency domain analysis using purpose written Matlab software. We obtained multiple parameters including the average value of the inter-pulse intervals, standard deviation, root mean square of the successive differences, and stress index. The stress score from the MMH2 screen indicated a decrease in overall stress by the participants. RMSSD decreased from pre-MMH2 to Post-MMH2 (4.6±5.9; 2.9±0.9; p=) whilst the Kubios Stress Index decreased as well (0.74±1.5; 0.44±1.1, p =) MMH2 can be used to help reduce stress and anxiety levels, making it a potentially useful tool for home use.","PeriodicalId":407282,"journal":{"name":"2020 Computing in Cardiology","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126612948","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}

引用次数: 1

A Noninvasive Cardiac Output Trend Monitor Targeting Telemedicine Applications 针对远程医疗应用的无创心输出量趋势监测仪

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.177

Ovidio López, Rafael Maestre, Andrés L. Bleda, R. Ruiz, J. Corral

{"title":"A Noninvasive Cardiac Output Trend Monitor Targeting Telemedicine Applications","authors":"Ovidio López, Rafael Maestre, Andrés L. Bleda, R. Ruiz, J. Corral","doi":"10.22489/CinC.2020.177","DOIUrl":"https://doi.org/10.22489/CinC.2020.177","url":null,"abstract":"This study aimed to investigate and validate a noninvasive affordable cardiac output (CO) trend monitor intended for telemedicine applications. The approach of this work will widely increase the availability of CO measurements, currently only available through expensive hospital equipment. The estimation method of the CO trend is based on the transient analysis of a PPG (photoplethys-mography) signal during venous occlusion. The PPG signal is acquired with an LED and a photodiode as in typical pulse oximeters, whereas a pneumatic cuff and pressure pump implement the occlusion and release cycles. The CO trend is given by the relative comparison of different CO measurements of the same individual. All the components used in this work have been already integrated into a portable device with wireless communications so it can be suitable for telemedicine applications. Different measures were taken on different individuals at different times of the day, several days per week during some weeks. The CO trend consistently reflected the expected daily CO variation patterns and events such as food intake and mild physical activities. The proposed methodology can be used to determine sudden CO changes or to analyze the underlying overall CO trend with measurements taken over multiple days.","PeriodicalId":407282,"journal":{"name":"2020 Computing in Cardiology","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126347316","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}

引用次数: 0

Automated Classification of Electrocardiograms Using Wavelet Analysis and Deep Learning 使用小波分析和深度学习的心电图自动分类

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.138

Andrew Demonbreun, Grace M. Mirsky

引用次数: 5

Multimodal Biosignal Analysis Algorithm for the Classification of Cardiac Rhythms During Resuscitation 复苏过程中心律分类的多模态生物信号分析算法

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/cinc.2020.347

Haizea Lasa, U. Irusta, T. Eftestøl, E. Aramendi, Ali Bahrami Rad, J. Kramer-Johansen, L. Wik

{"title":"Multimodal Biosignal Analysis Algorithm for the Classification of Cardiac Rhythms During Resuscitation","authors":"Haizea Lasa, U. Irusta, T. Eftestøl, E. Aramendi, Ali Bahrami Rad, J. Kramer-Johansen, L. Wik","doi":"10.22489/cinc.2020.347","DOIUrl":"https://doi.org/10.22489/cinc.2020.347","url":null,"abstract":"Monitoring the heart rhythm during out-of-hospital cardiac arrest (OHCA) is important to improve treatment quality. OHCA rhythms fall into five categories: asystole (AS), pulseless electrical activity (PEA), pulse-generating rhythms (PR), ventricular fibrillation (VF) and ventricular tachycardia (VT). This paper introduces an algorithm to classify these OHCA rhythms using the ECG and the thorax impedance (TI) signals recorded by the defibrillation pads. The dataset consisted of 100 OHCA patient files from which 2833 4-s signal segments were extracted: 423 AS, 912 PE, 689 PR, 643 VF, and 166 VT The Stationary Wavelet Transform (SWT) was used to obtain 95 features from the ECG and the TI. Random Forest classifiers were used, features were ranked during training using random forest importance, and models with increasing number of features were evaluated. The optimal classifier was obtained combining 50 ECG and TI features, with a median (80% confidence interval) average recall of 86.5% (80.6-89.4). The recall for AS/PEA/PR/VF/VT were 96.3% (93.0-98.5), 77.8% (68.1-89.2), 88.7% (79.5-93.6), 94.4% (90.2-97.4) and 77.3% (52.9-91.3), respectively.","PeriodicalId":407282,"journal":{"name":"2020 Computing in Cardiology","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124914471","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}

引用次数: 1

Classification of 12-lead ECG With an Ensemble Machine Learning Approach 基于集成机器学习方法的12导联心电图分类

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.406

Matteo Bodini, M. Rivolta, R. Sassi

{"title":"Classification of 12-lead ECG With an Ensemble Machine Learning Approach","authors":"Matteo Bodini, M. Rivolta, R. Sassi","doi":"10.22489/CinC.2020.406","DOIUrl":"https://doi.org/10.22489/CinC.2020.406","url":null,"abstract":"The PhysioNet 2020 Challenge focused on the automatic classification of 27 cardiac abnormalities (CAs) from 12-lead ECG signals. We investigated on a hybrid approach, combining average-template-based algorithms with deep neural networks (DNNs), to build an ensemble classification model. We calibrated the model on the available 40,000+ ECGs, while organizers tested the model on a private test set. Standard ECG preprocessing was applied. For ECGs related to CAs altering the ECG morphology, multi-lead average P, QRS, and T segments were computed. For signals associated with irregular rhythms, time dependent features were computed. The ensemble model comprised of: i) three DNNs to classify morphology-related CAs. ii) a fully connected neural network to classify irregular rhythm; and iii) a threshold-based classifier for premature ventricular beat detection. The organizers designed a score for ranking the models. The ensemble model proposed by our team “BiSP Lab” reached the 40th position, and obtained a score of -0.179 on the private test set. Despite the low performance obtained on the private test set, our ensemble model showed potential for classification of CAs from ECGs.","PeriodicalId":407282,"journal":{"name":"2020 Computing in Cardiology","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124970262","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}

引用次数: 2

Predicting Atrial Fibrillation Recurrence After Catheter Ablation Through Time Variability of P-wave Features 通过p波特征的时间变异性预测导管消融后房颤复发

2020 Computing in Cardiology Pub Date : 2020-09-13 DOI: 10.22489/CinC.2020.366

Antonio Ruiz, M. A. Arias, A. Puchol, M. Pachón, J. J. Rieta, R. Alcaraz

{"title":"Predicting Atrial Fibrillation Recurrence After Catheter Ablation Through Time Variability of P-wave Features","authors":"Antonio Ruiz, M. A. Arias, A. Puchol, M. Pachón, J. J. Rieta, R. Alcaraz","doi":"10.22489/CinC.2020.366","DOIUrl":"https://doi.org/10.22489/CinC.2020.366","url":null,"abstract":"Nowadays, the first-line therapy for paroxysmal atrial fibrillation (PAF) is pulmonary vein isolation through catheter ablation. However, the success rate of this procedure is still not as high as desirable. Thus, preoperative prediction of early AF recurrence after ablation is a challenge to select optimal candidates for the intervention. To this end, some promising predictors based on the P-wave in short ECG signals have been proposed in the last years. However, evolution of the P-wave along the time has still not been analyzed. Hence, the present work studies how time variability of two features of the P-wave predicts midterm cryoablation failure. For 45 PAF patients, a standard 12-lead ECG signal was obtained for 5 minutes before ablation. An automatic algorithm was then used to delineate all P-waves in lead II, and duration and amplitude were computed. The resulting time series were characterized by their mean, standard deviation and coefficient of variation (CV). Correlating these measures with ablation outcome, the CV for both parameters obtained the best discrimination between patients. In fact, compared with the mean value, the CV for both features obtained accuracies 10% greater, thus achieving values of 70%. These outcomes entail that time variability of the P-wave can reveal new information about the proarrhythmic condition of the patients, thus improving predictions of ablation failure.","PeriodicalId":407282,"journal":{"name":"2020 Computing in Cardiology","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124976236","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}

引用次数: 0