2022 Computing in Cardiology (CinC)最新文献_第2页

ACQuA: Anomaly Classification with Quasi-Attractors 基于拟吸引子的异常分类

2022 Computing in Cardiology (CinC) Pub Date : 2022-09-04 DOI: 10.22489/CinC.2022.201

William Rudman, Jack Merullo, L. Mercurio, Carsten Eickhoff

{"title":"ACQuA: Anomaly Classification with Quasi-Attractors","authors":"William Rudman, Jack Merullo, L. Mercurio, Carsten Eickhoff","doi":"10.22489/CinC.2022.201","DOIUrl":"https://doi.org/10.22489/CinC.2022.201","url":null,"abstract":"In recent years, deep learning has redefined algorithms for detecting cardiac abnormalities. However, many state of the art algorithms still rely on calculating handcrafted features from a given heart signal that are then fed into shallow 1D convolutional networks or transformer architectures. We propose ACQuA (Anomaly Classification with Quasi Attractors), a task agnostic algorithm that can be used in a wide variety of cardiac settings, from classifying cardiac arrhythmias from ECG signals to detecting heart murmurs from PCG signals. Using theorems from dynamical analysis and topological data analysis, we create informative attractor images that 1) are human distinguishable and 2) can be used to train neural networks for anomaly classification. In the George B. Moody 2022 Challenge, our team, BrownBAI, received an official score of 0.406 (38/40) for murmur classification and a score of 16773 (39/40) for outcome classification. Additionally, we evaluate our model on the CinC 2017 Challenge data that tasks practitioners to classify cardiac arrhythmias from ECG signals. On the CinC 2017 Challenge data, we improve upon the winning F1 scores by approximately 14% on the hidden validation data.","PeriodicalId":117840,"journal":{"name":"2022 Computing in Cardiology (CinC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123484183","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

An LSTM-based Listener for Early Detection of Heart Disease 一种基于lstm的心脏病早期检测监听器

2022 Computing in Cardiology (CinC) Pub Date : 2022-09-04 DOI: 10.22489/CinC.2022.151

Philip Gemke, Nicolai Spicher, T. Kacprowski

引用次数: 1

Classification of Murmurs in PCG Using Combined Frequency Domain and Physician Inspired Features 结合频域和医生启发特征的PCG杂音分类

2022 Computing in Cardiology (CinC) Pub Date : 2022-09-04 DOI: 10.22489/CinC.2022.065

Julia Ding, Jing-Jing Li, Max Xu"

{"title":"Classification of Murmurs in PCG Using Combined Frequency Domain and Physician Inspired Features","authors":"Julia Ding, Jing-Jing Li, Max Xu\"","doi":"10.22489/CinC.2022.065","DOIUrl":"https://doi.org/10.22489/CinC.2022.065","url":null,"abstract":"Physiological machine learning methods have a unique opportunity to augment deep-learning engineered features with additional features derived from prior pathological knowledge. We propose an phonocardiogram (PCG) classifier that combines raw spectrogram features with crafted, physician-inspired features with an end-to-end neural network architecture. Learned spectrogram features were obtained by training a convolutional neural network (CNN) directly on the raw mel-spectrogram representation of the PCG time-series. Crafted features were based on the four stages of the cardiac cycle (S1, systole, S2, and diastole). The spectrogram features have the advantage of introducing flexibility for the model to learn abstract, low-level information that captures a variety of different rhythmic abnormalities and the latter has the advantage of using segmentation to elucidate specific, high-level, human-interpretable information. Combined features are fed into a fully connected neural network which is able to learn the relationship between the two feature types. In the George B. Moody PhysioNet Challenge 2022 test set, our team (“lubdub”) received a weighted accuracy score of 0.835 with a cost of 14905 in the clinical outcome task (ranked 31/39). For the murmur prediction task, our model received a weighted accuracy score of 0.525 and a cost of 15083 (ranked 33/40).","PeriodicalId":117840,"journal":{"name":"2022 Computing in Cardiology (CinC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125440366","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

Exploring a Segmentation-Classification Deep Learning-based Heart Murmurs Detector 基于分割分类深度学习的心脏杂音检测器的研究

2022 Computing in Cardiology (CinC) Pub Date : 2022-09-04 DOI: 10.22489/CinC.2022.266

Daniel Enériz, Antonio Rodriguez-Almeida, H. Fabelo, S. Ortega, Francisco Balea-Fernández, N. Medrano, Belén Calvo, G. Callicó

{"title":"Exploring a Segmentation-Classification Deep Learning-based Heart Murmurs Detector","authors":"Daniel Enériz, Antonio Rodriguez-Almeida, H. Fabelo, S. Ortega, Francisco Balea-Fernández, N. Medrano, Belén Calvo, G. Callicó","doi":"10.22489/CinC.2022.266","DOIUrl":"https://doi.org/10.22489/CinC.2022.266","url":null,"abstract":"This work presents the advances of the UZ-ULPGC team in the Heart Murmur Detection from Phonocardiogram Recordings: The George B. Moody PhysioNet Challenge 2022. As the 2016 challenge proved the success of the combination of a segmentation algorithm and a classifier, a deep learning-based murmur detector is developed using the sequence segmentation-classification. A U-Net-based segmentation model is used to extract each cardiac cycle from the PCG with state-of-the-art accuracy. Three deep models are tested for the classification: a model based on four independent 1D-convolutional feature extractors; its variation enabling combination of the features; and an autoencoder. Furthermore, to enable unique patient diagnostic, a decision model gathering all the patient-related cardiac cycles information is added. All classifiers show limited performance, probably due to the heavy class imbalance of the data at the cardiac cycle level and the minimal preprocessing chosen in the architecture. Note that our models have not been tested in the hidden challenge data and therefore we are not ranked. Hence, a 10-fold cross-validation over the training set is used to evaluate their performance, with the best model getting a weighted accuracy score in the presence task of $0.58pm 0.10$ and 10 $735pm 2208$ in Challenge cost score for the outcome.","PeriodicalId":117840,"journal":{"name":"2022 Computing in Cardiology (CinC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125497539","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

ECG and PPG-Based Hypertension Screening Under Non-Hypertensive Blood Pressure Recordings 基于心电图和ppg的非高血压记录下的高血压筛查

2022 Computing in Cardiology (CinC) Pub Date : 2022-09-04 DOI: 10.22489/CinC.2022.093

Jesús Cano, V. Bertomeu-González, Lorenzo Fácia, J. Moreno-Arribas, R. Alcaraz, J. J. Rieta

{"title":"ECG and PPG-Based Hypertension Screening Under Non-Hypertensive Blood Pressure Recordings","authors":"Jesús Cano, V. Bertomeu-González, Lorenzo Fácia, J. Moreno-Arribas, R. Alcaraz, J. J. Rieta","doi":"10.22489/CinC.2022.093","DOIUrl":"https://doi.org/10.22489/CinC.2022.093","url":null,"abstract":"Blood pressure (BP) fluctuates throughout the day, mainly due to circadian oscillations as well as a response to physical and mental stimuli. This study aims at investigating whether machine learning (ML) classifiers can detect hypertension pathology regardless of absolute BP values. The goal is to identify HTS patients from non-HTS recordings and NTS subjects from non-NTS recordings using photoplethysmography (PPG) and electrocardiography (ECG). 803 simultaneous PPG, ECG and invasive BP recordings from 51 subjects were analyzed. 668 were coherent BP segments, with high BP for HTS patients and normal BP for NTS subjects, and 135 were incoherent segments, with normal BP for HTS patients and high BP for NTS subjects. PPG and BP relationship was evaluated with discriminant features and classification models were employed to classify incoherent segments. Using the discriminant features of coherent segments for training and the set of incoherent segments for validation, K-nearest neighbors provided the best outcomes, with F1-score of 88.30%. Combining PPG and ECG recordings with ML-based methodologies would be of high interest for hypertension screening, so that HTS and NTS subjects could be properly discerned even in the case of incoherent or altered BP values. This method could be used as a support for clinical decision-making when diagnosing hypertension.","PeriodicalId":117840,"journal":{"name":"2022 Computing in Cardiology (CinC)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125562418","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

A CNN for COVID-19 Detection Using ECG signals 基于心电信号的新型冠状病毒检测CNN

2022 Computing in Cardiology (CinC) Pub Date : 2022-09-04 DOI: 10.22489/CinC.2022.196

Federico M. Muscato, V. Corino, M. Rivolta, P. Cerveri, A. Sanzo, A. Vicentini, R. Sassi, L. Mainardi

{"title":"A CNN for COVID-19 Detection Using ECG signals","authors":"Federico M. Muscato, V. Corino, M. Rivolta, P. Cerveri, A. Sanzo, A. Vicentini, R. Sassi, L. Mainardi","doi":"10.22489/CinC.2022.196","DOIUrl":"https://doi.org/10.22489/CinC.2022.196","url":null,"abstract":"We developed an end-to-end automatic algorithm for the detection of signs of COVID-19 virus infection in ECGs. We analyzed 12-lead ECGs from patients infected by COVID-19 (C-group) and from a control group (NC-group). The C-group (896 cases) included patients (age range [19–96] years) hospitalized at Ospedale San Matteo in Pavia (Italy) during the first 2020 pandemic outbreak. Infection was confirmed by nasal swab testing. The NC-group (also 896 cases) was built by collecting ECG in sinus rhythm from 3 datasets: Georgia ECG (USA), PTB-XL (Germany) and CPSC 2018 (China). Control ECGs were matched by gender, age and heart rate. An additional control group, only used for testing, was extracted from the Ningbo (China) database. A 4-layers convolutional neural network (CNN), with increasing filter size plus a final fully connected (FC) layer, was designed to classify C vs NC-group. The CNN was trained and k-fold cross validated $(k=7)$ on 1536 ECGs (1316 for testing-220 for validation). Every fold model was used to classify the remaining, separate common test set of 256 ECGs. The accuracy was $0.86pm 0.01$ on validation, $0.86pm 0.01$ on the test set. The FPR on the NC-group was $0.14pm 0.03$ on validation, $0.13pm$ 0.02 on test and $0.10pm 0.01$ on the Ningbo test set $(p > 0.05,ns)$ showing that no bias was induced by the selection of datasets.","PeriodicalId":117840,"journal":{"name":"2022 Computing in Cardiology (CinC)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126769230","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

A New Approach for Mapping Electrical Conduction in Ventricular Tachycardia 室性心动过速电传导成像的新方法

2022 Computing in Cardiology (CinC) Pub Date : 2022-09-04 DOI: 10.22489/CinC.2022.381

C. Fabbri, Matteo Diani, N. Trevisi, C. Corsi

{"title":"A New Approach for Mapping Electrical Conduction in Ventricular Tachycardia","authors":"C. Fabbri, Matteo Diani, N. Trevisi, C. Corsi","doi":"10.22489/CinC.2022.381","DOIUrl":"https://doi.org/10.22489/CinC.2022.381","url":null,"abstract":"Detection of slow electrical conduction areas is crucial for providing an effective ablation therapy in ventricular tachycardia. To this aim local activations and their duration should be accurately identified. Currently mapping systems identify the precocity or lateness of a local activation with respect to a fixed reference without considering its duration. In this study we developed an automatic approach to compute local activation durations from electrograms (EGMs) and electrographic signals (ECGs). EGMs were acquired during both sinus rhythm and ventricular tachycardia with a commercial mapping catheter (Abbott Advisor HD Grid) in six patients. EGMs were band-pass filtered before processing and the analysis was based on the EGMs histogram and similarity techniques, only when a repeatable rhythm was detected in the ECGs the proposed approach was validated against 2846 activations manually annotated (GS) by an expert electrophysiologist. The mean error in the computation of the activation durations over each signal for each patient was -0.1±1.8ms (GS activation duration: 54.4±9.3ms). The developed algorithm is accurate, and the 3D dynamic maps showing slow electrical conduction areas may represent a useful tool to be integrated with activation and voltage maps to plan and assist therapeutic interventions in ventricular arrhythmias.","PeriodicalId":117840,"journal":{"name":"2022 Computing in Cardiology (CinC)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122110866","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

Predicting Daytime Sleepiness from Electrocardiography Based Respiratory Rate Using Deep Learning 利用深度学习从基于心电图的呼吸频率预测白天嗜睡

2022 Computing in Cardiology (CinC) Pub Date : 2022-09-04 DOI: 10.22489/CinC.2022.100

Emmi Antikainen, R. Rehman, T. Ahmaniemi, M. Chatterjee

{"title":"Predicting Daytime Sleepiness from Electrocardiography Based Respiratory Rate Using Deep Learning","authors":"Emmi Antikainen, R. Rehman, T. Ahmaniemi, M. Chatterjee","doi":"10.22489/CinC.2022.100","DOIUrl":"https://doi.org/10.22489/CinC.2022.100","url":null,"abstract":"Daytime sleepiness impairs the activities of daily living, especially in chronic disease patients. Typically, daytime sleepiness is measured with subjective patient reported outcomes (PROs), which could be prone to recall bias. Objective measures of daytime sleepiness, which are sensitive to change, would benefit the assessment of disease states and novel therapies that impact the quality of life. The presented study aimed to predict daytime sleepiness from two hours of continuously measured respiratory rate using a 1-dimensional convolutional neural network. A wearable biosensor was used to continuously measure electrocardiography (ECG) based respiratory rate, while the participants $(N=82)$ were asked to fill in Karolinska Sleepiness Scale three times a day. Considering the need for a sleepiness measure for chronic diseases, neurodegenerative disease (NDD, $N=14)$ patients, immune-mediated inflammatory disease (IMID, $N=42$) patients, as well as healthy participants $(N=26)$ were included in the study. The diseaseagnostic model achieved an accuracy of 63% between non-sleepy and sleepy states. The result demonstrates the potential of using respiratory rate with deep learning for an objective measure of daytime sleepiness.","PeriodicalId":117840,"journal":{"name":"2022 Computing in Cardiology (CinC)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128528373","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

Is the Dominant Frequency Accurate Enough for Atrial Fibrillation Signals? 主导频率对房颤信号足够准确吗?

2022 Computing in Cardiology (CinC) Pub Date : 2022-09-04 DOI: 10.22489/CinC.2022.418

"aline cabasson, Olivier Meste, S. Zeemering, U. Schotten, P. Bonizzi

{"title":"Is the Dominant Frequency Accurate Enough for Atrial Fibrillation Signals?","authors":"\"aline cabasson, Olivier Meste, S. Zeemering, U. Schotten, P. Bonizzi","doi":"10.22489/CinC.2022.418","DOIUrl":"https://doi.org/10.22489/CinC.2022.418","url":null,"abstract":"In noninvasive studies of atrial fibrillation (AF), especially in body surface potential map (BSPM) measurements, the dominant frequency (DF) is usually defined as the highest peak in the power spectrum, after prior cancellation or removal of the ECG components related to the ventricular activity. However, the power spectrum is often hampered by phase breaks presence in atrial signals due to either signal concatenation or to chaotic behavior. Fourier analysis (including multiple frequency components models) is used as a starting point to develop methods adapted to handle phase breaks. Fourier analysis and the average frequency derived from the phase of the analytic signal (within an AF cycle or globally) were selected as estimators of the single frequency model, and compared by means of simulations. It is found that for large phase breaks (±T/2 every half-second), and for a SNR of 5db, the 95 % confidence interval were smaller for the estimates based on the phase, within an AF cycle, of the analytic signal. For the more realistic multiple frequency model, the Fourier decomposition is extended by using a Least Mean Squares (LMS) adaptive algorithm, with or without imposing a constant magnitude. Slight differences in performances of the presented methods are exemplified on a single AF subject where the DF is computed over all the leads of the BSPM records.","PeriodicalId":117840,"journal":{"name":"2022 Computing in Cardiology (CinC)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128694729","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

Identification of Myocardial Infarction by High Frequency Serial ECG Measurement 高频连续心电测量识别心肌梗死

2022 Computing in Cardiology (CinC) Pub Date : 2022-09-04 DOI: 10.22489/CinC.2022.185

Jonas Sandelin, T. Koivisto, Jukka Sirkiä, A. Anzanpour

引用次数: 0