Development of entropy based algorithm for cardiac beat detection in 12-lead electrocardiogram

Measurement and Rate Correction of the QT Interval

Setting a New Gold Standard

The diagnostic values of the aVR lead or "Vereckei algorithm," and the lead II R-wave peak time (RWPT) criterion, recently devised for the differential diagnosis of wide QRS complex tachycardias (WCTs), were compared. A total of 212 WCTs (142 ventricular tachycardias [VTs], 62 supraventricular tachycardias [SVT], and eight preexcitation SVTs) from 145 patients with proven electrophysiologic diagnoses were retrospectively analyzed by seven examiners blinded to the electrophysiologic diagnoses. The overall test accuracy of the Vereckei algorithm was superior to that of the RWPT criterion (84.3% vs. 79.6%; p = 0.0003). The sensitivity of the Vereckei algorithm for VT diagnosis was greater than that of RWPT criterion (92.4% vs. 79.1%; p < 0.0001). The negative predictive value (NPV) for the Vereckei algorithm was also greater (77.8%; 95% confidence interval [CI] = 73.6% to 82.1%) than that of the RWPT criterion (61.6%; 95% CI = 57.6% to 65.6%). The specificity of the Vereckei algorithm was lower than that of the RWPT criterion (64.7% vs. 80.9%; p < 0.0001). The positive predictive value (PPV) was also lower for the Vereckei algorithm (86.4%; 95% CI = 84.4% to 88.4%) than for the RWPT criterion (90.9%; 95% CI = 89.1% to 92.8%). Incorrect diagnoses made by the Vereckei algorithm were mainly due to misdiagnosis of SVT as VT (65.7% of cases), and those made by the RWPT criterion were due to the more dangerous misdiagnosis of VT as SVT (72.5% of cases). The Vereckei algorithm was superior in overall test accuracy, sensitivity, and NPV for VT diagnosis and inferior in specificity and PPV to the RWPT criterion. All of these parameters were lower in "real life" than those reported by the original authors for each of the particular electrocardiographic methods.

Objective. Automatic detection of arrhythmia based on electrocardiogram (ECG) plays a critical role in early prevention and diagnosis of cardiovascular diseases. With the increase in widely available digital ECG data and the development of deep learning, multi-class arrhythmia classification based on automatic feature extraction of ECG has become increasingly attractive. However, the majority of studies cannot accept varied-length ECG signals and have limited performance in detecting multi-class arrhythmias. Approach. In this study, we propose a multi-branch signal fusion network (MBSF-Net) for multi-label classification of arrhythmia in 12-lead varied-length ECG. Our model utilizes the complementary power between different structures, which include Inception with depthwise separable convolution (DWS-Inception), spatial pyramid pooling (SPP) Layer, and multi-scale fusion Resnet (MSF-Resnet). The proposed method can extract features from each lead of 12-lead ECG recordings separately and then effectively fuse the features of each lead by integrating multiple convolution kernels with different receptive fields, which can achieve the information of complementation between different angles of the ECG signal. In particular, our model can accept 12-lead ECG signals of arbitrary length. Main results. The experimental results show that our model achieved an overall classification F1 score of 83.8% in the 12-lead ECG data of CPSC-2018. In addition, the F1 score of the MBSF-Net performed best among the MBF-Nets which are removed the SPP layer from MBSF-Net. In comparison with the latest ECG classification algorithms, the proposed model can be applied in varied-length signals and has an excellent performance, which not only can fully retain the integrity of the original signals, but also eliminates the cropping/padding signal beforehand when dealing with varied-length signal database. Significance. MBSF-Net provides an end-to-end multi-label classification model with outperfom performance, which allows detection of disease in varied-length signals without any additional cropping/padding. Moreover, our research is beneficial to the development of computer-aided diagnosis.

This focus issue of Physiological Measurement follows the 38th Annual International Computing in Cardiology (CinC) Conference, hosted in Hangzhou, China in September 2011 by Zhejiang University. Each year, the NIH-sponsored PhysioNet resource (http://physionet.org/) runs an open competition lasting several months, aimed at encouraging the development of solutions to an unsolved or poorly solved problem in biomedicine, in most cases making use of relevant clinical and experimental data provided freely by PhysioNet. Participants in these annual challenges discuss their diverse approaches to the Challenge problems during dedicated scientific sessions at CinC. The topics of these PhysioNet/CinC Challenges range from physiologic signal processing and analysis to forecasting and modelling clinically important events and processes.

A QRS detection method is proposed based on the principal component analysis (PCA) and the combined wavelet entropy for 12-lead electrocardiogram (ECG) signals. Firstly, the base line wander and the high frequency interference are removed for ECG signals. The PCA method is employed to reduce the dimension of filtered signals. Then, the quasi-period sorting method is proposed to reorder principal components (PCs), which may help the following combined wavelet entropy based method detecting the QRS complex in the lower sorted PCs easily. The proposed method is evaluated against the standard St. Petersburg institute of cardiological technics 12-lead arrhythmia database with other two different QRS detection methods for the single-lead ECG signal and two-lead ECG signals respectively. Experimental results show that the proposed method gives the best overall performance. It achieves an average detection rate of 99.980%, a sensitivity of 99.997%, and a positive prediction of 99.987%.

PO-04-175 RECONSTRUCTING TWELVE-LEAD ELECTROCARDIOGRAM SIGNALS FROM A SINGLE-LEAD SIGNAL USING GENERATIVE ADVERSARIAL NETWORKS

This paper proposes a simple technique for automatic detection and delineation of cardiac beat (QRS-complex) in Electrocardiogram (ECG) using Fuzzy C-Means (FCM) clustering algorithm. The power line interference and baseline wander present in the ECG signal is removed using digital filtering techniques. Absolute slope of the filtered ECG signal is calculated to enhance the QRS-complexes in the ECG signal. FCM technique is then used to delineate QRS and non-QRS-regions. Algorithm performance is validated using original single lead ECG recordings from the dataset-3 of the CSE multi-lead measurement library. Detection rate of 98.74% is achieved. The percentage of false positive and false negative detection is very low. The delineation performance of the algorithm is validated by calculating the mean and standard deviation of the errors between automatic and manual annotations by the referee's cardiologists/combined program median in the CSE study.

Background: Electrocardiograms (ECGs) are frequently stored as printed images, but tools developed for artificial intelligence (AI)-based automated diagnosis and patient phenotyping exclusively rely on signal data, limiting their application. Methods: We developed a model that simultaneously incorporates both signal and image-based ECG data in clinical diagnosis and patient phenotyping. We used all 12-lead ECGs from a large deidentified database from Brazil, with signals transformed to a standard 12-lead ECG image. Both ECG signals and images were randomly subset into training, validation and test sets (90%-5%-5%). We used 6 physician-defined clinical labels spanning rhythm and conduction disorders as labels (atrial fibrillation [AF], sinus tachy/bradycardia [ST/SB], 1 st degree AV block [1dAVB], RBBB, LBBB). In addition, ECG-based identification of patient sex was assessed as a higher dimensional label. Convolutional Neural Networks were constructed with custom Inception net architecture for signals and EfficientNet-B3 for images. Model performance was evaluated in the holdout test set and US-based external validation dataset, PTB-XL. Results: The derivation dataset included 2,228,236 ECGs in 1,506,112 individuals (mean age 54y, 60% female). The ECG-DualNet model successfully labeled all 6 clinical diagnoses and patient gender with a high model performance (AUC of .81-.99 for signal and .92-.99 for images, F1 scores, .31-.78, and .49-82) in the 111,412 held-out test ECGs (Fig A-B). In external validation, with 21,785 ECGs, the model had excellent performance on both ECG signals and images (AUCs .75-.99 and .89-.99, F1 scores, .25-82, and .44-.86) (Fig C-D). Conclusion: A novel multilabel dual signal and image-based model achieved high performance in both clinical diagnosis and higher dimensional phenotypic labels across distinct ECG sources, with the ability to standardize automated diagnosis and patient phenotyping across ECG data formats.

The American College of Cardiology (ACC)/American Heart Association (AHA) Task Force on Practice Guidelines was formed to make recommendations regarding the diagnosis and treatment of patients with known or suspected cardiovascular disease. Coronary artery disease (CAD) is the leading cause of death in the United States. Unstable angina (UA) and the closely related condition non–ST-segment elevation myocardial infarction (NSTEMI) are very common manifestations of this disease. These life-threatening disorders are a major cause of emergency medical care and hospitalizations in the United States. In 1996, the National Center for Health Statistics reported 1 433 000 hospitalizations for UA or NSTEMI. In recognition of the importance of the management of this common entity and of the rapid advances in the management of this condition, the need to revise guidelines published by the Agency for Health Care Policy and Research (AHCPR) and the National Heart, Lung and Blood Institute in 1994 was evident. This Task Force therefore formed the current committee to develop guidelines for the management of UA and NSTEMI. The present guidelines supersede the 1994 guidelines. The customary ACC/AHA classifications I, II, and III summarize both the evidence and expert opinion and provide final recommendations for both patient evaluation and therapy: Class I: Conditions for which there is evidence and/or general agreement that a given procedure or treatment is useful and effective . Class II: Conditions for which there is conflicting evidence and/or a divergence of opinion about the usefulness/efficacy of a procedure or treatment. Class IIa: Weight of evidence/opinion is in favor of usefulness/efficacy. Class IIb: Usefulness/efficacy is less well established by evidence/opinion. Class III: Conditions for which there is evidence and/or general agreement that the procedure/treatment is not useful/effective and in some cases may be harmful. The weight of the evidence was ranked highest (A) if the data …

Present paper deals with design and development of digital FIR filters. Heart diseases, which are one of the death reasons, are among the several serious problems in this century and as per the latest survey, 60% of the patients die due to Heart diseases. Electrocardiogram (ECG) signal plays a vital role in the monitoring and diagnosis of the health conditions of human heart. The basic ECG signal has the frequency range from .5Hz to 100Hz. The most significant noises that corrupt ECG signal are power line interference and baseline wanders. It becomes difficult for the Specialist to diagnose the diseases if artifacts are present in the ECG signal. Designing digital filters to reduce these noises is very important in ECG signal processing. In this work a digital FIR filter for reducing 50Hz power line noise in ECG signal is designed and implemented with different windowing methods as of Hamming, Kaiser, and Chebyshev. The work has been done in MATLAB environment. The result obtained for all FIR filters are compared by comparing the waveforms of the original and filtered ECG signals. The filter which gives the best results is the one using Chebyshev Window.

A software-based pacemaker pulse detection and paced rhythm classification algorithm

Electrocardiogram (ECG) signals are widely used to diagnose heart health. Experts can detect multiple cardiac abnormalities using the ECG signal. In a clinical setting, 12-lead ECG is mainly used. But using fewer leads can make the ECG more pervasive as it can be integrated with wearable devices. At the same time, we need to build systems that can diagnose cardiac abnormalities automatically. This work develops a channel self-attention-based deep neural network to diagnose cardiac abnormality using a different number of ECG lead combinations. Our approach takes care of the temporal and spatial interdependence of multi-lead ECG signals. Our team participates under the name &#x201C;cardiochallenger&#x201D; in the &#x201C;PhysioNetl-Computing in Cardiology Challenge 2021&#x201D;. Our method achieves the challenge metric score of 0.55, 0.51, 0.53, 0.51, and 0.53 (ranked 2^nd, 5^th, 4^th, 5^th and 4th) for the 12-lead, 6-lead, 4-lead, 3-lead, and 2-lead cases, respectively, on the test data set.

Influence of the vectorcardiogram synthesis matrix on the power of the electrocardiogram-derived spatial QRS-T angle to predict arrhythmias in patients with ischemic heart disease and systolic left ventricular dysfunction

Improving the diagnosis of bundle branch block by analysis of body surface potential maps