P wave detection and delineation in the ECG based on the phase free stationary wavelet transform and using intracardiac atrial electrograms as reference

2016 ◽  
Vol 61 (1) ◽  
pp. 37-56 ◽  
Author(s):  
Gustavo Lenis ◽  
Nicolas Pilia ◽  
Tobias Oesterlein ◽  
Armin Luik ◽  
Claus Schmitt ◽  
...  
Keyword(s):  
Wavelet Transformation ◽  
Optimal Parameter ◽  
P Wave ◽  
P Waves ◽  
Wave Detection ◽  
Local Extrema ◽  
Parameter Configuration ◽  
Ecg Leads ◽  
Electrocardiogram Ecg

Abstract Robust and exact automatic P wave detection and delineation in the electrocardiogram (ECG) is still an interesting but challenging research topic. The early prognosis of cardiac afflictions such as atrial fibrillation and the response of a patient to a given treatment is believed to improve if the P wave is carefully analyzed during sinus rhythm. Manual annotation of the signals is a tedious and subjective task. Its correctness depends on the experience of the annotator, quality of the signal, and ECG lead. In this work, we present a wavelet-based algorithm to detect and delineate P waves in individual ECG leads. We evaluated a large group of commonly used wavelets and frequency bands (wavelet levels) and introduced a special phase free wavelet transformation. The local extrema of the transformed signals are directly related to the delineating points of the P wave. First, the algorithm was studied using synthetic signals. Then, the optimal parameter configuration was found using intracardiac electrograms and surface ECGs measured simultaneously. The reverse biorthogonal wavelet 3.3 was found to be optimal for this application. In the end, the method was validated using the QT database from PhysioNet. We showed that the algorithm works more accurately and more robustly than other methods presented in literature. The validation study delivered an average delineation error of the P wave onset of -0.32±12.41 ms when compared to manual annotations. In conclusion, the algorithm is suitable for handling varying P wave shapes and low signal-to-noise ratios.

scholarly journals P-Wave Detection Using a Fully Convolutional Neural Network in Electrocardiogram Images

2020 ◽  
Vol 10 (3) ◽  
pp. 976
Author(s):  
Rana N. Costandy ◽  
Safa M. Gasser ◽  
Mohamed S. El-Mahallawy ◽  
Mohamed W. Fakhr ◽  
Samir Y. Marzouk
Keyword(s):  
Neural Network ◽  
Atrial Fibrillation ◽  
Convolutional Neural Network ◽  
P Wave ◽  
Ecg Signal ◽  
P Waves ◽  
Ecg Signals ◽  
Wave Detection ◽  
Automated Algorithm ◽  
Electrocardiogram Ecg

Electrocardiogram (ECG) signal analysis is a critical task in diagnosing the presence of any cardiac disorder. There are limited studies on detecting P-waves in various atrial arrhythmias, such as atrial fibrillation (AFIB), atrial flutter, junctional rhythm, and other arrhythmias due to P-wave variability and absence in various cases. Thus, there is a growing need to develop an efficient automated algorithm that annotates a 2D printed version of P-waves in the well-known ECG signal databases for validation purposes. To our knowledge, no one has annotated P-waves in the MIT-BIH atrial fibrillation database. Therefore, it is a challenge to manually annotate P-waves in the MIT-BIH AF database and to develop an automated algorithm to detect the absence and presence of different shapes of P-waves. In this paper, we present the manual annotation of P-waves in the well-known MIT-BIH AF database with the aid of a cardiologist. In addition, we provide an automatic P-wave segmentation for the same database using a fully convolutional neural network model (U-Net). This algorithm works on 2D imagery of printed ECG signals, as this type of imagery is the most commonly used in developing countries. The proposed automatic P-wave detection method obtained an accuracy and sensitivity of 98.56% and 98.78%, respectively, over the first 5 min of the second lead of the MIT-BIH AF database (a total of 8280 beats). Moreover, the proposed method is validated using the well-known automatically and manually annotated QT database (a total of 11,201 and 3194 automatically and manually annotated beats, respectively). This results in accuracies of 98.98 and 98.9%, and sensitivities of 98.97 and 97.24% for the automatically and manually annotated QT databases, respectively. Thus, these results indicate that the proposed automatic method can be used for analyzing long-printed ECG signals on mobile battery-driven devices using only images of the ECG signals, without the need for a cardiologist.

Abstract P504: Quality Validation Of Different ECG Leads, And An Automatic P-wave Detection Algorithm For Af, Based On Real-world Data

Circulation ◽  
2020 ◽  
Vol 141 (Suppl_1) ◽  
Author(s):  
Magnus Samuelsson ◽  
Åke Olsson
Keyword(s):  
Respiratory Sinus Arrhythmia ◽  
Real World ◽  
Detection Algorithm ◽  
P Wave ◽  
Detection Accuracy ◽  
Real World Data ◽  
P Waves ◽  
Sinus Arrhythmia ◽  
Ecg Leads

Background: Single-lead ECG has shown in research to be affected by artifacts leading to lower diagnostic yield of Atrial Fibrillation (AF). Use of multiple ECG leads and algorithms for detection of AF has shown to increase detection accuracy and reduce false positives. Methods: A novel RR- and P-wave based automatic algorithm implemented in the 2-lead Coala Heart Monitor (Coala) was evaluated for detection accuracy and quality by the comparison to blinded manual ECG interpretation. Evaluation was conducted on 100 consecutive anonymous printouts of chest- and thumb-ECG waveforms, where both an irregular RR-rhythm and strong P-waves in either chest or thumb recording were detected.The recordings, without exclusions, were generated from 5,512 real-world data recordings from actual Coala users in Sweden (both OTC and Rx users) during the period of March 5 to March 22, 2019, with no control or influence by the researchers or any other organization or individual. The prevalence of cardiac conditions in the user population was unknown. The blinded recordings were each manually interpreted and assessed for quality by a trained cardiologist. The manual interpretation was compared with the automatic analysis performed by the cloud-based detection algorithm to determine the detection quality of the respective ECG leads. Results: Strong P-waves were detected more often in the chest ECG as compared to the thumb ECG (90 vs 32 recordings). The assessed quality of the ECG tracings was higher in the chest ECGs as compared to the thumb ECGs (4.61 vs 3.88). Irregular RR-rhythms were detected in 569 recordings (10 % of a total of 5,512 recordings), the 100 non-AF recordings containing both irregular RR-rhythms and strong P-waves thus constituted 18% of all recordings with irregular RR-rhythms. Non-pathological rhythm (normal) was present in 84% of the recordings although all of these recordings contained irregular rhythm disturbances (respiratory sinus arrhythmia, PAC/PVC etc). Respiratory sinus arrhythmia was the single most prevalent condition and found in 47% of the recordings with irregular RR-rhythms with strong detected P-waves. Conclusion: The combination of chest and thumb ECG for detection of AF by an automatic P-wave based algorithm is shown to be more than 300% superior to thumb ECG alone with the majority of automatically detected P-waves and highest assessed ECG quality in the chest recordings.

P wave dispersion – fading light of a popular parameter

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
J Zawadzki ◽  
J Mercik ◽  
A Marecka ◽  
G Zawadzki ◽  
J Adamowicz ◽  
...  
Keyword(s):  
Wave Dispersion ◽  
P Wave ◽  
P Wave Dispersion ◽  
P Waves ◽  
Wave Duration ◽  
P Wave Duration ◽  
Basic Measurement ◽  
Atrial Activation ◽  
Electrophysiological System ◽  
Ecg Leads

Abstract Background The P wave dispersion concept was created to describe the non-uniform atrial conduction as a separate AF factor. However the major assumptions of the theory are inconsistent with the main principle of electrocardiography, which assumes that 12 leads of ECG, recorded simultaneously, register the same events at the same time. The presence of dispersion suggests the presence of a P wave in one lead, while in the other one it has ended and no longer exists. This observation per se could be considered as a methodological artifact. Objective The major objective is to present that the P wave dispersion descends from imprecise measurements in various ECG leads. We intend to demonstrate that more accurate measurements make this parameter disappear. Methods Our study included 150 patients (89F, 61M) assessed using the electrophysiological system, which allowed to assess the sinus P waves. The P wave duration was measured by 3 independent researchers in all leads twice: 1. paper speed=50 mm/s, enhancement 16x (basic measurement) 2. paper speed=200 mm/s, enhancement 128–256x, simultaneously measuring the P wave dispersion. All measurements were repeated 3 times. Results The results are presented in Table 1 Conclusion 1. The P wave dispersion is the artifact of measurement. It is clear that after using much more accurate tools, the parameter disappears. 2. The P-wave dispersion is connected with Pmax, therefore may be apparently clinically useful but as a matter of fact, doesn't carry any meaning itself. 3.The significant P wave duration parameter should be a total atrial activation time, from the beginning of the earliest recorded P wave, till the end of the last Pwave recorded in any lead. Funding Acknowledgement Type of funding source: None

scholarly journals Brno University of Technology ECG Signal Database with Annotations of P Wave (BUT PDB)

2021 ◽  
Author(s):  
Lucie Maršánová ◽  
Radovan Smíšek ◽  
Andrea Němcová ◽  
Lukáš Smital ◽  
Martin Vítek
Keyword(s):  
Biomedical Engineering ◽  
P Wave ◽  
Ecg Signal ◽  
Supraventricular Arrhythmia ◽  
P Waves ◽  
Detection Algorithms ◽  
Wave Detection ◽  
University Of Technology ◽  
Development Evaluation

Abstract Background: Brno University of Technology ECG Signal Database with Annotations of P Wave (BUT PDB) is an ECG signal database with marked peaks of P waves created by the cardiology team at the Department of Biomedical Engineering, Brno University of Technology. Results: The database consists of 50 2-minute 2-lead ECG signal records with various types of pathologies. The ECGs were selected from 3 existing databases of ECG signal - MIT-BIH Arrhythmia Database, MIT-BIH Supraventricular Arrhythmia Database and Long Term AF Database. The P waves positions were manually annotated by two ECG experts in all 50 records. Each record contains also dominant diagnosis (pathology) present in the record and annotated positions and types of QRS complexes (from the original database). Conclusion: The database is created for the development, evaluation and objective comparison of P wave detection algorithms.

scholarly journals Dynamic Electrocardiogram under P Wave Detection Algorithm Combined with Low-Dose Betaloc in Diagnosis and Treatment of Patients with Arrhythmia after Hepatocarcinoma Resection

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Fenfen Jiang ◽  
Haokai Xu ◽  
Xiaowen Shi ◽  
Bingjiang Han ◽  
Zhenliang Chu ◽  
...  
Keyword(s):  
Liver Cancer ◽  
Low Dose ◽  
Primary Liver Cancer ◽  
Diagnostic Value ◽  
Detection Algorithm ◽  
P Wave ◽  
Control Group ◽  
High Dose ◽  
P Waves ◽  
Wave Detection

This work aimed to study the diagnostic value of dynamic electrocardiogram (ECG) based on P wave detection algorithm for arrhythmia after hepatectomy in patients with primary liver cancer, and to compare the therapeutic effect of different doses of Betaloc. P wave detection algorithm was introduced for ECG automatic detection and analysis, which can be used for early diagnosis of arrhythmia. Sixty patients with arrhythmia after hepatectomy for primary liver cancer were selected as the research objects. They were randomly divided into control group, SD group, MD group, and HD group, with 15 cases in each group. No Betaloc, low-dose (≤47.5 mg), medium-dose (47.5–95 mg), and high-dose (142.5–190 mg) Betaloc were used for treatment. As a result, P wave detection algorithms can mark P waves that may be submerged in strong interference. P waves from arrhythmia database were used to verify the performance of the proposed algorithm. The prediction precision (Pp) of ventricular arrhythmia and atrial arrhythmia was 98.53% and 98.76%, respectively. Systolic blood pressure (117.35 ± 7.33, 126.44 ± 9.38, and 116.02 ± 8.2) mmHg in SD group, MD group, and HD group was significantly lower than that in control group (140.3 ± 7.21) mmHg after two weeks of treatment. Moreover, those of SD group and HD group were significantly lower than MD group ( P < 0.05 ). The effective rate of cardiac function improvement in SD group (72.35 ± 1.21%) was significantly higher than that in control group, MD group, and HD group (38.2 ± 0.98%, 65.12 ± 1.33%, and 60.43 ± 1.25%; P < 0.05 ). In short, dynamic ECG based on P wave detection algorithm had high diagnostic value for arrhythmia after hepatectomy in patients with primary liver cancer. It was safe and effective for patients to choose small dose of Betaloc.

scholarly journals Advanced P Wave Detection in Ecg Signals During Pathology: Evaluation in Different Arrhythmia Contexts

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Lucie Maršánová ◽  
Andrea Němcová ◽  
Radovan Smíšek ◽  
Martin Vítek ◽  
Lukáš Smital
Keyword(s):  
Automated Analysis ◽  
P Wave ◽  
Algorithm Performance ◽  
Ecg Signals ◽  
Wave Detection ◽  
Pathological Conditions ◽  
Novel Method ◽  
Electrocardiogram Ecg ◽  
Term Monitoring

AbstractReliable P wave detection is necessary for accurate and automatic electrocardiogram (ECG) analysis. Currently, methods for P wave detection in physiological conditions are well-described and efficient. However, methods for P wave detection during pathology are not generally found in the literature, or their performance is insufficient. This work introduces a novel method, based on a phasor transform, as well as innovative rules that improve P wave detection during pathology. These rules are based on the extraction of a heartbeats’ morphological features and knowledge of heart manifestation during both physiological and pathological conditions. To properly evaluate the performance of the proposed algorithm in pathological conditions, a standard database with a sufficient number of reference P wave positions is needed. However, such a database did not exist. Thus, ECG experts annotated 12 chosen pathological records from the MIT-BIH Arrhythmia Database. These annotations are publicly available via Physionet. The algorithm performance was also validated using physiological records from the MIT-BIH Arrhythmia and QT databases. The results for physiological signals were Se = 98.42% and PP = 99.98%, which is comparable to other methods. For pathological signals, the proposed method reached Se = 96.40% and PP = 85.84%, which greatly outperforms other methods. This improvement represents a huge step towards fully automated analysis systems being respected by ECG experts. These systems are necessary, particularly in the area of long-term monitoring.

Electrocardiogram Recognition and Ablation of Atrial Tachycardia

2011 ◽  
Vol 3 (1) ◽  
pp. 80
Author(s):  
Alexander Feldman ◽  
Jonathan M Kalman ◽  
◽  
Keyword(s):  
Catheter Ablation ◽  
Atrial Tachycardia ◽  
Primary Treatment ◽  
P Wave ◽  
Wave Morphology ◽  
Focal Atrial Tachycardia ◽  
Mapping Techniques ◽  
Electrocardiogram Ecg ◽  
Surface Electrocardiogram

Focal atrial tachycardia (AT) is a relatively uncommon cause of supraventricular tachycardia, but when present is frequently difficult to treat medically. Atrial tachycardias tend to originate from anatomically determined atrial sites. The P-wave morphology on surface electrocardiogram (ECG) together with more sophisticated contemporary mapping techniques facilitates precise localisation and ablation of these ectopic foci. Catheter ablation of focal AT is associated with high long-term success and may be viewed as a primary treatment strategy in symptomatic patients.

scholarly journals Detection of Myocardial Infarction Using ECG and Multi-Scale Feature Concatenate

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1906
Author(s):  
Jia-Zheng Jian ◽  
Tzong-Rong Ger ◽  
Han-Hua Lai ◽  
Chi-Ming Ku ◽  
Chiung-An Chen ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Network Structure ◽  
Class Imbalance ◽  
Class Imbalance Problem ◽  
Multi Scale ◽  
Imbalance Problem ◽  
Average Accuracy ◽  
Significant Difference ◽  
Electrocardiogram Ecg

Diverse computer-aided diagnosis systems based on convolutional neural networks were applied to automate the detection of myocardial infarction (MI) found in electrocardiogram (ECG) for early diagnosis and prevention. However, issues, particularly overfitting and underfitting, were not being taken into account. In other words, it is unclear whether the network structure is too simple or complex. Toward this end, the proposed models were developed by starting with the simplest structure: a multi-lead features-concatenate narrow network (N-Net) in which only two convolutional layers were included in each lead branch. Additionally, multi-scale features-concatenate networks (MSN-Net) were also implemented where larger features were being extracted through pooling the signals. The best structure was obtained via tuning both the number of filters in the convolutional layers and the number of inputting signal scales. As a result, the N-Net reached a 95.76% accuracy in the MI detection task, whereas the MSN-Net reached an accuracy of 61.82% in the MI locating task. Both networks give a higher average accuracy and a significant difference of p < 0.001 evaluated by the U test compared with the state-of-the-art. The models are also smaller in size thus are suitable to fit in wearable devices for offline monitoring. In conclusion, testing throughout the simple and complex network structure is indispensable. However, the way of dealing with the class imbalance problem and the quality of the extracted features are yet to be discussed.

scholarly journals Differential Transform Method as an Effective Tool for Investigating Fractional Dynamical Systems

2021 ◽  
Vol 11 (15) ◽  
pp. 6955
Author(s):  
Andrzej Rysak ◽  
Magdalena Gregorczyk
Keyword(s):  
Optimal Parameter ◽  
Differential Transform Method ◽  
Bifurcation Diagrams ◽  
Transform Method ◽  
Rossler System ◽  
Differential Transform ◽  
Rössler System ◽  
Fractional System ◽  
Parameter Values

This study investigates the use of the differential transform method (DTM) for integrating the Rössler system of the fractional order. Preliminary studies of the integer-order Rössler system, with reference to other well-established integration methods, made it possible to assess the quality of the method and to determine optimal parameter values that should be used when integrating a system with different dynamic characteristics. Bifurcation diagrams obtained for the Rössler fractional system show that, compared to the RK4 scheme-based integration, the DTM results are more resistant to changes in the fractionality of the system.

scholarly journals Multiscale Analysis of Surface Texture Quality of Models Manufactured by Laser Powder-Bed Fusion Technology and Machining from 316L Steel

Materials ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 2794
Author(s):  
Damian Gogolewski ◽  
Tomasz Bartkowiak ◽  
Tomasz Kozior ◽  
Paweł Zmarzły
Keyword(s):  
Multiscale Analysis ◽  
Wavelet Transformation ◽  
316L Stainless Steel ◽  
Multiscale Methods ◽  
Fusion Technology ◽  
Current Standard ◽  
Statistical Research ◽  
Powder Bed ◽  
316L Steel

The paper presents the results of tests aimed at evaluating the surface textures of samples manufactured from material based on 316L stainless steel. The analysis of the surface topography was conducted based on the classical approach in accordance with the current standard and with the use of multiscale methods; i.e., wavelet transformation and geometric via curvature. Selective laser melting 3D printing technology was used to produce samples for surface testing. Furthermore, additional assessment of surfaces created as result of milling was conducted. Statistical research demonstrated a differentiation in the distribution of particular morphological features in certain ranges of the analyzed scales.

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