Methodology for detection of paroxysmal atrial fibrillation based on P-Wave, HRV and QR electrical alternans features

The detection of Paroxysmal Atrial Fibrillation (PAF) is a fairly complex process performed manually by cardiologists or electrophysiologists by reading an electrocardiogram (ECG). Currently, computational techniques for automatic detection based on fast Fourier transform (FFT), Bayes optimal classifier (BOC), k-nearest neighbors (K-NNs), and artificial neural network (ANN) have been proposed. In this study, six features were obtained based on the morphology of the P-Wave, the QRS complex and the heart rate variability (HRV) of the ECG. The performance of this methodology was validated using clinical ECG signals from the Physionet arrhythmia database MIT-BIH. A feedforward neural network was used to detect the presence of PAF reaching a general accuracy of 97.4%. The results obtained show that the inclusion of the information of the P-Wave, HRV and QR Electrical alternans increases the accuracy to identify the PAF event compared to other works that use the information of only one or at most two of them.

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

Applied Sciences ◽

10.3390/app10030976 ◽

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.

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Multiscale convolutional neural network for detecting paroxysmal atrial fibrillation from single lead ECG signals

2020 IEEE Applied Signal Processing Conference (ASPCON) ◽

10.1109/aspcon49795.2020.9276690 ◽

2020 ◽

Author(s):

Eedara Prabhakararao ◽

Samarendra Dandapat

Keyword(s):

Neural Network ◽

Atrial Fibrillation ◽

Convolutional Neural Network ◽

Paroxysmal Atrial Fibrillation ◽

Ecg Signals

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P-Wave Configuration in the Signal Averaged Electrocardiogram: Which Filter Technique Differentiates Best Between Patients With Paroxysmal Atrial Fibrillation and Healthy Volunteers?

Journal of the American College of Cardiology ◽

10.1016/s0735-1097(97)83917-4 ◽

1998 ◽

Vol 31 (2) ◽

pp. 47A

Author(s):

M Hofmann

Keyword(s):

Atrial Fibrillation ◽

Healthy Volunteers ◽

Paroxysmal Atrial Fibrillation ◽

P Wave ◽

Filter Technique

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P Wave Dispersion in Hypertensive Patients with Paroxysmal Atrial Fibrillation

Pacing and Clinical Electrophysiology ◽

10.1111/j.1540-8159.2000.tb07038.x ◽

2000 ◽

Vol 23 (11P2) ◽

pp. 1859-1862 ◽

Cited By ~ 54

Author(s):

NECLA ÖZER ◽

KUDRET AYTEMIR ◽

ENVER ATALAR ◽

ELIF SADE ◽

SERDAR AKSÖYEK ◽

...

Keyword(s):

Atrial Fibrillation ◽

Paroxysmal Atrial Fibrillation ◽

Wave Dispersion ◽

P Wave ◽

P Wave Dispersion ◽

Hypertensive Patients

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Cardiac Arrhythmia Detection using Dual Tree Wavelet Transform and Convolutional Neural Network

10.21203/rs.3.rs-875283/v1 ◽

2021 ◽

Author(s):

K Reddy Madhavi ◽

Padmavathi kora ◽

L Venkateswara Reddy ◽

J Avanija ◽

KLS Soujanya ◽

...

Keyword(s):

Neural Network ◽

Wavelet Transform ◽

Cardiac Cells ◽

P Wave ◽

Computer Assisted ◽

Ecg Signals ◽

Coronary Diseases ◽

Life Saving ◽

Life Threatening ◽

Assisted Diagnosis

Abstract The non-stationary ECG signals are used as a key tools in screening coronary diseases. ECG recording is collected from millions of cardiac cells’ and depolarization and re-polarization conducted in a synchronized manner as: The P-wave occurs first, followed by the QRScomplex and the T-wave, which will repeat in each beat. The signal is altered in a cardiac beat period for different heart conditions. This change can be observed in order to diagnose the patient’s heart status. There are life-threatening (critical) and non-life - threatening (noncritical) arrhythmia (abnormal Heart). Critical arrhythmia gives little time for surgery, whereas non-critical needs additional life-saving care. Simple naked eye diagnosis can mislead the detection. At that point, Computer Assisted Diagnosis (CAD) is therefore required. In this paper Dual Tree Wavelet Transform (DTWT) used as a feature extraction technique along with Convolution Neural Network (CNN) to detect abnormal Heart. The findings of this research and associated studies are without any cumbersome artificial environments. The CAD method proposed has high generalizability; it can help doctors efficiently identify diseases and decrease misdiagnosis.

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Signal-averaged P-wave abnormalities and atrial size in patients with and without idiopathic paroxysmal atrial fibrillation

American Heart Journal ◽

10.1016/s0002-8703(00)90048-6 ◽

2000 ◽

Vol 139 (4) ◽

pp. 684-689 ◽

Cited By ~ 34

Author(s):

Naoko Ishimoto ◽

Makoto Ito ◽

Masahiko Kinoshita

Keyword(s):

Atrial Fibrillation ◽

Paroxysmal Atrial Fibrillation ◽

P Wave ◽

Atrial Size

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Beat-to-Beat P-Wave Analysis Outperforms Conventional P-Wave Indices in Identifying Patients with a History of Paroxysmal Atrial Fibrillation during Sinus Rhythm

Diagnostics ◽

10.3390/diagnostics11091694 ◽

2021 ◽

Vol 11 (9) ◽

pp. 1694

Author(s):

Dimitrios Tachmatzidis ◽

Dimitrios Filos ◽

Ioanna Chouvarda ◽

Anastasios Tsarouchas ◽

Dimitrios Mouselimis ◽

...

Keyword(s):

Atrial Fibrillation ◽

Sinus Rhythm ◽

Paroxysmal Atrial Fibrillation ◽

Univariate Analysis ◽

Multivariate Logistic Regression Analysis ◽

P Wave ◽

P Value ◽

Wave Analysis ◽

P Waves ◽

History Of

Early identification of patients at risk for paroxysmal atrial fibrillation (PAF) is essential to attain optimal treatment and a favorable prognosis. We compared the performance of a beat-to-beat (B2B) P-wave analysis with that of standard P-wave indices (SPWIs) in identifying patients prone to PAF. To this end, 12-lead ECG and 10 min vectorcardiogram (VCG) recordings were obtained from 33 consecutive, antiarrhythmic therapy naïve patients, with a short history of low burden PAF, and from 56 age- and sex-matched individuals with no AF history. For both groups, SPWIs were calculated, while the VCG recordings were analyzed on a B2B basis, and the P-waves were classified to a primary or secondary morphology. Wavelet transform was used to further analyze P-wave signals of main morphology. Univariate analysis revealed that none of the SPWIs performed acceptably in PAF detection, while five B2B features reached an AUC above 0.7. Moreover, multivariate logistic regression analysis was used to develop two classifiers—one based on B2B analysis derived features and one using only SPWIs. The B2B classifier was found to be superior to SPWIs classifier; B2B AUC: 0.849 (0.754–0.917) vs. SPWIs AUC: 0.721 (0.613–0.813), p value: 0.041. Therefore, in the studied population, the proposed B2B P-wave analysis outperforms SPWIs in detecting patients with PAF while in sinus rhythm. This can be used in further clinical trials regarding the prognosis of such patients.

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Convolution Neural Network Based Ecg Classifier

International Journal of Research in Pharmaceutical Sciences ◽

10.26452/ijrps.v10i3.1327 ◽

2019 ◽

Vol 10 (3) ◽

pp. 1626-1630

Author(s):

Sharanya S ◽

Sridhar PA ◽

Poornakala J ◽

Muppala Vasishta ◽

Tharani U

Keyword(s):

Neural Network ◽

Noise Removal ◽

Convolution Neural Network ◽

P Wave ◽

Time Interval ◽

Ecg Signal ◽

Related Condition ◽

Simple Method ◽

Medical Practitioners ◽

Ecg Signals

Classification of Electrocardiogram (ECG) signals plays a significant role in the identification of the functioning of the heart. This work pertains with the ECG signals, where the classifier is developed for identification of normal or abnormal conditions of the heart. The raw ECG signals are collected from an online database (www.physioNet.org) for classification. The raw ECG signal is pre-processed for noise removal, and the frequency spectrum is analysed to compare raw and denoised ECG signal. Attributes (P, Q, R, S, T time intervals) from denoised ECG signal is analysed and classified using Convolution Neural Network (CNN). The paper reports a classification technique to differentiate ECG signals from the MIT-BIH database (arrhythmia database, arrhythmia p-wave annotations, atrial fibrillation). The CNN analyses the deviation between nominal ranges of attributes (amplitude and time interval) and classifies between the abnormality and normal ECG wave. This work provides a simple method for interpreting ECG related condition for the clinician and helps medical practitioners to make diagnostic decisions.

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