Feasibility of neural network based QRS-T cancellation schemes for P-wave detection

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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A new approach to the P-wave detection and classification based upon application of wavelet neural network

2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society ◽

10.1109/iembs.2001.1020559 ◽

2005 ◽

Cited By ~ 2

Author(s):

T. Domider ◽

E.J. Tkacz ◽

P. Kostka ◽

A. Wrzesniowski

Keyword(s):

Neural Network ◽

Wavelet Neural Network ◽

P Wave ◽

New Approach ◽

Wave Detection

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

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v10i4.pp4023-4034 ◽

2020 ◽

Vol 10 (4) ◽

pp. 4023

Author(s):

Henry Castro ◽

Juan David Garcia-Racines ◽

Alvaro Bernal-Noreña

Keyword(s):

Neural Network ◽

Atrial Fibrillation ◽

Paroxysmal Atrial Fibrillation ◽

P Wave ◽

Computational Techniques ◽

K Nearest Neighbors ◽

Ecg Signals ◽

Complex Process ◽

Artificial Neural Network Ann ◽

Electrical Alternans

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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An Accurate QRS Complex and P Wave Detection in ECG Signals Using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise Approach

IEEE Access ◽

10.1109/access.2019.2939943 ◽

2019 ◽

Vol 7 ◽

pp. 128869-128880 ◽

Cited By ~ 11

Author(s):

Md Billal Hossain ◽

Syed Khairul Bashar ◽

Allan J. Walkey ◽

David D. McManus ◽

Ki H. Chon

Keyword(s):

Empirical Mode Decomposition ◽

Ensemble Empirical Mode Decomposition ◽

P Wave ◽

Qrs Complex ◽

Ecg Signals ◽

Wave Detection ◽

Mode Decomposition ◽

Adaptive Noise

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A New Method to Detect P-Wave Based on Quadratic Function

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.267.462 ◽

2011 ◽

Vol 267 ◽

pp. 462-467

Author(s):

Nan Quan Zhou

Keyword(s):

Quadratic Function ◽

Least Square Method ◽

Detection Algorithm ◽

Least Square ◽

P Wave ◽

Good Effect ◽

Rate Variation ◽

Optimal Interval ◽

Wave Detection ◽

Wide Range

The paper presents a P-wave detection algorithm based on fitting function in the optimal interval. In the algorithm we used quadratic function to fit the P wave by this means of least square method in every interval, which was shifted in local range. Then we found the optimal fitting interval of P wave by comparing the error of fitting. Finally, we obtained the characteristic points of P wave by using the fitting function to fit P wave in the optimal interval. The performance of the algorithm tested using the records of the MIT-BIH database was effective and accurate. The algorithm on the wide range of heart rate variation and small P wave of ECG P-wave detection has good effect. Also it has some capabilities of anti-interference, particularly the false dismissal probability is quite low.

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End-to-end PGA estimation for earthquake early warning using transformer networks

10.5194/egusphere-egu2020-5107 ◽

2020 ◽

Author(s):

Jannes Münchmeyer ◽

Dino Bindi ◽

Ulf Leser ◽

Frederik Tilmann

Keyword(s):

Neural Network ◽

Real Time ◽

Ground Motion ◽

Early Warning ◽

Earthquake Early Warning ◽

P Wave ◽

Lead Times ◽

Ground Shaking ◽

Target Sites ◽

End To End

The key task of earthquake early warning is to provide timely and accurate estimates of the ground shaking at target sites. Current approaches use either source or propagation based methods. Source based methods calculate fast estimates of the earthquake source parameters and apply ground motion prediction equations to estimate shaking. They suffer from saturation effects for large events, simplified assumptions and the need for a well known hypocentral location, which usually requires arrivals at multiple stations. Propagation based methods estimate levels of shaking from the shaking at neighboring stations and therefore have short warning times and possibly large blind zones. Both methods only use specific features from the waveform. In contrast, we present a multi-station neural network method to estimate horizontal peak ground acceleration (PGA) anywhere in the target region directly from raw accelerometer waveforms in real time.The three main components of our model are a convolutional neural network (CNN) for extracting features from the single-station three-component accelerograms, a transformer network for combining features from multiple stations and for transferring them to the target site features and a mixture density network to generate probabilistic PGA estimates. By using a transformer network, our model is able to handle a varying set and number of stations as well as target sites. We train our model end-to-end using recorded waveforms and PGAs. We use data augmentation to enable the model to provide estimations at targets without waveform recordings. Starting with the arrival of a P wave at any station of the network, our model issues real-time predictions at each new sample. The predictions are Gaussian mixtures, giving estimates of both expected value and uncertainties. The model can be used to predict PGA at specific target sites, as well as to generate ground motion maps.We analyze the model on two strong motion data sets from Japan and Italy in terms of standard deviation and lead times. Through the probabilistic predictions we are able to give lead times for different levels of uncertainty and ground shaking. This allows to control the ratio of missed detections to false alerts. Preliminary analysis suggest that for levels between 1%g and 10%g our model achieves multi-second lead times even for the closest stations at a false-positive rate below 25%. For an example event at 50 km depth, lead times at the closest stations with epicentral distances below 20 km are 6 s and 7.5 s. This suggests that our model is able to effectively use the difference between P and S travel time and accurately assess the future level of ground shaking from the first parts of the P wave. It additionally makes effective use of the information contained in the absence of signal at other stations.

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