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.

scholarly journals Recent Research for Unobtrusive Atrial Fibrillation Detection Methods Based on Cardiac Dynamics Signals: A Survey

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3814
Author(s):  
Fangfang Jiang ◽  
Yihan Zhou ◽  
Tianyi Ling ◽  
Yanbing Zhang ◽  
Ziyu Zhu
Keyword(s):  
Atrial Fibrillation ◽  
Detection Methods ◽  
Ecg Signals ◽  
Non Invasive ◽  
Cardiac Dynamics ◽  
Feature Extraction And Selection ◽  
Future Work ◽  
Electrocardiogram Ecg ◽  
Term Monitoring

Atrial fibrillation (AF) is the most common cardiac arrhythmia. It tends to cause multiple cardiac conditions, such as cerebral artery blockage, stroke, and heart failure. The morbidity and mortality of AF have been progressively increasing over the past few decades, which has raised widespread concern about unobtrusive AF detection in routine life. The up-to-date non-invasive AF detection methods include electrocardiogram (ECG) signals and cardiac dynamics signals, such as the ballistocardiogram (BCG) signal, the seismocardiogram (SCG) signal and the photoplethysmogram (PPG) signal. Cardiac dynamics signals can be collected by cushions, mattresses, fabrics, or even cameras, which is more suitable for long-term monitoring. Therefore, methods for AF detection by cardiac dynamics signals bring about extensive attention for recent research. This paper reviews the current unobtrusive AF detection methods based on the three cardiac dynamics signals, summarized as data acquisition and preprocessing, feature extraction and selection, classification and diagnosis. In addition, the drawbacks and limitations of the existing methods are analyzed, and the challenges in future work are discussed.

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.

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 A Wearable Electrocardiogram Telemonitoring System for Atrial Fibrillation Detection

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 606 ◽  
Author(s):  
Minggang Shao ◽  
Zhuhuang Zhou ◽  
Guangyu Bin ◽  
Yanping Bai ◽  
Shuicai Wu
Keyword(s):  
Atrial Fibrillation ◽  
Ecg Monitoring ◽  
Web Browser ◽  
Ecg Signals ◽  
Cloud Server ◽  
Android App ◽  
Electrocardiogram Ecg ◽  
The Web ◽  
Ecg Data

In this paper we proposed a wearable electrocardiogram (ECG) telemonitoring system for atrial fibrillation (AF) detection based on a smartphone and cloud computing. A wearable ECG patch was designed to collect ECG signals and send the signals to an Android smartphone via Bluetooth. An Android APP was developed to display the ECG waveforms in real time and transmit every 30 s ECG data to a remote cloud server. A machine learning (CatBoost)-based ECG classification method was proposed to detect AF in the cloud server. In case of detected AF, the cloud server pushed the ECG data and classification results to the web browser of a doctor. Finally, the Android APP displayed the doctor’s diagnosis for the ECG signals. Experimental results showed the proposed CatBoost classifier trained with 17 selected features achieved an overall F1 score of 0.92 on the test set (n = 7270). The proposed wearable ECG monitoring system may potentially be useful for long-term ECG telemonitoring for AF detection.

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.

A REAL-TIME QT INTERVAL DETECTION ALGORITHM

2008 ◽  
Vol 08 (02) ◽  
pp. 251-263 ◽  
Author(s):  
Z. E. HADJ SLIMANE ◽  
F. BEREKSI REGUIG
Keyword(s):  
Real Time ◽  
Qt Interval ◽  
Detection Algorithm ◽  
Qrs Detection ◽  
Signal To Noise ◽  
Ecg Signals ◽  
Wave Detection ◽  
T Wave ◽  
Electrocardiogram Ecg ◽  
Ventricular Depolarization

The QT interval is the electrocardiographic representation of the duration of ventricular depolarization and repolarization. In this paper, we have developed a new real-time QT interval detection algorithm for automatically locating the onset of QRS and the end of the T wave. The algorithm consists of several steps: signal-to-noise enhancement, QRS detection, QRS onset, and T-wave end definition. The detection algorithm is tested on electrocardiogram (ECG) signals from the universal MIT-BIH Arrhythmia Database. The resulting QRS detection algorithm has a sensitivity of 99.79% and a specificity of 99.72%. The QRS onset and T-wave detection algorithm is tested using several data records from the MIT/BIH Arrhythmia Database. The results obtained are shown to be highly satisfactory.

scholarly journals A Novel Heart Rate Robust Method for Short-Term Electrocardiogram Biometric Identification

2019 ◽  
Vol 9 (1) ◽  
pp. 201 ◽  
Author(s):  
Di Wang ◽  
Yujuan Si ◽  
Weiyi Yang ◽  
Gong Zhang ◽  
Tong Liu
Keyword(s):  
Heart Rate ◽  
Principal Component ◽  
Identification Accuracy ◽  
Ecg Signal ◽  
Identification Task ◽  
Short Term ◽  
Ecg Signals ◽  
Subject Identification ◽  
Novel Method ◽  
Electrocardiogram Ecg

In the past decades, the electrocardiogram (ECG) has been investigated as a promising biometric by exploiting the subtle discrepancy of ECG signals between subjects. However, the heart rate (HR) for one subject may vary because of physical activities or strong emotions, leading to the problem of ECG signal variation. This variation will significantly decrease the performance of the identification task. Particularly for short-term ECG signal without many heartbeats, the hardly measured HR makes the identification task even more challenging. This study aims to propose a novel method suitable for short-term ECG signal identification. In particular, an improved HR-free resampling strategy is proposed to minimize the influence of HR variability during heartbeat processing. For feature extraction, the Principal Component Analysis Network (PCANet) is implemented to determine the potential difference between subjects. The proposed method is evaluated using a public ECG-ID database that contains various HR data for some subjects. Experimental results show that the proposed method is robust to HR change and can achieve high subject identification accuracy (94.4%) on ECG signals with only five heartbeats. Thus, the proposed method has the potential for application to systems that use short-term ECG signals for identification (e.g., wearable devices).

A novel method for long-term monitoring of intracranial pressure in rats

2014 ◽  
Vol 227 ◽  
pp. 1-9 ◽  
Author(s):  
Maria Uldall ◽  
Marianne Juhler ◽  
Anders Daehli Skjolding ◽  
Christina Kruuse ◽  
Inger Jansen-Olesen ◽  
...  
Keyword(s):  
Intracranial Pressure ◽  
Novel Method ◽  
Long Term Monitoring ◽  
Term Monitoring

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.

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