Novel decision strategy for p wave detection utilising non-linearly synthesised ECG components and their enhanced pseudospectral resonances

2000 ◽  
Author(s):  
M. Sabry-Rizk
Keyword(s):  
P Wave ◽  
Decision Strategy ◽  
Wave Detection

Novel decision strategy for P-wave detection utilising nonlinearly synthesised ECG components and their enhanced pseudospectral resonances

2000 ◽  
Vol 147 (6) ◽  
pp. 389-396 ◽  
Author(s):  
M. Sabry-Rizk ◽  
E.R. Carson ◽  
W. Zgallai ◽  
C. Morgan ◽  
K.T.V. Grattan ◽  
...  
Keyword(s):  
P Wave ◽  
Decision Strategy ◽  
Wave Detection

P-wave Detection Using a Parallel Convolutional Neural Network in Electrocardiogram

2021 ◽  
Author(s):  
Jinlei Liu ◽  
Yunqing Liu ◽  
Yanrui Jin ◽  
Xiaojun Chen ◽  
Liqun Zhao ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
P Wave ◽  
Wave Detection

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

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 128869-128880 ◽  
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

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

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.

P Wave and Far-Field R Wave Detection in Pacemaker Patient Atrial Electrograms

2000 ◽  
Vol 23 (4) ◽  
pp. 434-440 ◽  
Author(s):  
HEINZ THERES ◽  
WEIMIN SUN ◽  
WILLIAM COMBS ◽  
ERIC PANKEN ◽  
HARDWIN MEAD ◽  
...  
Keyword(s):  
P Wave ◽  
Far Field ◽  
Wave Detection ◽  
Atrial Electrograms

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 Evaluation of Percutaneous Coronary Intervention for Acute ST-Segment Elevation Myocardial Infarction with Hypertension by Dynamic Electrocardiogram Feature Data

2022 ◽  
Vol 2022 ◽  
pp. 1-8
Author(s):  
Guoqiang Wang ◽  
Yu Wang ◽  
Ru Zhao
Keyword(s):  
Myocardial Infarction ◽  
Percutaneous Coronary Intervention ◽  
Coronary Intervention ◽  
Detection Algorithm ◽  
P Wave ◽  
St Segment Elevation ◽  
Segment Elevation Myocardial Infarction ◽  
Wave Detection ◽  
St Segment ◽  
Percutaneous Coronary

This work was to study the application value of dynamic electrocardiogram (ECG) feature data in evaluating the curative effect of percutaneous coronary intervention in acute ST-segment elevation myocardial infarction with hypertension, so as to facilitate the early diagnosis and treatment of the disease. In this study, 90 patients with acute ST-segment elevation myocardial infarction accompanied by hypertension were selected as the study subjects and randomly divided into group A (oral aspirin antiplatelet therapy), group B (thrombolytic drug streptokinase (SK) therapy), and group C (percutaneous coronary intervention), with 30 cases in each group. In addition, a P-wave detection algorithm was introduced for automatic detection and analysis of electrocardiograms, and the efficacy of patients was assessed by Holter feature data based on the P-wave detection algorithm. The results showed that the diagnostic error rate, sensitivity, and predictive accuracy of the P-wave detection algorithm for ST-segment elevation myocardial infarction caused by acute occlusion of left main coronary artery (LMCA) were 0.24%, 95.41%, and 92.33%, respectively; the diagnostic error rate, sensitivity, and predictive accuracy for non-LMCA (nLMCA) ST-segment elevation myocardial infarction were 0.28%, 95.32%, and 96.07%, respectively; the proportion of patients with symptom to blood flow patency time <3 h in group C (55.3%) was significantly higher than that in groups A and B (22.1% and 22.6%) ( P  < 0.05). Compared with group A, the content of B-type natriuretic peptide (pre-proBNP) at 1 week, 2 weeks, and 3 weeks after treatment in groups B and C was significantly lower and group C was significantly lower than group B ( P  < 0.05). In summary, the P-wave detection algorithm has a high application value in the diagnosis and early prediction of acute ST-segment elevation myocardial infarction. Percutaneous coronary intervention in the treatment of acute ST-segment elevation myocardial infarction with hypertension can shorten the opening time of infarction blood flow, so as to effectively protect the heart function of patients.

scholarly journals P-wave picking for earthquake early warning: Refinement of a T pd method

2021 ◽  
Author(s):  
Masumi Yamada ◽  
Jim Mori
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Arrival Time ◽  
Early Warning Systems ◽  
Earthquake Early Warning ◽  
P Wave ◽  
Warning Systems ◽  
Wave Detection ◽  
Earthquake Early Warning Systems ◽  
Wave Arrival

Summary Detecting P-wave onsets for on-line processing is an important component for real-time seismology. As earthquake early warning systems around the world come into operation, the importance of reliable P-wave detection has increased, since the accuracy of the earthquake information depends primarily on the quality of the detection. In addition to the accuracy of arrival time determination, the robustness in the presence of noise and the speed of detection are important factors in the methods used for the earthquake early warning. In this paper, we tried to improve the P-wave detection method designed for real-time processing of continuous waveforms. We used the new Tpd method, and proposed a refinement algorithm to determine the P-wave arrival time. Applying the refinement process substantially decreases the errors of the P-wave arrival time. Using 606 strong motion records of the 2011 Tohoku earthquake sequence to test the refinement methods, the median of the error was decreased from 0.15 s to 0.04 s. Only three P-wave arrivals were missed by the best threshold. Our results show that the Tpd method provides better accuracy for estimating the P-wave arrival time compared to the STA/LTA method. The Tpd method also shows better performance in detecting the P-wave arrivals of the target earthquakes in the presence of noise and coda of previous earthquakes. The Tpd method can be computed quickly so it would be suitable for the implementation in earthquake early warning systems.

A new algorithm for P-wave detection in the ECG signal

2003 ◽  
Author(s):  
Hee-Kyo Joeng ◽  
Kwang-Keun Kim ◽  
Sun-Chul Hwang ◽  
Myoung-Ho Lee
Keyword(s):  
P Wave ◽  
Ecg Signal ◽  
Wave Detection
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