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A new method of filtering T waves to detect hidden P waves in electrocardiogram signals
Abstract and Figures
A correct identification of the P wave is crucial for the diagnosis of narrow QRS tachycardias. This is sometimes difficult because atrial activity is hidden in the T wave. The aim of this study is to evaluate the usefulness of a T wave filtering technique based on wavelet transformation to identify atrial activity.
Forty-two patients with narrow QRS tachycardias and regular atrial activity were studied. A surface electrocardiogram (ECG), intra-atrial recording, and the T wave filtering ECG were compared simultaneously to check the accuracy of the filtering system in detecting atrial activity. The sensitivity of the T wave filtering and P wave detection algorithm was 85.8% [95% confidence interval (CI): 81.2-89.4%] and the specificity was 89.4% (95% CI: 87.1-91.4%), with a global accuracy of 88.5% (95% CI: 86.5-90.3%). The expert cardiologist's accuracy in distinguishing between atrioventricular nodal reentry tachycardia and atrioventricular reentry tachycardia was 75% in the surface ECG vs. 100% in the ECG with the T wave filtering process (P<0.01).
T wave filtering based on wavelet transformation improves the capacity of the surface ECG to identify atrial activity in cases of regular narrow QRS supraventricular tachycardias.
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... However, the localization of P waves becomes very difficult, because its low amplitude, short duration, easy to drown in noise, or similar characteristics to noise. Various techniques are employed for detecting P-waves in ECG signals, such as the low-pass difference method (LPD) [2], wavelet transform method (WT) [3][4][5][6][7][8], Block Gibbs Sampler [9], and morphological transformation [10]. A precise and dependable P-wave detection method has been introduced by researchers at Brno University of Technology [11]. ...
Electrocardiogram (ECG) signal can reflect the health of the heart, and P wave is an important part of ECG signal, which reflects the excitatory conduction process of the heart. By precisely locating the P waves in the ECG signal, important information about heart disease can be obtained. In this paper, a method of P-wave peak localization based on Pan-Tompkins algorithm is proposed. Firstly, the ECG signal is decomposed by wavelet transform, and the coefficients of each layer are processed to filter out the noise such as baseline drift and electromyographic interference in the ECG signal. After the location of R wave in the signal was found by the Pan-Tompkins algorithm, the QRS wave was eliminated, and then the possible P wave location was found by the improved Pan-Tompkins algorithm. Finally, the correct P wave was screened by a screening strategy. Experimental results show that the sensitivity and accuracy of the P-wave localization algorithm proposed in this paper reach 94.78% and 95.10%, respectively. It shows that the proposed algorithm has good performance for P-wave localization.
... Goya-Esteban et al. [49] have proposed TWA detection and estimation method using signal processing stages, as depicted below Goldwasser et al. have discussed about the possibility of atrial activity (i.e., P wave) over-shadowed in T wave and the importance of proper identification of T wave. Moreover, the T wave filtering was performed using wavelet transformation, which improvises the detection possibility of atrial activity through surface ECG [50]. ...
This chapter presents some comments about the importance of the clinical context for electrocardiogram (ECG) interpretation, and describes the utility of other electrocardiologic techniques. The ECG can be normal even just before the patient experiences sudden death. Vectorcardiography is a technique that records the cardiac electrical activity as closed loops: the atrial depolarization, ventricular depolarization, and ventricular repolarization loops. Vectorcardiography is useful both as a clinical and teaching tool, especially for training in electrocardiography. Changes may be observed with exercise testing in patients with ischemic heart disease. Ischemia can be detected by: ECG alterations, hemodynamic changes, and/or clinical signs and symptoms. Holter ECG monitoring is very useful for the diagnosis and evaluation of arrhythmias. Intracavitary ECG and electrophysiologic studies allow for a better assessment of cardiac electrical activity. Portable and fast electrode placement devices allowing for good‐quality ECG tracings are available.
This chapter provides an overview of the most relevant advantages and disadvantages, as well as the utility and limitations, of the surface electrocardiogram (ECG). The ECG may perform the differential diagnosis of some types of ECG pattern such as Brugada phenocopies. The ECG is currently of great utility, not only from a diagnostic point of view, but also from a prognostic one, in addition to its use in the management of heart disease. The correlation between ECG patterns and coronarography in cases of acute coronary syndromes is valuable to better locate the occlusion site and area at risk. Despite the usefulness of the ECG in acute myocardial infarction (MI), many patients with old MI or chronic ischemic heart disease present with a normal or non‐definitively abnormal ECG at rest and often even during exercise. Undoubtedly, what is most important for the future is to try to overcome the limitations that still exist in electrocardiography.
The cadence of atrial activity may be regular or irregular. In sinus rhythm, the cadence is regular, although it usually shows a little variability, especially during respiration. Ectopic atrial waves may show a regular or irregular cadence. The presence of premature atrial or ventricular complexes may convert a regular rhythm into an irregular one. The presence of wide QRS complexes and irregular rhythm is seen in the case of atrial fibrillation or atrial flutter with variable conduction and aberrant intraventricular conduction or pre‐excitation. High ventricular rates correspond to active arrhythmias, but in many cases normal and low ventricular rates may also be seen in active rhythms, as in the case with regular rhythms or irregular rhythms. In children and athletes, pauses are occasionally caused by a marked physiologic sinus arrhythmia and excessive vagal tone. The most frequent causes of premature complexes are extrasystoles of supraventricular or ventricular origin.
UtilityLimitationsThe future of electrocardiographyReferences
IntroductionInterpretation of the surface ECG in light of the patient's clinical settingAdditional value of other techniquesReferences
This chapter describes an arrhythmia and its classification. The most important clinical significance of arrhythmias is related to an association with sudden cardiac death. It is also important to remember that frequently arrhythmias (especially atrial fibrillation) may lead to embolism, including cerebral emboli, often with severe consequences. Also, it must be remembered that sometimes fast arrhythmias may trigger or worsen heart failure (HF). The chapter comments on these aspects. In order to make a diagnosis of arrhythmia, two factors in particular should be taken into consideration by a physician when examining a patient: the rate of the heart rhythm and heart rhythm regularity or irregularity. Surface electrocardiogram (ECG) recording is the key technique used for diagnosis of a cardiac arrhythmia. Carotid sinus massage with electrocardiographic recording may help in the differential diagnosis of the different types of tachyarrhythmias, according to the results of this maneuver.
Determining the presence of a dominant rhythmAtrial wave analysisQRS complex analysisAtrioventricular relationship analysisPremature complex analysisPause analysisDelayed complex analysisAnalysis of the P wave, the QRS complexes and the ST-T of variable morphology (Figures 18.6–18.9 and Table 18.1)Repetitive arrhythmia analysis: bigeminal rhythmDifferential diagnosis between several arrhythmias in special situationsReferences
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Mallat's book is the undisputed reference in this field - it is the only one that covers the essential material in such breadth and depth. - Laurent Demanet, Stanford University The new edition of this classic book gives all the major concepts, techniques and applications of sparse representation, reflecting the key role the subject plays in today's signal processing. The book clearly presents the standard representations with Fourier, wavelet and time-frequency transforms, and the construction of orthogonal bases with fast algorithms. The central concept of sparsity is explained and applied to signal compression, noise reduction, and inverse problems, while coverage is given to sparse representations in redundant dictionaries, super-resolution and compressive sensing applications. Features: * Balances presentation of the mathematics with applications to signal processing * Algorithms and numerical examples are implemented in WaveLab, a MATLAB toolbox * Companion website for instructors and selected solutions and code available for students New in this edition * Sparse signal representations in dictionaries * Compressive sensing, super-resolution and source separation * Geometric image processing with curvelets and bandlets * Wavelets for computer graphics with lifting on surfaces * Time-frequency audio processing and denoising * Image compression with JPEG-2000 * New and updated exercises A Wavelet Tour of Signal Processing: The Sparse Way, third edition, is an invaluable resource for researchers and R&D engineers wishing to apply the theory in fields such as image processing, video processing and compression, bio-sensing, medical imaging, machine vision and communications engineering. Stephane Mallat is Professor in Applied Mathematics at École Polytechnique, Paris, France. From 1986 to 1996 he was a Professor at the Courant Institute of Mathematical Sciences at New York University, and between 2001 and 2007, he co-founded and became CEO of an image processing semiconductor company. Includes all the latest developments since the book was published in 1999, including its application to JPEG 2000 and MPEG-4 Algorithms and numerical examples are implemented in Wavelab, a MATLAB toolbox Balances presentation of the mathematics with applications to signal processing.
Recent studies have emphasized the role of concealed accessory pathways in reciprocating supraventricular tachycardia. Diagnosis has generally required multicatheter electrophysiologic study. We recorded esophageal electrograms during study in 16 patients with reciprocating tachycardia due to reentry using an accessory alriovenlricular pathway, and in 12 patients with reciprocating tachycardia due to reentry in the AV node. The interval from onset of ventricular depolarization to earliest atrial activation (V-AMIN), ear-liest atrial activity on the esophageal lead (V-AESO).and high right atrium (V-HRA) was measured. No patient with RT due to an accessory atrioventricular pathway had a V-AMIN or V-AESO less than 70 ms, or a V-HRA less than 95 ms. In contrast, 11 of 12 patients with reentry in the AV node had V-Aggo intervals less than 70 ms. Esophageol recording during reciprocating tachycardia provides a simple screening procedure available to all practicing physicians to exclude the diagnosis of accessory atrioventricular pathways in the genesis of paroxysmal supraventricular tachycardia.
Mallat's book is the undisputed reference in this field - it is the only one that covers the essential material in such breadth and depth. - Laurent Demanet, Stanford University The new edition of this classic book gives all the major concepts, techniques and applications of sparse representation, reflecting the key role the subject plays in today's signal processing. The book clearly presents the standard representations with Fourier, wavelet and time-frequency transforms, and the construction of orthogonal bases with fast algorithms. The central concept of sparsity is explained and applied to signal compression, noise reduction, and inverse problems, while coverage is given to sparse representations in redundant dictionaries, super-resolution and compressive sensing applications. Features: * Balances presentation of the mathematics with applications to signal processing * Algorithms and numerical examples are implemented in WaveLab, a MATLAB toolbox * Companion website for instructors and selected solutions and code available for students New in this edition * Sparse signal representations in dictionaries * Compressive sensing, super-resolution and source separation * Geometric image processing with curvelets and bandlets * Wavelets for computer graphics with lifting on surfaces * Time-frequency audio processing and denoising * Image compression with JPEG-2000 * New and updated exercises A Wavelet Tour of Signal Processing: The Sparse Way, third edition, is an invaluable resource for researchers and R&D engineers wishing to apply the theory in fields such as image processing, video processing and compression, bio-sensing, medical imaging, machine vision and communications engineering. Stephane Mallat is Professor in Applied Mathematics at École Polytechnique, Paris, France. From 1986 to 1996 he was a Professor at the Courant Institute of Mathematical Sciences at New York University, and between 2001 and 2007, he co-founded and became CEO of an image processing semiconductor company. Includes all the latest developments since the book was published in 1999, including its application to JPEG 2000 and MPEG-4 Algorithms and numerical examples are implemented in Wavelab, a MATLAB toolbox Balances presentation of the mathematics with applications to signal processing.