In this paper, we developed and evaluated a robust single-lead electrocardiogram (ECG) delineation system based on the wavelet transform (WT). In a first step, QRS complexes are detected. Then, each QRS is delineated by detecting and identifying the peaks of the individual waves, as well as the complex onset and end. Finally, the determination of P and T wave peaks, onsets and ends is performed. We evaluated the algorithm on several manually annotated databases, such as MIT-BIH Arrhythmia, QT, European ST-T and CSE databases, developed for validation purposes. The QRS detector obtained a sensitivity of Se=99.66% and a positive predictivity of P+=99.56% over the first lead of the validation databases (more than 980,000 beats), while for the well-known MIT-BIH Arrhythmia Database, Se and P+ over 99.8% were attained. As for the delineation of the ECG waves, the mean and standard deviation of the differences between the automatic and manual annotations were computed. The mean error obtained with the WT approach was found not to exceed one sampling interval, while the standard deviations were around the accepted tolerances between expert physicians, outperforming the results of other well known algorithms, especially in determining the end of T wave.

A wavelet-based ECG delineator: evaluation on standard databases

Atrial fibrillation (AF) is the most common sustained arrhythmia and is associated with significant morbidity and mortality. Timely diagnosis of the arrhythmia, particularly transient episodes, can be difficult since patients may be asymptomatic. In this study, we describe a robust algorithm for automatic detection of AF based on the randomness, variability and complexity of the heart beat interval (RR) time series. Specifically, we employ a new statistic, the Turning Points Ratio, in combination with the Root Mean Square of Successive RR Differences and Shannon Entropy to characterize this arrhythmia. The detection algorithm was tested on two databases, namely the MIT-BIH Atrial Fibrillation Database and the MIT-BIH Arrhythmia Database. These databases contain several long RR interval series from a multitude of patients with and without AF and some of the data contain various forms of ectopic beats. Using thresholds and data segment lengths determined by Receiver Operating Characteristic (ROC) curves we achieved a high sensitivity and specificity (94.4% and 95.1%, respectively, for the MIT-BIH Atrial Fibrillation Database). The algorithm performed well even when tested against AF mixed with several other potentially confounding arrhythmias in the MIT-BIH Arrhythmia Database (Sensitivity = 90.2%, Specificity = 91.2%).

Automatic Real Time Detection of Atrial Fibrillation

An open access database for evaluating the algorithms of electrocardiogram rhythm and morphology abnormality detection

Automatic detection of atrial fibrillation using stationary wavelet transform and support vector machine

This work introduces a new single-lead ECG delineator based on phasor transform. The method is characterized by its robustness, low computational cost and mathematical simplicity. It converts each instantaneous ECG sample into a phasor, and can precisely manage P and T waves, which are of notably lower amplitude than the QRS complex. The method has been validated making use of synthesized and real ECG sets, including the MIT-BIH arrhythmia, QT, European ST-T and TWA Challenge 2008 databases. Experiments with the synthesized recordings reported precise detection and delineation performances in a wide variety of ECGs, with signal-to-noise ratios of 10 dB and above. For real ECGs, the QRS detection was characterized by an average sensitivity of 99.81% and positive predictivity of 99.89%, for all the analyzed databases (more than one million beats). Regarding delineation, the maximum localization error between automatic and manual annotations was lower than 6 ms and its standard deviation was in agreement with the accepted tolerances for expert physicians in the onset and offset identification for QRS, P and T waves. Furthermore, after revising and reannotating some ECG recordings by expert cardiologists, the delineation error decreased notably, becoming lower than 3.5 ms, on average, and reducing by a half its standard deviation. This new proposed strategy outperforms the results provided by other well-known delineation algorithms and, moreover, presents a notably lower computational cost.

http://iopscience.iop.org/article/10.1088/0967-3334/31/11/005/pdf

Application of the phasor transform for automatic delineation of single-lead ECG fiducial points.

A method is presented for automatic analysis of the P-wave, based on lead II of a 12-lead standard ECG, in resting conditions during a routine examination for the detection of patients prone to atrial fibrillation (AF), one of the most prevalent arrhythmias. First, the P-wave was delineated, and this was achieved in two steps: the detection of the QRS complexes for ECG segmentation, using a wavelet analysis method, and a hidden Markov model to represent one beat of the signal for P-wave isolation. Then, a set of parameters to detect patients prone to AF was calculated from the P-wave. The detection efficiency was validated on an ECG database of 145 patients, including a control group of 63 people and a study group of 82 patients with documented AF. A discriminant analysis was applied, and the results obtained showed a specificity and a sensitivity between 65% and 70%.

/pdf/automatic-p-wave-analysis-of-patients-prone-to-atrial-4yhuvz6ue1.pdf

Automatic P-wave analysis of patients prone to atrial fibrillation.

Presents an automatic analysis method of the P-wave, based on lead II of a 12 lead standard ECG, which will be applied to the detection of patients prone to atrial fibrillation (AF), one of the most frequent arrhythmias. It focuses first on the segmentation of the electrocardiogram P-wave, which is performed in two steps: first, detection of the QRS complexes, then association of a wavelet analysis method and a bidden Markov model to represent one beat of the signal. After segmentation, the P-wave is isolated and a set of parameters, which have the ability to detect patients prone to AF, is calculated from it. The detection efficiency is validated on an ECG database of 145 patients including a control group and a study group with documented AF. A discriminant analysis is applied and the results obtained show a specificity and a sensitivity between 65% and 70%.

/pdf/ecg-segmentation-and-p-wave-feature-extraction-application-3l6049bxuh.pdf

ECG segmentation and P-wave feature extraction: application to patients prone to atrial fibrillation

Atrial fibrillation (AF) is a frequent heart disease but can still not be predicted However, several studies of the electrocardiogram P-wave have shown that some parameters, in the time domain as well as in the frequency domain, can discriminate people at risk of AF The goal of this study is to find the best parameters and to evaluate their limits We have classified people at risk of AF in four categories, depending on their P-wave pattern, trying to link each pattern to anatomical and electrical features

http://ieeexplore.ieee.org/iel4/5216/14119/00647457.pdf

J.-J. Blanc

Papers

Automatic P-wave analysis of patients prone to atrial fibrillation.

ECG segmentation and P-wave feature extraction: application to patients prone to atrial fibrillation

P-wave parameters for atrial fibrillation risk detection