Annotated QT Database Research Articles

A novel and lightweight P, QRS, and T peaks detector using adaptive thresholding and template waveform

Accurate detection of key components in an electrocardiogram (ECG) plays a vital role in identifying cardiovascular diseases. In this work, we proposed a novel and lightweight P, QRS, and T peaks detector using adaptive thresholding and template waveform. In the first stage, we proposed a QRS complex detector, which utilises a novel adaptive thresholding process followed by threshold initialisation. Moreover, false positive QRS complexes were removed using the kurtosis coefficient computation. In the second stage, the ECG segment from the S wave point to the Q wave point was extracted for clustering. The template waveform was generated from the cluster members using the ensemble average method, interpolation, and resampling. Next, a novel conditional thresholding process was used to calculate the threshold values based on the template waveform morphology for P and T peaks detection. Finally, the min-max functions were used to detect the P and T peaks. The proposed technique was applied to the MIT-BIH arrhythmia database (MIT-AD) and the QT database for QRS detection and validation. Sensitivity (Se%) values of 99.81 and 99.90 and positive predictivity (+P%) values of 99.85 and 99.94 were obtained for the MIT-AD and QT database for QRS complex detection, respectively. Further, we found that Se% = 96.50 and +P% = 96.08 for the P peak detection, Se% = 100 and +P% = 100 for the R peak detection, and Se% = 99.54 and +P% = 99.68 for the T peak detection when using the manually annotated QT database. The proposed technique exhibits low computational complexity and can be implemented on low-cost hardware, since it is based on simple decision rules rather than a heuristic approach.

An Automatic R and T Peak Detection Method Based on the Combination of Hierarchical Clustering and Discrete Wavelet Transform.

The detection and delineation of QRS-complexes and T-waves in Electrocardiogram (ECG) is an important task because these features are associated with the cardiac abnormalities including ventricular arrhythmias that may lead to sudden cardiac death. In this paper, we propose a novel method for the R-peak and the T-peak detection using hierarchical clustering and Discrete Wavelet Transform (DWT) from the ECG signal. In the first step, a template of the single ECG beat is identified. Secondly, all R-peaks are detected by using hierarchical clustering. Then, each corresponding T-wave boundary is delineated based on the template morphology. Finally, the determination of T wave peaks is achieved based on the Modulus-Maxima Analysis (MMA) of the DWT coefficients. We evaluated the algorithm by using all records from the MIT-BIH arrhythmia database and QT database. The R-peak detector achieved a sensitivity of 99.89%, a positive predictivity of 99.97% and 99.83% accuracy over the validation MIT-BIH database. In addition, it shows a sensitivity of 100%, a positive predictivity of 99.83% in manually annotated QT database. It also shows 99.92% sensitivity and 99.96% positive predictivity over the automatic annotated QT database. In terms of the T-peak detection, our algorithm is verified with 99.91% sensitivity and 99.38% positive predictivity in manually annotated QT database.

P-Wave Detection Using a Fully Convolutional Neural Network in Electrocardiogram Images

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.

P- and T-wave delineation in ECG signals using parametric mixture Gaussian and dynamic programming

Detection and tracking of the P- and T-waves are important issues in the analysis and interpretation of the ECG signals. This paper addresses the problem by using two mixture Gaussian function and the Dynamic programming. A key feature of the proposed algorithm is that it allows to incorporate the prior knowledge about the P/T wave location variations and robustness to errors in QRS detection. The proposed algorithm is evaluated on the annotated QT-database and compared against the algorithms based on differential evolution optimization strategy (DEOS) and generating blocks of interest (GBI). The experiments show that the proposed method determines the P- and T-peak locations with a root mean square error of 0.085s and 0.091s respectively. Both these values are better than the corresponding values from DEOS and GBI. Similarly, the proposed algorithm achieves a sensitivity of 96.13% and predictivity of 97.70%. While the predictivity is higher than both DEOS and GBI, the sensitivity is on par with GBOI and higher than that of DEOS.

Sequential beat-to-beat P and T wave delineation and waveform estimation in ECG signals: Block Gibbs sampler and marginalized particle filter

For ECG interpretation, the detection and delineation of P and T waves are challenging tasks. This paper proposes sequential Bayesian methods for simultaneous detection, threshold-free delineation, and waveform estimation of P and T waves on a beat-to-beat basis. By contrast to state-of-the-art methods that process multiple-beat signal blocks, the proposed Bayesian methods account for beat-to-beat waveform variations by sequentially estimating the waveforms for each beat. Our methods are based on Bayesian signal models that take into account previous beats as prior information. To estimate the unknown parameters of these Bayesian models, we first propose a block Gibbs sampler that exhibits fast convergence in spite of the strong local dependencies in the ECG signal. Then, in order to take into account all the information contained in the past rather than considering only one previous beat, a sequential Monte Carlo method is presented, with a marginalized particle filter that efficiently estimates the unknown parameters of the dynamic model. Both methods are evaluated on the annotated QT database and observed to achieve significant improvements in detection rate and delineation accuracy compared to state-of-the-art methods, thus providing promising approaches for sequential P and T wave analysis.

An innovative approach of QRS segmentation based on first-derivative, Hilbert and Wavelet Transforms

The QRS detection and segmentation processes constitute the first stages of a greater process, e.g., electrocardiogram (ECG) feature extraction. Their accuracy is a prerequisite to a satisfactory performance of the P and T wave segmentation, and also to the reliability of the heart rate variability analysis. This work presents an innovative approach of QRS detection and segmentation and the detailed results of the proposed algorithm based on First-Derivative, Hilbert and Wavelet Transforms, adaptive threshold and an approach of surface indicator. The method combines the adaptive threshold, Hilbert and Wavelet Transforms techniques, avoiding the whole ECG signal preprocessing. After each QRS detection, the computation of an indicator related to the area covered by the QRS complex envelope provides the detection of the QRS onset and offset. The QRS detection proposed technique is evaluated based on the well-known MIT-BIH Arrhythmia and QT databases, obtaining the average sensitivity of 99.15% and the positive predictability of 99.18% for the first database, and 99.75% and 99.65%, respectively, for the second one. The QRS segmentation approach is evaluated on the annotated QT database with the average segmentation errors of 2.85±9.90ms and 2.83±12.26ms for QRS onset and offset, respectively. Those results demonstrate the accuracy of the developed algorithm for a wide variety of QRS morphology and the adaptation of the algorithm parameters to the existing QRS morphological variations within a single record.

P- and T-Wave Delineation in ECG Signals Using a Bayesian Approach and a Partially Collapsed Gibbs Sampler

Detection and delineation of P- and T-waves are important issues in the analysis and interpretation of electrocardiogram (ECG) signals. This paper addresses this problem by using Bayesian inference to represent a priori relationships among ECG wave components. Based on the recently introduced partially collapsed Gibbs sampler principle, the wave delineation and estimation are conducted simultaneously by using a Bayesian algorithm combined with a Markov chain Monte Carlo method. This method exploits the strong local dependency of ECG signals. The proposed strategy is evaluated on the annotated QT database and compared to other classical algorithms. An important feature of this paper is that it allows not only for the detection of P- and T-wave peaks and boundaries, but also for the accurate estimation of waveforms for each analysis window. This can be useful for some ECG analysis that require wave morphology information.