New approach for T-wave peak detection and T-wave end location in 12-lead paced ECG signals based on a mathematical model

Independent Detection of T-Waves in Single Lead ECG Signal Using Continuous Wavelet Transform

INTRODUCTION

In the ECG signals, T-waves play a very important role in the detection of cardiac arrest. During myocardial ischemia, the first significant change occurs on the T-wave. These waves are generated due to the repolarization of the heart ventricle. The independent detection of T-waves is a bit challenging due to its variable nature, therefore, most of the algorithms available in the literature for T-wave detection use the detection of the QRS complex as the starting point. But accurate detection of Twave is very much required, as clinically, the first indication of a shortage of blood supply to the heart muscle (myocardial ischemia) shows up as changes in T-wave followed by other changes in the morphology of the ECG signal.

MATERIALS AND METHODS

In this paper, an efficient and novel algorithm based on Continuous Wavelet Transform (CWT) is presented to detect the Twave independently. In CWT, for better matching, a new mother wavelet is designed using the pattern and shape of the Twave. This algorithm is validated on all the signals of the QT database.

CONCLUSION

The algorithm attains an average sensitivity of 99.88% and positive predictivity of 99.81% for the signals annotated by the cardiologists in the database.

Weighted Time Warping Improves T-wave Morphology Markers Clinical Significance

Background: T-wave (TW) morphology indices based on time-warping (d_w) have shown significant cardiovascular risk stratification value. However, errors in the location of TW boundaries may impact their prognostic power. Our aim was to test the hypothesis that a weighted time-warping function (WF) would reduce the sensitivity of d_w to these errors and improve their clinical significance. Methods: The WFs were proportional to (i) the reference TW (T), and (ii) the absolute value of its derivative (D). The index d_w was recalculated using these WFs, and its performance was compared to the unweighted control case (C) in four different scenarios: 1) robustness against simulated TW boundaries location errors; 2) ability to retain physiological information in an electrophysiological cardiac model; 3) ability to monitor blood potassium concentration changes ([K⁺]) in 29 hemodialysis (HD) patients; 4) and the sudden cardiac death (SCD) risk stratification value of the TW morphology restitution (TMR) index, derived from d_w, in 651 chronic heart failure (CHF) patients. Results and Discussion: The WFs led to a reduced sensitivity (R) of d_w to TW boundary location errors as compared to C (median R=0.19 and 0.22 and 0.35 for T, D and C, respectively). They also preserved the physiological relationship between d_w and repolarization dispersion changes at ventricular level. No improvements in [K⁺] tracking were observed for the HD patients (Pearsons median correlation [r] between [K⁺] and d_w was 0.86r0.90 for T, D and C). In CHF patients, the SCD risk stratification value of TMR was improved by applying T (hazard ratio, HAR, of 2.80), followed by D (HAR=2.32) and C (HAR=2.23). Conclusions and Significance: The proposed WFs, with T showing the best performance, increased the robustness of time-warping based markers against TW location errors preserving their physiological information content and boosting their SCD risk stratification value. Results from this work support the use of T when deriving d_w for future clinical applications.

An enhanced T-wave delineation method using phasor transform in the electrocardiogram

Accurate detection of key components plays a vital role in determining cardiovascular diseases in the ECG. In this method, we propose an enhanced T-wave delineation method using the phasor transform. Discrete Wavelet Transform (DWT) and median filters were used to suppress the high-frequency noise and baseline drift during pre-processing. The phasor transform was used to detect and locate the delineation points before and after the T-wave. The proposed method was tested on the QTDB for R-peak, T-peak, and T_offdetection. It achieved both sensitivity (Se%) and positive predictivity (+P%) values of 100 for R-peak detection. In T-peak detection, method shows Se % = 99.46 and +P % = 99.54, respectively. This method has reported Se% = 99.34 and +P% = 99.48 for T_offdetection in the ECG. The achieved results show that the method can be used for cardiac arrhythmia detection related to the morphology of T-wave.

Application of two novel electrical restitution-based ECG markers of ventricular arrhythmia to patients with nonischemic cardiomyopathy

INTRODUCTION

Sudden cardiac death (SCD) risk assessment is limited, particularly in patients with nonischemic cardiomyopathies. This is the first application, in patients with cardiomyopathies, of two novel risk markers, regional restitution instability index (R2I2) and peak electrocardiogram restitution slope (PERS), which have been shown to be predictive of ventricular arrhythmias (VA) or death in ischemic heart disease patients.

METHODS

Blinded retrospective study of 50 patients: 33 dilated cardiomyopathy and 17 other; undergoing electrophysiological study (EPS) for SCD risk stratification, and 29 controls with structurally normal hearts undergoing EPS. R2I2 was calculated from an EPS using electrocardiogram surrogates for action potential duration and diastolic interval. Cut-offs for high and low R2I2/PERS were predefined.

RESULTS

R2I2 was significantly higher in study than control patients (0.99 ± 0.05 vs. 0.63 ± 0.04, p < .001). PERS showed a trend to higher values in the study group (1.18[0.63] vs. 1.09[0.54], p = .07). During median follow up of 5.6 years [interquartile range 1.9 years], nine study patients reached the endpoint of VA/death. Patients who experienced VA/death showed trends to higher mean R2I2 (1.14 ± 0.07 vs.0.95 ± 0.05, p = .12) and PERS (1.46[0.49] vs. 1.13[0.62], p = .22). A Cox proportional hazards model using grouped markers: R2I2 < 1.03 + PERS < 1.21/either R2I2 ≥ 1.03 or PERS ≥ 1.21/R2I2 ≥ 1.03 + PERS ≥ 1.21; significantly predicted VA/death (p = .02) with a hazard ratio per positive component of 3.2 (95% confidence interval 1.2-8.8).

CONCLUSION

R2I2≥ 1.03 + PERS ≥ 1.21 may predict VA/death in patients with cardiomyopathies. R2I2 ≥ 1.03 + PERS ≥ 1.21 have the potential to play an important role in SCD risk stratification in cardiomyopathies but their validity should be confirmed in a larger study.

Robust T-End Detection via T-End Signal Quality Index and Optimal Shrinkage

An automatic accurate T-wave end (T-end) annotation for the electrocardiogram (ECG) has several important clinical applications. While there have been several algorithms proposed, their performance is usually deteriorated when the signal is noisy. Therefore, we need new techniques to support the noise robustness in T-end detection. We propose a new algorithm based on the signal quality index (SQI) for T-end, coined as tSQI, and the optimal shrinkage (OS). For segments with low tSQI, the OS is applied to enhance the signal-to-noise ratio (SNR). We validated the proposed method using eleven short-term ECG recordings from QT database available at Physionet, as well as four 14-day ECG recordings which were visually annotated at a central ECG core laboratory. We evaluated the correlation between the real-world signal quality for T-end and tSQI, and the robustness of proposed algorithm to various additive noises of different types and SNR’s. The performance of proposed algorithm on arrhythmic signals was also illustrated on MITDB arrhythmic database. The labeled signal quality is well captured by tSQI, and the proposed OS denoising help stabilize existing T-end detection algorithms under noisy situations by making the mean of detection errors decrease. Even when applied to ECGs with arrhythmia, the proposed algorithm still performed well if proper metric is applied. We proposed a new T-end annotation algorithm. The efficiency and accuracy of our algorithm makes it a good fit for clinical applications and large ECG databases. This study is limited by the small size of annotated datasets.

A novel adaptive window based technique for T wave detection and delineation in the ECG

The electrocardiogram (ECG) morphology determines the overall activity of the heart and is the most widely used tool in the diagnostic processes. T wave is a crucial wave component that reveals very useful information regarding various cardiac disorders. In this paper we have proposed a novel T wave detection technique based on adaptive window and simple decision rule. The proposed technique uses two-stage median filters followed by the Savitzky-Golay filter at the pre-processing stage to remove the noises in the ECG signal. The QRS complex is detected for locating the T wave as a reference in one ECG cycle. An R-R interval based window is considered for detecting the T wave, and decision logic depends on the iso-electric line value. The proposed technique is tested on the QT database and self-recorded dataset for its performance evaluation. In the present work, the results achieved for T wave detection sensitivity (Se), positive predictivity (+P), detection error rate (DER), and accuracy (Acc) on the QT database are Se = 97.57%, +P = 99.63%, DER = 2.78%, and Acc = 97.22% with an average time error of (3.468 ± 5.732) ms. The proposed technique shows Se = 99.94%, +P = 99.94%, DER = 0.01%, and Acc = 99.89% on the self-recorded dataset. The proposed technique is also capable of detecting both the upward and downward T wave efficiently in the ECG signal.

An Improved Sliding Window Area Method for T Wave Detection

BACKGROUND

The T wave represents ECG repolarization, whose detection is required during myocardial ischemia, and the first significant change in the ECG signal is being observed in the ST segment followed by changes in other waves like P wave and QRS complex. To offer guidance in clinical diagnosis, decision-making, and daily mobile ECG monitoring, the T wave needs to be detected firstly. Recently, the sliding area-based method has received an increasing amount of attention due to its robustness and low computational burden. However, the parameter setting of the search window's boundaries in this method is not adaptive. Therefore, in this study, we proposed an improved sliding window area method with more adaptive parameter setting for T wave detection.

METHODS

Firstly, k-means clustering was used in the annotated MIT QT database to generate three piecewise functions for delineating the relationship between the RR interval and the interval from the R peak to the T wave onset and that between the RR interval and the interval from the R peak to the T wave offset. Then, the grid search technique combined with 5-fold cross validation was used to select the suitable parameters' combination for the sliding window area method.

RESULTS

With respect to onset detection in the QT database, F1 improved from 54.70% to 70.46% and 54.05% to 72.94% for the first and second electrocardiogram (ECG) channels, respectively. For offset detection, F1 also improved in both channels as it did in the European ST-T database.

CONCLUSIONS

F1 results from the improved algorithm version were higher than those from the traditional method, indicating a potentially useful application for the proposed method in ECG monitoring.

Quantitative Evaluation of the Relationship between T-Wave-Based Features and Serum Potassium Level in Real-World Clinical Practice

BACKGROUND

Proper management of hyperkalemia that leads to fatal cardiac arrhythmia has become more important because of the increased prevalence of hyperkalemia-prone diseases. Although T-wave changes in hyperkalemia are well known, their usefulness is debatable. We evaluated how well T-wave-based features of electrocardiograms (ECGs) are correlated with estimated serum potassium levels using ECG data from real-world clinical practice.

METHODS

We collected ECGs from a local ECG repository (MUSE™) from 1994 to 2017 and extracted the ECG waveforms. Of about 1 million reports, 124,238 were conducted within 5 minutes before or after blood collection for serum potassium estimation. We randomly selected 500 ECGs and two evaluators measured the amplitude (T-amp) and right slope of the T-wave (T-right slope) on five lead waveforms (V3, V4, V5, V6, and II). Linear correlations of T-amp, T-right slope, and their normalized feature (T-norm) with serum potassium levels were evaluated using Pearson correlation coefficient analysis.

RESULTS

Pearson correlation coefficients for T-wave-based features with serum potassium between the two evaluators were 0.99 for T-amp and 0.97 for T-right slope. The coefficient for the association between T-amp, T-right slope, and T-norm, and serum potassium ranged from -0.22 to 0.02. In the normal ECG subgroup (normal ECG or otherwise normal ECG), there was no correlation between T-wave-based features and serum potassium level.

CONCLUSIONS

T-wave-based features were not correlated with serum potassium level, and their use in real clinical practice is currently limited.

Investigation of the relationship between two novel electrocardiogram-based sudden cardiac death risk markers and autonomic function

BACKGROUND

Regional Restitution Instability Index (R2I2) and Peak ECG Restitution Slope (PERS) are promising sudden cardiac death (SCD) risk markers. R2I2 and PERS use the standard 12‑lead ECG to measure properties of electrical restitution implicated in ventricular arrhythmogenesis. We investigated the relationship between R2I2, PERS and autonomic function to inform future application of these risk markers.

METHODS

Blinded, prospective, observational study of 44 patients with ischaemic cardiomyopathy undergoing risk stratification for an ICD. Patients underwent an electrophysiological study for determination of R2I2 and PERS. 24-hour ambulatory ECG monitoring was carried out for determination of time-domain heart rate variability (HRV).

RESULTS

During median follow up of 22 months, 11 patients experienced ventricular arrhythmia (VA)/SCD. Weak inverse correlation was seen between R2I2 and HRV-i (rho: -0.36, p = 0.02). R2I2 and PERS were significantly higher in patients experiencing VA/SCD than those not (mean ± SEM:1.14 ± 0.11 vs 0.84 ± 0.05, p = 0.01) and (1.73 ± 0.27 vs 1.07 ± 0.08, p = 0.002) respectively. Patients with low HRV-i and high PERS had an incidence rate ratio for VA/SCD 14.5 times that of patients with high HRV-i and low PERS (p = 0.02).

CONCLUSION

This small study suggests that there is minimal correlation between R2I2, PERS and autonomic function as measured by HRV. Combining PERS with HRV identified patients at particularly high risk of ventricular arrhythmia/SCD. A combined PERS+HRV risk marker may improve SCD risk stratification in patients with ischaemic cardiomyopathy.

T-wave end detection using neural networks and Support Vector Machines

BACKGROUND AND OBJECTIVE

In this paper we propose a new approach for detecting the end of the T-wave in the electrocardiogram (ECG) using Neural Networks and Support Vector Machines.

METHODS

Both, Multilayer Perceptron (MLP) neural networks and Fixed-Size Least-Squares Support Vector Machines (FS-LSSVM) were used as regression algorithms to determine the end of the T-wave. Different strategies for selecting the training set such as random selection, k-means, robust clustering and maximum quadratic (Rényi) entropy were evaluated. Individual parameters were tuned for each method during training and the results are given for the evaluation set. A comparison between MLP and FS-LSSVM approaches was performed. Finally, a fair comparison of the FS-LSSVM method with other state-of-the-art algorithms for detecting the end of the T-wave was included.

RESULTS

The experimental results show that FS-LSSVM approaches are more suitable as regression algorithms than MLP neural networks. Despite the small training sets used, the FS-LSSVM methods outperformed the state-of-the-art techniques.

CONCLUSION

FS-LSSVM can be successfully used as a T-wave end detection algorithm in ECG even with small training set sizes.

Extracting cardiac dynamics within ECG signal for human identification and cardiovascular diseases classification

Cardiac characteristics underlying the time/frequency domain features are limited and not comprehensive enough to reflect the temporal/dynamical nature of ECG patterns. This paper proposes a dynamical ECG recognition framework for human identification and cardiovascular diseases classification via a dynamical neural learning mechanism. The proposed method consists of two phases: a training phase and a test phase. In the training phase, cardiac dynamics within ECG signals is extracted (approximated) accurately by using radial basis function (RBF) neural networks through deterministic learning mechanism. The obtained cardiac system dynamics is represented and stored in constant RBF networks. An ECG signature is then derived from the extracted cardiac dynamics along the periodic ECG state trajectories. A bank of estimators is constructed using the extracted cardiac dynamics to represent the trained gait patterns. In the test phase, recognition errors are generated and taken as the similarity measure by comparing the cardiac dynamics of the trained ECG patterns and the dynamics of the test ECG pattern. Rapid recognition of a test ECG pattern begins with measuring the state of test pattern, and automatically proceeds with the evolution of the recognition error system. According to the smallest error principle, the test ECG pattern can be rapidly recognized. This kind of cardiac dynamics information represents the beat-to-beat temporal change of ECG modifications and the temporal/dynamical nature of ECG patterns. Therefore, the amount of discriminability provided by the cardiac dynamics is larger than the original signals. This paper further discusses the extension of the proposed method for cardiovascular diseases classification. The constructed recognition system can distinguish and assign dynamical ECG patterns to predefined classes according to the similarity of cardiac dynamics. Experiments are carried out on the FuWai and PTB ECG databases to demonstrate the effectiveness of the proposed method.

ECG Parameters for Malignant Ventricular Arrhythmias: A Comprehensive Review

Many studies showed electrocardiogram (ECG) parameters are useful for predicting fatal ventricular arrhythmias (VAs). However, the studies have several shortcomings. Firstly, all studies lack of effective way to present behavior of various ECG parameters prior to the occurrence of the VAs. Secondly, they also lack of discussion on how to consider the parameters as abnormal. Thirdly, the reports do not include approaches to increase the detection accuracy for the abnormal patterns. The purpose of this study is to address the aforementioned issues. It identifies ten ECG parameters from various sources and then presents a review based on the identified parameters. From the review, it has been found that the increased risk of VAs can be represented by presence and certain abnormal range of the parameters. The variation of parameters range could be influenced by either gender or age. This study also has discovered the facts that averaging, outliers elimination and morphology detection algorithms can contribute to the detection accuracy.

Application of a new robust ECG T-wave delineation algorithm for the evaluation of the autonomic innervation of the myocardium

T-wave amplitude (TWA) is a well know index of the autonomic innervation of the myocardium. However, until now it has been evaluated only manually or with simple and inefficient algorithms. In this paper, we developed a new robust single-lead electrocardiogram (ECG) T-wave delineation algorithm that is able to detect the T-wave with a wavelet based method and automatically calculate the TWA. We evaluated the algorithm on the QT database, achieving a sensitivity of 99.92% for the T wave peak and 99.38% for the T wave end. In addition, the percentage of records automatically delineated with high precision was higher than previous published works. Finally, the algorithm was applied to study the influence of anticholinergic and antiadrenergic drugs (i.e. atropine and metoprolol) on the TWA. It was observed that atropine significantly decreased the TWA when compared to baseline level, that head-up tilt caused a decrease of TWA and that metoprolol blunted this decrease. Through the development of a robust algorithm, this study opens the way for further research on the T-wave analysis for the assessment of the autonomic innervation of the ventricular myocardium.

Real-time electrocardiogram P-QRS-T detection-delineation algorithm based on quality-supported analysis of characteristic templates

The main objective of this study is to introduce a simple, low-latency, and accurate algorithm for real-time detection of P-QRS-T waves in the electrocardiogram (ECG) signal. In the proposed method, real-time signal preprocessing, which includes high frequency noise filtering and baseline wander reduction, is performed by applying discrete wavelet transform (DWT). A method based on signal first-order derivative and adaptive threshold adjustment is employed for real-time detection of the QRS complex. Moreover, detection and delineation of P- and T-waves are achieved by correlation analysis conducted between signal and their templates. Besides, signal quality is investigated online, and if the quality of the analysis window is unacceptable, then the algorithm will guess (estimate) the locations of P- and T-waves. The operating characteristics of the proposed algorithm are evaluated by its implementation to an artificially generated ECG signal whose quality is adjustable from the best (Quality, 100%) to the worst (Quality, ≤40%) cases based on the random-walk noise theory. The algorithm was applied to the MIT-BIH arrhythmia database, QT database, and Physionet/CinC challenge 2011competition database. The obtained results, which were based on the QT database, showed sensitivity and positive predictivity of Se=99.63% and P+=99.83%, Se=99.83% and P+=99.98%, and Se=99.74% and P+=99.89% for the detection of P-, QRS-, and T-waves, respectively, and the obtained results, which were based on the MIT-BIH arrhythmia database, showed Se=99.81% and P+=99.70% for the detection of the QRS complex. Moreover, it will be shown that the results of the proposed method are reliable for a minimum signal quality value of 70%. According to numerical assessments, 8-ms after the occurrence of R-wave, its location will be identified by the computer code of the proposed algorithm. This parameter is 198-ms and 177-ms for P- and T-waves, respectively.

HIGH-SPEED APPROACH FOR DELINEATING P AND T WAVES CHARACTERISTICS IN ELECTROCARDIOGRAM SIGNAL

Identifying and delineating P and T wave characteristics are greatly important in interpreting and diagnosing electrocardiogram (ECG) signals. P and T waves with high accuracy are more difficult to delineate because of their various shapes, positions, directions and boundaries. This paper proposes a high-speed approach to delineate P and T waves in a single lead using two high-speed algorithms of high detection accuracy. This approach presents a simple, adaptive and intelligent P and T wave scan method that determines the onset, peak and end time locations within an adaptive period appointed by previous records of the QRS complex. By using a translating (rising to/from falling) interval inside the scan wave, the peak time location of P and T waves and the T wave sign (upward or downward) are determined. Continuously, this time location is considered a reference point for determining the onset and the end time locations based on a series of computed outcomes related to amplitude and slope difference. The new approach is validated by 105 annotated records from the QT database collected from seven different categories of ECG signals. Simulation results show that the average detection rates of sensitivity and positive predictivity are equal to 99.97% and 99.36% for P wave and 99.98% and 99.26% for T wave, respectively. The average time errors computed by the mean and standard deviation for the P wave onset, peak and end time locations are -3.00 ± 2.94, -0.69 ± 4.42 and 0.67 ± 4.56 ms, respectively. The values for T wave are -3.33 ± 4.96, 0.24 ± 5.36 and -0.36 ± 5.68 ms. Results demonstrate the reliability, accuracy and forcefulness of the proposed approach in delineating various categories of P and T waves.