A new approach for TU complex characterization

Improved T-wave detection in electrocardiogram signals based non-stationary wavelet transform and QRS complex cancellation with kurtosis analysis

Objective. The T-wave in electrocardiogram (ECG) signal has the potential to enumerate various cardiac dysfunctions in the cardiovascular system. The primary objective of this research is to develop an efficient method for detecting T-waves in ECG signals, with potential applications in clinical diagnosis and continuous patient monitoring.Approach. In this work, we propose a novel algorithm for T-wave peak detection, which relies on a non-decimated stationary wavelet transform method (NSWT) and involves the cancellation of the QRS complex by utilizing its local extrema. The proposed scheme contains three stages: firstly, the technique is pre-processed using a two-stage median filter and Savitzky-Golay (SG) filter to remove the various artifacts from the ECG signal. Secondly, the NSWT technique is implemented using the bior 4.4 mother wavelet without downsampling, employing 24scale analysis, and involves the cancellation of QRS-complex using its local positions. After that, Sauvola technique is used to estimate the baseline and remove the P-wave peaks to enhance T-peaks for accurate detection in the ECG signal. Additionally, the moving average window and adaptive thresholding are employed to enhance and identify the location of the T-wave peaks. Thirdly, false positive T-peaks are corrected using the kurtosis coefficients method.Main results. The robustness and efficiency of the proposed technique have been corroborated by the QT database (QTDB). The results are also validated on a self-recorded database. In QTDB database, the sensitivity of 98.20%, positive predictivity of 99.82%, accuracy of 98.04%, and detection error rate of 1.95% have been achieved. The self-recorded dataset attains a sensitivity, positive predictivity, accuracy, and detection error rate of 99.94%, 99.96%, 99.90%, and 0.09% respectively.Significance. A T-wave peak detection based on NSWT and QRS complex cancellation, along with kurtosis analysis technique, demonstrates superior performance and enhanced detection accuracy compared to state-of-the-art techniques.

A wavelet-based VCG QRS loop boundaries and isoelectric coordinates detector

This paper deals with a wavelet-based algorithm for automatic detection of isoelectric coordinates of individual QRS loops of VCG record. Fiducial time instants of QRS peak, QRS onset, QRS end, and isoelectric PQ interval are evaluated on three VCG leads (X, Y, Z) together with global QRS boundaries of a record to spatiotemporal QRS loops alignment. The algorithm was developed and optimized on 161 VCG records of PTB diagnostic database of healthy control subjects (HC), patients with myocardial infarction (MI) and patients with bundle branch block (BBB) and validated on CSE multilead measurement database of 124 records of the same diagnostic groups. The QRS peak was evaluated correctly for all of 1,467 beats. QRS onset, QRS end were detected with standard deviation of 5,5 ms and 7,8 ms respectively from the referee annotation. The isoelectric 20 ms length PQ interval window was detected correctly between the P end and QRS onset for all the cases. The proposed algorithm complies the (2σCSE) limits for the QRS onset and QRS end detection and provides comparable or better results to other well-known algorithms. The algorithm evaluates well a wide QRS based on automated wavelet scale switching. The designed multi-lead approach QRS loop detector accomplishes diagnostic VCG processing, aligned QRS loops imaging and it is suitable for beat-to-beat variability assessment and further automatic VCG classification.

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.

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.

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.

A Real-Time QRS Detection System With PR/RT Interval and ST Segment Measurements for Wearable ECG Sensors Using Parallel Delta Modulators

This paper presents a real-time electrocardiogram (ECG) monitoring system for wearable devices. The system is based on the proposed parallel delta modulator architecture with local maximum point and local minimum point algorithms to detect QRS and PT waves. Therefore, using the proposed system and algorithm, real-time PR and RT intervals, and ST segment measurements can be achieved in long-term wearable ECG recording. The algorithm is tested with the MIT-BIH Arrhythmia Database for QRS complex detection and with the QT Database for the P and T wave detections. The simulation result shows that the algorithm achieves above 99%, 91%, and 98% accuracy in the QRS complex, P wave, and T wave detections, respectively. Experimental results are presented from the system prototype, in which the parallel delta modulator circuits are fabricated in IBM 0.13 $\mu \text{m}$ standard CMOS technology and the algorithms are implemented in a Xilinx Spartan-6 field programmable gate array (FPGA). The parallel delta modulators consume 720 nW at 1 kHz sampling rate with $\pm$0.6 V power supply. The proposed system has the potential to be applied in future long-term wearable ECG recording devices.

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.

A New Wavelet-Based ECG Delineator for the Evaluation of the Ventricular Innervation

T-wave amplitude (TWA) has been proposed as a marker of the innervation of the myocardium. Until now, TWA has been calculated manually or with poor algorithms, thus making its use not efficient in a clinical environment. We introduce a new wavelet-based algorithm for the delineation QRS complexes and T-waves, and the automatic calculation of TWA. When validated in the MIT/BIH Arrhythmia database, the QRS detector achieved sensitivity and positive predictive value of 99.84% and 99.87%, respectively. The algorithm was validated also on the QT database and it achieved sensitivity of 99.50% for T-peak detection. In addition, the algorithm achieved delineation accuracy that is similar to the differences in delineation between expert cardiologists. We applied the algorithm for the evaluation of the influence in TWA of anticholinergic and antiadrenergic drugs (i.e., atropine and metoprolol) for healthy subjects. We found that the TWA decreased significantly with atropine and that metoprolol caused a significant increase in TWA, thus confirming the clinical hypothesis that the TWA is a marker of the innervation of the myocardium. The results of this paper show that the proposed algorithm can be used as a useful and efficient tool in clinical practice for the automatic calculation of TWA and its interpretation as a non-invasive marker of the autonomic ventricular innervation.

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.

Fetal QT Interval Estimation Using Sequential Hypothesis Testing

Objective: Recent studies utilizing fetal magnetocardiography have demonstrated the efficacy of corrected QT interval (QTc) measurement for in utero diagnosis and prognosis of long QT syndrome, a leading cause of sudden death in early life. The objective of the study was to formulate and test a novel statistical estimation method to detect the end of the fetal T-wave and thereby improve the accuracy of fetal QT interval measurement. Methods: To detect the end of the T-wave, we apply a sequential composite hypothesis test to decide when the T-wave has returned to baseline. The method uses the generalized likelihood ratio test in conjunction with a low-rank spatiotemporal model that exploits the repetitive nature of cardiac signals. The unknown model parameters are determined using maximum likelihood estimation. Results: In realistic simulations, the detector was shown to be accurate to within 10 ms (95% prediction interval), even at noise-to-signal ratios as high as 6. When applied to real data from normal fetuses, the detector agreed well with measurements made by cardiologists ( 1.4 6.9 ms). Conclusions: The method was effective and practical. Detector performance was excellent despite the continual presence of strong maternal interference. Significance: This detector serves as a valuable adjunct to traditional measurement based on subjective assessment.Objective: Recent studies utilizing fetal magnetocardiography have demonstrated the efficacy of corrected QT interval (QTc) measurement for in utero diagnosis and prognosis of long QT syndrome, a leading cause of sudden death in early life. The objective of the study was to formulate and test a novel statistical estimation method to detect the end of the fetal T-wave and thereby improve the accuracy of fetal QT interval measurement. Methods: To detect the end of the T-wave, we apply a sequential composite hypothesis test to decide when the T-wave has returned to baseline. The method uses the generalized likelihood ratio test in conjunction with a low-rank spatiotemporal model that exploits the repetitive nature of cardiac signals. The unknown model parameters are determined using maximum likelihood estimation. Results: In realistic simulations, the detector was shown to be accurate to within 10 ms (95% prediction interval), even at noise-to-signal ratios as high as 6. When applied to real data from normal fetuses, the detector agreed well with measurements made by cardiologists ( 1.4 6.9 ms). Conclusions: The method was effective and practical. Detector performance was excellent despite the continual presence of strong maternal interference. Significance: This detector serves as a valuable adjunct to traditional measurement based on subjective assessment.

Fast T Wave Detection Calibrated by Clinical Knowledge with Annotation of P and T Waves

Background

There are limited studies on the automatic detection of T waves in arrhythmic electrocardiogram (ECG) signals. This is perhaps because there is no available arrhythmia dataset with annotated T waves. There is a growing need to develop numerically-efficient algorithms that can accommodate the new trend of battery-driven ECG devices. Moreover, there is also a need to analyze long-term recorded signals in a reliable and time-efficient manner, therefore improving the diagnostic ability of mobile devices and point-of-care technologies.

Methods

Here, the T wave annotation of the well-known MIT-BIH arrhythmia database is discussed and provided. Moreover, a simple fast method for detecting T waves is introduced. A typical T wave detection method has been reduced to a basic approach consisting of two moving averages and dynamic thresholds. The dynamic thresholds were calibrated using four clinically known types of sinus node response to atrial premature depolarization (compensation, reset, interpolation, and reentry).

Results

The determination of T wave peaks is performed and the proposed algorithm is evaluated on two well-known databases, the QT and MIT-BIH Arrhythmia databases. The detector obtained a sensitivity of 97.14% and a positive predictivity of 99.29% over the first lead of the validation databases (total of 221,186 beats).

Conclusions

We present a simple yet very reliable T wave detection algorithm that can be potentially implemented on mobile battery-driven devices. In contrast to complex methods, it can be easily implemented in a digital filter design.

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.

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.

New approach for T-wave end detection on electrocardiogram: performance in noisy conditions

Background

The detection of T-wave end points on electrocardiogram (ECG) is a basic procedure for ECG processing and analysis. Several methods have been proposed and tested, featuring high accuracy and percentages of correct detection. Nevertheless, their performance in noisy conditions remains an open problem.

Methods

A new approach and algorithm for T-wave end location based on the computation of Trapezium's areas is proposed and validated (in terms of accuracy and repeatability), using signals from the Physionet QT Database. The performance of the proposed algorithm in noisy conditions has been tested and compared with one of the most used approaches for estimating the T-wave end point: the method based on the threshold on the first derivative.

Results

The results indicated that the proposed approach based on Trapezium's areas outperformed the baseline method with respect to accuracy and repeatability. Also, the proposed method is more robust to wideband noise.

Conclusions

The trapezium-based approach has a good performance in noisy conditions and does not rely on any empirical threshold. It is very adequate for use in scenarios where the levels of broadband noise are significant.

R-point detection for noise affected ECG recording through signal segmentation

In this work we propose a novel approach for filtering noise-affected electrocardiogram (ECG) signals. The proposed method, mainly based on signal approximation by means of linear segments, has been applied for R-peaks recognition and has been compared with both cardiologists' manual marking and the automatic Laguna's method. The obtained results show that when compared to the Laguna's method the proposed algorithm provides a smaller mean error and a better error distribution.

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.

Finding events of electrocardiogram and arterial blood pressure signals via discrete wavelet transform with modified scales

A robust electrocardiogram (ECG) wave detection-delineation algorithm that can be applied to all ECG leads is developed in this study on the basis of discrete wavelet transform (DWT). By applying a new simple approach to a selected scale obtained from DWT, this method is capable of detecting the QRS complex, P-wave, and T-wave as well as determining parameters such as start time, end time, and wave sign (upward or downward). In the proposed method, the selected scale is processed by a sliding rectangular window of length n and the curve length in each window is multiplied by the area under the absolute value of the curve. In the next step, an adaptive thresholding criterion is conducted on the resulted signal. The presented algorithm is applied to various databases including the MIT-BIH arrhythmia database, European ST-T database, QT database, CinC Challenge 2008 database as well as high-resolution Holter data gathered in the DAY Hospital. As a result, the average values of sensitivity and positive prediction Se = 99.84 per cent and P+ = 99.80 per cent were obtained for the detection of QRS complexes with an average maximum delineation error of 13.7, 11.3, and 14.0 ms for the P-wave, QRS complex, and T-wave respectively. The presented algorithm has considerable capability in cases of a low signal-to-noise ratio, high baseline wander, and in cases where QRS complexes and T-waves appear with abnormal morphologies. Especially, the high capability of the algorithm in the detection of the critical points of the ECG signal, i.e. the beginning and end of the T-wave and the end of the QRS complex was validated by the cardiologist and the maximum values of 16.4 and 15.9 ms were recognized as absolute offset error of localization respectively. Finally, in order to illustrate an alternative capability of the algorithm, it is applied to all 18 subjects of the MIT-BIH polysomnographic database and the end-systolic and end-diastolic points of the blood pressure waveform were extracted and values of sensitivity and positive prediction Se = 99.80 per cent and P+ = 99.86 per cent were obtained for the detection of end-systolic, end-diastolic pulses.

Improving ECG beats delineation with an evolutionary optimization process

As in other complex signal processing tasks, ECG beat delineation algorithms are usually constituted of a set of processing modules, each one characterized by a certain number of parameters (filter cutoff frequencies, threshold levels, time windows, etc.). It is well recognized that the adjustment of these parameters is a complex task that is traditionally performed empirically and manually, based on the experience of the designer. In this paper, we propose a new automated and quantitative method to optimize the parameters of such complex signal processing algorithms. To solve this multiobjective optimization problem, an evolutionary algorithm (EA) is proposed. This method for parameter optimization is applied to a wavelet-transform-based ECG delineator that has previously shown interesting performance. An evaluation of the final delineator, using the optimal parameters, has been performed on the QT database from Physionet and results are compared with previous algorithms reported in the literature. The optimized parameters provide a more accurate delineation, with a global improvement of 7.7%, over all the criteria evaluated, and over the best results found in the literature, which is a proof of interest in the approach.

Automatic ECG wave extraction in long-term recordings using Gaussian mesa function models and nonlinear probability estimators

This paper describes the automatic extraction of the P, Q, R, S and T waves of electrocardiographic recordings (ECGs), through the combined use of a new machine-learning algorithm termed generalized orthogonal forward regression (GOFR) and of a specific parameterized function termed Gaussian mesa function (GMF). GOFR breaks up the heartbeat signal into Gaussian mesa functions, in such a way that each wave is modeled by a single GMF; the model thus generated is easily interpretable by the physician. GOFR is an essential ingredient in a global procedure that locates the R wave after some simple pre-processing, extracts the characteristic shape of each heart beat, assigns P, Q, R, S and T labels through automatic classification, discriminates normal beats (NB) from abnormal beats (AB), and extracts features for diagnosis. The efficiency of the detection of the QRS complex, and of the discrimination of NB from AB, is assessed on the MIT and AHA databases; the labeling of the P and T wave is validated on the QTDB database.

ECG Parameter Extractor of Intelligent Home Healthcare Embedded System

Automatic ECG QRS complex detection has been widely studied and used over the past decade. Although QRS complex is the most prominent feature in ECG and can provide useful information about the heart status, other parts of the ECG (P-wave, T-wave, etc) are also significant in determining the health status. Recently, researches for P-wave and T-wave detection algorithms started to appear but a parameter extractor in obtaining most essential ECG parameters (PQRST) is still not very popular. Considering that all these can be integrated together, we propose an Intelligent Home Health Care Embedded System (IHHCS) with essential ECG parameters extraction that can provide diagnosis about health status of patients directly at home. Inconvenient visits and precious time spent in health checking at hospitals or clinics can be saved.

A new approach to QRS segmentation based on wavelet bases and adaptive threshold technique

In this paper, we develop and evaluate a new approach to QRS segmentation based on the combination of two techniques: wavelet bases and adaptive threshold. Firstly, QRS complexes are identified without a preprocessing stage. Then, each QRS is segmented by identifying the complex onset and offset. We evaluated the algorithm on two manually annotated databases, the QT-database and the MIT-BIH Arrhythmia database. The QRS detector obtained a sensitivity of 99.02% and a positive predictivity of 99.35% over the first lead of the validation databases (more than 192,000 beats), while for the QT-database, values larger than 99.6% were attained. As for the delineation of the QRS complex, the mean and the standard deviation of the differences between the automatic and the manual annotations were computed. Using QT-database which contains recordings of annotated ECG with a sampling rate of 250 Hz, we obtain the average of the differences not exceeding two sampling intervals, while the standard deviations were within acceptable range of values.

An Algorithm for Robust and Efficient Location of T-Wave Ends in Electrocardiograms

The purpose of this paper is to propose a new algorithm for T-wave end location in electrocardiograms, mainly through the computation of an indicator related to the area covered by the T-wave curve. Based on simple assumptions, essentially on the concavity of the T-wave form, it is formally proved that the maximum of the computed indicator inside each cardiac cycle coincides with the T-wave end. Moreover, the algorithm is robust to acquisition noise, to wave form morphological variations and to baseline wander. It is also computationally very simple: the main computation can be implemented as a simple finite impulse response filter. When evaluated with the PhysioNet QT database in terms of the mean and the standard deviation of the T-wave end location errors, the proposed algorithm outperforms the other algorithms evaluated with the same database, according to the most recent available publications up to our knowledge.

Delineation of T-wave in ECG by wavelet transform using multiscale differential operator

This paper proposes an improved wavelet-based method for the delineation of T wave in the electrocardiogram by using multiscale dif-operator (MDO). MDO was employed to automatically categorize the T wave into one of the three categories of T wave morphologies and to improve the precision of T wave delineation for each category. The new algorithm was evaluated on QT database (QTDB) and new annotations of 2160 beats from QTDB were performed by two cardiologists. To evaluate the performance of the wave delineation, the time differences between automatic detection versus cardiologists' annotations and intercardiologist differences were measured. The new algorithm can attain better results than the previous methods by achieving the smallest standard deviation of the differences and qualifying the strict error criterion for T-off measurement. This new algorithm also exhibited excellent T wave categorization agreement with the cardiologists, resulting in kappa values above 0.75.

Dataset of manually measured QT intervals in the electrocardiogram

Background

The QT interval and the QT dispersion are currently a subject of considerable interest. Cardiac repolarization delay is known to favor the development of arrhythmias. The QT dispersion, defined as the difference between the longest and the shortest QT intervals or as the standard deviation of the QT duration in the 12-lead ECG is assumed to be reliable predictor of cardiovascular mortality.

The seventh annual PhysioNet/Computers in Cardiology Challenge, 2006 addresses a question of high clinical interest: Can the QT interval be measured by fully automated methods with accuracy acceptable for clinical evaluations?

Method

The PTB Diagnostic ECG Database was given to 4 cardiologists and 1 biomedical engineer for manual marking of QRS onsets and T-wave ends in 458 recordings. Each recording consisted of one selected beat in lead II, chosen visually to have minimum baseline shift, noise, and artifact.

In cases where no T wave could be observed or its amplitude was very small, the referees were instructed to mark a 'group-T-wave end' taking into consideration leads with better manifested T wave.

A modified Delphi approach was used, which included up to three rounds of measurements to obtain results closer to the median.

Results

A total amount of 2*5*548 Q-onsets and T-wave ends were manually marked during round 1. To obtain closer to the median results, 8.58 % of Q-onsets and 3.21 % of the T-wave ends had to be reviewed during round 2, and 1.50 % Q-onsets and 1.17 % T-wave ends in round 3.

The mean and standard deviation of the differences between the values of the referees and the median after round 3 were 2.43 ± 0.96 ms for the Q-onset, and 7.43 ± 3.44 ms for the T-wave end.

Conclusion

A fully accessible, on the Internet, dataset of manually measured Q-onsets and T-wave ends was created and presented in additional file: 1 (Table 4) with this article. Thus, an available standard can be used for the development of automated methods for the detection of Q-onsets, T-wave ends and for QT interval measurements.

LF/(LF+HF) index in ventricular repolarization variability correlated and uncorrelated with heart rate variability

The purpose of this study, was to asses whether LF/(LF+HF) obtained from ventricular repolarization variability (VRV) reflects the state of sympathovagal balance. The VRV time series and heart rate variability (HRV) time series from seventy two electrocardiogram (ECG) records in four different autonomic nervous system (ANS) profiles (athletes, cardiac transplant patient, heart failure patients and normal subjects) were extracted. A dynamic linear parametric model was applied to separate the VRV in two parts, VRV correlated with HRV (VRV(r)) and VRV uncorrelated with HRV (VRV(u)). Spectral indices were obtained from HRV, VRV, VRV(u) and VRV(u) time series. Changes of these indicators from rest to tilt position were analyzed. Results showed that: i) only LF/(LF+HF) from HRV time series increases significantly from rest to tilt in all ANS profiles, this information could not be retrieved in the other three series (VRV, VRV (u) and VRV(u)) ii) LF/(LF+HF) index in HRV series are significantly different between normal subjects and heart failure patients, while cardiac transplant patients show a low coherence between HRV and VRV power spectra and iii) HF rhythm in VRV series seem to be related to the mechanical effect of respiration.

A wavelet-based ECG delineator: evaluation on standard databases

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.

Safety of non-antiarrhythmic drugs that prolong the QT interval or induce torsade de pointes: an overview

The long and growing list of non-antiarrhythmic drugs associated with prolongation of the QT interval of the electrocardiogram has generated concern not only for regulatory interventions leading to drug withdrawal, but also for the unjustified view that QT prolongation is usually an intrinsic effect of a whole therapeutic class [e.g. histamine H(1) receptor antagonists (antihistamines)], whereas, in many cases, it is displayed only by some compounds within a given class of non-antiarrhythmic drugs because of an effect on cardiac repolarisation. We provide an overview of the different classes of non-antiarrhythmic drugs reported to prolong the QT interval (e.g. antihistamines, antipsychotics, antidepressants and macrolides) and discusses the clinical relevance of the QT prolonging effect. Drug-induced torsade de pointes are sometimes considered idiosyncratic, totally unpredictable adverse drug reactions, whereas a number of risk factors for their occurrence is now recognised. Widespread knowledge of these risk factors and implementation of a comprehensive list of QT prolonging drugs becomes an important issue. Risk factors include congenital long QT syndrome, clinically significant bradycardia or heart disease, electrolyte imbalance (especially hypokalaemia, hypomagnesaemia, hypocalcaemia), impaired hepatic/renal function, concomitant treatment with other drugs with known potential for pharmacokinetic/pharmacodynamic interactions (e.g. azole antifungals, macrolide antibacterials and class I or III antiarrhythmic agents). This review provides insight into the strategies that should be followed during a drug development program when a drug is suspected to affect the QT interval. The factors limiting the predictive value of preclinical and clinical studies are also outlined. The sensitivity of preclinical tests (i.e. their ability to label as positive those drugs with a real risk of inducing QT pronglation in humans) is sufficiently good, but their specificity (i.e. their ability to label as negative those drugs carrying no risk) is not well established. Verapamil is a notable example of a false positive: it blocks human ether-a-go-go-related (HERG) K(+) channels, but is reported to have little potential to trigger torsade de pointes. Although inhibition of HERG K(+) channels has been proposed as a primary test for screening purposes, it is important to remember that several ion currents are involved in the generation of the cardiac potential and that metabolites must be specifically tested in this in vitro test. At the present state of knowledge, no preclinical model has an absolute predictive value or can be considered as a gold standard. Therefore, the use of several models facilitates decision making and is recommended by most experts in the field.

Evaluation of drug-induced QT interval prolongation: implications for drug approval and labelling

Assessment of proarrhythmic toxicity of newly developed drugs attracts significant attention from drug developers and regulatory agencies. Although no guidelines exist for such assessment, the present experience allows several key suggestions to be made and an appropriate technology to be proposed. Several different in vitro and in vitro preclinical models exist that, in many instances, correctly predict the clinical outcome. However, the correspondence between different preclinical models is not absolute. None of the available models has been demonstrated to be more predictive and/or superior to others. Generally, compounds that do not generate any adverse preclinical signal are less likely to lead to cardiac toxicity in humans. Nevertheless, differences in likelihood offer no guarantee compared with entities with a preclinical signal. Thus, the preclinical investigations lead to probabilistic answers with the possibility of both false positive and false negative findings. Clinical assessment of drug-induced QT interval prolongation is crucially dependent on the quality of electrocardiographic data and the appropriateness of electrocardiographic analyses. An integral part of this is a precise heart rate correction of QT interval, which has been shown to require the assessment of QT/RR relationship in each study individual. The numbers of electrocardiograms required for such an assessment are larger than usually obtained in pharmacokinetic studies. Thus, cardiac safety considerations need to be an integral part of early phase I/II studies. Once proarrhythmic safety has been established in phase I/II studies, large phase III studies and postmarketing surveillance can be limited to less strict designs. The incidence of torsade de pointes tachycardia varies from 1 to 5% with clearly proarrhythmic drugs (e.g. quinidine) to 1 in hundreds of thousands with drugs that are still considered unsafe (e.g. terfenadine, cisapride). Thus, not recording any torsade de pointes tachycardia during large phase III studies offers no guarantee, and the clinical premarketing evaluation has to rely on the assessment of QT interval changes. However, since QT interval prolongation is only an indirect surrogate of predisposition to the induction of torsade de pointes tachycardia, any conclusion that a drug is safe should be reserved until postmarketing surveillance data are reviewed. The area of drug-related cardiac proarrhythmic toxicity is fast evolving. The academic perspective includes identification of markers more focused compared with simple QT interval measurement, as well as identification of individuals with an increased risk of torsade de pointes. The regulatory perspective includes careful adaptation of new research findings.