A Real-Time Algorithm to Detect Inverted and Symmetrical T-Wave

Zarrini, Mahdi; Sadr, Ali

doi:10.1109/iccee.2009.61

Cited by 5 publications

(4 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nevertheless, various techniques have been proposed for automatic T-end detection. This includes threshold on the first derivative [ 3 , 4 ], threshold on an area connected by points around the T-wave [ 5 , 6 , 7 ], wavelet transform [ 8 , 9 ], mathematical model [ 10 ], support vector machine [ 11 ], artificial neural network (ANN) [ 12 , 13 , 14 ], hidden Markov model (HMM) [ 15 , 16 ], partially collapsed Gibbs sample and Bayesian [ 17 ], “wings” function [ 18 ], derivative curve [ 19 ], adaptive technique [ 20 ], TU complex analyses [ 21 ], correlation analysis [ 22 ], and k-nearest neighbor [ 23 ]. While those algorithms are widely applied, they are only validated on databases without severe noise contamination.…”

Section: Introductionmentioning

confidence: 99%

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

Soliman

2020

Sensors

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

Soliman

2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Besides, most of the ischemic cases suffering from earlier STEMI (ST-elevation myocardial infarction) have a prominent ST elimination or depression, which significantly affects the detection of the T onsets. Nowadays, various approaches based on different techniques have been proposed for T wave detection, and those typical techniques are wavelet [13, 14], mathematical model [15], support vector machine (SVM) [16], artificial neural network (ANN) [17–19], low-pass differentiation (LPD) [20], hidden Markov model (HMM) [21, 22], partially collapsed Gibbs sample and Bayesian (PCGS) [23], “wings” function [24], derivative curve [25], adaptive technique [26], computing the Trapezium's area [27], TU complex analyses [28], correlation analysis [29], k -nearest neighbor [30], and sliding window area (SWA) [31]. In these aforementioned methods, the wavelet-based method is robust to waveform morphological variations but is sensitive to noise [13, 14].…”

Section: Introductionmentioning

confidence: 99%

An Improved Sliding Window Area Method for T Wave Detection

Shāng

Wei

Liu

et al. 2019

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

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.

show abstract

“…In 2009 Zarrini et al [7] applied the average slopes S1,S2 to analyze the symmetry of T-wave morphologies. In this paper an integrated method was used for T-wave morphology identification.…”

Section: Introductionmentioning

confidence: 99%

Research on T-wave morphology analysis in ECG signal

Song

Yan

Xiao

2010

2010 3rd International Conference on Biomedical Engineering and Informatics

View full text Add to dashboard Cite

T-wave morphology classification and recognition plays an important role in clinical diagnosis based on Electrocardiogram (ECG). The integrated method based on Principal Component Analysis (PCA), threshold, linear regression, and symbolic method was used to analyze T-wave morphologies in this paper, and it was simple and convenient to implement. All kinds of T-wave shapes, expressed by symbol 'ABCDE', could be included by this method, and the recognition results were obvious, which could provide a reliable scientific basis for the clinical detection. After European ST-T database verification, the accuracy of T-wave morphology analysis was above 92%.

show abstract

A Real-Time Algorithm to Detect Inverted and Symmetrical T-Wave

Cited by 5 publications

References 14 publications

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

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

An Improved Sliding Window Area Method for T Wave Detection

Research on T-wave morphology analysis in ECG signal

Contact Info

Product

Resources

About