A Robust and Efficient Algorithm for St–t Complex Detection in Electrocardiograms

Song, Jinzhong; Yan, Hong; Xiao, Zhijun; Yang, Xianglin; Zhang, Xiaodong

doi:10.1142/s0219519411004198

Cited by 8 publications

(8 citation statements)

References 9 publications

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“…Figure 6 shows the detection examples of the proposed method, compared with the traditional methods, Zhang's method for T wave offset detection [31] and Song's method for T wave onset detection [32]. Figure 6(a) shows the inverted T wave detections, Figure 6(b) shows the biphasic T wave detections, and Figure 6(c) shows the normal T wave detections.…”

Section: Resultsmentioning

confidence: 99%

“…We firstly tested the performance of the improved SWA method on the QT database. The traditional SWA methods (Song's method [32] and Zhang's method [31]) were used as comparators.…”

Section: Resultsmentioning

confidence: 99%

“…Figure 2 illustrates the detection for T on . Firstly, with the location of R peak, the left and right boundaries ( t 1 and t 2 , respectively) of search window are determined based on the current RR interval as suggested in the study by Song et al [32]:t1=0.5×RRi+Ri+0.08s,t2=0.15×RRi+Ri+0.12s,if RRi<0.88 s,t1=0.5×RRi+Ri+0.1s,t2=Ri+0.32s,if RRi≥0.88 s,where RR i is the i th RR interval and R i is the i th position of R peak.…”

Section: Methodsmentioning

confidence: 99%

“…In 2006, Zhang et al first proposed the SWA method for detecting T wave offsets and confirmed its efficiency in the QT database [31]. Subsequently, Song et al improved this method for detecting T wave onsets [32]. Afterwards, our team combined onsets and offsets detection for classifying the morphology of the ST segment [33].…”

Section: Introductionmentioning

confidence: 99%

“…Afterwards, our team combined onsets and offsets detection for classifying the morphology of the ST segment [33]. In 2017, our team analyzed its efficiency in the QT database with a different evaluation index ( F 1 measure), and we found that there is still some space for further improvement since the parameter settings in the transitional SWA method are not adaptive [34], and the parameters given by Zhang et al [31]and Song et al [32] are empiric values and there is no optimization step included.…”

Section: Introductionmentioning

confidence: 99%

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An Improved Sliding Window Area Method for T Wave Detection

Shāng

Wei

Liu

et al. 2019

Computational and Mathematical Methods in Medicine

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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.

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Section: Resultsmentioning

confidence: 99%

“…We firstly tested the performance of the improved SWA method on the QT database. The traditional SWA methods (Song's method [32] and Zhang's method [31]) were used as comparators.…”

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Improved Sliding Window Area Method for T Wave Detection

Shāng

Wei

Liu

et al. 2019

Computational and Mathematical Methods in Medicine

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ECG‐based cardiodynamicsgram can reflect anomalous functional information in coronary artery disease

Wang

Sun

Kim

et al. 2023

Clinical Cardiology

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Background: The cardiodynamicsgram (CDG), a novel noninvasive method, extracts dynamic ST-T segment information from an electrocardiogram (ECG) through deterministic learning. Hypothesis:The CDG can reflect anomalous functional information in coronary artery disease (CAD).Methods: We retrospectively enrolled 456 patients with suspected CAD who underwent coronary computed tomography angiography (CCTA) from January 2020 to 2022, followed immediately by standard 12-lead ECG acquisition. Positivity for CAD were defined as CCTA ≥ 50% or CT-derived fractional flow reserve (CT-FFR) ≤ 0.8. A CDG value <0 was considered negative; otherwise, it was considered positive. We also evaluated the diagnostic performance of the CDG in the ECGdiagnosis-negative subgroup and in patients who had undergone invasive coronary angiography (ICA) after CCTA.Results: Of 362 patients, 168 (46.41%) were positive for CAD, and 178 (49.17%) were men. The median age was 59 (52−66) years. The accuracy of the CDG in the diagnosis of CAD was 79.56%, with a sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC) of 75.60%, 82.99%, and 0.836 (95% CI: 0.794−0.878), respectively. Similarly, in the ECG-diagnosis-negative subgroup (n = 223), the accuracy of the CDG was 80.27%, with an AUC of 0.842 (95% CI: 0.790−0.895). Among the 11 patients with CAD confirmed by ICA, 10 were diagnosed positive by the CDG. Furthermore, the CDG values and CT-FFR were correlated (r = −.395; p < .001). Conclusions:The ECG-based CDG has relatively high specificity and accuracy for the diagnosis of CAD and reflects functional cardiac information to some extent. It has

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A new method for early detection of myocardial ischemia: cardiodynamicsgram (CDG)

Dong

Wang

et al. 2015

Sci. China Inf. Sci.

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A Robust and Efficient Algorithm for St–t Complex Detection in Electrocardiograms

Cited by 8 publications

References 9 publications

An Improved Sliding Window Area Method for T Wave Detection

An Improved Sliding Window Area Method for T Wave Detection

ECG‐based cardiodynamicsgram can reflect anomalous functional information in coronary artery disease

A new method for early detection of myocardial ischemia: cardiodynamicsgram (CDG)

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