Performance Analysis of Ten Common QRS Detectors on Different ECG Application Cases

Liu, Feifei; Liu, Chengyu; Jiang, Xinge; Zhang, Zhimin; Zhang, Yatao; Li, Jianqing; Wei, Shoushui

doi:10.1155/2018/9050812

Cited by 90 publications

(72 citation statements)

References 35 publications

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“…A preprocessing step was brought in for denoising, mainly including power frequency interference, baseline drift, myoelectricity interference, motion artifacts, and electrode contact noise [24]. The annotation of QRS complex is the most important task, where an algorithm based on the Pan and Tompkins has a preferable performance [23,25,26]. Features are used to represent patterns with minima loss of important information.…”

Section: Ecg Cloud Platformmentioning

confidence: 99%

See 1 more Smart Citation

An Open-Access Arrhythmia Database of Wearable Electrocardiogram

Shen

Gao

et al. 2020

J. Med. Biol. Eng.

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Purpose Heart arrhythmias result from any disturbance in the rate, regularity, and site of origin or conduction of the cardiac electric pulse. Sporadic and underappreciated characteristics make diagnosis less timely, leading to stroke, heart failure, or even sudden death. Wearable electrocardiogram (ECG) devices are gradually becoming the main trend of intelligent diagnosis alongside the improvement of information transmission and computation power of the hardware. Therefore, a database for arrhythmia detection was planned to construct. Methods Collaborating with hospital, this study presents a bipolar limb two-lead wearable device by collecting a large amount of real-time data and giving rise to readily available ECG databases. Results In total, the database contains 2000 30-s recordings of sinus, atrial and ventricular arrhythmias collected from more than 200 voluntary patients who had been diagnosed with heart diseases, ranging in age from 18 to 82. Meanwhile, manual annotations by cardiologists were proposed to benefit and instruct non-medical researchers to design the algorithm reasonably.

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Section: Ecg Cloud Platformmentioning

confidence: 99%

“…The most commonly used databases on published researches for arrhythmia are the MIT-BIH Arrhythmia Database, QT Database, CSE Database, and AHA Database [20]. Although classical they are, the characteristic of non-wearable makes them not perfect for dynamic automatic analysis algorithm designing [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

An Open-Access Arrhythmia Database of Wearable Electrocardiogram

Shen

Gao

et al. 2020

J. Med. Biol. Eng.

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“…The six detectors were separately named as Hamilton-median algorithm [18], sixth-power algorithm [19], U3 transform algorithm (U3 algorithm) [20], difference operation algorithm (DOM algorithm) [21], 'jqrs' algorithm [22]- [24], optimized knowledge based algorithm (OKB algorithm) [25] in this paper. Though they were built on different basic theories, they all had high efficiencies and could be executed in nearly real-time processing on the mobile devices [26].…”

Section: ) Heart Rate Annotationsmentioning

confidence: 99%

Continuous-Valued Annotations Aggregation for Heart Rate Detection

Xie

Zhu

et al. 2019

IEEE Access

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In the medical field, experts usually annotate the bio-signals manually, and this is regarded as the gold standard. The manual annotating mode is time-consuming so widely replaced by an automated annotating algorithm. To address the low precision and low robustness of algorithm, we used a probabilistic model to synthesize the heart rate (HR) annotations from multiple annotators for electrocardiograph (ECG) signals and inferred the underlying true annotations and the precision of each annotator when the ground truth was not available. We further introduced signal quality indices in the model to improve our estimation. The 100 noisy ECG recordings in 2014 PhysioNet/computing in cardiology challenge database were divided into two parts, and various annotations for HR were generated by six available annotators. By employing the expectation maximization algorithm, we obtained the estimated true annotations for 80 recordings, and this result had an improvement not only over the best single annotator (17.46%) used in this paper but also to the mean and median strategies (the highest of 23.12% and 42.23%). Furthermore, the estimated precision of the single annotator from the proposed model served as the weight of the test data. In independent test, the weighted average of multiple annotations was superior to the single annotator and the mean strategy on 20 recordings, and its root mean square error (14.22 bpm) was close to that (13.96 bpm) of the proposed model on 80 recordings, demonstrating the robustness of the proposed continuous-valued annotation aggregation model. INDEX TERMS Annotation aggregation, electrocardiograph, heart rate, ground truth, probabilistic model.

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“…A recent study demonstrated that even when using state-of-the-art QRS detectors, an 80% or higher accuracy of QRS detection is not achieved. By contrast, these methods can easily obtain a 99% accuracy using conventional ECG databases such as the PhysioNet/MIT Arrythmias database [ 37 ]. Potential detection errors from the automatic analysis of dynamic ECGs also bring abnormal RR intervals, i.e., RR intervals lasting for too much or too little time.…”

Section: Introductionmentioning

confidence: 99%

Suppressing the Influence of Ectopic Beats by Applying a Physical Threshold-Based Sample Entropy

Zhao

Xiong

et al. 2020

Entropy

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Sample entropy (SampEn) is widely used for electrocardiogram (ECG) signal analysis to quantify the inherent complexity or regularity of RR interval time series (i.e., heart rate variability (HRV)), with the hypothesis that RR interval time series in pathological conditions output lower SampEn values. However, ectopic beats can significantly influence the entropy values, resulting in difficulty in distinguishing the pathological situation from normal situations. Although a theoretical operation is to exclude the ectopic intervals during HRV analysis, it is not easy to identify all of them in practice, especially for the dynamic ECG signal. Thus, it is important to suppress the influence of ectopic beats on entropy results, i.e., to improve the robustness and stability of entropy measurement for ectopic beats-inserted RR interval time series. In this study, we introduced a physical threshold-based SampEn method, and tested its ability to suppress the influence of ectopic beats for HRV analysis. An experiment on the PhysioNet/MIT RR Interval Databases showed that the SampEn use physical meaning threshold has better performance not only for different data types (normal sinus rhythm (NSR) or congestive heart failure (CHF) recordings), but also for different types of ectopic beat (atrial beats, ventricular beats or both), indicating that using a physical meaning threshold makes SampEn become more consistent and stable.

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Performance Analysis of Ten Common QRS Detectors on Different ECG Application Cases

Cited by 90 publications

References 35 publications

An Open-Access Arrhythmia Database of Wearable Electrocardiogram

An Open-Access Arrhythmia Database of Wearable Electrocardiogram

Continuous-Valued Annotations Aggregation for Heart Rate Detection

Suppressing the Influence of Ectopic Beats by Applying a Physical Threshold-Based Sample Entropy

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