Threshold-Independent QRS Detection Using the Dynamic Plosion Index

Ramakrishnan, A. G.; Prathosh, A P; Ananthapadmanabha, T.

doi:10.1109/lsp.2014.2308591

Cited by 46 publications

(13 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The worst cases for each database are shown, and the median result for each set of the corpus. In this work we also evaluated the gqrs detector [1] and a threshold independent algorithm [6] under the rodent set. Results where S=72.1; P =61.2 for the gqrs, and S=74.5; P =87.4 for the threshold independent algorithm.…”

Section: Resultsmentioning

confidence: 99%

Versatile Detector of Pseudo-periodic Patterns

Santini

Diez

Llamedo

2019

2019 XVIII Workshop on Information Processing and Control (RPIC)

View full text Add to dashboard Cite

This study aimed to develop a detector with few physiologically-meaningful parameters, that could be capable of detecting pseudo periodic patterns. The algorithm is based in a signal detection based on a matched filter, and a threshold calculation based on robust stadistics. The evaluation of the detector was performed under a corpus consisting in two sets. One set of human ECGs, and one set of rodent pseudo ECGs. The evaluation was performed with respect to the gold standard annotations, and was calculated in terms of sensitivity (S) and positive predictive value (P). For the human ECG set of recordings, the detector had 100 S and 99.9 P, while for the rodent pseudo ECG set of recordings the results where 97.1 S and 79.1 P. Both sets results are median values. The results obtained under the rodent set where compared to those obtained with another two detectors, where our detector obtained the best results. The algorithm achieved promising results, in a broad set of ECG recordings of very different nature, with the additional capability of further adaptation provided by expert assistance.

show abstract

Section: Resultsmentioning

confidence: 99%

Versatile Detector of Pseudo-periodic Patterns

Santini

Diez

Llamedo

2019

2019 XVIII Workshop on Information Processing and Control (RPIC)

View full text Add to dashboard Cite

show abstract

“…The results showed that our scheme achieved higher sensitivity and comparable positive predictivity compared to other reported detection schemes. Note that both DPI [4] and WT [11] were tuned and evaluated solely on the MIT-BIH database. It demonstrated that our novel RSWT scheme has a robust cross-database performance.…”

Section: Qrs Detection Results On Mit-bih Databasementioning

confidence: 99%

“…For determining the position of ECG characteristic points several QRS complex detection and ECG delineation methods are used. Some methods focus on detection of QRS complexes [2]- [4], while others use empirical mode decomposition [3] and threshold-independent QRS detection algorithm [4]. However, most of the QRS detection methods cannot be straightforwardly applied to the detection of P and T waves.…”

Section: Introductionmentioning

confidence: 99%

ECG Delineation with Randomly Selected Wavelet Feature and Random Forest Classifier

Xia

Gao

et al. 2018

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

Objective: Detection of Electrocardiogram (ECG) characteristic points can provide critical diagnostic information about heart diseases. We proposed a novel feature extraction and machine learning scheme for automatic detection of ECG characteristic points. Methods: A new feature, termed as randomly selected wavelet transform (RSWT) feature, was devised to represent ECG characteristic points. A random forest classifier was adapted to infer the characteristic points position with high sensitivity and precision. Results: Compared with other state-ofthe-art algorithms' testing results on QT database, our detection results of RSWT scheme showed comparable performance (similar sensitivity, precision, and detection error for each characteristic point). RSWT testing on MIT-BIH database also demonstrated promising cross-database performance. Conclusion: A novel RSWT feature and a new detection scheme was fabricated for ECG characteristic points. The RSWT demonstrated a robust and trustworthy feature for representing ECG morphologies. Significance: With the effectiveness of the proposed RSWT feature we presented a novel machine learning based scheme to automatically detect all types of ECG characteristic points at a time. Furthermore, it showed that our algorithm achieved better performance than other reported machine learning based methods.

show abstract

“…Therefore, it is worth evaluating how the performance of a QRS detection algorithm varies with the number of measurements. To achieve this goal, the threshold-independent QRS detector proposed in [23] is used. The ground truth for the R peaks is determined by the QRS detection provided by the algorithm when using the original ECG signal as input.…”

Section: Qrs Detection Performancementioning

confidence: 99%

Compressed sensing ECG using restricted Boltzmann machines

Polanía

Plaza

2018

Biomedical Signal Processing and Control

View full text Add to dashboard Cite

Recently, it has been shown that compressed sensing (CS) has the potential to lower energy consumption in wireless electrocardiogram (ECG) systems. By reducing the number of acquired measurements, the communication burden is decreased and energy is saved. In this paper, we aim at further reducing the number of necessary measurements to achieve faithful reconstruction by exploiting the representational power of restricted Boltzmann machines (RBMs) to model the probability distribution of the sparsity pattern of ECG signals. The motivation for using this approach is to capture the higher-order statistical dependencies between the coefficients of the ECG sparse representation, which in turn, leads to superior reconstruction accuracy and reduction in the number of measurements, as it is shown via experiments.

show abstract

Threshold-Independent QRS Detection Using the Dynamic Plosion Index

Cited by 46 publications

References 16 publications

Versatile Detector of Pseudo-periodic Patterns

Versatile Detector of Pseudo-periodic Patterns

ECG Delineation with Randomly Selected Wavelet Feature and Random Forest Classifier

Compressed sensing ECG using restricted Boltzmann machines

Contact Info

Product

Resources

About