Principal Component Analysis as a Tool for Analyzing Beat-to-Beat Changes in ECG Features: Application to ECG-Derived Respiration

Langley, Philip; Bowers, E.J.; Murray, Alan

doi:10.1109/tbme.2009.2018297

Cited by 154 publications

(89 citation statements)

References 17 publications

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“…With respect to the ECG-derived respiration, Langley et al pointed out that a segment size containing the length of a QRS complex provided the best results [ 16 ]. In the scope of this study, we also found extracting a whole QRS complex to be most promising and, therefore, set the window size to N = 41 samples ( 164 ms).…”

Section: Online Ecg Processingmentioning

confidence: 80%

“…Next, the PCs have been calculated offline using the singular value decomposition (SVD) [ 8 ], and the online RTpca algorithm was applied to estimate the PC containing the respiratory activity. In [ 16 ], it was already shown that the offline SVD achieves good results in respiratory signal extraction. To double check, we compared the estimated PC by RTpca with the calculated PC by SVD, on the one hand and the estimated PC with the reference signal on the other hand.…”

Section: Resultsmentioning

confidence: 99%

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Online learning algorithms for principal component analysis applied on single-lead ECGs

Pflugradt

Mann²,

Feller³

et al. 2013

Biomedizinische Technik/Biomedical Engineering

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This article evaluates several adaptive approaches to solve the principal component analysis (PCA) problem applied on single-lead ECGs. Recent studies have shown that the principal components can indicate morphologically or environmentally induced changes in the ECG signal and can be used to extract other vital information such as respiratory activity. Special interest is focused on the convergence behavior of the selected gradient algorithms, which is a major criterion for the usability of the gained results. As the right choice of learning rates is very data dependant and subject to movement artifacts, a new measurement system was designed, which uses acceleration data to improve the performance of the online algorithms. As the results of PCA seem very promising, we propose to apply a single-channel independent component analysis (SCICA) as a second step, which is investigated in this paper as well.

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Section: Online Ecg Processingmentioning

confidence: 80%

Section: Resultsmentioning

confidence: 99%

Online learning algorithms for principal component analysis applied on single-lead ECGs

Pflugradt

Mann²,

Feller³

et al. 2013

Biomedizinische Technik/Biomedical Engineering

View full text Add to dashboard Cite

show abstract

“…With the help of the supplied database-ECG-annotations 161 samples including P-wave, QRS-complex and T-Wave have been extracted, thus obtaining N beats of N consecutively following QRS complexes of one specific dataset. A 161xN input matrix X is then built by these N beats according to [3]. Finally the algorithms are consecutively supplied with vector-samples x(i) from X.…”

Section: Datasetmentioning

confidence: 99%

“…Convergence of the first PCs for a) RTpca-and b) the SGA-algorithm According to [3] respiration can be found in the third or fourth component of the ECG. This could be confirmed by the results of our implementations.…”

Section: Figmentioning

confidence: 99%

“…It was also shown, that even from a single lead ECG, valuable information like morphologic variability, ventricular repolarization, atrial fibrillation or myocardial ischemia can be extracted by PCA [2]. Langley et al have successfully used PCA by eigenvalue and eigenvector decomposition to extract an ECG-Derived Respiration feature from a single lead ECG [3]. The PCA Problem can be stated as follows: Find an orthogonal transformation matrix A such that the elements of a centered measurement vector x become uncorrelated:…”

Section: Introductionmentioning

confidence: 99%

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On-line Learning Algorithms for extracting respiratory activity from Single Lead ECGs based on Principal Component Analysis

Pflugradt

Mann

Feller

et al. 2012

Biomedical Engineering / Biomedizinische Technik

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In this paper we present several statistic gradient algorithms from literature to solve the Principal Component Analysis (PCA) problem. We used a linear artificial neural network forming the basis of the implemented algorithms which is a neat way for on-line computation of the PCA expansion. As convergence is a key-aspect of these algorithms and is crucial for the usefulness in particular applications, we compared the different learning rules with respect to their suitability in ECG signal processing. Recent studies have shown, that a surrogate respiratory signal can be derived from single-lead ECGs by applying PCA. Since the traditionally applied closed-form computations of PCA are numerically demanding, it seems promising to resort to an adaptive approach when dealing with changing environments like the ECG.

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A novel transformer‐based ECG dimensionality reduction stacked auto‐encoders for arrhythmia beat detection

et al. 2023

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BackgroundElectrocardiogram (ECG) is a powerful tool for studying cardiac activity and diagnosing various cardiovascular diseases, including arrhythmia. While machine learning and deep learning algorithms have been applied to ECG interpretation, there is still room for improvement. For instance, the commonly used Recurrent Neural Networks (RNNs), reply on its previous state to update and is therefore ineffective for parallel computing. RNN also struggles to efficiently address the issue of long‐distance reliance.PurposeTo reduce computational complexity by dimensionality reduction of ECG signals we constructed a Stacked Auto‐encoders model using Transformer for ECG‐based arrhythmia detection. And overcome the challenges of long‐term dependencies and limited parallelizability in traditional RNNs when applied to ECG signal processing.MethodsIn this paper, a Transformer‐Based ECG Dimensionality Reduction Stacked Auto‐encoders model is proposed for ECG‐based arrhythmia detection. The transformer is used to encode ECG signals into a feature matrix, which is then dimensionally reduced using unsupervised greedy training through the four linear layers. This resulted in a low‐dimensional representation of ECG features, which are subsequently classified using support vector machines (SVM) to minimize overfitting.ResultsThe proposed method is benchmarked on the MIT‐BIH Arrhythmia database. In the 10‐fold cross validation of beat‐based arrhythmia detection, the average accuracy, sensitivity, specificity and F1 score of the proposed method are 99.83%, 98.84%, 99.84% and 99.13%, respectively, for the record‐based arrhythmia detection which refers to the approach where the training and testing sets use ECG data from independent recorded patients are 88.10%, 49.79%, 91.56% and 39.95%, respectively.ConclusionsCompared to other existing ECG‐based arrhythmia detection methods, our proposed approach exhibits improved detection accuracy and stronger generalization for arrhythmia beats. Additionally, the use of the record‐based data division method makes our approach more suitable for clinical practice.

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Principal Component Analysis as a Tool for Analyzing Beat-to-Beat Changes in ECG Features: Application to ECG-Derived Respiration

Cited by 154 publications

References 17 publications

Online learning algorithms for principal component analysis applied on single-lead ECGs

Online learning algorithms for principal component analysis applied on single-lead ECGs

On-line Learning Algorithms for extracting respiratory activity from Single Lead ECGs based on Principal Component Analysis

A novel transformer‐based ECG dimensionality reduction stacked auto‐encoders for arrhythmia beat detection

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