Quantification of fragmented QRS complex using intrinsic time-scale decomposition

Jin, Feng; Sugavaneswaran, Lakshmi; Krishnan, Sridhar; Chauhan, Vijay S.

doi:10.1016/j.bspc.2016.09.015

Cited by 17 publications

(9 citation statements)

References 27 publications

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“…Table IV finally summarizes the sensitivity, specificity and accuracy results obtained by applying the proposed method (with RBF kernel) and the three alternative algorithms [6]- [8] on the first test set. The other approaches use the wavelet transform [6], [8] or intrinsic time-scale decomposition [7] to decompose the ECG signal and detect fQRS from features obtained from the decomposed signal.…”

Section: Classifier Performancementioning

confidence: 99%

A Machine-Learning Approach for Detection and Quantification of QRS Fragmentation

Goovaerts

Padhy

Vandenberk

et al. 2019

IEEE J. Biomed. Health Inform.

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Objective: Fragmented QRS (fQRS) is an accessible biomarker and indication of myocardial scarring that can be detected from the electrocardiogram (ECG). Nowadays, fQRS scoring is done on a visual basis, which is timeconsuming and leads to subjective results. This study proposes an automated method to detect and quantify fQRS in a continuous way using features extracted from Variational Mode Decomposition (VMD) and Phase-Rectified Signal Averaging (PRSA). Methods: In the proposed framework, QRS complexes in the ECG signals were first segmented using VMD. Then, 10 VMD-and PRSA-based features were computed and fed into well-known classifiers such as Support Vector Machine (SVM), K-nearest neighbors (KNN), Naive Bayesian (NB), and TreeBagger (TB) in order to compare their performance. The proposed method was evaluated with 12-lead ECG data of 616 patients from the University Hospitals Leuven. The presence of fQRS in each ECG lead was scored by five raters. Both detection and quantification of fQRS could be achieved in this way. Results: The experimental results indicated that the proposed method achieved AUC values of 0.95, 0.94, 0.90, and 0.89 using SVM, KNN, NB and TB classifiers respectively for detecting QRS fragmentation. Assessment of quantification performance was done by comparing the fQRS score with the total score, obtained by summing the scores from the individual raters. Results showed that the fQRS score clearly correlated with this estimate of fQRS certainty. Conclusion:The proposed method obtained good results in both fQRS detection and quantification, and is a novel way of assessing the certainty of QRS fragmentation in the ECG signal.

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Section: Classifier Performancementioning

confidence: 99%

A Machine-Learning Approach for Detection and Quantification of QRS Fragmentation

Goovaerts

Padhy

Vandenberk

et al. 2019

IEEE J. Biomed. Health Inform.

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“…In Reference [10], the authors presented an algorithm based on intrinsic time-scale decomposition (ITD). First, the algorithm computes the first four components of ITD from the ECG.…”

Section: Introductionmentioning

confidence: 99%

Electrocardiographic Fragmented Activity (II): A Machine Learning Approach to Detection

et al. 2019

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Hypertrophic cardiomyopathy, according to its prevalence, is a comparatively common disease related to the risk of suffering sudden cardiac death, heart failure and stroke. This illness is characterized by the excessive deposition of collagen among healthy myocardium cells. This situation, which is medically known as fibrosis, constitutes effective conduction obstacles in the myocardium electrical path, and when severe enough, it can be outlined as additional peaks or notches in the QRS, clinically entitled as fragmentation. Nowadays, the fragmentation detection is performed by visual inspection, but the fragmented QRS can be confused with the noise present in the electrocardiogram (ECG). On the other hand, fibrosis detection is performed by magnetic resonance imaging with late gadolinium enhancement, the main drawback of this technique being its cost in terms of time and money. In this work, we propose two automatic algorithms, one for fragmented QRS detection and another for fibrosis detection. For this purpose, we used four different databases, including the subrogated database described in the companion paper and incorporating three additional ones, one compounded by more accurate subrogated ECG signals and two compounded by real and affected subjects as labeled by expert clinicians. The first real-world database contains QRS fragmented records and the second one contains records with fibrosis and both were recorded in Hospital Clínico Universitario Virgen de la Arrixaca (Spain). To deeply analyze the scope of these datasets, we benchmarked several classifiers such as Neural Networks, Support Vector Machines (SVM), Decision Trees and Gaussian Naïve Bayes (NB). For the fragmentation dataset, the best results were 0.94 sensitivity, 0.88 specificity, 0.89 positive predictive value, 0.93 negative predictive value and 0.91 accuracy when using SVM with Gaussian kernel. For the fibrosis databases, more limited accuracy was reached, with 0.47 sensitivity, 0.91 specificity, 0.82 predictive positive value, 0.66 negative predictive value and 0.70 accuracy when using Gaussian NB. Nevertheless, this is the first time that fibrosis detection is attempted automatically from ECG postprocessing, paving the way towards improved algorithms and methods for it. Therefore, we can conclude that the proposed techniques could offer a valuable tool to clinicians for both fragmentation and fibrosis diagnoses support.

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“…Resolution, cross term interference and unadaptiveness are the issues observed in those methods. To solve the above mentioned issues, the proposed work is oriented to use ITD method to distinguish S3 and S4 and this is the crucial level in the given frame work [8], [9]. Then, the extraction of features such as shape and structures related parameters along with the time intervals of heart sound components.…”

Section: Sai Bharadwaj B Ch Sumanth Kumarmentioning

confidence: 99%

“…and 3 which are normalized completely in time as well as in amplitude and ITD method is then applied. The normalization defined in(8) doesnot show any impact in the calculation of the time distances. The normalized heart sound signal with S3 The normalized heart sound signal with S4…”

mentioning

confidence: 99%

Classification of the Third and Fourth Heart Sounds using Intrinsic Time-Scale Decomposition and Support Vector Machine Technique

B *,

Kumar

2019

IJITEE

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The two diastolic heart sounds reflecting the malfunctionality of heart are third and fourth heartsounds(S3 and S4). Early detection of heart failures can decrease the risk by identifying the abnormal heart sounds through Phonocardiogram (PCG) signal analysis. In this paper abnormal heart sounds are identified and classified using Intrinsic time scale decomposition (ITD) and Support vector machine (SVM). The proposed framework has been tested on authenticated database signals under abnormal conditions. The success rate is really conquering for the SVM classifier with an accuracy over 94% in the S3 detection and 91% for the S4, which reveals the effectiveness and high efficiency of the proposed work

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Quantification of fragmented QRS complex using intrinsic time-scale decomposition

Cited by 17 publications

References 27 publications

A Machine-Learning Approach for Detection and Quantification of QRS Fragmentation

A Machine-Learning Approach for Detection and Quantification of QRS Fragmentation

Electrocardiographic Fragmented Activity (II): A Machine Learning Approach to Detection

Classification of the Third and Fourth Heart Sounds using Intrinsic Time-Scale Decomposition and Support Vector Machine Technique

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