Detection of Shockable Ventricular Arrhythmia using Variational Mode Decomposition

Tripathy, Rajesh Kumar; Sharma, L. N.; Dandapat, Samarendra

doi:10.1007/s10916-016-0441-5

Cited by 96 publications

(54 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other studies that have also used the class VFVT [1] have evaluated both time domain (e.g., energy, permutation entropy) and frequency domain (e.g., renyi entropy) features. The classification is done by using a Random Forest (RF) classifier aiming to identify shockable and non-shockable ventricular arrhythmia with CUDB and MITDB databases with results of Acc = 97.23%, Sens = 96.54%, Spe = 97.97% [50]. Thirteen time-frequency and statistical features were extracted and applied to the C4.5 classifier [51], resulting in Acc = 97.02%, Sens = 90.97%, Spe = 97.86% for VFVT detection (including ventricular flutter).…”

Section: Algorithm An Nc_kn N_an Nc(%) An Nc_kn N_l2rlr(%) An Nc_kn Nmentioning

confidence: 99%

Detection of Ventricular Fibrillation Using the Image from Time-Frequency Representation and Combined Classifiers without Feature Extraction

et al. 2018

View full text Add to dashboard Cite

Due the fact that the required therapy to treat Ventricular Fibrillation (V F) is aggressive (electric shock), the lack of a proper detection and recovering therapy could cause serious injuries to the patient or trigger a ventricular fibrillation, or even death. This work describes the development of an automatic diagnostic system for the detection of the occurrence of V F in real time by means of the time-frequency representation (T F R) image of the ECG. The main novelties are the use of the T F R image as input for a classification process, as well as the use of combined classifiers. The feature extraction stage is eliminated and, together with the use of specialized binary classifiers, this method improves the results of the classification. To verify the validity of the method, four different classifiers in different combinations are used: Regression Logistic with L2 Regularization (L 2 R L R), adaptive neural network (A N N C), Bagging (B A G G), and K-nearest neighbor (K N N). The Hierarchical Method (HM) and Voting Majority Method (VMM) combinations are used. ECG signals used for evaluation were obtained from the standard MIT-BIH and AHA databases. When the classifiers were combined, it was observed that the combination of B A G G , K N N , and A N N C using the Hierarchical Method (HM) gave the best results, with a sensitivity of 95.58 ± 0.41%, a 99.31 ± 0.08% specificity, a 98.6 ± 0.04% of overall accuracy, and a precision of 98.25 ± 0.29% for V F . Whereas a sensitivity of 94.02 ± 0.58%, a specificity of 99.31 ± 0.08%, an overall accuracy of 99.14 ± 0.43%, and a precision of 98.59 ± 0.09% was obtained for V T with a run time between 0.07 s and 0.12 s. Results show that the use of T F R image data to feed the combined classifiers yields a reduction in execution time with performance values above to those obtained by individual classifiers. This is of special utility for V F detection in real time.

show abstract

Section: Algorithm An Nc_kn N_an Nc(%) An Nc_kn N_l2rlr(%) An Nc_kn Nmentioning

confidence: 99%

Detection of Ventricular Fibrillation Using the Image from Time-Frequency Representation and Combined Classifiers without Feature Extraction

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Ensemble empirical mode decomposition (EEMD) and empirical mode decomposition (EMD) are adaptive signal decomposition methods that decompose the signals into intrinsic mode functions (IMFs) without prior knowledge and only according to the characteristics of the signal itself, which is vitally important for non-linear and non-stationary signal analysis (Wu and Huang, 2009). These signal decomposition methods have shown their capacity in various applications, such as the classification of ECG heartbeats (Rajesh and Dhuli, 2017), detection of shockable ventricular arrhythmia (Tripathy et al, 2016), and automated identification of congestive heart failure (Acharya et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Early Detection of Sudden Cardiac Death by Using Ensemble Empirical Mode Decomposition-Based Entropy and Classical Linear Features From Heart Rate Variability Signals

Shi

Geng

et al. 2020

Front. Physiol.

View full text Add to dashboard Cite

Sudden cardiac death (SCD), which can deprive a person of life within minutes, is a destructive heart abnormality. Thus, providing early warning information for patients at risk of SCD, especially those outside hospitals, is essential. In this study, we investigated the performances of ensemble empirical mode decomposition (EEMD)-based entropy features on SCD identification. EEMD-based entropy features were obtained by using the following technology: (1) EEMD was performed on HRV beats to decompose them into intrinsic mode functions (IMFs), (2) five entropy parameters, namely Rényi entropy (RenEn), fuzzy entropy (FuEn), dispersion Entropy (DisEn), improved multiscale permutation entropy (IMPE), and Renyi distribution entropy(RdisEn), were computed from the first four IMFs obtained, which were named EEMD-based entropy features. Additionally, an automated scheme combining EEMD-based entropy and classical linear (time and frequency domains) features was proposed with the intention of detecting SCD early by analyzing 14 min (at seven successive intervals of 2 min) heart rate variability (HRV) in signals from a normal population and subjects at risk of SCD. Firstly, EEMDbased entropy and classical linear measurements were extracted from HRV beats, and then the integrated measurements were ranked by various methodologies, i.e., t-test, entropy, receiver-operating characteristics (ROC), Wilcoxon, and Bhattacharyya. Finally, these ranked features were fed into a k-Nearest Neighbor algorithm for classification. Compared with several state-of-the-art methods, the proposed scheme firstly predicted subjects at risk of SCD up to 14 min earlier with an accuracy of 96.1%, a sensitivity of 97.5%, and a specificity of 94.4% 14 min before SCD onset. The simulation results exhibited that EEMD-based entropy estimators showed significant difference between SCD patients and normal individuals and outperformed the classical linear estimators

show abstract

“…Compared with the EMD and EEMD algorithms, VMD has not only a solid theoretical foundation but also good robustness to noise. It has been applied in the fields of biomedical sciences [8,9], mechanical diagnosis [10] and underwater acoustic signal processing [11]. PE is one of the most effective ways to detect the randomness and dynamic changes of time sequence based on comparison of neighboring values [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Denoising and Feature Extraction Algorithms Using NPE Combined with VMD and Their Applications in Ship-Radiated Noise

Xiao

et al. 2017

Symmetry

View full text Add to dashboard Cite

Abstract:A new denoising algorithm and feature extraction algorithm that combine a new kind of permutation entropy (NPE) and variational mode decomposition (VMD) are put forward in this paper. VMD is a new self-adaptive signal processing algorithm, which is more robust to sampling and noise, and also can overcome the problem of mode mixing in empirical mode decomposition (EMD) and ensemble EMD (EEMD). Permutation entropy (PE), as a nonlinear dynamics parameter, is a powerful tool that can describe the complexity of a time series. NPE, a new version of PE, is interpreted as distance to white noise, which shows a reverse trend to PE and has better stability than PE. In this paper, three kinds of ship-radiated noise (SN) signal are decomposed by VMD algorithm, and a series of intrinsic mode functions (IMF) are obtained. The NPEs of all the IMFs are calculated, the noise IMFs are screened out according to the value of NPE, and the process of denoising can be realized by reconstructing the rest of IMFs. Then the reconstructed SN signal is decomposed by VMD algorithm again, and one IMF containing the most dominant information is chosen to represent the original SN signal. Finally, NPE of the chosen IMF is calculated as a new complexity feature, which constitutes the input of the support vector machine (SVM) for pattern recognition of SN. Compared with the existing denoising algorithms and feature extraction algorithms, the effectiveness of proposed algorithms is validated using the numerical simulation signal and the different kinds of SN signal.

show abstract

Detection of Shockable Ventricular Arrhythmia using Variational Mode Decomposition

Cited by 96 publications

References 39 publications

Detection of Ventricular Fibrillation Using the Image from Time-Frequency Representation and Combined Classifiers without Feature Extraction

Detection of Ventricular Fibrillation Using the Image from Time-Frequency Representation and Combined Classifiers without Feature Extraction

Early Detection of Sudden Cardiac Death by Using Ensemble Empirical Mode Decomposition-Based Entropy and Classical Linear Features From Heart Rate Variability Signals

Denoising and Feature Extraction Algorithms Using NPE Combined with VMD and Their Applications in Ship-Radiated Noise

Contact Info

Product

Resources

About