Exploiting correlation of ECG with certain EMD functions for discrimination of ventricular fibrillation

Anas, Emran Mohammad Abu; Lee, Soo Yeol; Hasan, Md. Kamrul

doi:10.1016/j.compbiomed.2010.12.005

Cited by 23 publications

(12 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Alonso-Atienza et al have applied bootstrap resampling-based feature extraction SVM classifier for VF detection [ 20 ]. A study by Anas et al describes how empirical mode decomposition method is used to discriminate VF and non-VF rhythms [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

Arrhythmia Evaluation in Wearable ECG Devices

Sadrawi

Lin²,

Lin³

et al. 2017

Sensors

View full text Add to dashboard Cite

This study evaluates four databases from PhysioNet: The American Heart Association database (AHADB), Creighton University Ventricular Tachyarrhythmia database (CUDB), MIT-BIH Arrhythmia database (MITDB), and MIT-BIH Noise Stress Test database (NSTDB). The ANSI/AAMI EC57:2012 is used for the evaluation of the algorithms for the supraventricular ectopic beat (SVEB), ventricular ectopic beat (VEB), atrial fibrillation (AF), and ventricular fibrillation (VF) via the evaluation of the sensitivity, positive predictivity and false positive rate. Sample entropy, fast Fourier transform (FFT), and multilayer perceptron neural network with backpropagation training algorithm are selected for the integrated detection algorithms. For this study, the result for SVEB has some improvements compared to a previous study that also utilized ANSI/AAMI EC57. In further, VEB sensitivity and positive predictivity gross evaluations have greater than 80%, except for the positive predictivity of the NSTDB database. For AF gross evaluation of MITDB database, the results show very good classification, excluding the episode sensitivity. In advanced, for VF gross evaluation, the episode sensitivity and positive predictivity for the AHADB, MITDB, and CUDB, have greater than 80%, except for MITDB episode positive predictivity, which is 75%. The achieved results show that the proposed integrated SVEB, VEB, AF, and VF detection algorithm has an accurate classification according to ANSI/AAMI EC57:2012. In conclusion, the proposed integrated detection algorithm can achieve good accuracy in comparison with other previous studies. Furthermore, more advanced algorithms and hardware devices should be performed in future for arrhythmia detection and evaluation.

show abstract

Section: Introductionmentioning

confidence: 99%

Arrhythmia Evaluation in Wearable ECG Devices

Sadrawi

Lin²,

Lin³

et al. 2017

Sensors

View full text Add to dashboard Cite

show abstract

“…Because of the strict requirement that the signal must be monocomponent in frequency, the HT cannot be used in the HRV signal directly. The EMD method expanded the use of the HT because the IMF decomposed by EMD was a monocomponent real-valued signal [20], [35].…”

Section: ) Multi-frequency Components Analysismentioning

confidence: 99%

“…The HHT consists of the empirical mode decomposition (EMD) method and the Hilbert transform (HT) algorithm [19]. The EMD method has been proven to be suitable for analyzing the HRV signal of ECG and decomposing the signal into a set of intrinsic mode functions (IMFs) [20]. Then, the HT can be adopted at the IMFs to reveal the instantaneous information of the signal in different frequency components.…”

Section: Introductionmentioning

confidence: 99%

Multi-Frequency Components Entropy as Novel Heart Rate Variability Indices in Congestive Heart Failure Assessment

Pan

Feng

et al. 2019

IEEE Access

View full text Add to dashboard Cite

In this paper, novel heart rate variability (HRV) indices were extracted for the autonomic nervous system (ANS) activity assessment in congestive heart failure (CHF). It has been reported that CHF is a chronic cardiovascular syndrome along with ANS dysfunction, and HRV is a useful tool for ANS assessment. The multi-frequency components Entropy (MFC-En), which is obtained by the Hilbert-Huang transform and the entropy algorithm, was proposed as novel HRV indices for analyzing ANS with CHF. This paper included 24-h HRV signals of 98 subjects collected with Holter (54 healthy, 12 low-risk CHF, and 32 high-risk CHF subjects). The MFC-En indices successfully showed a statistical significance between the control and CHF groups (p < 0.001). The CHF classification accuracy of the MFC-En was 86.7%, while the ratio of the low-and high-frequency power was only 79.6%. Moreover, statistical significances were found among the control, low-risk CHF, and high-risk CHF groups (p < 0.01). Therefore, the MFC-En is a useful tool for CHF assessment that revealed the ANS of CHF patient is more activated by measuring the complexity of the rhythms changes of the ANS throughout the day. INDEX TERMS Congestive heart failure (CHF), heart rate variability (HRV), Hilbert-Huang transform (HHT), instantaneous frequency (IF), multi-frequency components analysis (MFCA).

show abstract

“…DiScRi is a diabetes complications screening program in Australia where members of the general public participate in a comprehensive health review consisting of tests including electrocardiogram (ECG), the Ewing battery, retinal scans, peripheral nerve function and assessment of diverse biomarkers associated with risk and early detection of diabetes and cardio-vascular disease. ECG data is crucial for medical applications, as illustrated, for example, by [30][31][32][33]. The DiScRi database is more than ten times larger than [20].…”

Section: Diabetes Screening Research Initiative Databasementioning

confidence: 99%

Predicting cardiac autonomic neuropathy category for diabetic data with missing values

Abawajy

Kelarev

Chowdhury

et al. 2013

Computers in Biology and Medicine

View full text Add to dashboard Cite

Cardiovascular autonomic neuropathy (CAN) is a serious and well known complication of diabetes. Previous articles circumvented the problem of missing values in CAN data by deleting all records and fields with missing values and applying classifiers trained on different sets of features that were complete. Most of them also added alternative features to compensate for the deleted ones. Here we introduce and investigate a new method for classifying CAN data with missing values. In contrast to all previous papers, our new method does not delete attributes with missing values, does not use classifiers, and does not add features. Instead it is based on regression and meta-regression combined with the Ewing formula for identifying the classes of CAN. This is the first article using the Ewing formula and regression to classify CAN. We carried out extensive experiments to determine the best combination of regression and meta-regression techniques for classifying CAN data with missing values. The best outcomes have been obtained by the additive regression meta-learner based on M5Rules and combined with the Ewing formula. It has achieved the best accuracy of 99.78% for two classes of CAN, and 98.98% for three classes of CAN. These outcomes are substantially better than previous results obtained in the literature by deleting all missing attributes and applying traditional classifiers to different sets of features without regression. Another advantage of our method is that it does not require practitioners to perform more tests collecting additional alternative features.

show abstract

Exploiting correlation of ECG with certain EMD functions for discrimination of ventricular fibrillation

Cited by 23 publications

References 17 publications

Arrhythmia Evaluation in Wearable ECG Devices

Arrhythmia Evaluation in Wearable ECG Devices

Multi-Frequency Components Entropy as Novel Heart Rate Variability Indices in Congestive Heart Failure Assessment

Predicting cardiac autonomic neuropathy category for diabetic data with missing values

Contact Info

Product

Resources

About