A new semantic mining approach for detecting ventricular tachycardia and ventricular fibrillation

Othman, Mohamed; Safri, Norlaili Mat; Ghani, Ismawati Abdul; Harun, Fauzan Khairi Che; Ariffin, Ismail

doi:10.1016/j.bspc.2012.10.001

Cited by 17 publications

(5 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Othman et al [10], used semantic mining (SM) based algorithm for detecting VT and VF. The database set considered three types of ECG signal normal, VT and VF obtained from same MIT-BIH arrhythmia database.…”

Section: Previous Researchmentioning

confidence: 99%

Computer Aided Diagnosis of Ventricular Arrhythmias from Electrocardiogram Lead II Signals

Alzahrani¹,

Alqasemi²

2018

SIPIJ

View full text Add to dashboard Cite

In this work, we use computer aided diagnosis (CADx) to extract features from ECG signals and detect different types of cardiac ventricular arrhythmias including Ventricular Tachycardia (VT),Ventricular Fibrillation (VF), Ventricular Couplet (VC), and Ventricular Bigeminy (VB).Our methodology is unique in computing features of lower and higher order statistical parameters from six different data domains: time domain, Fourier domain, and four Wavelet domains (Daubechies, Coiflet, Symlet, and Meyer). These features proved to give superior classification performance, in general, regardless of the type of classifier used as compared with previous studies. However, Support Vector Machine (SVM) and Artificial Neural Network (ANN) classifiers got better performance than other classifiers tried including KNN and Naïve Bayes classifiers. Our unique features enabled classifiers to perform better in comparison with previous studies: for VT, 100% accuracy while best previous work got 95.8%, for VF, 100% accuracy while best previous work got 97.5%, for VC, 100% sensitivity while best previous work got 71.8%, and for VB, 100% sensitivity while best previous work got 84.6%.

show abstract

Section: Previous Researchmentioning

confidence: 99%

Computer Aided Diagnosis of Ventricular Arrhythmias from Electrocardiogram Lead II Signals

Alzahrani¹,

Alqasemi²

2018

SIPIJ

View full text Add to dashboard Cite

show abstract

“…The preprocessing contains three steps [11,12,21]: a) A moving average filter is applied in order to remove high frequency noise like muscle noise. b) Using a high pass filter with a cut-off frequency of 1 Hz to eliminate interference baseline drift.…”

Section: A Review Of Tcsc Algorithmmentioning

confidence: 99%

Ventricular Fibrillation Detection by an Improved Time Domain Algorithm Combined with SVM

Hou

Zhang

2014

2014 International Conference on Medical Biometrics

View full text Add to dashboard Cite

correct detection of ventricular fibrillation (VF) is of great importance to real-time electrocardiogram (ECG) monitoring systems and automatic external defibrillator (AED). First, the paper gives a brief review of threshold crossing sample count algorithm (TCSC), and analyzes this algorithm's drawbacks. Then the authors present an improved algorithm combined TCSC with support vector machine (SVM), which is more accuracy than the TCSC algorithm. For assessment of the performance of the algorithm, the complete CU database and MIT-BIH database are used. The authors compare the new algorithm with other VF detection methods under the same conditions. The ROC curve is created and the AUC is also calculated. The results show that the proposed algorithm has a high Accuracy of 91.2%, Specificity of 96.8%, and the AUC is 92.5. The new algorithm is fast, accurate and reliable, showing strong potential to be applied in real-time ECG monitor system.

show abstract

“…Defibrillation is the only definitive treatment for VF. It consists of applying a high voltage electric shock on the patient's chest, facilitating the restart of a normal electrical cardiac activity [1][2][3]. However, the success of defibrillation is inversely proportional to the interval of time lapsed from the beginning of the episode to the application of the discharge.…”

Section: Introductionmentioning

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

A new semantic mining approach for detecting ventricular tachycardia and ventricular fibrillation

Cited by 17 publications

References 11 publications

Computer Aided Diagnosis of Ventricular Arrhythmias from Electrocardiogram Lead II Signals

Computer Aided Diagnosis of Ventricular Arrhythmias from Electrocardiogram Lead II Signals

Ventricular Fibrillation Detection by an Improved Time Domain Algorithm Combined with SVM

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

Contact Info

Product

Resources

About