In this paper we describe a technique that has successfully classified arrhythmia from an ECG dataset using a least square support vector machine (LSSVM). LSSVM was applied to the ECG dataset to distinguish between healthy persons and diseased persons (arrhythmia). The LSSVM classifier trained with four train-test parts including a training-to-test split of 50–50%, a training-to-test split of 70–30%, and a training-to-test split of 80–20%. We have used the classification accuracy, sensitivity and specificity analysis, and ROC curves to test the performance of LSSVM classifier on the detection of ECG arrhythmia. The classification accuracies obtained are 100% for all the training-to-test splits. These results show that the proposed method is more promising than previously reported classification techniques. The results suggest that the proposed method can be used to enhance the performance of a new intelligent assistance diagnosis system.
"The ECG signals have been widely used for detection and diagnosis of MI , . However, the ECG signals are examined manually by cardiologists or specialized device only available in large hospitals . Visual inspection of the whole ECG trend even for one individual is cumbersome. "
[Show abstract][Hide abstract] ABSTRACT: Portable, Wearable and Wireless electrocardiogram (ECG) Systems have the
potential to be used as point-of-care for cardiovascular disease diagnostic
systems. Such wearable and wireless ECG systems require automatic detection of
cardiovascular disease. Even in the primary care, automation of ECG diagnostic
systems will improve efficiency of ECG diagnosis and reduce the minimal
training requirement of local healthcare workers. However, few fully automatic
myocardial infarction (MI) disease detection algorithms have well been
developed. This paper presents a novel automatic MI classification algorithm
using second order ordinary differential equation (ODE) with time varying
coefficients, which simultaneously captures morphological and dynamic feature
of highly correlated ECG signals. By effectively estimating the unobserved
state variables and the parameters of the second order ODE, the accuracy of the
classification was significantly improved. The estimated time varying
coefficients of the second order ODE were used as an input to the support
vector machine (SVM) for the MI classification. The proposed method was applied
to the PTB diagnostic ECG database within Physionet. The overall sensitivity,
specificity, and classification accuracy of 12 lead ECGs for MI binary
classifications were 98.7%, 96.4% and 98.3%, respectively. We also found that
even using one lead ECG signals, we can reach accuracy as high as 97%.
Multiclass MI classification is a challenging task but the developed ODE
approach for 12 lead ECGs coupled with multiclass SVM reached 96.4% accuracy
for classifying 5 subgroups of MI and healthy controls.
"do not trap in local minima points and need less training input, various methods of SVM have been adopted for ECG signals classification and proved to be effective    . Although many ECG arrhythmia classification methods show good performance in the laboratory, there are only few techniques gaining popularity in practical applications. "
[Show abstract][Hide abstract] ABSTRACT: This paper presents a novel maximum margin clustering method with immune evolution (IEMMC) for automatic diagnosis of electrocardiogram (ECG) arrhythmias. This diagnostic system consists of signal processing, feature extraction, and the IEMMC algorithm for clustering of ECG arrhythmias. First, raw ECG signal is processed by an adaptive ECG filter based on wavelet transforms, and waveform of the ECG signal is detected; then, features are extracted from ECG signal to cluster different types of arrhythmias by the IEMMC algorithm. Three types of performance evaluation indicators are used to assess the effect of the IEMMC method for ECG arrhythmias, such as sensitivity, specificity, and accuracy. Compared with K-means and iterSVR algorithms, the IEMMC algorithm reflects better performance not only in clustering result but also in terms of global search ability and convergence ability, which proves its effectiveness for the detection of ECG arrhythmias.
Computational and Mathematical Methods in Medicine 04/2013; 2013:453402. DOI:10.1155/2013/453402 · 0.77 Impact Factor
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