Ischemia episode detection in ECG using kernel density estimation, support vector machine and feature selection

School of Information and Communications, Gwangju Institute of Science and Technology 1, , Oryong-dong, Buk-gu, Gwangju, Republic of Korea. .
BioMedical Engineering OnLine (Impact Factor: 1.43). 06/2012; 11(30):30. DOI: 10.1186/1475-925X-11-30
Source: PubMed


Myocardial ischemia can be developed into more serious diseases. Early Detection of the ischemic syndrome in electrocardiogram (ECG) more accurately and automatically can prevent it from developing into a catastrophic disease. To this end, we propose a new method, which employs wavelets and simple feature selection.
For training and testing, the European ST-T database is used, which is comprised of 367 ischemic ST episodes in 90 records. We first remove baseline wandering, and detect time positions of QRS complexes by a method based on the discrete wavelet transform. Next, for each heart beat, we extract three features which can be used for differentiating ST episodes from normal: 1) the area between QRS offset and T-peak points, 2) the normalized and signed sum from QRS offset to effective zero voltage point, and 3) the slope from QRS onset to offset point. We average the feature values for successive five beats to reduce effects of outliers. Finally we apply classifiers to those features.
We evaluated the algorithm by kernel density estimation (KDE) and support vector machine (SVM) methods. Sensitivity and specificity for KDE were 0.939 and 0.912, respectively. The KDE classifier detects 349 ischemic ST episodes out of total 367 ST episodes. Sensitivity and specificity of SVM were 0.941 and 0.923, respectively. The SVM classifier detects 355 ischemic ST episodes.
We proposed a new method for detecting ischemia in ECG. It contains signal processing techniques of removing baseline wandering and detecting time positions of QRS complexes by discrete wavelet transform, and feature extraction from morphology of ECG waveforms explicitly. It was shown that the number of selected features were sufficient to discriminate ischemic ST episodes from the normal ones. We also showed how the proposed KDE classifier can automatically select kernel bandwidths, meaning that the algorithm does not require any numerical values of the parameters to be supplied in advance. In the case of the SVM classifier, one has to select a single parameter.

Download full-text


Available from: Jinho Park,
  • Source
    • "They have compared their results with MIT/BIH database and obtained a sensitivity of 99.53% and a positive predictivity of 99.73 and a sensitivity of 99.7% and a positive predictivity of 99.68%. Park et al. (2012) proposes a wavelet interpolation filter to remove motion artefacts around the ST-segment of stress ECG. Inoue and Miyazaki (1998) "
    [Show abstract] [Hide abstract]
    ABSTRACT: The aim of this paper is to highlight the biomedical applications of wavelet transform and corresponding research efforts in imaging techniques. A brief introduction of wavelet transform and its properties that are vital for biomedical applications touched by various researchers has been discussed. Application of electrocardiography in terms of QRS complex is briefly spotlighted. Recent survey of wavelet expansion in medical imaging is another facet of this paper, which includes biomedical image denoising, image enhancement and functional neuro-imaging, including positron emission tomography and functional MRI.
    International Journal of Biomedical Engineering and Technology 07/2015; 19(19 1):1-25. DOI:10.1504/IJBET.2015.071405
  • [Show abstract] [Hide abstract]
    ABSTRACT: This paper explains the development of a delineation algorithm for ECG signals and ST segment classification, based on both wavelet transform and support vector machine (SVM), taking advantage of their specific characteristics. The discrete transform with the mother wavelet Daubechies 4 was used to make the pre-processing, signal filtering and QRS complex detection. The selection of the set of coefficients was made according to the energy of each wavelet's decomposition level. The continuous transform was implemented for T and P wave detection.The detection of the onsets and offsets of each of these waves was evaluated using a combination of both types of wavelet transform, allowing the identification of the characteristic components in ECG signal. Samples of different kinds of diseases contained in QT Database were used for the validation. For the QRS complex it was found a sensivility Se=99,8% and a positive predictivity of P+=99,8%; and for P, QRS and T delineation values of sensibility and positive predictivity over 96% were found applied on different morphologies and different leads. For the ST classification with the SVM it was used different kinds of characteristic vectors, it was found a highest sensitivity of 98.8% and a average near to 80%.
    Health Care Exchanges (PAHCE), 2013 Pan American; 04/2013
  • [Show abstract] [Hide abstract]
    ABSTRACT: This study demonstrates the effect of polydopamine coating on separator membranes used in liquid electrolyte batteries as a function of membrane porosity. We select two typical separators that differed only in porosity. High-porosity (16H) and low-porosity (16L) separators are coated with polydopamine by simple dip-coating. Their properties are evaluated via scanning electronic microscopy (SEM) and determining the water contact angle, Gurley number, ionic conductivity, and uptake volume of liquid electrolyte. In addition, the effect of polydopamine coating on electrochemical properties is tested using CR2032 coin-type half-cells (LiMn2O4/Li metal). With enhanced hydrophilic properties of surfaces as keeping pore structures, both of polydopamine coated high and low porous separators show enhanced rate capability and cell performance compared to uncoated versions. The effect of polydopamine coating is greatly enhanced in the low-porosity separators, with up to 40% increase in power capability (at 5 C rate) and a 290% increase in cycle performance (after 500 cycles, at C/2 rate), compared to the high-porosity type (13% increase in power capability, 43% increase in cycle performance).
    Electrochimica Acta 12/2013; 113:433-438. DOI:10.1016/j.electacta.2013.09.104 · 4.50 Impact Factor
Show more