Automated Detection of Sudden Cardiac Death by Discrete Wavelet Transform of Electrocardiogram Signal

Abstract

Sudden cardiac death (SCD) results in millions of deaths annually; as it is a fatal heart abnormality, early prediction of SCD could save peoples’ lives to the greatest extent. Symmetry and asymmetry play an important role in many fields. Electrocardiograms (ECG) as a noninvasive process for acquiring the electrical activity of the heart, has both asymmetric and non-stationary characteristics; it is frequently employed to diagnose and evaluate the heart’s condition. In this work, we have detected SCD 14 min (separately for each one-minute interval) prior to its occurrence by analyzing ECG signals using discrete wavelet transform (DWT) and locality preserving projection (LPP). In the experiment, we have performed DWT on ECG signals to obtain coefficients, then LPP as a reduction methodology was used to cut down these obtained coefficients. Then, the acquired LPP features were ranked using various methods, including the T-test, Bhattacharyya, Wilcoxon, and entropy. At last, the highly ranked LPP features were subjected to decision tree, k-nearest neighbor (KNN), and support vector machine classifiers for distinguishing normal from SCD ECG signals. Our proposed technique has achieved a highest accuracy of 97.6% for the detection of SCD 14 min prior using the KNN classifier, compared to the existing works. Our proposed method is capable of predicting the people at risk of developing SCD 14 min before its onset, and, hence, clinicians would have enough time to provide treatment in intensive care units (ICU) for a subject at risk of SCD. Thus, this proposed technique as a useful tool can increase the survival rate of many cardiac patients.

Automated characterization of sudden cardiac death by using empirical mode decomposition and fuzzy entropy of ECG signals

“…Additionally, we developed a combined model with DWT and LPP using only one ECG beat, capable of predicting SCD 13 minutes before onset with an accuracy of 97.6% [9].…”

“…Considering different noises present in the ECG records, such as power line interference (>50 Hz) and baseline wander (<0.5 Hz), we applied Discrete Wavelet Transform (DWT) with Daubechies wavelet 6 (db6) to the ECG recordings. The signals were decomposed into 11 levels, and then the first three detail coefficients and the highest-level approximation coefficients were set to zero for denoising purposes [9]. Furthermore, the denoised ECG records were obtained by performing the inverse wavelet transform using the fourth to the eleventh level detail sub-bands.…”

“…Therefore, it is extremely important to early predict the unexpected SCD risk and consequently provide timely treatment for individuals experiencing ventricular fibrillation (VF) with the aim of increasing the survival rate. In recent years, research worldwide has focused on SCD identification using non-invasive methodologies, such as electrocardiogram (ECG) [6][7][8][9]or heart rate variability (HRV) signal analysis [10][11][12][13][14][15][16][17],. These methods can detect subtle changes within signals and predict the risk of SCD.…”

Extend Nearly Pseudo Quasi-2-Absorbing submodules(II)

“…The wavelets transform is first introduced for transient continuous signal time -frequency domain analysis, and subsequently expanded to the concept for multi-resolution wavelet transform utilizing filtering approximations [20]. A signal is represented by a wavelet transform in the form of specific short time intervals [21][22][23]:…”

Forecasting medical state transition using machine learning methods

“…The maximal overlap discrete wavelet transform is used to explore the original variables on different resolution levels 5 . In order to improve the performance of automated detection of sudden cardiac death, the discrete wavelet transform is used to explore the non-stationary characteristics involved in the electrocardiograms signals 6 . These methods aimed to improve the model performance by mining more information contained in the original input variables.…”