QRS Complex Detection Using STFT, Chaos Analysis, and PCA in Standard and Real-Time ECG Databases

The early detection of heart abnormalities through electrocardiography (ECG) is essential for reducing the prevalence of cardiac arrest worldwide. Often, subjects are unaware of the condition of their hearts until detected at the last stage. In this study, various records in real-time and PhysioNet databases were examined using chaos analysis. Chaos analysis was implemented by plotting different attractors against various time-delay dimensions. The main advantages of chaos analysis approach include: (1) a preprocessing stage is not demanded to the recorded ECG signal, and (2) it helps to estimate the reliable and robust thresholds for QRS detection using time-delay dimension (embedding), correlation dimension, Lyapunov exponent, and entropy. ECG may be a useful candidate to classify heart diseases; however, visualization through ECG may not be sufficient because of the minute differences that exist in the ECG recordings. Therefore, the effective automatic detection of ECG signals is essential. Further, ECG datasets should be analyzed using time–frequency representations for getting frequency contents of the signal at each time point. ECG signals are nonstationary in nature; the assumption of stationarity is valid on a short-time basis. For this purpose, a short-time spectrum is computed using the short-time Fourier transform (STFT) as a feature extraction tool in this paper. Noise and baseline wander are filtered before the STFT operation to ensure correct frequency components of the QRS complex. For filtering, a digital band-pass filter has been used since its filtering characteristics are invariant with drift and temperature. The automatic detection of QRS complex has been proposed which is useful in early diagnosis of cardiac diseases. The essential feature of detection stage is to build feature selection approach for having a minimal feature set which includes ample information about data for the planned application. In this paper, the QRS complex is detected by applying principal component analysis (PCA) on the fused results of individual features extracted using chaos analysis and STFT. Using PCA, the estimated principal components show the degree of morphological beat-to-beat variability. The detection performance is evaluated in terms of sensitivity (Se), positive predictivity (PP), detection error rate (DER), and accuracy (Acc). The proposed technique yields encouraging performance parameter values such as 99.93% Se, 99.97% PP, 0.0895% DER, and 99.91% Acc in the analysis of data from the PhysioNet database and 99.93% Se, 99.96% PP, 0.097% DER, and 99.90% Acc in the analysis of data from the real-time database. Suitable comparisons have been presented with the existing techniques.