ECG signal analysis using modified S‐transform

Abstract

Accurate detection of QRS complexes is essential for the investigation of heart rate variability. Several transform techniques have been proposed and extensively used for the detection and analysis of QRS complexes. In this proposed work, the de-noised ECG signal is subjected to a modified S-transform for QRS complex detection.The performance analysis of the proposed work is evaluated using parameters such as sensitivity, positive predictivity and accuracy. The algorithm delivers sensitivity, positive predictivity and overall accuracy of 99.91, 99.91 and 99.77%, respectively. Furthermore, a search back mechanism is employed, which specifies the filtered electrocardiogram (ECG) segment, which was traced for the true R-peak locations. The modified S-transform based QRS complex detection algorithm provides an excellent search back range of only ±2 samples in comparison with other earlier proposed algorithms.

Automated ECG Analysis for Localizing Thrombus in Culprit Artery Using Rule Based Information Fuzzy Network

“…For point identification, an ECG signal has to be converted into its time-frequency distribution and frequency-dependent resolution, while preserving a direct relationship with time spectrum component. For this purpose, the Stockwell Transform [37], is used. The advantage of using Stockwell Transform is that it gives an accurate Time-Frequency representation and gives information about all points present in the signal.…”

“…One series of each pair includes a spontaneous episode of Ventricular Tachycardia (VT) or Ventricular Fibrillation (VF) and the other is a sample of the intrinsic (usually sinus) rhythm. T-Wave Alternans Challenge Database contains 100, 2, 3, and 12-lead ECG records sampled at 500 Hz with 16-bit resolution over a ± 32 mV range, including subjects with risk factors of sudden cardiac death as well as healthy controls and synthetic cases with calibrated amounts of T-wave Alternans [37]. The output ECG signals and results are illustrated in Fig.…”

“…1. [37] is an improvisation and combination of Short-Time Fourier Transform (STFT) and wavelet transform. The main function of this method is to analyze time-frequency and this paper implements Stockwell transform for point identification in ECG signal to locate the starting point, peak point, and end point of the ECG signal.…”

Optimal SSA‐based wideband digital differentiator design for cardiac QRS complex detection application

“…Several QRS complex detection techniques have been reported in the recent literature. These include quadratic filter, level crossing sampling‐based analog to digital conversion logic, integrate and fire sampling, least mean square algorithm‐based adaptive linear predictor, empirical mode decomposition (EMD), multiscale mathematical morphology (MM), sigmoidal radial basis function artificial neural network (ANN), max‐min difference (MMD) algorithm, filter banks (FBs), quadratic spline wavelet transform (WT), daubechies (db10) WT, Harr WT, wavelet filter bank, digital filtering with dynamic threshold, combination of WT, derivative, and Hilbert transform (HT), adaptive MM, phase space reconstruction and box‐scoring calculation, ECG structural analysis (SA), relative energy (RE), parallel delta modulator (PDM), modified S‐transform (ST), and deterministic finite automata (DFA) …”

“…The Shannon energy and Hilbert transform‐based methods detect several false peaks for long pause ECG signals . The QRS detection accuracy of the methods reported in the literature is very poor, which reduces the diagnostic correctness and operational reliability, whereas, the high accuracy methods employ costly signal processing operations.…”