Electrocardiogram Fiducial Point Detector Using a Bilateral Filter and Symmetrical Point-Filter Structure

Abstract

The characteristics or aspects of important fiducial points (FPs) in the electrocardiogram (ECG) signal are complicated because of various factors, such as non-stationary effects and low signal-to-noise ratio. Due to the various noises caused by the ECG signal measurement environment and by typical ECG signal deformation due to heart diseases, detecting such FPs becomes a challenging task. In this study, we introduce a novel PQRST complex detector using a one-dimensional bilateral filter (1DBF) and the temporal characteristics of FPs. The 1DBF with noise suppression and edge preservation preserves the P- or T-wave whereas it suppresses the QRS-interval. The 1DBF acts as a background predictor for predicting the background corresponding to the P- and T-waves and the remaining flat interval excluding the QRS-interval. The R-peak and QRS-interval are founded by the difference of the original ECG signal and the predicted background signal. Then, the Q- and S-points and the FPs related to the P- and T-wave are sequentially detected using the determined searching range and detection order based on the detected R-peak. The detection performance of the proposed method is analyzed through the MIT-BIH database (MIT-DB) and the QT database (QT-DB).

Multilayer Perceptron-Based Real-Time Intradialytic Hypotension Prediction Using Patient Baseline Information and Heart-Rate Variation

“…Figure 11 shows the HRV results at the time of hypotension in five hemodialysis patients using the HRV analysis method introduced in [ 43 , 44 ]. Among the HRV parameters, HR per minute (beats per minute (BPM)), RR interval trend, RR histogram, power spectrum, and Poincaré plot were used.…”

Fast Parabolic Fitting: An R-Peak Detection Algorithm for Wearable ECG Devices

“…To be consistent with most of the R-peak detection studies [3,5,[8][9][10][11][12]14,15], we used quantitative comparisons in terms of sensitivity (S), positive predictability (P), and detection error (DER), as defined in Equations ( 1)-(3):…”

“…Several notable studies have recently been conducted [4,6,7]. However, a problem with most R-peak detection algorithms is that the computational complexity could be higher, and they require impractical assumptions, e.g., they usually need a global statistical knowledge of the entire input signal [3,[8][9][10][11].…”

ELEKTRA: ELEKTRokardiomatrix application to biometric identification with convolutional neural networks

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“…But, unfortunately, many of the existing works base their study on very complex DL architectures making it difficult to reproduce those systems in a real-life situation. Moreover, computationally speaking, many of the studies in the current literature perform a fiducial analysis, which is very costly despite having good performances [11].…”