Key result
A novel QRS complex detection algorithm using Smoothed Nonlinear Energy Operator achieved an average sensitivity of 99.70%, positive predictivity of 99.78%, and detection error rate of 0.52%.
Why the study?
Does the SNEO-based algorithm improve QRS complex detection in ECG signals compared to recent techniques?
Does the SNEO-based algorithm improve QRS complex detection in ECG signals compared to recent techniques?
The novel SNEO-based algorithm demonstrates high accuracy and low error rates for QRS complex detection in ECG signals.
Promising QRS detection metrics on benchmark data merit validation; leaves open superiority over existing methods in clinical ECGs.
In this work, we present a novel algorithm for QRS complex detection in the Electrocardiogram (ECG) signal. The proposed algorithm is based on a combination of an Infinite Impulse Response (IIR) Filter, a Nonlinear transform termed Smoothed Nonlinear Energy Operator (SNEO) and an efficient simple thresholding process. In our scheme, ButterWorth filter is used to enhance the raw ECG signal and the SNEO is applied to accentuate the QRS complex features. The simulation tests and experimental results carried out on the MIT-BIH Arrhythmia Database show that our proposed approach gives a higher detection performances in comparison with some recently developed techniques with an average Sensitivity (Se) of 99.70%, average Positive Predictivity (P+) of 99.78% and a Detection Error Rate (DER) of 0.52%.
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Bouny et al. (2018) studied Arrhythmia. Smoothed Nonlinear Energy Operator (SNEO) based algorithm vs. Recently developed techniques was evaluated on QRS complex detection performance (Sensitivity, Positive Predictivity, Detection Error Rate). A novel QRS complex detection algorithm using Smoothed Nonlinear Energy Operator achieved an average sensitivity of 99.70%, positive predictivity of 99.78%, and detection error rate of 0.52%.
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