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The proposed ECG signal processing algorithm using Shannon energy and median filtering achieved a sensitivity of 99.92% and a positive predictivity of 99.96% for R-peak detection.
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A novel ECG processing algorithm using Shannon energy improves R peak detection and signal quality, potentially aiding clinical diagnosis.
A novel method for processing Electrocardiogram (ECG) signals, with a focus on interpreting the QRS complex and extracting its characteristics. The algorithm incorporates a variety of techniques, including median filtering, the Shannon energy transform, segmentation, time and amplitude thresholds, and statistical false peak elimination (SFPE). These filters play a crucial role in the preprocessing phase, reducing undesired background noise and interference. With in the algorithm, the Shannon energy (SE) is computed, and an SE envelope is generated by applying a predefined threshold. This envelope serves the purpose of pinpointing R waves in the time domain. False peaks, stemming from residual noise, are eliminated by averaging peak-to-peak intervals. The algorithm's performance is evaluated using MIT-BIH arrhythmia database, demonstrating significant improvements in signal quality, reduced noise, and closer alignment to the original signal. This underscores the efficiency of the proposed signal processing technique.
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Alajangi Soniya P. Pushpalatha (2023) studied Arrhythmia (n=47). Shannon energy transform and median filtering algorithm vs. Previous QRS detection methods was evaluated on Sensitivity of R-peak detection. The proposed ECG signal processing algorithm using Shannon energy and median filtering achieved a sensitivity of 99.92% and a positive predictivity of 99.96% for R-peak detection.
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