A K-Nearest Neighbor algorithm was developed and tested for automated delineation of ECG wave boundaries and interval computation.
Answers are grounded in this paper and cite their sources.
KNN ECG delineation lacks reported metrics for clinical use; leaves open need for validated comparisons.
Detection of the boundaries of electrocardiogram (ECG) characteristic waves with a reasonable accuracy has been a difficult task. As a classical statistical pattern recognition algorithm characterized with high accuracy and stability, KNN has been proposed for locating the waveform boundaries (the onsets and offsets of P, QRS, and T waves) in ECG signals. First, the QRS-complex of each beat is detected from the ECG signal. Next, the onset and offset of each QRS complex are located. The P wave and T wave, relative to each QRS complex along with their onset and offset points, are then identified using this algorithm. Further, QRS duration, heart rate, QT-interval, P-wave duration and PR-interval have also been computed using ECG wave fiducial points. This algorithm is tested on the ECG dataset acquired using ATRIA ® 6100 ECG machine in our own laboratory. The results obtained using the proposed algorithm presented for the assessment of performance, has been compared with the output of inbuilt software based detector of ATRIA machine.
No takes yet. Share an insight, caveat, or question.
Saini et al. (2013) studied this question.
Synapse has enriched 8 closely related papers on similar clinical questions. Consider them for comparative context: