The proposed modular low-complexity ECG delineation algorithm achieved 100% sensitivity and 99.51% positive predictive value for QRS peak detection on the QT database in high-accuracy mode.
A novel modular, low-complexity ECG delineation algorithm enables real-time, accurate detection of ECG fiducial points on resource-constrained wearable devices with minimal computational load.
This work presents a new modular and low-complexity algorithm for the delineation of the different ECG waves (QRS, P and T peaks, onsets, and end). Involving a reduced number of operations per second and having a small memory footprint, this algorithm is intended to perform real-time delineation on resource-constrained embedded systems. The modular design allows the algorithm to automatically adjust the delineation quality in runtime to a wide range of modes and sampling rates, from a ultralow-power mode when no arrhythmia is detected, in which the ECG is sampled at low frequency, to a complete high-accuracy delineation mode, in which the ECG is sampled at high frequency and all the ECG fiducial points are detected, in the case of arrhythmia. The delineation algorithm has been adjusted using the QT database, providing very high sensitivity and positive predictivity, and validated with the MIT database. The errors in the delineation of all the fiducial points are below the tolerances given by the Common Standards for Electrocardiography Committee in the high-accuracy mode, except for the P wave onset, for which the algorithm is above the agreed tolerances by only a fraction of the sample duration. The computational load for the ultralow-power 8-MHz TI MSP430 series microcontroller ranges from 0.2% to 8.5% according to the mode used.
Bote et al. (Fri,) conducted a other in Electrocardiogram (ECG) delineation. Modular low-complexity ECG delineation algorithm vs. Manual annotations and state-of-the-art algorithms was evaluated on Sensitivity and Positive Predictive Value for QRS peak detection. The proposed modular low-complexity ECG delineation algorithm achieved 100% sensitivity and 99.51% positive predictive value for QRS peak detection on the QT database in high-accuracy mode.