A Fast Multimodal Ectopic Beat Detection Method Applied for Blood Pressure Estimation Based on Pulse Wave Velocity Measurements in Wearable Sensors

Abstract

Automatic detection of ectopic beats has become a thoroughly researched topic, with literature providing manifold proposals typically incorporating morphological analysis of the electrocardiogram (ECG). Although being well understood, its utilization is often neglected, especially in practical monitoring situations like online evaluation of signals acquired in wearable sensors. Continuous blood pressure estimation based on pulse wave velocity considerations is a prominent example, which depends on careful fiducial point extraction and is therefore seriously affected during periods of increased occurring extrasystoles. In the scope of this work, a novel ectopic beat discriminator with low computational complexity has been developed, which takes advantage of multimodal features derived from ECG and pulse wave relating measurements, thereby providing additional information on the underlying cardiac activity. Moreover, the blood pressure estimations’ vulnerability towards ectopic beats is closely examined on records drawn from the Physionet database as well as signals recorded in a small field study conducted in a geriatric facility for the elderly. It turns out that a reliable extrasystole identification is essential to unsupervised blood pressure estimation, having a significant impact on the overall accuracy. The proposed method further convinces by its applicability to battery driven hardware systems with limited processing power and is a favorable choice when access to multimodal signal features is given anyway.

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“…Thus, overall, the proposed augmentation is favorable in that only a minor performance drop (if at all) is experienced when both D † 1 n,I,p,q and D † 2 n,I,p,q perform well, whereas a lot is gained when they do not. Furthermore, it should be pointed out that, as has already been observed by other authors (see, e.g., [52]), there are some inconsistencies in the PC15 records in that the some of the supposedly synchronized PPG traces exhibit an unusual delay not consistent with the assumption that the PPG pulse peak should have an offset equal to the PTT with respect to the respective R peak in the ECG. Pflugradt et al attribute this occasional unusual offset to glitches in the original measurement setup (see [52] at 11) and we assume it to have negatively impacted the performance characteristics presented in Table 2 since we allow only for a very limited detection delay τ d .…”

“…Furthermore, it should be pointed out that, as has already been observed by other authors (see, e.g., [52]), there are some inconsistencies in the PC15 records in that the some of the supposedly synchronized PPG traces exhibit an unusual delay not consistent with the assumption that the PPG pulse peak should have an offset equal to the PTT with respect to the respective R peak in the ECG. Pflugradt et al attribute this occasional unusual offset to glitches in the original measurement setup (see [52] at 11) and we assume it to have negatively impacted the performance characteristics presented in Table 2 since we allow only for a very limited detection delay τ d . Focusing on Table 3, which directly compares our results to those obtained by Pflugradt on the almost identical subset of PC15 records, we observe that on average our l-SSA-CPD outperforms Pflugradt et al's Fast Multimodal Ectopic Beat Detector (MEBD) on ECG records, achieving both higher sensitivity and specificity.…”

“…To evaluate and assess the performance and utility of our method we use records which are publicly available through Physiobank [51], a vast and commonly used resource for ECG and other biophysiological data. In particular, since we claim our method to be suitable for both ECG and PPG data, we found the Physionet Challenge 2015 training database (PC15) [50] to be of particular interest as it provides a collection of synchronized ECG and PPG recordings from which we chose a subset similar to the one used in [52]. With PC15 records not being annotated, we purposely chose to limit our evaluation to records containing PVCs (with different frequencies of occurrence) since they can quite accurately be spotted by careful visual inspection.…”

“…The PC15 database [50] provides a collection of ECG and PPG recordings from which we chose a subset similar to the one used in [52]. With PC15 records not being annotated, we purposely chose to limit our evaluation to records containing PVCs (with different frequencies of occurrence), since they can quite accurately be spotted by careful visual inspection.…”

“…A performance evaluation of l-SSA-CPD using ECG and PPG records from the publicly available Physionet Challenge 2015 training database (PC15) [50,51] are presented in Section 4.3 and compared to a recently proposed competing algorithm by Pflugradt et al [52].…”