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

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

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

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

AN INITIAL EVALUATION OF IBI VIZEDIT: AN RSHINY APPLICATION FOR OBTAINING ACCURATE ESTIMATES OF AUTONOMIC REGULATION OF CARDIAC ACTIVITY

Photoplethysmogram (PPG) sensors are increasingly used to collect individual heart rate data during laboratory assessments and psychological experiments. PPG sensors are relatively cheap, easy to use, and non-invasive alternatives to the more common electrodes used to produce electrocardiogram recordings. The downside is that these sensors are more susceptible to signal distortion. Often, the most relevant measures for understanding psychological processes that underlie emotions and behaviors are measures of heart rate variability. As with all measures of variability, outliers (i.e., signal artifacts) can have outsized effects on the final estimates; and, given that these scores represent a primary variable of interest in many research contexts, the successful elimination of artefactual points is critical to the ability to make valid inferences with the data. Prior to the development of IBI VizEdit, there was no single, integrated processing and editing pipeline for PPG data. The present pair of studies offers and initial evaluation of the program’s performance. Study 1 is focused on the efficacy of a novel approach to imputing sections of particularly corrupted PPG signal. Study 2 tests the ability of trained editors to reliably use IBI VizEdit as well as the validity of estimates of cardiac activity during a prescribed set of laboratory tasks. Study 1 suggests that the novel imputation approach, under certain conditions and using certain parameterizations may hold promise as a means of accurately imputing missing sections of data. However, Study 1 also clearly demonstrates the need for further refinement and the consideration of alternative implementations. The results from Study 2 indicate that IBI VizEdit can be reliably used by trained editors and that estimates of cardiac activity derived from its output are likely valid