Scalzo, F. | bioRxiv Science

bioRxiv2019

Objective Assessment of Beat Quality in Transcranial Doppler Measurement of Blood Flow Velocity in Cerebral Arteries

ObjectiveTranscranial Doppler (TCD) ultrasonography measures pulsatile cerebral blood flow velocity in the arteries and veins of the head and neck. Similar to other real-time measurement modalities, especially in healthcare, the identification of high quality signals is essential for clinical interpretation. Our goal is to identify poor quality beats and remove them prior to further analysis of the TCD signal. MethodsWe selected objective features for this purpose including Euclidean distance between individual and average beat waveform, cross-correlation between individual and average beat waveform, ratio of the high frequency power to the total beat power, beat length, and variance of the diastolic portion of the beat waveform. We developed an iterative outlier detection algorithm to identify and remove the beats that are different from others in a recording. Finally, we tested the algorithm on a dataset consisting of more than 16 hours of TCD data recorded from 48 stroke and 35 in-hospital control subjects. ResultsWe assessed the performance of the algorithm in estimating clinically important TCD parameters by comparison to those identified from beats hand-annotated by an expert. The results show that there is strong correlation between the two that delineates the algorithm has successfully recovered clinically important features. We obtained significant improvement in estimating the TCD parameters using the algorithm accepted beats compared to using all beats (r>0.78, p<0.01). SignificanceOur algorithm provides a valuable tool to the clinicians for automated detection of the reliable portion of the data. Moreover, it can be used as a pre-processing tool to improve the data quality for machine learning algorithms for automated diagnosis of pathologic beat waveform.

bioinformatics↗

bioRxiv2018

Algorithm for Reliable Detection of Beat Onsets in Cerebral Blood Flow Velocity Signals

Transcranial Doppler (TCD) ultrasound has been demonstrated to be a valuable tool for assessing cerebral hemodynamics via measurement of cerebral blood flow velocity (CBFV), with a number of established clinical indications. However, CBFV waveform analysis depends on reliable pulse onset detection, an inherently difficult task for CBFV signals acquired via TCD. We study the application of a new algorithm for CBFV pulse segmentation, which locates pulse onsets in a sequential manner using a moving difference filter and adaptive thresholding. The test data set used in this study consists of 92,012 annotated CBFV pulses, whose quality is representative of real world data. On this test set, the algorithm achieves a true positive rate of 99.998% (2 false negatives), positive predictive value of 99.998% (2 false positives), and mean temporal offset error of 6.10 {+/-} 4.75 ms. We do note that in this context, the way in which true positives, false positives, and false negatives are defined caries some nuance, so care should be taken when drawing comparisons to other algorithms. Additionally, we find that 97.8% and 99.5% of onsets are detected within 10 ms and 30 ms, respectively, of the true onsets. The algorithms performance in spite of the large degree of variation in signal quality and waveform morphology present in the test data suggests that it may serve as a valuable tool for the accurate and reliable identification of CBFV pulse onsets in neurocritical care settings.

bioinformatics↗