Abstract
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 and 30 ms, respectively, of the true onsets. The algorithm's 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.
Highlights
Assessment of cerebrovascular function is imperative in the diagnosis and management of numerous conditions common in neurological care
We report on a number of metrics to evaluate the performance of the algorithm. These metrics are based on the number of true positives (TP), the number of false positives, the number of false negatives, and the temporal separation between the annotated beat and the detected beat or temporal offset error ( t)
The scans were performed on patients experiencing subarachnoid hemorrhage, and the overall quality of the data generally ranges from very poor to acceptable
Summary
Assessment of cerebrovascular function is imperative in the diagnosis and management of numerous conditions common in neurological care. Transcranial Doppler (TCD) ultrasound has been used since the 1980s to measure cerebral blood flow velocity (CBFV), which can serve as a means of assessing cerebral hemodynamics [1, 2]. CBFV waveform analysis often utilizes individual CBFV pulse morphology and depends heavily on reliable pulse onset detection. Accurate pulse delineation in CBFV waveforms presents a significant challenge for a number of reasons. CBFV signals are prone to noise artifacts as a result of motion of the (not uncommonly handheld) probe or subject. The wide range of possible waveform morphologies that can occur in CBFV signals, in pathological populations, presents yet another challenge, as it may be difficult to rely on the presence of any one uniform and consistent feature to aid in onset detection
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