Identification of Pulse Onset on Cerebral Blood Flow Velocity Waveforms: A Comparative Study.

Area of Science:

• Neuroscience
• Biomedical Engineering
• Medical Signal Processing

Background:

• Transcranial Doppler (TCD) enables noninvasive measurement of cerebral blood flow velocity (CBFV).
• Accurate CBFV waveform analysis is essential for assessing cerebral hemodynamics, intracranial pressure, and mild traumatic brain injury.
• Detecting the pulse onset in CBFV signals is challenging due to noise, artifacts, and diverse pathological morphologies.

Purpose of the Study:

• To conduct a comprehensive comparative analysis of three popular pulse onset detection methods for CBFV signals.
• To evaluate the accuracy, computational complexity, and parameter sensitivity of these detection methods.
• To identify the most reliable algorithm for accurate beat-to-beat delineation of CBFV pulses.

Main Methods:

• A large annotated dataset of 92,794 CBFV pulses from 108 subarachnoid hemorrhage patients was utilized.
• Three distinct pulse onset detection algorithms were compared.
• Sensitivity analysis was performed to determine optimal parameter values for each method.

Main Results:

• One method, with optimized parameters, achieved a true positive rate (TPR) of 97.06% and a positive predictivity value (PPV) of 96.48% for pulse onset detection within a 10 ms error threshold.
• This optimal method demonstrated high accuracy and low computational complexity, with an average running time of 4ms per pulse.
• The study highlighted the significant impact of parameter selection on detection method performance.

Conclusions:

• The validated pulse onset detection method offers a reliable and computationally efficient solution for analyzing CBFV signals.
• This algorithm's high accuracy and speed support its clinical application in real-time CBFV monitoring.
• Accurate pulse onset detection is fundamental for advancing noninvasive assessments in cerebrovascular conditions.