Intracranial Pressure Peak Extraction Using Arterial Blood Pressure Morphology
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Solution Overview
Problem
Current methods for extracting peaks from intracranial pressure (ICP) waveforms are inefficient due to morphological diversity and require a vast reference pulse library, limiting accuracy and clinical application, especially in offline environments where real-time individual pulse characteristics are not accounted for.
Innovation Solution
A method and device that extract peaks P1, P2, and P3 from ICP waveforms using morphological features of arterial blood pressure waveforms by deriving derivative values, calculating latency, clustering peaks, and searching notches, allowing for accurate peak extraction without a reference pulse library.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a reference pulse library is used to detect peaks in ICP waveform, then the accuracy of peak detection is improved, but the device complexity and requirement for vast data storage increase
Solution Approach 1:
The system performs self-calibration by automatically adapting to each patient's unique ICP waveform characteristics during an initial calibration period. The algorithm learns the individual patient's waveform morphology and uses this adapted reference for subsequent peak detection, eliminating the need for external reference pulse libraries while maintaining high accuracy
Solution Approach 2:
The system dynamically adjusts detection parameters such as threshold values, time constants, and waveform filtering characteristics based on the calibrated patient-specific waveform patterns. This adaptive parameter adjustment allows accurate peak detection across diverse waveform morphologies without requiring pre-stored reference data
2Reliability
If individual pulses are averaged to reduce noise and defects, then the signal quality is improved, but the real-time individual pulse characteristics are lost
Solution Approach 1:
The calibration period is divided into multiple segments where individual pulse waves are processed separately. The system identifies corresponding peaks in each segment and calculates timing intervals individually, then uses these segmented measurements to establish patient-specific reference values without averaging away important individual variations
Solution Approach 2:
The system performs preliminary calibration during an initial period to establish patient-specific reference parameters, then uses these pre-established references for rapid real-time detection. This preliminary action captures individual characteristics before clinical use begins, enabling both noise reduction and preservation of individual features during actual monitoring
3Measurement precision
If the system adapts to individual patient characteristics through calibration, then the measurement accuracy is improved, but the time required for setup increases
Solution Approach 1:
The calibration process uses a partial calibration approach where only the essential parameters needed for accurate peak detection are measured and stored during calibration. Non-critical parameters are not calibrated, reducing calibration time while maintaining sufficient accuracy for clinical purposes
Solution Approach 2:
The system focuses calibration efforts on the specific waveform regions and parameters that most critically affect peak detection accuracy, such as the systolic upstroke and dicrotic notch areas. By concentrating calibration resources on these local critical regions rather than the entire waveform, accurate detection is achieved with minimal calibration time
Data Source
AI summary
The present invention relates to a device and a method for detecting a peak of an intracranial pressure (ICP) waveform using a morphological feature of an arterial blood pressure waveform. A peak extracting method of an ICP waveform using a morphological feature of an arterial blood pressure waveform according to an aspect of the present invention includes: extracting a pulse onset from a continuous ICP waveform based on systolic peak from arterial blood pressure waveform; dividing individual ICP waveforms in the continuous ICP waveform based on the pulse onset; deriving a derivative value from each of the ICP waveforms to extract a peak, a trough, and a flat; calculating latencies from the pulse onset extracted in each of the ICP waveforms to the extracted peaks to cluster peaks with a similar time interval and generate a peak cluster; searching a notch from each of the ICP waveforms based on the latency of a dicrotic notch of the arterial blood pressure waveform; and extracting P1, P2, and P3 peaks from each of the ICP waveforms by referring to the searched notch of the ICP.