State-Based P-Wave Detection for Far-Field Cardiac Sensing
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Solution Overview
Problem
Existing CRT techniques face challenges in accurately detecting P-waves due to low amplitude and frequency content of subcutaneous or substernal sensing, which can lead to improper timing of ventricular pacing, particularly when relying on far-field signals.
Innovation Solution
Implementing state-based sequencing and heuristics-driven training to detect P-waves using state-transition probabilities and morphological values, leveraging contextual information for precise P-wave detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If subcutaneous or substernal sensing is used for P-wave detection, then the device can be implanted with minimal invasive procedures, but the P-wave amplitude and frequency content become low making detection difficult
Solution Approach 1:
The system performs preliminary action by detecting the R-wave first and then using state-transition probabilities to predict and detect the subsequent P-wave. This sequential approach allows the system to compensate for the low amplitude P-waves by using contextual information from the cardiac cycle timing and morphology patterns established after R-wave detection.
Solution Approach 2:
The system uses feedback from the detected R-wave and subsequent cardiac cycle monitoring to refine P-wave detection. By continuously monitoring the cardiac signal and comparing morphological values against stored criteria for state transitions, the system adjusts and refines its P-wave detection accuracy based on the actual cardiac cycle characteristics observed.
2Device complexity
If traditional P-wave detection methods are used, then the detection process is simple, but the timing of ventricular pacing becomes improper when relying on far-field signals
Solution Approach 1:
The system changes the parameters used for P-wave detection by incorporating state-transition probabilities and morphological values derived from the cardiac cycle model. Instead of relying solely on traditional amplitude-based P-wave detection, the system uses temporal and morphological parameters to identify P-waves, thereby improving pacing timing accuracy while maintaining reasonable detection process complexity.
3Measurement precision
If state-based sequencing with state-transition probabilities is implemented, then P-wave detection accuracy improves, but the system complexity increases
Solution Approach 1:
The system segments the cardiac cycle into distinct states (P-wave state, R-wave state, etc.) and uses state-transition probabilities to navigate between these states. This segmentation approach organizes the complex detection process into manageable phases, improving P-wave detection accuracy while keeping the system architecture structured and manageable.
Data Source
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AI summary
An implantable medical device includes a memory storing criteria for transitioning between states of a cardiac cycle model, the states including a P-wave state. The device also includes sensing circuitry that senses a cardiac signal that varies as a function of a cardiac cycle of a patient, and also includes processing circuitry coupled to the sensing circuitry. The processing circuitry is configured to detect an R-wave in the sensed cardiac signal, to determine an elapsed time since the detection of the R-wave, to determine one or more morphological values of a post-R-wave segment of the cardiac signal to compare the elapsed time and the one or more morphological values to the stored criteria for transitioning between the plurality of states of the cardiac cycle model, and to detect a P-wave in the sensed cardiac signal in response to a transition to the P-wave state of the cardiac cycle model.