Cardiac Event Identification Using Threshold Crossing Intervals
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Solution Overview
Problem
Existing implantable medical devices (IMDs) face challenges in accurately identifying cardiac events due to limitations in battery energy reserves, which restrict them to simple time-domain processing techniques, leading to errors in distinguishing between cardiac signal waveform segments.
Innovation Solution
The implementation of a system that senses cardiac signals, determines dominant frequencies by measuring time intervals between threshold crossings, and compares these intervals to predetermined patterns to identify cardiac events, reducing battery consumption and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If frequency domain processing techniques such as Fourier transforms and wavelet transforms are used to improve cardiac event identification accuracy, then measurement precision is improved, but use of energy increases beyond available battery reserves
Solution Approach 1:
The cardiac cycle is segmented into distinct phases (QRS complex, T-wave, etc.) and each phase is analyzed separately using time-domain threshold crossing methods. This segmentation allows accurate identification of individual waveform segments without requiring computationally intensive global frequency domain analysis of the entire cardiac cycle.
Solution Approach 2:
The patent extracts only the essential temporal information (time intervals between threshold crossings) needed for cardiac event identification, discarding the computationally expensive frequency domain transformation step. This extraction approach retains sufficient diagnostic information while dramatically reducing computational energy requirements.
2Use of energy by moving object
If simple time-domain processing techniques such as comparing cardiac signals to a single threshold are used to conserve battery energy, then use of energy is reduced, but measurement precision deteriorates leading to misidentification of cardiac signals
Solution Approach 1:
The system dynamically adjusts the threshold level based on the instantaneous amplitude of the cardiac signal. Rather than using a fixed threshold, the threshold adapts to signal variations, allowing accurate detection of waveform segments across different cardiac conditions while maintaining low computational complexity.
Solution Approach 2:
The patent introduces a temporal dimension by analyzing multiple time intervals between successive threshold crossings. Instead of relying solely on amplitude comparison at a single point, the system evaluates the pattern of time intervals, adding temporal pattern recognition to the detection process without requiring frequency domain analysis.
3Measurement precision
If multiple threshold crossings are measured and compared to predetermined patterns to improve cardiac event identification, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent creates simplified temporal templates (predetermined patterns of time intervals) that represent normal cardiac waveforms. These templates are stored in memory and used for comparison against measured time intervals from patient signals. This copying approach enables pattern recognition without requiring complex real-time signal processing or machine learning algorithms.
Data Source
AI summary
An implantable medical device includes leads having electrodes that are positioned within a heart. The electrodes sense signals derived from the heart that include waveform segments. The device includes a timing module that determines when the waveform segments cross a threshold and measures time intervals between at least two threshold crossings by the waveform segments. The device also includes event identification module that compares the time intervals to a predetermined pattern associated with a cardiac event. The event identification module identifies the cardiac event based on the time intervals and the predetermined pattern.