Cardiac Signal Processing System for Power-Efficient Feature Extraction
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Solution Overview
Problem
Conventional systems for monitoring physiological signals face challenges in accuracy, reliability, and power efficiency, particularly in ambulatory settings, due to the need for significant power and processing resources to communicate and process waveform data.
Innovation Solution
A system that computes parameter values for cardiac-related signals by identifying feature points, determining their validity based on signal-to-noise ratio, and using dynamic signal-to-noise ratio to reduce data volume and power consumption, while denoising and compressing signals for efficient wireless communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If waveform data is communicated from the subject to a data collection system, then information accuracy is improved, but power consumption increases
Solution Approach 1:
The patent extracts and transmits only essential physiological parameters and events rather than complete waveform data. The device identifies and communicates key feature points and clinically relevant events, eliminating redundant information while maintaining diagnostic accuracy.
Solution Approach 2:
The system transforms continuous waveform data into discrete parameter representations. By converting analog physiological signals into digital parameters and event codes, the device reduces data volume and transmission power requirements while preserving essential diagnostic information.
2Loss of information
If complete waveform data is transmitted, then data completeness is improved, but data volume increases
Solution Approach 1:
The patent extracts essential physiological parameters and events from complete waveform data. The device identifies key feature points such as R-waves, T-waves, and clinically significant events, transmitting only these extracted elements rather than the full continuous waveform.
Solution Approach 2:
The system segments continuous waveform data into discrete physiological events and parameter measurements. By dividing the continuous signal into meaningful segments (cardiac cycles, respiratory events, arrhythmia episodes), the device reduces overall data volume while maintaining diagnostic completeness.
3Use of energy by moving object
If signal processing is performed to denoise and compress signals, then power consumption is reduced, but processing complexity increases
Solution Approach 1:
The patent performs signal denoising, feature detection, and parameter extraction as preliminary actions within the implanted device before transmission. By preprocessing signals locally and eliminating noise early in the signal chain, the system reduces the power required for subsequent transmission and processing stages.
4Measurement precision
If feature points are validated using signal-to-noise ratio, then data accuracy is improved, but computational resources increase
Solution Approach 1:
The system uses signal-to-noise ratio as a validation parameter for feature points. By calculating SNR for detected features and comparing against thresholds, the device filters out spurious detections while maintaining sensitivity to genuine physiological events, improving overall measurement accuracy.
Data Source
AI summary
A parameter value is computed for a segment of a cardiac-related signal. In accordance with various example embodiments, a system includes a computer circuit configured to identify cardiac cycles within a segment of a cardiac-related signal, such as an ECG. At least one feature point is identified within the cardiac cycles. For each identified feature point, a signal-to-noise ratio (SNR) representative of the ratio of signal energy to noise energy is computed for a cardiac cycle subsegment containing the identified feature point. A validity characteristic of the feature point is determined based upon the signal-to-noise ratio, and a parameter value is computed by combining feature points contained within the segment, based upon the determined validity characteristics of the feature points.