Dynamic Threshold Control for ECG Peak Detection
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Solution Overview
Problem
Existing methods for detecting physiological signals, such as ECG, face challenges in accurately identifying peaks due to noise interference and varying signal intervals, leading to reduced accuracy in heart rate calculation and arrhythmia diagnosis.
Innovation Solution
A method and apparatus that dynamically control the threshold for peak detection in physiological signals by adjusting it based on a minimum threshold and feature values, using a combination of proportion and differential controlling units to ensure the threshold remains above a minimum value, thereby reducing noise interference and improving peak detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a fixed threshold is used for peak detection, then the device complexity is low, but the measurement precision deteriorates due to noise interference and varying signal intervals
Solution Approach 1:
The patent implements dynamic threshold adjustment by continuously updating the detection threshold based on the difference between the current signal value and the previous peak value. The threshold is modified using a differential component (α×(S(t)-P(t-1))) that adapts to signal variations, transforming the static threshold into a dynamic parameter that follows the signal's characteristics while maintaining noise rejection capability.
Solution Approach 2:
The system employs feedback mechanisms where the detected peak value P(t-1) and current signal value S(t) are fed back into the threshold calculation formula. This feedback loop allows the threshold to automatically adjust based on recent signal behavior, improving detection accuracy without requiring complex external control systems.
2Measurement precision
If the threshold is lowered to detect smaller peaks, then the measurement precision improves, but false detection increases due to noise interference
Solution Approach 1:
The patent applies local quality by making the threshold adaptation localized to each peak detection event. Instead of using a global fixed threshold or a uniformly adaptive threshold, the system adjusts the threshold locally based on the specific characteristics of each signal segment (the difference between current signal S(t) and previous peak P(t-1)), allowing sensitive detection of local variations while maintaining overall noise rejection.
Solution Approach 2:
The system dynamically changes the threshold parameter based on signal characteristics. The threshold is transformed from a fixed value to a variable parameter that changes with each detection cycle according to the formula involving α, S(t), and P(t-1), enabling the system to adapt its sensitivity to match the local signal-to-noise conditions.
3Reliability
If the threshold is raised to reduce false detection, then the reliability improves, but the measurement precision deteriorates due to missed peaks
Solution Approach 1:
The dynamic threshold adjustment mechanism allows the system to maintain higher reliability when signal variations are small while preserving sensitivity when variations are large. The threshold automatically rises or falls based on the actual signal behavior, preventing both false detections and missed peaks through continuous adaptation to signal conditions.
Data Source
AI summary
A method of controlling a threshold for detecting peaks of physiological signals includes: obtaining a physiological signal measured from a person being examined; determining whether a peak of the physiological signals is detected based on a result of comparing the physiological signals with a threshold; and controlling the threshold based on a minimum threshold and either the threshold or a feature value of the detected peak based on a result of the determining. When a threshold for detecting peaks of physiological signals is controlled, even if an interval between the peaks is irregular or there is a large difference in values of the peaks, the peaks can be accurately detected.