ECG Signal Quality Evaluation Using Peak Variability Thresholds
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Solution Overview
Problem
Electrocardiogram signals are susceptible to noise and interference, leading to reduced accuracy in early warning algorithms due to pathological features being disturbed by noise and abnormal signals.
Innovation Solution
An electrocardiogram signal quality evaluation method that calculates peak variability and peak number variability features to distinguish between normal, abnormal, and noise signals by setting thresholds based on collected electrocardiogram signal samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If multiple lead screws are used to drive the carriage in parallel, then the measurement speed is improved, but the device complexity and manufacturing difficulty increase
Solution Approach 1:
The measurement system is divided into multiple independent measurement units, each with its own lead screw and carriage. This segmentation allows parallel operation of multiple measurement channels, improving overall measurement speed while keeping each individual unit's structure relatively simple and modular.
Solution Approach 2:
The evaluation device is designed with a universal carriage structure that can be shared across multiple measurement units. The carriage serves multiple functions: supporting measurement probes, providing precise positioning, and enabling synchronized movement across different measurement channels, thereby reducing overall device complexity.
2Ease of manufacture
If the evaluation device is designed to be compact and simple, then the manufacturing cost and difficulty are reduced, but the measurement precision and reliability deteriorate
Solution Approach 1:
Multiple measurement functions are merged into a single integrated evaluation device. The carriage combines positioning, probe support, and coordinate transformation functions in one structure. This merging maintains compactness and manufacturing simplicity while achieving high measurement precision through coordinated multi-functional operation.
Solution Approach 2:
A coordinate transformation unit acts as an intermediary between the physical measurement coordinates and the evaluation coordinates. This intermediary component simplifies the overall system architecture by abstracting the complex coordinate relationships, making the device easier to manufacture while maintaining measurement precision through mathematical transformation rather than complex mechanical structures.
3Measurement precision
If the evaluation device uses complex coordinate transformation and multiple measurement units, then the measurement precision is improved, but the device complexity and processing time increase
Solution Approach 1:
The coordinate transformation parameters and measurement unit configurations are pre-calibrated and stored in the evaluation device before actual measurement. This preliminary action eliminates the need for complex real-time calculations during measurement, reducing processing time while maintaining high precision through pre-computed transformation matrices and geometric relationships.
Solution Approach 2:
The evaluation device creates virtual copies of the physical measurement coordinates through coordinate transformation. Instead of physically complex mechanisms, the system uses mathematical models to replicate and transform measurement data from multiple units into a unified evaluation coordinate system, achieving high precision without proportional increases in physical device complexity.
Data Source
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AI summary
This application provides an electrocardiogram signal quality evaluation method, an electronic device, and a chip system, and relates to the field of signal evaluation technologies. In this method, peak variability features and peak number variability features of a plurality of electrocardiogram signal samples are counted, based on the peak variability features of the plurality of electrocardiogram signal samples as one abnormal dividing point, and the peak number variability features of the plurality of electrocardiogram signal samples as another abnormal dividing point; and based on a relationship between a peak variability feature and a peak number variability feature of a to-be-evaluated electrocardiogram signal and the two abnormal dividing points, whether the to-be-evaluated electrocardiogram signal is a normal signal, an abnormal signal, or a noise signal is determined. Through this method, a normal electrocardiogram signal can be accurately detected.