ECG Signal Segmentation for Adaptive Noise Detection
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Solution Overview
Problem
Existing signal processing methods for biomedical signals like ECG struggle to effectively filter out noise components such as power-line interference and motion artifacts, which can significantly affect the accuracy of diagnoses.
Innovation Solution
A signal processing device and method that utilize sampling, segmentation, and detection modules to differentiate between noise and signal components by calculating segment values over varying time intervals, generating a detection signal to trigger noise reduction, and employing an adaptive filter with a baseline tracking unit and adjustable step sizes to generate a noise-reduced output signal.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If high pass filters or band pass filters are used to reduce noise components, then noise reduction capability is improved, but device complexity increases
Solution Approach 1:
The patent segments the signal processing task into multiple stages: segmentation module divides the input signal into multiple segments, then each segment is processed by calculation modules to generate segment values. This segmentation approach replaces complex filtering operations with simpler segment-based calculations, reducing device complexity while maintaining noise reduction capability.
Solution Approach 2:
The patent introduces intermediate segment values as mediators between the raw input signal and the final output signal. Calculation modules compute segment values from segmented signal portions, and comparison modules use these intermediate values to detect noise without requiring complex filter structures. This intermediary approach simplifies the overall device architecture.
2Measurement precision
If signal processing techniques are applied to reduce noise components, then measurement precision is improved, but loss of information increases
Solution Approach 1:
The patent employs dynamic threshold adjustment where comparison modules adaptively determine thresholds based on segment values and signal characteristics. This dynamic approach allows the system to maintain high measurement precision across varying signal conditions while preserving genuine signal variations, avoiding the information loss that occurs with fixed-threshold methods.
Solution Approach 2:
The patent applies different processing strategies to different segments of the signal. Each segment is evaluated independently with its own segment value calculation and comparison, allowing local adaptation to signal characteristics. This ensures that noise reduction is applied selectively without uniformly affecting the entire signal, thereby preserving important local signal information.
3Reliability
If noise reduction processing is continuously applied, then reliability is improved, but use of energy increases
Solution Approach 1:
The patent implements periodic noise reduction processing rather than continuous processing. The segmentation module divides the signal into discrete segments, and processing is applied periodically to each segment. This periodic approach maintains signal quality through regular processing intervals while significantly reducing overall energy consumption compared to continuous processing.
Solution Approach 2:
The patent applies noise reduction processing selectively to segments that require it, rather than processing the entire signal uniformly. Comparison modules identify segments with noise characteristics and apply processing only to those portions, implementing partial action that reduces energy consumption while maintaining reliability for affected signal portions.
Data Source
AI summary
Embodiments of the invention disclose a signal processing device and a signal processing method and a device and a method for signal processing. The signal processing device includes a sampling module, a first segmentation module, a second segmentation module, and a detection module. The sampling module samples an input signal to generate a sample signal. The first segmentation module calculates a first segment value according to the sample signal during a first time interval. The second segmentation module calculates a second segment value according to the sample signal during a second time interval different in length from the first time interval. The detection module generates a detection signal according to the determination of whether the first segment value lies out of a first range, and whether the second segment value lies out of a second range.