Automatic ECG Activity Labeling via Respiration Feature Extraction
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Solution Overview
Problem
Current ECG data analysis struggles with accurately mapping activity information with ECG signals due to independent collection methods, leading to reduced reliability in diagnosis, as accelerometers only detect motion and fail to differentiate between activities like sleep, rest, or sitting idle.
Innovation Solution
A method and system for automatically labeling ECG data by processing ECG signals to identify fiducial points, applying Karhunen Loeve Transform (KLT) for feature extraction, and using a classifier model to map these features to corresponding activities, incorporating respiration data for differentiation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If accelerometer is used to detect activity, then motion detection is achieved, but activity differentiation accuracy deteriorates
Solution Approach 1:
The patent combines ECG signal processing with activity detection to create a hybrid system. Instead of relying solely on accelerometer data, the system merges ECG features (such as heart rate variability, waveform morphology) with motion sensors to achieve both motion detection and accurate activity differentiation. This combination allows the system to distinguish between similar motions (e.g., eating vs. bicep curls) by analyzing physiological responses alongside mechanical motion.
Solution Approach 2:
The ECG signal serves as an intermediary that bridges the gap between motion detection and activity identification. By analyzing the relationship between cardiac electrical activity and physical motion, the system can infer activity type more accurately. The ECG acts as a mediator that provides physiological context to motion sensor data, enabling better differentiation between activities that produce similar motion patterns.
2Ease of manufacture
If ECG and activity information are collected independently, then data collection simplicity is maintained, but mapping reliability deteriorates
Solution Approach 1:
The system performs preliminary synchronization and alignment of ECG and activity sensor data streams before analysis. By establishing temporal and contextual relationships between the two independent data sources in advance, the system prepares merged datasets that maintain the simplicity of independent collection while achieving reliable mapping. This preliminary coordination ensures that ECG beats and motion events are properly associated without requiring complex real-time coordination.
Solution Approach 2:
The system implements feedback mechanisms that continuously refine the mapping between ECG and activity data. By analyzing the relationship between cardiac signals and motion patterns, the system adjusts its classification models to improve mapping accuracy. This feedback loop allows the system to learn from discrepancies between ECG-based and sensor-based activity detection, progressively improving reliability while maintaining the simplicity of independent data collection.
Data Source
AI summary
The present invention relates to a method of automatic labeling of activity of a subject on ECG data. The method of the invention comprises acquiring at least one physiological input signal purporting to an ECG signal and processing thereof. The processing of the at least one physiological input signal comprises conditioning the ECG signal and processing thereof, wherein the processing comprises obtaining respiration data from the ECG signal, identifying the activity pertaining to the said ECG data based on at least a signal specific feature of the said ECG signal, wherein the respiration data are used for differentiating activities performed by the subject, and labeling the said ECG data with the said activity, automatically. The present invention also relates to a system for automatic labeling of activity on ECG data in accordance with the method of the invention.