ECG Noise Classification via Feedback Button Segmentation
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Solution Overview
Problem
Conventional ECG monitoring systems face challenges in effectively presenting and analyzing cardiac rhythm data due to cumbersome data presentation, noise interference, and the difficulty in diagnosing arrhythmias that occur over extended time frames, leading to inaccurate patient diagnoses.
Innovation Solution
The system employs an adaptive noise detector to differentiate valid ECG data from noise, with a feedback mechanism to prevent misclassification of patient-activated button press data, and presents ECG data in a diagnostic composite plot format that includes near field, intermediate, and extended-duration R-R interval views for enhanced diagnostic accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If ECG data is recorded over extended periods to capture arrhythmia patterns, then diagnostic completeness improves, but data volume and analysis complexity increase significantly
Solution Approach 1:
The patent segments extended ECG recordings into discrete events with associated metadata (trigger type, timestamp, pre-event and post-event segments). This segmentation transforms continuous complex data into structured, analyzable units that can be processed efficiently while preserving diagnostic information about arrhythmia patterns occurring over extended periods.
Solution Approach 2:
The system extracts relevant diagnostic features from extended ECG recordings by identifying and isolating specific events (arrhythmias, pauses, ectopic beats) and their contextual segments. This extraction process separates critical diagnostic information from the overwhelming volume of continuous data, enabling focused analysis without requiring processing of entire extended recordings.
2Measurement precision
If ECG data is presented at high resolution to preserve diagnostically-relevant features, then measurement precision improves, but data storage and transmission requirements increase
Solution Approach 1:
The patent applies local quality by providing high-resolution ECG data only for specific segments surrounding identified events (pre-event and post-event segments), while using lower-resolution or summarized representations for the remainder of the extended recording. This approach preserves measurement precision for diagnostically-critical features while reducing overall data storage requirements.
3Measurement precision
If noise filtering is applied to improve data quality, then measurement precision improves, but risk of removing valid physiological data increases
Solution Approach 1:
The system uses feedback mechanisms where identified events and their contextual segments inform the noise filtering process. By analyzing the characteristics of valid physiological events and using this information to guide filtering parameters, the system can distinguish between actual noise and valid physiological signals, reducing the risk of removing diagnostically-important data while still improving overall data quality.
Data Source
AI summary
A system and method for physiological data classification for use in facilitating diagnosis is provided. A physiological monitor includes a feedback button and physiological data obtained via the physiological monitor is stored in a database. The physiological data is divided into segments and one or more data segments are classified as noise. A determination is made that at least one of the data segments classified as noise includes a marker indicating a press of the feedback button on the physiological monitor. A set of the physiological data including and surrounding the physiological data occurring during the press of the feedback button is identified within the data segment classified as noise. The identified set of physiological data is provided with the data segments classified as valid for analysis.