ECG Signal Segmentation for Long-Term Arrhythmia Detection
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Solution Overview
Problem
Existing ECG monitoring technologies are ineffective for diagnosing cardiac conditions that may not be captured during short-term recordings, necessitating long-term monitoring which generates large amounts of data requiring extensive processing by medical professionals.
Innovation Solution
Segmenting ECG signals into fixed-size segments surrounding consecutive heartbeats, including a QRS complex, P wave, and T wave, and generating a data set by joining these segments, with additional features like heart rate and sensor data, to facilitate machine learning and remote cardiac monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If long-term ECG monitoring is implemented to capture intermittent cardiac conditions, then diagnostic efficacy is improved, but data processing complexity increases
Solution Approach 1:
The patent segments long-term ECG data into individual heartbeat intervals (HBIs) centered around detected R-peaks. Each HBI contains a fixed number of samples representing one complete cardiac cycle, making the data manageable for analysis. This segmentation transforms continuous long-term monitoring data into discrete, analyzable units without losing diagnostic information about intermittent conditions.
Solution Approach 2:
The patent extracts relevant features from segmented HBI data, such as RR intervals, QRS duration, and morphological characteristics. By extracting only the most diagnostically relevant features from the raw ECG signals, the system reduces processing complexity while maintaining diagnostic efficacy for detecting intermittent cardiac conditions.
2Device complexity
If short-term ECG recording is used to reduce data processing requirements, then data processing complexity is reduced, but diagnostic efficacy deteriorates
Solution Approach 1:
Instead of processing entire long-term ECG recordings, the patent segments the data into individual heartbeat intervals and processes them independently. This allows the system to handle long-term monitoring data with the same computational resources required for short-term analysis, maintaining low processing complexity while enabling detection of intermittent conditions that would be missed in short-term recordings.
3Productivity
If ECG signal is segmented into fixed-size segments surrounding heartbeats, then data processing efficiency is improved, but information completeness may be compromised
Solution Approach 1:
The patent segments ECG data into fixed-size heartbeat intervals centered on R-peaks, with each segment containing a predetermined number of samples. This segmentation enables efficient processing while preserving complete cardiac cycle information, as each HBI captures the full P-QRS-T complex and surrounding intervals necessary for comprehensive cardiac analysis.
Solution Approach 2:
The patent applies different processing approaches to different parts of the ECG signal. Each heartbeat interval is processed with attention to its local characteristics, allowing the system to maintain information completeness for each cardiac cycle while achieving overall processing efficiency through standardized segment handling.
Data Source
AI summary
Techniques are disclosed for segmenting electrocardiogram (ECG) signals. In one example, a method to segment an electrocardiogram (ECG) signal may include detecting consecutive heartbeats in an ECG signal. The method also includes segmenting the ECG signal into multiple ECG segments surrounding the detected consecutive heartbeats and generating an ECG data set by joining consecutive ECG segments. The generated the ECG data set represents the detected heartbeats. In some such examples, each ECG segment is of a duration to include a QRS complex, a P wave, and a T wave.