Adaptive T-Wave Detection Algorithm for Deviated ECG Waveforms
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Solution Overview
Problem
Existing methods for analyzing electrocardiogram waveforms, such as those described in Hermans (2017), often fail to accurately detect the end point of the T-wave due to deviations in amplitude and time axis intervals caused by diseases or measurement environments, leading to incorrect peak detection.
Innovation Solution
A physiological information processing apparatus and method that acquires and classifies physiological information, selecting an appropriate algorithm from a plurality based on classification results to analyze the data accurately, using a controller with an acquiring, classifying, and analyzing section to perform suitable analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a fixed predetermined range (R+50ms to R+0.7RR interval) is used to search for the T-wave peak, then the detection process is simple and fast, but the detection accuracy deteriorates when the electrocardiogram waveform deviates from typical values due to diseases or measurement environments
Solution Approach 1:
The patent applies dynamics by making the T-wave search range adaptive rather than fixed. The upper limit of the search range is dynamically adjusted based on the actual RR interval measured from the electrocardiogram waveform. Specifically, the search range is set from R+50ms to R+(0.7×RR interval), allowing the range to expand or contract according to the patient's heart rate variability. This dynamic adjustment maintains detection speed while improving accuracy for waveforms with deviated time axis intervals.
2Ease of operation
If usual values from databases are used to set the search range, then the method is easy to implement, but it fails to accurately detect T-wave peaks in electrocardiogram waveforms with deviated amplitude or time axis intervals
Solution Approach 1:
The patent applies parameter changes by modifying the search range parameters based on actual measured values from the electrocardiogram waveform. Instead of using fixed database values, the upper limit parameter is changed to 0.7×RR interval, where RR interval is the actual measured time between R waves. This parameter adjustment allows the search range to adapt to different heart rates and waveform characteristics, maintaining ease of implementation while significantly improving detection accuracy for deviated waveforms.
3Device complexity
If a single algorithm is used for all electrocardiogram waveforms, then the processing system is simple, but it cannot accurately analyze waveforms with deviated characteristics caused by diseases or measurement conditions
Solution Approach 1:
The patent applies local quality by implementing different search strategies for different portions of the T-wave based on its characteristics. The method divides the T-wave analysis into two parts: the initial portion (from T-wave start to peak) and the terminal portion (from peak to end). Different algorithms are applied to each portion - a first algorithm for the initial part and a second algorithm for the terminal part. This localized approach allows the system to maintain simplicity while achieving high accuracy for various waveform types including deviated waveforms.
Data Source
AI summary
A physiological information processing apparatus includes an acquiring section that acquires physiological information of a subject, a classifying section that classifies the physiological information that is acquired by the acquiring section, and an analyzing section that selects an algorithm from a plurality of algorithms according to a result of the classification performed by the classifying section, and that analyzes the classified physiological information by using the selected algorithm.