Key result
Novel processing chain for non-uniformly sampled ECG achieves ~100% QRS detection accuracy.
Why the study?
Growing daily ECG data volume and long storage requirements motivated assessing whether medical-grade diagnostics are feasible directly from storage-efficient, non-uniformly sampled ECG representations.
Does an interpretation algorithm adapted for non-uniformly sampled ECGs maintain diagnostic accuracy compared to uniformly sampled processing?
Does an interpretation algorithm adapted for non-uniformly sampled ECGs maintain diagnostic accuracy compared to uniformly sampled processing?
Medical-grade diagnostics based directly on storage-efficient, non-uniformly sampled ECG representations are highly accurate and feasible without reconstructing uniform signals.
We present a set of three fundamental methods for electrocardiogram (ECG) diagnostic interpretation adapted to process non-uniformly sampled signal. The growing volume of ECGs recorded daily all over the world (roughly estimated to be 600 TB) and the expectance of long persistence of these data (on the order of 40 years) motivated us to challenge the feasibility of medical-grade diagnostics directly based on arbitrary non-uniform (i.e., storage-efficient) ECG representation. We used a refined time-independent QRS detection method based on a moving shape matching technique. We applied a graph data representation to quantify the similarity of asynchronously sampled heartbeats. Finally, we applied a correlation-based non-uniform to time-scale transform to get a multiresolution ECG representation on a regular dyadic grid and to find precise P, QRS and T wave delimitation points. The whole processing chain was implemented and tested with MIT-BIH Database (probably the most referenced cardiac database) and CSE Multilead Database (used for conformance testing of medical instruments) signals arbitrarily sampled accordingly to a perceptual model (set for variable sampling frequency of 100-500 Hz, compression ratio 3.1). The QRS detection shows an accuracy of 99.93% with false detection ratio of only 0.18%. The classification shows an accuracy of 99.27% for 14 most frequent MIT-BIH beat types and 99.37% according to AAMI beat labels. The wave delineation shows cumulative (i.e., sampling model and non-uniform processing) errors of: 9.7 ms for P wave duration, 3.4 ms for QRS, 6.7 ms for P-Q segment and 17.7 ms for Q-T segment, all the values being acceptable for medical-grade interpretive software.
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Piotr Augustyniak (2021) studied Non-uniformly sampled electrocardiogram (ECG). Algorithm for non-uniformly sampled ECG interpretation vs. Original uniform test ECGs and commercial interpretive software was evaluated on QRS detection accuracy. The proposed processing chain for non-uniformly sampled ECG signals achieved a QRS detection accuracy of 99.93% and beat classification accuracy up to 99.37%.
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