Why the study?
Does a two-stage classifier improve ECG biometric verification accuracy compared to single-stage classifiers in subjects under different health conditions?
Population
184 subjects under different health conditions
Comparison
Two-stage classifier combining random forest and… vs Random forest alone and wavelet distance measure…
Design
Other
Authors
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ECG biometrics from mobile sensors may aid secure remote access; leaves open prospective validation before clinical or security adoption.
Does a two-stage classifier improve ECG biometric verification accuracy compared to single-stage classifiers in subjects under different health conditions?
A novel two-stage classifier combining random forest and wavelet distance measure achieves 99.52% accuracy for ECG-based biometric verification using mobile sensors.
Tan et al. (2017) studied this question.
Synapse has enriched 3 closely related papers on similar clinical questions. Consider them for comparative context: