A model-based approach to human identification using ECG
2009
Biometrics, such as fingerprint, iris scan, and face recognition, offer methods for identifying individuals based on a
unique physiological measurement. Recent studies indicate that a person's electrocardiogram (ECG) may also
provide a unique biometric signature. Current techniques for identification using ECG rely on empirical methods
for extracting features from the ECG signal. This paper presents an alternative approach based on a time-domain
model of the ECG trace. Because Auto-Regressive Integrated Moving Average (ARIMA) models form a rich class
of descriptors for representing the structure of periodic time series data, they are well-suited to characterizing the
ECG signal. We present a method for modeling the ECG, extracting features from the model representation, and
identifying individuals using these features.
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