Temporal and spectral features of single lead ECG for human identification

The electrocardiogram (ECG) is an electrophysiologic signal that conveys signature biometric data about individuals. While this signal varies across time and with mental state, and is susceptible to noise artifact, this paper presents an approach for classifying individuals that is robust to these signal properties and noise sources. To show maximal information extraction, we take the discrete wavelet transform of a single lead ECG for feature extraction and identify individuals using a multiclass support vector machine. This approach utilizes temporal and spectral information to characterize ECG waveform morphology and timing with low computational cost. To demonstrate the effectiveness of this approach, records from the MIT-BIH normal sinus rhythm database are used to test subject identification, yielding high accuracy and positive predictive value.