Discrete Wavelet Transform Applied on Personal Identity Verification With Ecg Signal

Abstract

The main purpose of this study is to present a novel personal authentication approach with the electrocardiogram (ECG) signal. The electrocardiogram is a recording of the electrical activity of the heart and the recorded signals can be used for individual verification because ECG signals of one person are never the same as those of others. The discrete wavelet transform was applied for extracting features that are the wavelet coefficients derived from digitized signals sampled from one-lead ECG signal. By the proposed approach applied on 35 normal subjects and 10 arrhythmia patients, the verification rate was 100% for normal subjects and 81% for arrhythmia patients. Furthermore, the performance of the ECG verification system was evaluated by the false acceptance rate (FAR) and false rejection rate (FRR). The FAR was 0.83% and FRR was 0.86% for a database containing only 35 normal subjects. When 10 arrhythmia patients were added into the database, FAR was 12.50% and FRR was 5.11%. The experimental results demonstrated that the proposed approach worked well for normal subjects. For this reason, it can be concluded that ECG used as a biometric measure for personal identity verification is feasible.

Heartbeat biometrics: a sensing system perspective

“…Chiu et al (2009) used energy attributes of ECGs computed from wavelets to classify heartbeats between normal patients and those with cardiac maladies. Palaniappan and Krishnan (2004) moved a window across the segmented heartbeats and generated textures for each window position.…”

Biometric Identification Using JPEG2000 Compressed ECG Signals

“…However, these biometrics either cannot provide reliable performance or not robust enough against falsification. Recent studies have suggested the possibility of using electrocardiogram (ECG) as a new biometric modality for person identification [2]- [9]. ECG signal is a recording of the electrical activity of the human heart, which is individual-specific in the sense of amplitude and time durations of the fiducial points.…”

“…Of these, ECG as a biometric based on a single channel is the most studied [3]- [9]. Based on the features that are extracted from ECG signals, we can further classify ECG biometrics as either fiducial points dependent [2]- [5] or independent [6]- [9]. Fiducial-based approaches rely on local features linked to the peak and time durations of the P-QRS-T waves.…”

“…To better exploit both intra-beat and inter-beat correlations, 2-D compression algorithms have been proposed by converting ECG signals into data arrays and then applying vector quantization [13] or the JPEG2000 image coding standard [14]. Irrespective of the underlying method used for data compression, compressed ECG imposes a new challenge for person identification as most existing algorithms have implicitly considered that biometric features are extracted from raw ECG signals [2]- [9]. Full decompression is then required to convert compressed data into ECG signals prior to feature extraction.…”

“…It is expected that considering the ECG as images and then applying the JPEG2000 will lead to better results. As the discrete wavelet transform (DWT) is an embedded part of the JPEG2000, and DWT itself is one of the best features for ECG biometrics [8], [9], working in DWT domain remains to be the most promising area for compressed ECG based biometric.…”