ECG personal identification in subspaces using radial basis neural networks

Biometric Applications of One-Dimensional Physiological Signals - Electrocardiograms

“…This section provides a detailed review of research geared toward the usefulness of ECGs as biometric indicators. Since the first such effort was reported in 1999 (Biel, et al, 1999), approximately 20 different groups have researched this interesting area (Israel, et al, 2005;Chan, et al, 2008;Biel, et al, 2001;Zhang & Wei, 2006;Biel, et al, 1999;Boumbarov, et al, 2009;Chan, et al, 2006;Chiu, et al, 2008;Fatemian & Hatzinakos, 2009;Gahi, et al, 2008;Israel, et al, 2003;Kim, et al, 2005;Kyoso, 2003;Kyoso & Uchiyama, 2001;Micheli-Tzanakou, et al, 2009;Nasri, et al, 2009;Plataniotis, et al, 2006;Shen, et al, 2002;Sufi & Khalil, 2008;Wang, et al, 2006;Yao & Wan, 2010) -see Table 1. Most of these published works use a similar approach to present research findings.…”

“…• ECG Data Sources: Multiple groups experimentally acquired ECG data from volunteers (Biel, et al, 1999(Biel, et al, 2001Chan, et al, 2006;Chan, et al, 2008;Gahi, et al, 2008;Israel, et al, 2005;Israel, et al, 2003;Kim, et al, 2005;Kyoso, 2003;Kyoso & Uchiyama, 2001;Sufi & Khalil, 2008;Wang, et al, 2006;.…”

“…• Selection of Classification Features: Most investigators assess time domain features (e.g., time intervals between P, Q, R, S, and T waves, along with their amplitudes) (Biel, et al, 2001;Boumbarov, et al, 2009;Gahi, et al, 2008;Israel, et al, 2005;Kyoso, 2003;Z. Zhang & Wei, 2006) and angle information (Singh & Gupta, 2008).…”

“…Others believe that post-transform features are more distinctive and will therefore improve identification performance. For example, wavelet transformation was used in (Chan, et al, 2006;Chan, et al, 2008;Chiu, et al, 2008; to find the wavelet coefficients and distances in the wavelet domain that optimally quantify the similarity between two ECGs. Autocorrelation coefficients are a third type of statistical feature under investigation (Agrafioti & Hatzinakos, 2008a;Plataniotis, et al, 2006).…”

“…While most of these approaches used variations of a "distance" concept (e.g., Euclidean distance (Israel, et al, 2003;Plataniotis, et al, 2006) or Mahalanobis' distance (Kyoso, 2003;Kim, et al, 2005) to quantify the similarities between the unknown data and the waveforms enrolled in the database, the classification algorithms they adopted were different, including (a) classic linear discriminate analysis (LDA) (Agrafioti & Hatzinakos, 2008a;Chan, et al, 2008;Kim, et al, 2005;Kyoso, 2003;Wang, et al, 2006), (b) neural networks (Saechia, et al, 2005;Shen, et al, 2002;Boumbarov, et al, 2009), and/or (c) voting after initial results were available from the first classification level (Israel, et al, 2005;Agrafioti & Hatzinakos, 2008b). Rather than use the intuitive distance concept, other investigators employed a sequential approach that employs a hidden Markov model (Boumbarov, et al, 2009) and a probabilistic, Bayesian-theorem-based approach (Z. Zhang & Wei, 2006 (Biel, et al, 2001;Agrafioti & Hatzinakos, 2008b) or (2) using methods such as principle component analysis (PCA) Sufi & Khalil, 2008;Z.…”