Abstract
This paper presents an interval-based LDA (Linear Discriminant Analysis) algorithm for individual verification using ECG (Electrocardiogram). In this algorithm, at first, unwanted noise and power-line interference are removed from the ECG signal. Then, the autocorrelation profile (ACP) of the ECG signal, which is a mathematical representation of the degree of similarity between a given time series and a lagged version of itself over successive time intervals, is calculated. Finally, the interval-based LDA algorithm is applied to extract unique individual feature vectors that represent distance and angle characteristics on short ACP segments. These feature vectors are used during the processes of enrollment and verification of individual identification. To validate our algorithm, we conducted experiments using the MIT-BIH ECG and achieved EERs (Equal Error Rate) of 0.143%, showing that the proposed algorithm is practically effective and robust in verifying the individual’s identity.
Highlights
Since the invention of electrocardiogram (ECG) recording in 1903 [1], a considerable number of studies using the electrocardiogram have been performed in the fields of diagnosing cardiac-related diseases [2,3,4,5], detecting sleep apnea [6], monitoring driver drowsiness [7], and measuring blood pressure [8], because of its advantage of noninvasive convenience
We propose an interval-based Linear Discriminant Analysis (LDA) algorithm, a type of hybrid algorithm that combines the complementary strengths of both fiducial dependent and fiducial independent features, and thereby is able to achieve higher accuracy of identification
To minimizethe thenegative negativeeffect effect random noises, applied scheme proposed by Sornmo in our study, in which noisy signals are passed through the bandpass filters (BPF)
Summary
Since the invention of electrocardiogram (ECG) recording in 1903 [1], a considerable number of studies using the electrocardiogram have been performed in the fields of diagnosing cardiac-related diseases [2,3,4,5], detecting sleep apnea [6], monitoring driver drowsiness [7], and measuring blood pressure [8], because of its advantage of noninvasive convenience. The fiducial dependent approach relies on local features, such as time duration and amplitude differences between specific points of interest in an ECG, so it is less affected by heart rate variations It has the possibility of missing the overall morphological information that might be useful in identifying individuals. The Pulse Active Ratio uses pulse width modulation (PWM) to generate new ECG feature vectors and thereby can adapt to changes in heart rate Such fiducial independent approaches require tremendous computational effort because the parameters should be generated and compared for every person listed in the database during the verification stage. The main characteristics of the proposed algorithm are as follows: (i) the use of an autocorrelation profile (ACP) of ECG signal; (ii) the use of short segments of the ACP to extract the interval-based feature vectors, which can be updated every 5 s, showing higher adaptability to intra-subject variation;.
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