Implementation of the ECG biometric identification by using Arduino Microprocessor

This paper proposes the method of implementing the ECG biometrics for person identification system by using the Arduino Microprocessor. The Lead-I ECG data had been self-setup and collected, then the achieved ECG features are investigated on the Arduino Uno R3 microprocessor and e-health sensor board. After that, the simulation processes for the identification process are begun with the selection Single Beat ECG and analyzed by the Continuous Wavelet Transform (CWT). Then RMS value of total energy of the wavelet coefficients of each P, QRS, and T segment is calculated. Next, the Fisher Linear discriminant analysis (FLDA) is applied to all set of RMS value for dimension reduction. Lastly the normalized Euclidean distance is implemented as the classifier. The experimental results demonstrate that: our proposed method is achieved the accuracy of classification with 96%.