Pattern Analysis of Personalized ECG Signal by Q, R, S Peak Variability

Abstract

Several algorithms have been developed to classify arrhythmia which rely on specific ECG(Electrocardiogram) database. Nevertheless personalized difference of ECG signal exist, performance degradation occurs because of carrying out diagnosis by general classification rule. Most methods require accurate detection of P-QRS-T point, higher computational cost and larger processing time. But it is difficult to detect the P and T wave signal because of person's individual difference. Therefore it is necessary to classify the pattern by analyzing personalized ECG signal and extracting minimal feature. Thus, QRS pattern Analysis of personalized ECG Signal by Q, R, S peak variability is presented in this paper. For this purpose, we detected R wave through the preprocessing method and extract eight feature by amplitude and phase variability. Also, we classified nine pattern in realtime through peak and morphology variability. PVC, PAC, Normal, LBBB, RBBB, Paced beat arrhythmia is evaluated by using 43 record of MIT-BIH arrhythmia database. The achieved scores indicate the average of 93.72% in QRS pattern detection classification.

Personal identification study for touchable devices with ECG

“…TDA contains a mean heart rate (MHR), standard deviation of the NN(R‐R) intervals (SDNN), root mean square of the successive differences (RMSSD), and proportion of NN50 divided by the total number of NN(R‐R) intervals (pNN50). Also, in the ECG signal, there are P, Q, R, S, T, PR segment, and ST segment, and according to their amplitudes around the R peak, Q, which is located on the left side of the R peak and S, which is positioned on the right side of the R peak, differs in every cycle …”