BRIEF REPORTS PHYSICAL REVIEW E84, 062901(2011) the time length(the higher the sampling frequency), the more 100% reliable the identification PSE Another more challenging problem is to predict the onset of 90% SP Vf based on the ventricular time series data. we have observed that the different dominant motifs of c and a work as a reliable indicator in distinguishing a ventricular fibrillation from the 80% normal sinus rhythm of a subject. Therefore, when a subject experiences a transition from the NSr physiological state to 70% he vF state, we conjecture that the percentage of motif C decreases while that of motif A increases significantly during the transition, with which we put a step forward in further predicting the onset of VE. To design the onset predictor of VF, we set each data episode Sampling Frequency [Hz] as five successive ecg beats in a time series Therefore after generating an associated network for each beat with the en. FIG. 4.( Color online) The reliability measures of sensitivity (SE) visibility graph method, we calculate M( as the difference episodes with different sampling frequencies of 25, 50, 100, 125, associated network for each beat i, i= 1, 2, 3, 4, 5, yielding and 250 Hz, respectively. All episodes are the same as those data the mean difference in a 5-beat episode as M=2_"o in Table I with the same time length of 10 s but different sampling We first target the subject of CU15 in the CUDB database, having 405 s of nsr data before the onset of VF. We examine the Cu15 data in every 60-s interval and calculate M FP is the number of false positive decisions(an NSR case 5-beat episode as shown in Fig. 6. We clearly observe that at being wrongly recognized as a VF one the 5-beat NSR episode 405 s before the VF onset [Fig. 6, We have found that motif C dominates in the associated inset(a)), the corresponding M=37.84%. While the one at etworks of NSR time series, while motif A dominates in those the 5-beat episode 105 s before the VF onset[Fig. 6, inset(b) of VF time series. Here we conjecture the dominance of motif is 3.99%, and that of after the vF onset is -13%[Fig. 6, A or motif C as an indicator in distinguishing the VF time inset(c)). Therefore, we set the alarm threshold as 5%and series from the NSR electrocardiogram. With the statistical design the onset predictor of VF as follows data in Table I covering total 35 subjects of the database, we if M<5%, the onset predictor predicts that a Vf will calculate that the sensitivity and specificity are 96. 5% and happen 89.5%(where TP= 193, TN= 179, FP= 21, Fn= 7 for In the Cudb database there are a total of 40 NSR records implicity), respectively, indicating a very effective indicator before the onset of vF with 32 subjects involved, and the data in identifying a ventricular fibrillation patient from a healthy We also consider the dependence of the above reliability results on different sampling frequencies and time lengths. As shown in Figs. 4 and 5, we clearly observe that the longer [onset predictor alarm ! 100% 95% 20% 90% 85% 80% 40534528522516510545 Time before VF [second] 75% FIG. 6.(Color online) The mean difference M between the percentages of motif C and motif A in associated networks generated Time Length [Second] from the NSr 5-beat episode before the onset of VF in CU15. The whole data series is 405 s before the VF onset, and every 5-beat FIG. 5.(Color online) The reliability measures of sensitivity (se) episode is examined in 60-s intervals. Inset (a): The 5-beat NSr and specificity (SP) of 200 NSR and 200 ventricular fibrillation episode is 405 s before the onset of VF; (b)the 5-beat NSR episode episodes with different time lengths of 2, 5, 8, and 10 s, respectively. is 105 s before the onset of VF; and(c)the VF episode is 0.4 s after All episodes are the same as those data in Table I with the same the onset of VE. The(red) dashed line is the alarm threshold of our sampling frequency 250 Hz but different time lengths. 62901-3BRIEF REPORTS PHYSICAL REVIEW E 84, 062901 (2011) FIG. 4. (Color online) The reliability measures of sensitivity (SE) and specificity (SP) of 200 NSR and 200 ventricular fibrillation episodes with different sampling frequencies of 25, 50, 100, 125, and 250 Hz, respectively. All episodes are the same as those data in Table I with the same time length of 10 s but different sampling frequencies. FP is the number of false positive decisions (an NSR case being wrongly recognized as a VF one). We have found that motif C dominates in the associated networks of NSR time series, while motif A dominates in those of VF time series. Here we conjecture the dominance of motif A or motif C as an indicator in distinguishing the VF time series from the NSR electrocardiogram. With the statistical data in Table I covering total 35 subjects of the database, we calculate that the sensitivity and specificity are 96.5% and 89.5% (where TP = 193, TN = 179, FP = 21, FN = 7 for simplicity), respectively, indicating a very effective indicator in identifying a ventricular fibrillation patient from a healthy subject. We also consider the dependence of the above reliability results on different sampling frequencies and time lengths. As shown in Figs. 4 and 5, we clearly observe that the longer FIG. 5. (Color online) The reliability measures of sensitivity (SE) and specificity (SP) of 200 NSR and 200 ventricular fibrillation episodes with different time lengths of 2, 5, 8, and 10 s, respectively. All episodes are the same as those data in Table I with the same sampling frequency 250 Hz but different time lengths. the time length (the higher the sampling frequency), the more reliable the identification. Another more challenging problem is to predict the onset of VF based on the ventricular time series data. We have observed that the different dominant motifs of C and A work as a reliable indicator in distinguishing a ventricular fibrillation from the normal sinus rhythm of a subject. Therefore, when a subject experiences a transition from the NSR physiological state to the VF state, we conjecture that the percentage of motif C decreases while that of motif A increases significantly during the transition, with which we put a step forward in further predicting the onset of VF. To design the onset predictor of VF, we set each data episode as five successive ECG beats in a time series. Therefore, after generating an associated network for each beat with the visibility graph method, we calculate M(i) as the difference between the percentages of motif C and motif A in the associated network for each beat i,i = 1, 2, 3, 4, 5, yielding the mean difference in a 5-beat episode as M = 5 i=1 M(i) 5 . We first target the subject of CU15 in the CUDB database, having 405 s of NSR data before the onset of VF. We examine the CU15 data in every 60-s interval and calculate M of each 5-beat episode as shown in Fig. 6. We clearly observe that at the 5-beat NSR episode 405 s before the VF onset [Fig. 6, inset (a)], the corresponding M = 37.84%. While the one at the 5-beat episode 105 s before the VF onset [Fig. 6, inset (b)] is 3.99%, and that of after the VF onset is −13% [Fig. 6, inset (c)]. Therefore, we set the alarm threshold as 5% and design the onset predictor of VF as follows: if M < 5%, the onset predictor predicts that a VF will happen. In the CUDB database there are a total of 40 NSR records before the onset of VF with 32 subjects involved, and the data (a) (b) (c) FIG. 6. (Color online) The mean difference M between the percentages of motif C and motif A in associated networks generated from the NSR 5-beat episode before the onset of VF in CU15. The whole data series is 405 s before the VF onset, and every 5-beat episode is examined in 60-s intervals. Inset (a): The 5-beat NSR episode is 405 s before the onset of VF; (b) the 5-beat NSR episode is 105 s before the onset of VF; and (c) the VF episode is 0.4 s after the onset of VF. The (red) dashed line is the alarm threshold of our designed onset predictor. 062901-3