A New Method Detecting Abrupt Change Base on Moving Cut Data-Permutation Entropy

Monitoring Autonomic and Central Nervous System Activity by Permutation Entropy during Short Sojourn in Antarctica

“…However, PE was the sole indicator of changing heartbeat dynamics when the subjects engaged in the space quantity perception test, and this finding was replicable in all three recordings. This finding emphasises the notion that PE analysis of short HRV recordings has the potential to outweigh time and frequency domain HRV parameters in monitoring psycho-physiological transitions [ 21 , 38 ]. In this particular case of HRV monitoring, PE analysis was able to capture the transition to a more irregular and constricted heartbeat interval sequence (see Figure 2 ), whereas the time and frequency domain analysis provided only slight and non-significant hints.…”

“…Both the challenges and advantages of PE should be tested by: (i) Extending the ordinal pattern statistics to the spatial domain, where analysis of the dynamical complexity of spatio-temporal systems by PE may be effective in large datasets [ 60 ]; (ii) Assessment of information transfer and coupling between time-series by PE and derived analytical tools [ 61 , 62 ], especially addressing the direction of coupling [ 63 ]; (iii) Examination of the effectiveness of ordinal pattern statistics in detecting non-linear transitions and pattern changes in time-series [ 21 , 38 ], especially for practical applications [ 64 ]; and (iv) Machine learning and data classification applications using deep learning algorithms [ 65 ].…”