introduction: in this paper, a novel complexity measure is proposed to detect dynamical changes in nonlinear systems using ordinal pattern analysis of time series data taken from the system. epilepsy is considered as a dynamical change in nonlinear and complex brain system. the ability of the proposed measure for characterizing the normal and epileptic eeg signals when the signal is short or is...

background: this paper proposes a new emotional stress assessment system using multi-modal bio-signals. electroencephalogram (eeg) is the reflection of brain activity and is widely used in clinical diagnosis and biomedical research. methods: we design an efficient acquisition protocol to acquire the eeg signals in five channels (fp1, fp2, t3, t4 and pz) and peripheral signals such as blood volu...

Most of the current epileptic seizure prediction algorithms require much prior knowledge of a patient’s pre-seizure electroencephalogram (EEG) patterns. They are impractical to be applied to a wide range of patients due to a very high inter-individual variability of EEG patterns. This paper proposes an adaptive prediction framework, which is capable of accumulating knowledge of pre-seizure EEG ...

We consider the problem of detecting temporal changes in the functional state of human subjects due to varying levels of cognitive load using real-time psychophysiological data. The proposed approach relies on monitoring several channels of electroencephalogram (EEG) and electrooculogram (EOG) signals using the methods of statistical process control. It is demonstrated that control charting met...

The diagnostic and prognostic value of dynamic video-electroencephalogram (V-EEG) monitoring in children with epilepsy were investigated. From February 2014 to February 2016, in total 200 children with epilepsy were selected during their stay in the First Hospital of Qinghuangdao and were randomly allocated to a dynamic and a conventional V-EEG monitoring group (n=100). The detection rate of ep...