We sought to determine the type of personality disorder cluster associated with patients with nonepileptic psychogenic seizures (NES) compared with that of patients with epileptic seizures (ES). Consecutive adult patients admitted for video/EEG monitoring found to have NES were compared with a simultaneously admitted patient with confirmed epilepsy. Personality was assessed using the Structured Clinical Interview for DSM-IV-TR Axis II Personality Disorders. Personality disorders were then divided into personality clusters described in the DSM-IV-TR: A = paranoid, schizotypal, schizoid; B = borderline, histrionic, antisocial, narcissistic; or C = avoidant, dependent, obsessive-compulsive. Thirteen of 16 patients with NES and 12 of 16 patients with ES met criteria for personality disorders. Patients with NES were more likely to meet criteria for a personality disorder in Cluster A or B, compared with patients with ES, who were more likely to have Cluster C personality disorders (chi(2) test, P=0.007). We propose that the personality traits of patients with NES contribute to the development of nonepileptic psychogenic seizures. However, the large proportion of patients with ES with Cluster C personality disorders was unexpected, and further, for the patients with epilepsy, the direction of the association of their personality traits with the development of epilepsy is unknown.
"The prevalence of borderline personality disorder has also shown to be significantly higher in patients with PNES than in either those with epilepsy or healthy controls  . Some common traits observed in both PNES and borderline personality disorder subjects include higher prevalence rates of sexual trauma, PTSD, dissociative disorders, somatoform disorders, depressive disorders, and suicide attempts     . Several behavioral and emotional traits of borderline personality disorder have been observed in patients with PNES. "
[Show abstract][Hide abstract] ABSTRACT: Intelligent control and estimation of power electronic systems by
fuzzy logic and neural network techniques show tremendous promise for
the future. The paper discusses primarily the work done in this area in
the University of Tennessee Power Electronics Laboratory. Altogether
seven projects are discussed These are: (1) fuzzy controlled DC motor
drive, (2) induction motor drive with fuzzy efficiency optimizer, (3)
fuzzy logic based control of wind generation system, (4) AC machine
temperature and winding resistance estimation by fuzzy logic, (3) neural
network based feedback signal estimation of AC drive, (6) neuro-fuzzy
efficiency optimization of induction motor drive, and (7) waveform
estimation by neural network
Electric Machines and Drives Conference Record, 1997. IEEE International; 06/1997
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