Tardive dyskinesia (TD) is a medication-induced hyperkinetic movement disorder, generally manifested as involuntary spasms or choreiform movements of the tongue, lower face and jaw, and limbs (lasting at least several weeks). It occurs after using neuroleptic medication for at least several months, causing irreversible injuries to the nerve system and significantly impacting patients′ daily function. This review summarizes recent research progress regarding TD risk factors, possible pathogenesis, clinical characteristics, assessment and identification, and treatment and management approaches. The goal is to raise awareness of TD to archive early prevention and identification, standardize treatment, and improve patients′ prognosis and quality of life.

Tardive dyskinesia (TD) is a medication-induced hyperkinetic movement disorder, generally manifested as involuntary spasms or choreiform movements of the tongue, lower face and jaw, and limbs (lasting at least several weeks). It occurs after using neuroleptic medication for at least several months, causing irreversible injuries to the nerve system and significantly impacting patients′ daily function. This review summarizes recent research progress regarding TD risk factors, possible pathogenesis, clinical characteristics, assessment and identification, and treatment and management approaches. The goal is to raise awareness of TD to archive early prevention and identification, standardize treatment, and improve patients′ prognosis and quality of life.

Since epileptic seizure is unpredictable and paroxysmal, an automatic system for seizure detecting could be of great significance and assistance to patients and medical staff. In this paper, a novel method is proposed for multichannel patient-specific seizure detection applying the earth mover's distance (EMD) in scalp EEG. Firstly, the wavelet decomposition is executed to the original EEGs with five scales, the scale 3, 4 and 5 are selected and transformed into histograms and afterwards the distances between histograms in pairs are computed applying the earth mover's distance as effective features. Then, the EMD features are sent to the classifier based on the Bayesian linear discriminant analysis (BLDA) for classification, and an efficient postprocessing procedure is applied to improve the detection system precision, finally. To evaluate the performance of the proposed method, the CHB-MIT scalp EEG database with 958 h EEG recordings from 23 epileptic patients is used and a relatively satisfactory detection rate is achieved with the average sensitivity of 95.65% and false detection rate of 0.68/h. The good performance of this algorithm indicates the potential application for seizure monitoring in clinical practice.
Classification ; Discriminant Analysis* ; Electroencephalography* ; Epilepsy* ; Humans ; Medical Staff ; Methods ; Scalp* ; Seizures ; Weights and Measures