Spatiotemporal chaos is an important hepatocellular carcinoma physical phenomenon which can be widely observed in physical systems, including Taylor�CCoquette flow, the atmosphere, lasers, and coupled-map lattices. However, asymmetric spatiotemporal chaos in biomedical systems has not received considerable investigation because of the complexity of biomedical systems and the limitation of measurement techniques. In the last decade, laryngeal pathology has been studied extensively from temporal perspectives.5, 6, 7, 8, 9, 10, 11, 12, 13 There is a lack of understanding of the asymmetric spatiotemporal aspect of disordered voice production from laryngeal pathologies. In this study, we applied measurement techniques of high-speed imaging and analysis based on spatiotemporal perspectives that were important for the investigation of complex spatiotemporal behaviors in laryngeal pathologies.

The results showed that asymmetric spatiotemporal chaos of pathological vocal folds may play an important role in understanding the mechanisms of vocal disorders from the laryngeal pathologies of vocal mass lesion and asymmetries. This study examines the potential contributions of spatiotemporal chaos to the understanding of pathological disorders, which may be clinically important to developing new methods for the further assessment and diagnosis of laryngeal diseases from high-speed imaging. ACKNOWLEDGMENTS This study was supported by NIH Grant Nos. 1-RO1DC006019 and 1-RO1DC05522 from the National Institute of Deafness and other Communication Disorders.
Epilepsy is the second most common neurological disorder, second only to stroke.

Epileptic seizures often occur without warning, may be associated with loss of consciousness and violent tremors, and significantly degrade quality of life for those suffering from epilepsy. The brain activity that gives rise to seizures can be monitored through electrodes on the scalp or in direct contact with the brain. This activity shows certain patient-specific stereotypical features, which may be detectable before the onset of behavioral manifestations, and this activity frequently appears more ��rhythmic�� than background brain activity. These rhythmic signals frequently consist of repetitions of similar waveform patterns. In this paper, we describe a technique for detecting this type of rhythmic signal, which is derived from a time series analysis method for detecting unstable periodic orbits.

Accurate detection of rhythmic signals, a subset of the vast variety of anomalous waveforms associated with epilepsy, may provide valuable information to benefit and improve implantable medical devices being developed to detect and disrupt epileptic signals. INTRODUCTION In the United States, epileptic seizures affect about 1% of the entire population. The abnormal brain activity associated Dacomitinib with seizures can be monitored via scalp (EEG) or intracranial electrodes (ECoG).