Neurophysiological correlates in Mild Cognitive Impairment detected using group Independent Component Analysis.

Alzheimer's disease is the most prevalent cause of dementia. Mild Cognitive Impairment (MCI) is defined as a grey area between intact cognitive functioning and clinical dementia. Electroencephalography (EEG) has been used to identify biomarkers in dementia. Currently, there is a great interest in translating the study from raw signals to signal generators, trying to keep the relationship with neurophysiology. In the current study, EEG recordings during an encoding task were acquired in MCI subjects and healthy controls. Data was decomposed using group Independent Component Analysis (gICA) and the most neuronal components were analyzed using Phase Intertrial Coherence (PIC) and Phase shift Intertrial Coherence (PsIC). MCI subjects exhibited an increase of PIC in the theta band, while controls showed increase in PsIC in the alpha band. Correlation between PIC and PsIC and clinical scales were also found. Those findings indicate that the methodology proposed based in gICA can help to extract information from EEG recordings with neurophysiological meaning.

A synchronization system for the analysis of biomedical signals recorded with different devices from mechanically ventilated patients.

Conducting research associated with mechanically ventilated patients often requires the recording of several biomedical signals to dispose of multiple sources of information to perform a robust analysis. This is especially important in the analysis of the relationship between pressure, volume and flow, signals available from mechanical ventilators, and other biopotentials such as the electromyogram of respiratory muscles, intrinsically related with the ventilatory process, but not commonly recorded in the clinical practice. Despite the usefulness of recording signals from multiple sources, few medical devices include the possibility of synchronizing its data with other provided by different biomedical equipment and some may use inaccurate sampling frequencies. Even thought a variant or inaccurate sampling rate does not affect the monitoring of critical patients, it restricts the study of simultaneous related events useful in research of respiratory system activity. In this article a device for temporal synchronization of signals recorded from multiple biomedical devices is described as well as its application in the study of patients undergoing mechanical ventilation with research purposes.