Sleep electroencephalographic characteristics of the Cynomolgus monkey measured by telemetry.

Cynomolgus monkeys are widely used as models of diseases and in pre-clinical studies to assess the impact of new pharmacotherapies on brain function and behaviour. However, the time course of electroencephalographic delta activity during sleep, which represents the main marker of sleep intensity associated with recovery during sleep, has never been described in this non-human primate. In this study, telemetry implants were used to record one spontaneous 24-h sleep-wake cycle in four freely-moving Cynomolgus monkeys, and to quantify the time course of electroencephalographic activity during sleep using spectral analysis. Animals presented a diurnal activity pattern interrupted by short naps. During the dark period, most of the time was spent in sleep with non-rapid eye movement sleep/rapid eye movement sleep alternations and sleep consolidation profiles intermediate between rodents and humans. Deep non-rapid eye movement sleep showed a typical predominance at the beginning of the night with decreased propensity in the course of the night, which was accompanied by a progressive increase in rapid eye movement sleep duration. Spectral profiles showed characteristic changes between vigilance states as reported in other mammalian species. Importantly, delta activity also followed the expected time course of variation, showing a build-up with wakefulness duration and dissipation across the night. Thus, Cynomolgus monkeys present typical characteristics of sleep architecture and spectral structure as those observed in other mammalian species including humans, validating the use of telemetry in this non-human primate model for translational sleep studies.

Simple, quantitative body surface potential map parameters in the diagnosis of remote Q wave and non-Q wave myocardial infarction.

BACKGROUND: Body surface potential mapping has been shown to be a useful tool in the diagnosis and localization of remote non-Q wave and Q wave myocardial infarction, but human expertise is required to interpret the maps. OBJECTIVE: To identify quantitative body surface potential mapping parameters that could enable a computer-based diagnosis. METHODS: Body surface isopotential maps (63 unipolar leads) were recorded in 86 patients with remote Q wave and 71 patients with remote non-Q wave myocardial infarction. Twenty-four healthy adults served as control subjects. Myocardial infarctions were classified using standard electrocardiogram leads in the acute and chronic phases, and were validated by coronary angiography, ventriculography and thallium scintigraphy. RESULTS: Two simple quantitative parameters with high diagnostic power were identified: the time interval between the peak minimum and the peak maximum potentials (time-shift), and the ratio of these potentials (maximum to minimum ratio [max/min]). Both parameters showed significant differences between infarction patients and normal control subjects, and optimum cut-off values were determined using receiver operating characteristic curves (anterior infarction: time-shift of -4 ms or less, max/min of 0.6 or less; posterior infarction: time-shift of 8 ms or greater, max/min of 1.25 or greater). The sensitivities of the two parameters were 100% and 97%, and the specificities were 99% and 100%, respectively, in the anterior Q wave infarction group, compared with sensitivities of 88% and 100%, and specificities of 94% and 95%, respectively, in the posterior Q wave infarction group. In the anterior non-Q wave infarction group, sensitivity was 35% for both parameters, specificity was 100% for both parameters, and only infarctions associated with a low ejection fraction were detected, indicating that infarction size may influence the power of the tests. CONCLUSIONS: Time-shift and max/min are two new, simple, powerful parameters for infarction diagnosis and may also be suitable for automated, computer-based processing.