Methods for transition toward computer assisted cognitive examination.

INTRODUCTION: We present a software framework which enables the extension of current methods for the assessment of cognitive fitness using recent technological advances. BACKGROUND: Screening for cognitive impairment is becoming more important as the world's population grows older. Current methods could be enhanced by use of computers. Introduction of new methods to clinics requires basic tools for collection and communication of collected data. OBJECTIVES: To develop tools that, with minimal interference, offer new opportunities for the enhancement of the current interview based cognitive examinations. METHODS: We suggest methods and discuss process by which established cognitive tests can be adapted for data collection through digitization by pen enabled tablets. We discuss a number of methods for evaluation of collected data, which promise to increase the resolution and objectivity of the common scoring strategy based on visual inspection. By involving computers in the roles of both instructing and scoring, we aim to increase the precision and reproducibility of cognitive examination. RESULTS: The tools provided in Python framework CogExTools available at http://bsp. brain.riken.jp/cogextools/ enable the design, application and evaluation of screening tests for assessment of cognitive impairment. The toolbox is a research platform; it represents a foundation for further collaborative development by the wider research community and enthusiasts. It is free to download and use, and open-source. CONCLUSION: We introduce a set of open-source tools that facilitate the design and development of new cognitive tests for modern technology. We provide these tools in order to enable the adaptation of technology for cognitive examination in clinical settings. The tools provide the first step in a possible transition toward standardized mental state examination using computers.

Sleep stage transitions in healthy humans altered by central monoaminergic antagonist.

OBJECTIVES: Sleep stage transitions constitute one of the key components of the dynamical aspect of sleep. However, neural mechanisms of sleep stage transitions have not, to date, been fully elucidated. We investigate the effects of administrating risperidone, a central serotonergic and dopaminergic antagonist, on sleep stage transitions inhumans, and also on ultradian rapid-eye-movement (REM) sleep rhythms. METHODS: Ten healthy young male volunteers (age: 22 ± 3.7 years) participated in this study. The subjects spent three nights in a sleep laboratory. The first was the adaptation night, and the second was the baseline night. On the third night, the subjects received risperidone (1 mg tablet) 30 min before the polysomnography recording. We measured and investigated transition probabilities between waking, REM and non-REM (stages I-IV) sleep stages. RESULTS: We found that the probability of transition from stage II to stage III was significantly greater for the risperidone night than for the baseline night. We also found that risperidone administration prolonged REM-onset intervals, when compared to the baseline night. CONCLUSIONS: We demonstrate that central serotonergic and/or dopaminergic neural transmissions are involved in the regulation of sleep stage transitions from light (stage II) to deep (stage III) sleep, and also in determining ultradian REM sleep rhythms.

Long-range correlated glucose fluctuations in diabetes.

OBJECTIVES: Our objective is to investigate diabetes-related alteration of glucose control in diurnal fluctuations in normal daily life by detrended fluctuation analysis (DFA). METHODS: The fluctuations of glucose of 12 non-diabetic subjects and 15 diabetic patients were measured using a continuous glucose monitoring system (CGMS) over a period of one day. The glucose data was calculated by the DFA method, which is capable of revealing the presence of long-range correlations in time series with inherent non-stationarity. RESULTS: Compared with the non-diabetic subjects, the mean glucose level and the standard deviation are significantly higher in the diabetic group. The DFA exponent alpha is calculated, and glucose time series are searched for the presence of negatively (0.5 < alpha < 1.5) or positively (1.5 < alpha) correlated fluctuations. A crossover phenomenon, i.e. a change in the level of correlations, is observed in the non-diabetic subjects at about two hours; the net effects of glucose flux/reflux causing temporal changes in glucose concentration are negatively correlated in a "long-range" (> two hours) regime. However, for diabetic patients, the DFA exponent alpha = 1.65 +/- 0.30, and in the same regime positively correlated fluctuations are observed, suggesting that the net effects of the flux and reflux persist for many hours. CONCLUSIONS: Such long-range positive correlation in glucose homeostasis may reflect pathogenic mechanisms of diabetes, i.e., the lack of the tight control in blood glucose regulation. Using modern time series analysis methods such as DFA, continuous evaluation of glucose dynamics could promote better diagnoses and prognoses of diabetes and a better understanding of the fundamental mechanism of glucose dysregulation in diabetes.

"Mobile Nurse" platform for ubiquitous medicine.

OBJECTIVES: We introduce "Mobile Nurse" (MN) - an emerging platform for the practice of ubiquitous medicine. METHODS: By implementing in a dynamic setting of daily life the patient care traditionally provided by the clinical nurses on duty, MN aims at integral data collection and shortening the response time to the patient. MN is also capable of intelligent interaction with the patient and is able to learn from the patient's behavior and disease sign evaluation for improved personalized treatment. RESULTS: In this paper, we outline the most essential concepts around the hardware, software and methodological designs of MN. We provide an example of the implementation, and elaborate on the possible future impact on medical practice and biomedical science research. CONCLUSIONS: The main innovation of MN, setting it apart from current tele-medicine systems, is the ability to integrate the patient's signs and symptoms on site, providing medical professionals with powerful tools to elucidate disease mechanisms, to make proper diagnoses and to prescribe treatment.

Autonomic imbalance induced breakdown of long-range dependence in healthy heart rate.

OBJECTIVES: The investigation of the relation between the long-range correlation property of heart rate and autonomic balance. METHODS: An investigation of the fractal scaling properties of heart rate variability was carried out by using detrended fluctuation analysis (DFA). Eleven healthy subjects were examined for two consecutive days, which included usual daily activity, strenuous prolonged experimental exercise, and sleep. We also considered two patient groups with autonomic dysfunction characterized by selective sympathetic and parasympathetic dominance. RESULTS: Robust long-range dependence in heart rate is observed only in the state of usual daily activity, characterized by normal heart rate typical of balanced autonomic sympathetic and parasympathetic regulation. This confirms the previously postulated behavioral independence of heart rate regulation, but reveals that the occurrence of 1/f, long-range dependence is restricted to only the state of autonomic balance. Both the sympathetic dominant high heart rate state, realized during strenuous experimental exercise, and the parasympathetic dominant low heart rate state, prevalent in (deep) sleep, are characterized by uncorrelated, near white-noise-like scaling, lacking long-range dependence. CONCLUSION: Remarkably, the breakdown of the long-range correlations observed in healthy heart rate in the states of sympathetic and parasympathetic dominance is in stark contrast to the increased correlations which have previously been observed in neurogenic parasympathetic and sympathetic dominance in patients suffering from primary autonomic failure and congestive heart failure, respectively. Our findings further reveal the diagnostic capabilities of heart rate dynamics, by differentiating physiological healthy states from pathology.

Multifractality in human heartbeat dynamics.

There is evidence that physiological signals under healthy conditions may have a fractal temporal structure. Here we investigate the possibility that time series generated by certain physiological control systems may be members of a special class of complex processes, termed multifractal, which require a large number of exponents to characterize their scaling properties. We report on evidence for multifractality in a biological dynamical system, the healthy human heartbeat, and show that the multifractal character and nonlinear properties of the healthy heart rate are encoded in the Fourier phases. We uncover a loss of multifractality for a life-threatening condition, congestive heart failure.