Sleep stability and cognitive function in an Arctic Martian analogue.

INTRODUCTION: Human performance is affected by sleep disruption and sleep deprivation can critically affect mission outcome in both spaceflight and other extreme environments. In this study, the seven-person crew (four men, three women) lived a Martian sol (24.65 h) for 37 d during a long-term stay at the Flashline Mars Arctic Research Station (FMARS) on Devon Island, Canada. Crewmembers underwent cardiopulmonary monitoring for signs of circadian disruption and completed a modified Pittsburgh Sleep Diary to monitor subjective fatigue. Crewmembers underwent cognitive testing to identify the effects, if any, of sleep disruption upon cognitive skill. METHODS: A Martian sol was implemented for 37 d during the Arctic mission. Each crewmember completed an adapted version of the Pittsburgh Sleep Diary in tandem with electrocardiograph (ECG) cardiopulmonary monitoring of sleep by the Cardiac Adapted Sleep Parameters Electrocardiogram Recorder (CASPER). Crewmembers also underwent cognitive testing during this time period. RESULTS: Sleep diary data indicate improvement in alertness with the onset of the sol (fatigue decreasing from 5.1 to 4.0, alertness increasing from 6.1 to 7.0). Cardiopulmonary data suggest sleep instability, though trends were not statistically significant. Crewmember decision speed time scores improved from pre-Mars to Mars (average improving from 66.5 to 84.0%), though the remainder of cognitive testing results were not significant. DISCUSSION: While subjective data demonstrate improved sleep and alertness during the sol, objective data demonstrate no significant alteration of sleep patterns. There was no apparent cognitive decline over the course of the mission.

Analysis of low-dimensional radio-frequency impedance-based cardio-synchronous waveforms for biometric authentication.

Over the past two decades, there have been a lot of advances in the field of pattern analyses for biomedical signals, which have helped in both medical diagnoses and in furthering our understanding of the human body. A relatively recent area of interest is the utility of biomedical signals in the field of biometrics, i.e., for user identification. Seminal work in this domain has already been done using electrocardiograph (ECG) signals. In this paper, we discuss our ongoing work in using a relatively recent modality of biomedical signals-a cardio-synchronous waveform measured using a Radio-Frequency Impedance-Interrogation (RFII) device for the purpose of user identification. Compared to an ECG setup, this device is noninvasive and measurements can be obtained easily and quickly. Here, we discuss the feasibility of reducing the dimensions of these signals by projecting onto various subspaces while still preserving interuser discriminating information. We compare the classification performance using classical dimensionality reduction methods such as principal component analysis (PCA), independent component analysis (ICA), random projections, with more recent techniques such as K-SVD-based dictionary learning. We also report the reconstruction accuracies in these subspaces. Our results show that the dimensionality of the measured signals can be reduced by 60 fold while maintaining high user identification rates.

Biometric identification of cardiosynchronous waveforms utilizing person specific continuous and discrete wavelet transform features.

In this paper we explore how a Radio Frequency Impedance Interrogation (RFII) signal may be used as a biometric feature. This could allow the identification of subjects in operational and potentially hostile environments. Features extracted from the continuous and discrete wavelet decompositions of the signal are investigated for biometric identification. In the former case, the most discriminative features in the wavelet space were extracted using a Fisher ratio metric. Comparisons in the wavelet space were done using the Euclidean distance measure. In the latter case, the signal was decomposed at various levels using different wavelet bases, in order to extract both low frequency and high frequency components. Comparisons at each decomposition level were performed using the same distance measure as before. The data set used consists of four subjects, each with a 15 minute RFII recording. The various data samples for our experiments, corresponding to a single heart beat duration, were extracted from these recordings. We achieve identification rates of up to 99% using the CWT approach and rates of up to 100% using the DWT approach. While the small size of the dataset limits the interpretation of these results, further work with larger datasets is expected to develop better algorithms for subject identification.

Classification of cardiosynchronous waveforms by projection to a Legendre Polynomial sub-space.

UNLABELLED: The use of Radio Frequency Impedance Interrogation (RFII) is being investigated for use as a noninvasive hemodynamic monitoring system and in the capacity of a biometric identifier. Biometric identification of subjects by cardiosynchronous waveform generated through RFII technology could allow the identification of subjects in operational and potentially hostile environments. Here, the filtering methods for extracting a unique biometric signature from the RFII signal are examined, including the use of Cepstral analysis for dynamically estimating the filter parameters. METHODS: The projection of that signature to a Legendre Polynomial sub-space is proposed for increased class separability in a low dimensional space. Support Vector Machine (SVM) and k-Nearest Neighbor (k=3) classification are performed in the Legendre Polynomial sub-space on a small dataset. RESULTS: Both the k-Nearest Neighbor and linear SVM methods demonstrated highly successful classification accuracy, with 93-100% accuracy demonstrated by various classification methods. CONCLUSIONS: The results are highly encouraging despite the small sample size. Further analysis with a larger dataset will help to refine this process for the eventual application of RFII as a robust biometric identifier.

Preliminary feasibility analysis of remote subject identification during hemodynamic monitoring by Radio Frequency Impedance.

Non-contact, non-invasive monitoring of hemodynamic parameters would be ideal for medical monitoring in a variety of environments. Radio Frequency Impedance Interrogation (RFII) measures hemodynamic function via resonance frequency coupling to a hydrophilic protein molecule. While the application of this technology to hemodynamic monitoring has demonstrated initial success, this preliminary study examined the use of RFII for subject identification by waveform signal analysis, which would allow confirmation of the identity of a subject in an operational setting prior to rescue efforts. Preliminary results demonstrate an excellent recognition rate using the RFII signature and pattern classification. Each individual has a consistent pattern during the initial waveform identification period that is visually distinct from the other individuals in the data set. These results suggest that RFII may be of great utility in the pre-hospital triage setting for patient monitoring and for the rapid identification of subjects in the operational setting.