B ecause stress is a leading cause of illness and disease and is so pervasive, there is an inherent need to be able to monitor stress in real time over extended periods. A real-time personal stress monitor would benefit individuals by providing continuous feedback about their stress levels and by helping their physicians to objectively evaluate stress exposure between visits. We are developing personal health monitors based on a wireless body area network (BAN) of intelligent sensors [1]. Individual monitors will be integrated into a distributed wireless system for synchronized monitoring of a group of subjects. This system could be used during the selection process and as part of a psychophysiological evaluation of military members undergoing intense training. We use measures of heart-rate variability (HRV) to quantify stress level prior to and during training as well as to predict stress resistance. This task requires reliable, high-precision instrumentation and synchronized measurements from a group of individuals over prolonged periods (days of training). Our preliminary results indicate that individuals who have better stress tolerance also exhibit significantly different patterns of HRV, both before and during stress exposure. These baseline differences in HRV are predictive of actual military and cognitive neuropsychological test performance scores assessed during and after stress exposure [1], [2]. During our preliminary investigations, we used a stressful component of aviation water survival training, the 9D5 Multi-place Underwater Egress Trainer, as our event for the whole group. The 9D5 is a reasonably realistic representation of a helicopter conducting an emergency landing, turning upside down, and sinking. Trainees report the 9D5 session as the most stressful training event during water survival training.
A novel approach for bidimensional empirical mode decomposition (BEMD) is proposed in this paper. BEMD decomposes an image into multiple hierarchical components known as bidimensional intrinsic mode functions (BIMFs). In each iteration of the process, two-dimensional (2D) interpolation is applied to a set of local maxima (minima) points to form the upper (lower) envelope. But, 2D scattered data interpolation methods cause huge computation time and other artifacts in the decomposition. This paper suggests a simple, but effective, method of envelope estimation that replaces the surface interpolation. In this method, order statistics filters are used to get the upper and lower envelopes, where filter size is derived from the data. Based on the properties of the proposed approach, it is considered as fast and adaptive BEMD (FABEMD). Simulation results demonstrate that FABEMD is not only faster and adaptive, but also outperforms the original BEMD in terms of the quality of the BIMFs.
Bidimensional empirical mode decomposition (BEMD) techniques are associated with high computation time and other artifacts because of the application of two dimensional (2D) scattered data interpolation methods. In this paper, order statistics filters are employed to get the upper and lower envelopes in the BEMD process, instead of the surface interpolation. Based on the achieved characteristics of the proposed approach, it is considered as fast and adaptive BEMD (FABEMD). Simulation results demonstrate that besides reducing the computation time, FABEMD outperforms the original BEMD in terms of the quality in some cases.
In this article, multiresolution analysis, specifically the discrete wavelet transform modulus-maxima (mod-max) method, is utilized for the extraction of mammographic mass shape features. These shape features are used in a classification system to classify masses as round, nodular, or stellate. The multiresolution shape features are compared with traditional uniresolution shape features for their class discriminating abilities. The study involved 60 digitized mammographic images. The masses were segmented manually by radiologists, prior to introduction to the classification system. The uniresolution and multiresolution shape features were calculated using the radial distance measure of the mass boundaries. The discriminating power of the shape features were analyzed via linear discriminant analysis (LDA). The classification system utilized a simple Euclidean metric to determine class membership. The system was tested using the apparent and leave-one-out test methods. The classification system when using the multiresolution and uniresolution shape features resulted in classification rates of 83% and 80% for the apparent and leave-one-out test methods, respectively. In comparison, when only the uniresolution shape features were used, the classification rates were 72 and 68% for the apparent and leave-one-out test methods, respectively.
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