Subjective social status, objective socioeconomic status, and cardiovascular risk in women.

Objective: Subjective perceptions of personal social status may relate to health beyond the effects of objective socioeconomic status (SES). The authors examined the relationship between subjective social status (SSS) and psychosocial, behavioral, and physical cardiovascular risk factors in middle-aged women. Design: Ninety-two women (90.2% White) completed ladder-based, pictorial self-report measures of SSS relative to others in their community and in the United States. Psychosocial measures of depression, anxiety, pessimism, stress, and social support and behavioral risk factors of fruit and vegetable consumption, leisure physical activity, and body-mass index were obtained. In addition, women underwent measurement of clinic blood pressure and assessment of daytime ambulatory systolic and diastolic blood pressure (SBP; DBP) over 2 consecutive days. Results: Community SSS was significantly inversely related to anxiety, pessimism, stress, and daytime ambulatory DBP after controlling for objective SES and U.S. SSS. Women with lower U.S. SSS showed less healthy dietary and exercise behaviors and, contrary to predictions, lower clinic and ambulatory DBP. Conclusion: This study provides additional evidence that perceptions of one's position in the social hierarchy could have important health implications beyond the impact of objective SES. Further, the cardiovascular risk implications of perceived community versus U.S. social status appear to be distinct. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Detection and extraction of periodic noises in audio and biomedical signals using Kalman filter

This paper studies the subject of adaptive noise cancelation using the Kalman filtering technique to achieve high precision and fast convergence. It is shown that the Kalman filter can successfully be designed to detect and extract periodic noises which may be constituted of different sinusoidal components with possibly unknown and/or time-varying frequencies. This highlights the feature of Kalman filter in synthesizing periodic noises in the time-domain which is not possible using Fourier-based methods such as DFT. Usefulness of the method is discussed in the context of two examples: active cancelation of periodic noises from audio waveforms and filtering of electrocardiogram measurements.