PurPose: Successful salvage of the threatened free flap is dependent upon prompt diagnosis of vascular occlusion and timely restoration of blood flow. Many monitoring systems can be used to augment clinical exam, but all suffer from drawbacks. The implantable venous Doppler offers rapid diagnosis of vascular compromise, but has a cumbersome transcutaneous wire and is reported to have high false positive rates largely due to inadvertent internal probe dislodgement. A wireless device would avoid these problems. This study describes the implementation of an entirely implanted Doppler sensor with wireless transmission of flow data in a pig femoral vein model.

Transcranial Doppler sonography was recently proposed as an approach for brain-machine interfaces. However, monitoring maximal cerebral blood flow velocity signals for extensive time periods can generate large volumes of data for processing. In this paper, a compressive sensing (CS) approach is proposed based on a time-frequency dictionary formed by modulated discrete prolate spheroidal sequences (MDPSS). To test the proposed scheme, we examined maximal cerebral blood flow velocity signals acquired from 20 healthy subjects during a resting state. The results of our analysis clearly depicted that these signals can be accurately reconstructed using only 30% and 50% of original samples. Hence, the proposed MDPSS-based CS approach is a valid tool for diminishing the number of acquired samples during brain-machine operations using transcranial Doppler sonography.

Nonstationarity relates to the variation over time of the statistics of a signal. Therefore, signals from practical applications that are realizations of nonstationary processes are difficult to represent and to process. In this article, we provide a comprehensive discussion of the asynchronous representation and processing of nonstationary signals using a time-frequency framework. Power consumption and type of processing imposed by the size of the devices in many applications motivate the use of asynchronous, rather than conventional synchronous, approaches. This leads to the consideration of nonuniform, signal-dependent level-crossing (LC) and asynchronous sigma delta modulator (ASDM)-based sampling. Reconstruction from a nonuniform sampled signal is made possible by connecting the sinc and the prolate spheroidal wave (PSW) functions?a more appropriate basis. Two decomposition procedures are considered. One is based on the ASDM that generalizes the Haar wavelet representation and is used for representing analog nonstationary signals. The second decomposer is for representing discrete nonstationary signals. It is based on a linear-chirp-based transform that provides local time-frequency parametric representations based on linear chirps as intrinsic mode functions (IMFs). Important applications of these procedures are the compression and processing of biomedical signals, as it will be illustrated in this article.

A recent rise of compressive sensing (CS) algorithms has prompted many questions about the analysis of such sensed signals. Specifically, calculating a time-frequency representation (TFR) of these signals is an open question. In this paper, we propose an approach for calculating TFRs of compressed sensed signals based on recently proposed CS algorithm using modulated discrete prolate spheroidal sequences (MDPSS). The results of our numerical analysis show that a visually reliable TFR of compressed sensed signals can be obtained using the proposed approach. Furthermore, these compressed sensed signals can also be used for accurate estimation of signal parameters such as the instantaneous frequency.