IEEE transactions on bio-medical engineering
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IEEE Trans Biomed Eng · Mar 2011
Time-varying autoregressive model-based multiple modes particle filtering algorithm for respiratory rate extraction from pulse oximeter.
We present a particle filtering algorithm, which combines both time-invariant (TIV) and time-varying autoregressive (TVAR) models for accurate extraction of breathing frequencies (BFs) that vary either slowly or suddenly. The algorithm sustains its robustness for up to 90 breaths/min (b/m) as well. ⋯ Furthermore, simulation examples show that the proposed algorithm remains accurate for SNR ratios as low as -20 dB. We are not aware of any other algorithms that are able to provide accurate TV BF over a wide range of respiratory rates directly from pulse oximeters.
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IEEE Trans Biomed Eng · Mar 2011
Low-power ultrawideband wireless telemetry transceiver for medical sensor applications.
An integrated CMOS ultrawideband wireless telemetry transceiver for wearable and implantable medical sensor applications is reported in this letter. This high duty cycled, noncoherent transceiver supports scalable data rate up to 10 Mb/s with energy efficiency of 0.35 nJ/bit and 6.2 nJ/bit for transmitter and receiver, respectively. A prototype wireless capsule endoscopy using the proposed transceiver demonstrated in vivo image transmission of 640 × 480 resolution at a frame rate of 2.5 frames/s with 10 Mb/s data rate.
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IEEE Trans Biomed Eng · Mar 2011
Toward unsupervised adaptation of LDA for brain-computer interfaces.
There is a step of significant difficulty experienced by brain-computer interface (BCI) users when going from the calibration recording to the feedback application. This effect has been previously studied and a supervised adaptation solution has been proposed. In this paper, we suggest a simple unsupervised adaptation method of the linear discriminant analysis (LDA) classifier that effectively solves this problem by counteracting the harmful effect of nonclass-related nonstationarities in electroencephalography (EEG) during BCI sessions performed with motor imagery tasks. ⋯ The chosen classifier is offline tested in data from 80 healthy users and four high spinal cord injury patients. Finally, for the first time in BCI literature, we apply this unsupervised classifier in online experiments. Additionally, we show that its performance is significantly better than the state-of-the-art supervised approach.