Name
Abstract | ||
|---|---|---|
Oscillometry is the most common measurement method used in automated electronic blood pressure (BP) monitors. A variety of oscillometric BP algorithms exist in the literature. However, most of these algorithms are without physiological and theoretical foundation. Moreover, most of the existing oscillometric algorithms estimate the BP from the envelope of the oscillometric pulses and ignore the wealth of information that the oscillometric pulses contain. More information could be obtained from the amplitude and time characteristics of the oscillometric pulses at different cuff pressures if an accurate mathematical model is developed. This paper reviews three novel model-based oscillometric BP estimation methods developed by our research group. These methods include (i) mathematical modeling of the oscillometric waveform envelope and BP estimation using neural networks, (ii) mathematical modeling of the oscillometric waveform and parameter estimation using extended Kalman filter, and (iii) mathematical modeling of the pulse transit time (PTT) and estimation of BP based on PTT-cuff pressure dependency. The performance of the proposed methods was evaluated on simulated and actual data in terms of mean error, mean absolute error, and standard deviation of error. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/MeMeA.2014.6860103 | Medical Measurements and Applications |
Keywords | Field | DocType |
Kalman filters,blood pressure measurement,medical signal processing,neural nets,parameter estimation,PTT-cuff pressure dependency,amplitude characteristics,automated electronic blood pressure monitors,extended Kalman filter,mean absolute error,mean error,model-based oscillometric blood pressure estimation,neural networks,oscillometric pulses,oscillometric waveform envelope,oscillometry,parameter estimation,pulse transit time,standard deviation of error,time characteristics,Kalman filter,blood pressure,electrocardiogram,estimation,mathematical model,neural network,oscillometry,pulse transit time | Extended Kalman filter,Waveform,Algorithm,Mean squared error,Oscillometry,Electronic engineering,Estimation theory,Engineering,Artificial neural network,Standard deviation,Amplitude | Conference |
Citations | PageRank | References |
1 | 0.36 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohamad Forouzanfar | 1 | 67 | 9.45 |
| Hilmi R. Dajani | 2 | 24 | 3.67 |
| Voicu Z. Groza | 3 | 51 | 9.03 |
| Miodrag Bolic | 4 | 503 | 58.17 |