Equipment/blood pressure

Blood pressure calibration

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Highlights

A Hammerstein–Windkessel model to represent the central blood pressure system.

A Kalman filter estimated the time varying Fourier series representing the Windkessel model's output signal.

The Fourier coefficients of this modeled oscillometry is then used in a next step to drive a logistic regression analysis.

A logistic regression analysis based on the signal features of the oscillometry can detect hyper- and hypotension and correct the blood pressures accordingly.

Oscillometric blood pressure (BP) monitors are omnipresent and used on a daily basis for personalized healthcare. Nevertheless, physicians generally approach these devices cautiously since the mercury Korotkoff sphygmomanometer remains the golden standard. Various reasons explain the hesitating attitude of the medical world towards automated BP monitors: (i) its principle is based on the pressure pulsations arriving at the cuff by the cardiac cycle instead of an audio wave used by physicians triggered by the turbulences in the artery, (ii) the actual computation of the systolic and diastolic BP from the measured oscillometry is manufacturer dependent and not based on general scientific principles, (iii) the quality of the oscillometric monitors is labeled by a trial such that the devices correspond well to the Korotkoff method for the average healthy patient but deviates for patients suffering from hypo- or hypertension. In this paper, we develop a statistical learning technique to calibrate and correct an oscillometric monitor such that the device better corresponds to the Korotkoff method regardless of the health status of the patient. The technique is based on logistic regression which allows correcting and eliminating systematic errors caused by patients suffering from hyper -or hypotension. No user interaction is required since the technique is able to train and validate the calibration procedure in an unsupervised way. In our case study, the systematic error is reduced by nearly 50% corresponding to the performance specifications of the device.

Keywords

  • Blood pressure measurement;
  • Oscillometric signal;
  • Pulse-pressure signals;
  • Bio-signal processing;
  • Heart frequency estimation;
  • Logistic regression;
  • Calibration
Source: www.sciencedirect.com
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