BMC Med Inform Decis Mak - An adaptive Kalman filter approach for cardiorespiratory signal extraction and fusion of non-contacting sensors.


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CKGROUND: Extracting cardiorespiratory signals from non-invasive and non-contacting sensor arrangements, i.e. magnetic induction sensors, is a challenging task. The respiratory and cardiac signals are mixed on top of a large and time-varying offset and are likely to be disturbed by measurement noise. Basic filtering techniques fail to extract relevant information for monitoring purposes.METHODS: We present a real-time filtering system based on an adaptive Kalman filter approach that separates signal offsets, respiratory and heart signals from three different sensor channels. It continuously estimates respiration and heart rates, which are fed back into the system model to enhance performance. Sensor and system noise covariance matrices are automatically adapted to the aimed application, thus improving the signal separation capabilities. We apply the filtering to two different subjects with different heart rates and sensor properties and compare the results to the non-adaptive version of the same Kalman filter. Also, the performance, depending on the initialization of the filters, is analyzed using three different configurations ranging from best to worst case.RESULTS: Extracted data are compared with reference heart rates derived from a standard pulse-photoplethysmographic sensor and respiration rates from a flowmeter. In the worst case for one of the subjects the adaptive filter obtains mean errors (standard deviations) of -0.2 min(-1) (0.3 min(-1)) and -0.7 bpm (1.7 bpm) (compared to -0.2 min(-1) (0.4 min(-1)) and 42.0 bpm (6.1 bpm) for the non-adaptive filter) for respiration and heart rate, respectively. In bad conditions the heart rate is only correctly measurable when the Kalman matrices are adapted to the target sensor signals. Also, the reduced mean error between the extracted offset and the raw sensor signal shows that adapting the Kalman filter continuously improves the ability to separate the desired signals from the raw sensor data. The average total computational time needed for the Kalman filters is under 25% of the total signal length rendering it possible to perform the filtering in real-time.CONCLUSIONS: It is possible to measure in real-time heart and breathing rates using an adaptive Kalman filter approach. Adapting the Kalman filter matrices improves the estimation results and makes the filter universally deployable when measuring cardiorespiratory signals.

Resumo Limpo

ckground extract cardiorespiratori signal noninvas noncontact sensor arrang ie magnet induct sensor challeng task respiratori cardiac signal mix top larg timevari offset like disturb measur nois basic filter techniqu fail extract relev inform monitor purposesmethod present realtim filter system base adapt kalman filter approach separ signal offset respiratori heart signal three differ sensor channel continu estim respir heart rate fed back system model enhanc perform sensor system nois covari matric automat adapt aim applic thus improv signal separ capabl appli filter two differ subject differ heart rate sensor properti compar result nonadapt version kalman filter also perform depend initi filter analyz use three differ configur rang best worst caseresult extract data compar refer heart rate deriv standard pulsephotoplethysmograph sensor respir rate flowmet worst case one subject adapt filter obtain mean error standard deviat min min bpm bpm compar min min bpm bpm nonadapt filter respir heart rate respect bad condit heart rate correct measur kalman matric adapt target sensor signal also reduc mean error extract offset raw sensor signal show adapt kalman filter continu improv abil separ desir signal raw sensor data averag total comput time need kalman filter total signal length render possibl perform filter realtimeconclus possibl measur realtim heart breath rate use adapt kalman filter approach adapt kalman filter matric improv estim result make filter univers deploy measur cardiorespiratori signal

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