Adaptive filtering of ECG signal for deriving respiratory activity

Abstract

A new approach is introduced for deriving the respiratory signal from a single-lead electrocardiogram (ECG) by adaptive filtering. The method uses the R-R interval and the R-wave amplitude time series, extracted from the ECG signal, as inputs to the filter, the respiratory activity is estimated as its output. The adaptive filtering is able to enhance the common component between the above series (namely the respiratory influence), attenuating the uncorrelated noise. More than 170 hours of ECG and respiratory signal were collected. Least mean squares (LMSs) and recursive least squares (RLSs) adaptive filtering methods were applied to obtain the estimate of the respiratory signal. Visual inspection and spectral analysis were used to evaluate the performance of the filtering by comparison with a true respiratory signal obtained by a piezoelectric transducer. The RLS adaptive filtering technique was more effective than LMS in producing an 'ECG-derived' respiratory signal. This approach adds clinically important information to conventional ECG analysis. >