No abstract

The monitoring of fetal heart rate (FHR) is commonly used in assessing the general health of the fetus. Although certain periodic cycles may be indicative of fetal problems, only short term observations are routinely employed in clinical practice. This is due to cost considerations, inconvenience to the patient and concern about long term ultrasonic monitoring. Therefore only a low confidence assessment can be established between detected rhythms and the health of the fetus. The technique advocated in this paper makes use of an inexpensive, non-invasive phonocardiographic (phono) transducer which facilitates safe long-term patient monitoring. A variable comb filter applied to the frequency domain is used in order to take full advantage of the harmonic content of fetal heart signals. Real time estimation of FHR has been achieved on pre-recorded phono signals lasting eight hours. Recordings with a reasonable signal quality were analysed and some of the results are given. Advanced signal processing techniques followed by Artificial Intelligence (AI) algorithms reduce the number of erroneous estimates during periods of low signal to noise ration (SNR). The resulting FHR time series is stored on the host computer for further processing, display and parameter extraction. This paper outlines the processing steps involved.

Foetal breathing movement (FBM) in utero has come to play an important role in foetal diagnosis. FBM may be monitored using real time ultrasound imaging of the foetus in utero. Foetal breathing activities can also be detected by monitoring maternal abdominal wall movement in the frequency range of 0.5-2.5 Hz. This paper presents a transducer which detects FBM non-invasively by monitoring maternal abdominal wall movements. Foetal heart sounds can also be monitored. The transducer presented uses piezo-electric film as the transducing medium. Results from preliminary clinical trials of prototype transducers on 10 patients are discussed.

No abstract

Fetal breathing movement (FBM) in utero may be an indicator of fetal health. This paper provides a second-by-second estimate of FBM rate. In the absence of a statistical model for the fetal breathing movement, block data structured autoregressive spectral estimation is used. The optimum tapered Burg algorithm provides a minimum variance breathing rate estimate from a short block of data. The data were recorded using a PVDF (PolyVinyliDeneFluoride) transducer which picks up maternal abdominal wall movements. A peak tracking algorithm is used to extract the fetal breathing rate. Results from these signals are presented in graphical form. Further analysis of the fetal breathing rate has revealed periodicities, similar to that observed in the fetal heart rate.