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Monitoring cardiovascular activity during pregnancy is of high importance for identifying abnormal development of the fetus. Automated cardiovascular auscultation of the abdomen in both infrasonic and audible frequencies is a non-invasive method for monitoring the maternal and fetal health, including blood flow to the placenta. However, the quality of such recordings is often compromised by artifacts. The purpose of this study was to automatically identify high-quality auscultation signals. 324 recordings were obtained with two microphones placed bilaterally on the abdomen of 90 pregnant women (gestational age of 28-41 weeks), with signal duration of 30 s - 180 s. The signals were band-pass filtered to infrasonic frequencies (2.5 Hz - 25 Hz) and audible low frequencies (25 Hz - 125 Hz), divided into 10 s segments, and areas with unwanted transients were removed. Five features were calculated for segments of at least five continuous seconds. A logistic regression model was trained and tested using the identified features, obtaining a maximum classification accuracy of 92.8% for the infrasonic frequencies (81.6% sensitivity and 97.0% specificity), and 96.1% accuracy for the audible frequencies (90.4% sensitivity and 97.2% specificity). These results demonstrate the feasibility of automatical identification of high-quality segments at infrasonic and audible frequencies.

Quality Assessment of Maternal and Fetal Cardiovascular Sounds Recorded From the Skin Near the Uterine Arteries During Pregnancy