A Phonography-Based Method Improved by Hidden Markov Model for Fetal Breathing Movement Detection

This paper proposes a novel phonography-based method for Fetal Breathing Movement (FBM) detection by its excitation sounds. It requires significantly less effort than the current procedures, and it allows long-term measurement, even at home. More than 50 pregnancies in the third trimester were examined, for a minimum of 20 minutes, taking synchronous long-term measurements using a commercial phonocardiographic fetal monitor and a 3D ultrasound machine. To analyze the gained chaotic signal, the frequency band was split into single test-frequencies in the 15-35 Hz frequency band, and their signal-free (silent) zones were regarded as the starting point (SP) of the next motions. The analysis made other disturbing signals, such as fetal hiccups, trunk rotation and limb movements, or maternal heart beats, distinguishable. The dominant test-frequencies of the analysis were predicted by a Hidden Markov Model (HMM). The SPs of the motion units (episodes) were determined by some features of the FBM, applying weighting factors. The recorded material lasted for 16 hours altogether (with nearly 3.5 hours of FBM). Based on the results of HMM method, nearly 7500 FBM episodes were identified in the phonogram signal with an average length of 0.96±0.13 seconds. The procedure for phonography-based breathing movement detection can be combined with a fetal heart activity measurement, and thus allows very intensive, long-term monitoring of the fetus.