Automatic computerized diagnosis of fetal sinusoidal heart rate.
OBJECTIVE
Computerized automatic detection of pathologic fetal sinusoidal heart rate (FSHR) and its differentiation from physiologic FSHR is the purpose of this study. The results will be applied in the objective evaluation of fetal heart rate (FHR) with artificial neural network computer.


METHODS
FHR tracings of pathologic FSHR of 9 cases of fetal-neonatal anemia, death, or severe asphyxia, those of 7 physiologic FSHR followed by normal outcome, and those of 5 normal FHR are processed with fast Fourier transform (FFT) analysis after digitization, and their power spectrums are obtained. The peak power spectrum frequency (PPSF), peak power spectrum density (PPSD), the area under the power spectrum of 0.03125-0.1 Hz (La), the area under the whole power spectrum (Ta), and the ratio of La/Ta (%) of pathologic FSHR are compared to those of physiologic FSHR and normal FHR.


RESULTS
The La/Ta ratio and PPSD are significantly larger in the pathologic FSHR than those of physiologic FSHR and normal FHR. The true positive rate is 100%, false negative and false positive rates are 0%, respectively, when the pathologic FSHR is diagnosed by such combined criteria as 39% or more of La/Ta ratio and 300 or more of PPSD.


CONCLUSIONS
Pathologic FSHR is clearly separated from physiologic FSHR and normal FHR by the La/Ta ratio and PPSD obtained by FFT frequency analysis of FHR. Consequently, it is capable to automatically diagnose pathologic FSHR, and to apply it to neural network computer evaluation of FHR.
Maeda
Kazuo
K
Professor Emeritus, Department of Obstetrics and Gynecology, Tottori University School of Medicine, 3-125 Nadamachi, Yonago, Tottoriken, Japan. maedak@mocha.ocn.ne.jp
Nagasawa
Takashi
T
D000740:Anemia / Q000175:diagnosis; D001237:Asphyxia / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted / Q000295:instrumentation; D005260:Female; D005315:Fetal Diseases / Q000175:diagnosis*; D005583:Fourier Analysis; D006340:Heart Rate, Fetal; D006801:Humans; D016571:Neural Networks, Computer; D011247:Pregnancy; D011296:Prenatal Diagnosis / Q000295:instrumentation*