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We propose a novel computational approach to automatically identify the fetal heart rate patterns (fHRPs), which are reflective of sleep/awake states. By combining these patterns with presence or absence of movements, a fetal behavioral state (fBS) was determined. The expert scores were used as the gold standard and objective thresholds for the detection procedure were obtained using Receiver Operating Characteristics (ROC) analysis. To assess the performance, intraclass correlation was computed between the proposed approach and the mutually agreed expert scores. The detected fHRPs were then associated to their corresponding fBS based on the fetal movement obtained from fetal magnetocardiogaphic (fMCG) signals. This approach may aid clinicians in objectively assessing the fBS and monitoring fetal wellbeing.

作者:S, Vairavan;U D, Ulusar;H, Eswaran;H, Preissl;J D, Wilson;S S, Mckelvey;C L, Lowery;R B, Govindan

来源:Computers in biology and medicine 2016 年 69卷

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作者:
S, Vairavan;U D, Ulusar;H, Eswaran;H, Preissl;J D, Wilson;S S, Mckelvey;C L, Lowery;R B, Govindan
来源:
Computers in biology and medicine 2016 年 69卷
标签:
Fetal behavioral states Fetal heart rate pattern Fetal movement Fetal, sleep-awake states
We propose a novel computational approach to automatically identify the fetal heart rate patterns (fHRPs), which are reflective of sleep/awake states. By combining these patterns with presence or absence of movements, a fetal behavioral state (fBS) was determined. The expert scores were used as the gold standard and objective thresholds for the detection procedure were obtained using Receiver Operating Characteristics (ROC) analysis. To assess the performance, intraclass correlation was computed between the proposed approach and the mutually agreed expert scores. The detected fHRPs were then associated to their corresponding fBS based on the fetal movement obtained from fetal magnetocardiogaphic (fMCG) signals. This approach may aid clinicians in objectively assessing the fBS and monitoring fetal wellbeing.