Background: To assess the type of infant nutrition at initiation of first feed in association with increasing maternal pre-pregnancy Body Mass Index in an Australian obstetric population.
Methods: A retrospective cohort study from 2008 to 2013 was undertaken. Body Mass Index was available for 12,347 women categorised into groups according to: underweight (≤18 kg/m); normal weight (19-24 kg/m); overweight (25-29 kg/m); obese class I (30-34 kg/m); obese class II (35-39/kg) and obese class III (40+ kg/m).
Objective: To assess maternal and neonatal outcomes associated with increasing body mass index (BMI) and interpregnancy BMI changes in an Australian obstetric population.
Methods: A retrospective cohort study from 2008 to 2013 was undertaken. BMI for 14 875 women was categorised as follows: underweight (≤18 kg/m(2)); normal weight (19-24 kg/m(2)); overweight (25-29 kg/m(2)); obese class I (30-34 kg/m(2)); obese class II (35-39 kg/m(2)) and obese class III (40+ kg/m(2)).
This paper presents mathematical techniques for automatically extracting and analyzing bioacoustic signals. Automatic techniques are described for isolation of target signals from background noise, extraction of features from target signals and unsupervised classification (clustering) of the target signals based on these features. The only user-provided inputs, other than raw sound, is an initial set of signal processing and control parameters.
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