  Infants born before 37 weeks of pregnancy are considered to be preterm.
Typically, preterm infants have to be strictly monitored since they are highly
susceptible to health problems like hypoxemia (low blood oxygen level), apnea,
respiratory issues, cardiac problems, neurological problems as well as an
increased chance of long-term health issues such as cerebral palsy, asthma and
sudden infant death syndrome. One of the leading health complications in
preterm infants is bradycardia - which is defined as the slower than expected
heart rate, generally beating lower than 60 beats per minute. Bradycardia is
often accompanied by low oxygen levels and can cause additional long term
health problems in the premature infant. The implementation of a non-parametric
method to predict the onset of bradycardia is presented. This method assumes no
prior knowledge of the data and uses kernel density estimation to predict the
future onset of bradycardia events. The data is preprocessed, and then analyzed
to detect the peaks in the ECG signals, following which different kernels are
implemented to estimate the shared underlying distribution of the data. The
performance of the algorithm is evaluated using various metrics and the
computational challenges and methods to overcome them are also discussed. It is
observed that the performance of the algorithm with regards to the kernels used
are consistent with the theoretical performance of the kernel as presented in a
previous work. The theoretical approach has also been automated in this work
and the various implementation challenges have been addressed.
