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Infants born before 37 weeks of pregnancy are considered to be preterm.Typically, preterm infants have to be strictly monitored since they are highlysusceptible to health problems like hypoxemia (low blood oxygen level), apnea,respiratory issues, cardiac problems, neurological problems as well as anincreased chance of long-term health issues such as cerebral palsy, asthma andsudden infant death syndrome. One of the leading health complications inpreterm infants is bradycardia - which is defined as the slower than expectedheart rate, generally beating lower than 60 beats per minute. Bradycardia isoften accompanied by low oxygen levels and can cause additional long termhealth problems in the premature infant. The implementation of a non-parametricmethod to predict the onset of bradycardia is presented. This method assumes noprior knowledge of the data and uses kernel density estimation to predict thefuture onset of bradycardia events. The data is preprocessed, and then analyzedto detect the peaks in the ECG signals, following which different kernels areimplemented to estimate the shared underlying distribution of the data. Theperformance of the algorithm is evaluated using various metrics and thecomputational challenges and methods to overcome them are also discussed. It isobserved that the performance of the algorithm with regards to the kernels usedare consistent with the theoretical performance of the kernel as presented in aprevious work. The theoretical approach has also been automated in this workand the various implementation challenges have been addressed.

Computational Challenges in Non-parametric Prediction of Bradycardia in Preterm Infants