  The heart sound signals (Phonocardiogram - PCG) enable the earliest
monitoring to detect a potential cardiovascular pathology and have recently
become a crucial tool as a diagnostic test in outpatient monitoring to assess
heart hemodynamic status. The need for an automated and accurate anomaly
detection method for PCG has thus become imminent. To determine the
state-of-the-art PCG classification algorithm, 48 international teams competed
in the PhysioNet (CinC) Challenge at 2016 over the largest benchmark dataset
with 3126 records with the classification outputs, normal (N), abnormal (A) and
unsure - too noisy (U). In this study, our aim is to push this frontier
further; however, we focus deliberately on the anomaly detection problem while
assuming a reasonably high Signal-to-Noise Ratio (SNR) on the records. By using
1D Convolutional Neural Networks trained with a novel data purification
approach, we aim to achieve the highest detection performance and a real-time
processing ability with significantly lower delay and computational complexity.
The experimental results over the high-quality subset of the same benchmark
dataset shows that the proposed approach achieves both objectives. Furthermore,
our findings reveal the fact that further improvements indeed require a
personalized (patient-specific) approach to avoid major drawbacks of a global
PCG classification approach.
