Key result
Adaptive 1D CNN with data purification achieves ~96% sensitivity for real-time PCG anomaly detection.
Why the study?
Heart sound signals enable early cardiovascular pathology detection, creating an imminent need for automated, accurate, and real-time PCG anomaly detection.
Does an adaptive 1D Convolutional Neural Network improve anomaly detection performance and real-time processing of PCG signals compared to standard global classification approaches?
Does an adaptive 1D Convolutional Neural Network improve anomaly detection performance and real-time processing of PCG signals compared to standard global classification approaches?
An adaptive 1D Convolutional Neural Network provides highly accurate, real-time anomaly detection in phonocardiogram signals, though future improvements may require patient-specific models.
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.
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Kıranyaz et al. (2019) studied Phonocardiogram (PCG) anomalies (n=3,126). Adaptive 1D Convolutional Neural Network vs. Ensemble of 20 feedforward sigmoidal Neural Networks was evaluated on Anomaly detection sensitivity and specificity. An adaptive 1D Convolutional Neural Network with data purification achieved a sensitivity of nearly 0.96 for PCG anomaly detection while enabling real-time processing at 0.29 milliseconds per beat.
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