Abstract 4366212: Artificial Intelligence with Augmented Data Increases Accuracy and Speed of Heart Murmur Detection in Phonocardiogram Data

Introduction: Cardiac auscultation often provides the first indication of underlying cardiac conditions through identification of heart murmurs. Auscultation is conducted during routine physical exams and is traditionally performed via stethoscope. However, conventional auscultation has limited sensitivity and accuracy due to high inter-rater discrepancy. Given its importance in the diagnostic process, an enhanced protocol is necessary. Artificial intelligence (AI) has been implemented in various medical settings. However, AI is limited by its need for vast quantities of high-quality training data. Data augmentation (DA) can be used to generate new samples from existing data by applying various transformations, which increases algorithm robustness and generalizability. Hypothesis: We hypothesized that implementing the Minimally Random Convolutional Kernel Transform (MiniROCKET) AI model with DA techniques improves the accuracy and speed of murmur detection in phonocardiogram (PCG) data. Methods: We used data from the 2022 George Moody PhysioNet Heart Sound Classification Challenge, containing PCG recordings of individuals under 21 years of age in Northeast Brazil. Patients without recordings from all four heart valves were excluded. Each patient’s audio files were synchronized at the first heartbeat. The file group then underwent 10 random time-series DA techniques, yielding four files from each original. Then, a Mel spectrogram was generated from each file, and one random DA technique was applied to each to make three spectrograms (Figure 1). Through DA, our sample size was increased from 928 spectrograms to 14,848. After pre-processing, MiniROCKET classified each patient as: “murmur present,” “murmur absent,” or “further assessment required” - if sufficient certainty is not achieved. Results: We assessed the effect of varying levels of DA, the overall accuracy of our MiniROCKET methodology, and the speed of our model by comparing with existing models (Tables 1&2). Overall, our method yielded improved quality assessment metrics and enhanced speed compared to existing models and was further improved through DA techniques. Conclusion: With DA (p=8.600e-05), our method boasts rapid and precise detection of murmurs in PCG data, with a weighted accuracy of 96.4% and an evaluation time of 0.02 seconds per patient, outperforming existing methods. Implementing this method may streamline diagnosis, promote scalability and adaptability, and allow for early-stage treatment.

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