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Automated heart sound classification system from unsegmented phonocardiogram (PCG) using deep neural network

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Abstract

Given the patient to doctor ratio of 50,000:1 in low income and middle-income countries, there is a need for automated heart sound classification system that can screen the Phonocardiogram (PCG) records in real-time. This paper proposes deep neural network architectures such as a one-dimensional convolutional neural network (1D-CNN) and Feed-forward Neural Network (F-NN) for the classification of unsegmented phonocardiogram (PCG) signal. The research paper aims to automate the feature engineering and feature selection process used in the analysis of the PCG signal. The original PCG signal is down-sampled at 500 Hz. Then they are divided into smaller time segments of 6 s epochs. Savitzky–Golay filter is used to suppress the high-frequency noises in the signal by data point smoothening. The processed data was then provided as an input to the proposed deep neural network (DNN) architectures. 1081 PCG records were used for training and validating the proposed DNN models. The Feed-forward Neural Network model with five hidden layers provided a better overall accuracy of 0.8565 with a sensitivity of 0.8673, and specificity of 0.8475. The balanced accuracy of the model was found to be 0.8574. The performance of the model was also studied using the Receiver Operating Characteristic (ROC) plot, which produced an Area Under the Curve (AUC) value of 0.857. The classification accuracy of the proposed models was compared to the related works on PCG signal analysis for cardiovascular disease detection. The DNN models studied in this study provided comparable performance in heart sound classification without the requirement of feature engineering and segmentation of heart sound signals.

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Data availability

The patient medical data used in this study were obtained from a publicly available source: Physionet.org.

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Funding

This article does not receive funding from any agencies. All expenditures for this study have been managed by the first author.

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Correspondence to Snekhalatha Umapathy.

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Krishnan, P.T., Balasubramanian, P. & Umapathy, S. Automated heart sound classification system from unsegmented phonocardiogram (PCG) using deep neural network. Phys Eng Sci Med (2020). https://doi.org/10.1007/s13246-020-00851-w

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Keywords

  • Phonocardiogram
  • Heart sound
  • Convolutional neural network
  • Deep learning
  • Feature extraction
  • Time series classification
  • Feedforward neural network