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

Physical and Engineering Sciences in Medicine
Palani Thanaraj KrishnanSnekhalatha Umapathy

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 stud...Continue Reading

References

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Citations

Aug 14, 2021·Physiological Measurement·Kaleem Nawaz KhanMuhammad Salman Khan

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Methods Mentioned

BETA
feature extraction

Software Mentioned

Google Cloud
MFSC
DEEPCOGNITION . AI
MXNet
Physionet

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