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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The patient medical data used in this study were obtained from a publicly available source: Physionet.org.
Cardiovascular diseases (CVDs): WHO Fact Sheet. WHO (2017) https://www.who.int/en/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds). Accessed 1 May 2019
Liu C, Springer D, Clifford GD (2017) Performance of an open-source heart sound segmentation algorithm on eight independent databases. Physiol Meas 38:1730–1745. https://doi.org/10.1088/1361-6579/aa6e9f
Liu C, Springer D, Li Q, Moody B, Juan RA, Chorro FJ et al (2016) An open access database for the evaluation of heart sound algorithms. Physiol Meas 37:2181–2213. https://doi.org/10.1088/0967-3334/37/12/2181
Redlarski G, Gradolewski D, Palkowski A (2014) A system for heart sounds classification. PLoS ONE 9:e112673. https://doi.org/10.1371/journal.pone.0112673
Choi S, Jiang Z (2010) Cardiac sound murmurs classification with autoregressive spectral analysis and multi-support vector machine technique. Comput Biol Med 40:8–20. https://doi.org/10.1016/J.COMPBIOMED.2009.10.003
Zheng Y, Guo X, Ding X (2015) A novel hybrid energy fraction and entropy-based approach for systolic heart murmurs identification. Expert Syst Appl 42:2710–2721. https://doi.org/10.1016/J.ESWA.2014.10.051
Zhang W, Han J, Deng S (2017) Heart sound classification based on scaled spectrogram and partial least squares regression. Biomed Signal Process Control 32:20–28. https://doi.org/10.1016/J.BSPC.2016.10.004
Hamidi M, Ghassemian H, Imani M (2018) Classification of heart sound signal using curve fitting and fractal dimension. Biomed Signal Process Control 39:351–359. https://doi.org/10.1016/J.BSPC.2017.08.002
Springer D, Tarassenko L, Clifford G (2015) Logistic Regression-HSMM-based heart sound segmentation. IEEE Trans Biomed Eng 63:1–1. https://doi.org/10.1109/TBME.2015.2475278
Whitaker BM, Suresha PB, Liu C, Clifford GD, Anderson DV (2017) Combining sparse coding and time-domain features for heart sound classification. Physiol Meas 38:1701–1713. https://doi.org/10.1088/1361-6579/aa7623
Plesinger F, Viscor I, Halamek J, Jurco J, Jurak P (2017) Heart sounds analysis using probability assessment. Physiol Meas 38:1685–1700. https://doi.org/10.1088/1361-6579/aa7620
Maknickas V, Maknickas A (2017) Recognition of normal–abnormal phonocardiographic signals using deep convolutional neural networks and mel-frequency spectral coefficients. Physiol Meas 38:1671–1684. https://doi.org/10.1088/1361-6579/aa7841
Langley P, Murray A (2017) Heart sound classification from unsegmented phonocardiograms. Physiol Meas 38:1658–1670. https://doi.org/10.1088/1361-6579/aa724c
Kay E, Agarwal A (2017) DropConnected neural networks trained on time-frequency and inter-beat features for classifying heart sounds. Physiol Meas 38:1645–1657. https://doi.org/10.1088/1361-6579/aa6a3d
Nabhan Homsi M, Warrick P (2017) Ensemble methods with outliers for phonocardiogram classification. Physiol Meas 38:1631–1644. https://doi.org/10.1088/1361-6579/aa7982
Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PC, Mark RG et al (2000) PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101:e215–e220. https://doi.org/10.1161/01.CIR.101.23.e215
Karim F, Majumdar S, Darabi H, Chen S (2018) LSTM Fully convolutional networks for time series classification. IEEE Access 6:1662–1669. https://doi.org/10.1109/ACCESS.2017.2779939
Sadouk L. CNN Approaches for time series classification. Time Series Analysis, IntechOpen; 2019. doi:10.5772/intechopen.81170.
Acharya UR, Fujita H, Lih OS, Adam M, Tan JH, Chua CK (2017) Automated detection of coronary artery disease using different durations of ECG segments with convolutional neural network. Knowl-Based Syst 132:62–71. https://doi.org/10.1016/j.knosys.2017.06.003
Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adam M, Gertych A et al (2017) A deep convolutional neural network model to classify heartbeats. Comput Biol Med 89:389–396. https://doi.org/10.1016/j.compbiomed.2017.08.022
Deep Cognition—DeepCognition.ai n.d. https://deepcognition.ai/. Accessed 23 Nov 2018
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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
- Heart sound
- Convolutional neural network
- Deep learning
- Feature extraction
- Time series classification
- Feedforward neural network