Abstract
Phonocardiogram (PCG) is the recording of heart sounds and murmurs. PCG compliments electrocardiogram in detection of heart diseases especially in the initial screenings due to its simplicity and low cost. Detecting abnormal heart sounds by algorithms is important for remote health monitoring and other scenarios where having an experienced physician is not possible. While several studies exist, we explore the possibility of detecting anomalies in heart sounds and murmurs using Deep-learning algorithms on well-known Physionet Dataset. We performed the experiments by employing various algorithms such as RNN, LSTM, GRU, B-RNN, B-LSTM and CNN. We achieved 80% accuracy in CNN 3 layer Deep learning model on the raw signals without performing any preprocessing methods. To our knowledge this is the highest reported accuracy that employs analyzing the raw PCG data.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Barabasa, Constantin, Maria Jafari, and Mark D. Plumbley. “A robust method for S1/S2 heart sounds detection without ECG reference based on music beat tracking.” Electronics and Telecommunications (ISETC), 2012 10th International Symposium on. IEEE, 2012.
Chen, Tien-En, et al. “S1 and S2 heart sound recognition using deep neural networks.” IEEE Transactions on Biomedical Engineering 64.2 (2017): 372–380.
Jusak, Jusak, Ira Puspasari, and Pauladie Susanto. “Heart murmurs extraction using the complete Ensemble Empirical Mode Decomposition and the Pearson distance metric.” Information Communication Technology and Systems (ICTS), 2016 International Conference on. IEEE, 2016.
Stainton, Scott, Charalampos Tsimenidis, and Alan Murray. “Characteristics of phonocardiography waveforms that influence automatic feature recognition.” Computing in Cardiology Conference (CinC), 2016. IEEE, 2016.
Mondal, Ashok, et al. “A noise reduction technique based on nonlinear kernel function for heart sound analysis.” IEEE Journal of Biomedical and Health Informatics (2017).
Faradisa, Irmalia Suryani, et al. “Identification of phonocardiogram signal based on STFT and Marquart Lavenberg Backpropagation.” Intelligent Technology and Its Applications (ISITIA), 2016 International Seminar on. IEEE, 2016.
Chakir, Fatima, et al. “Phonocardiogram signals classification into normal heart sounds and heart murmur sounds.” Intelligent Systems: Theories and Applications (SITA), 2016 11th International Conference on. IEEE, 2016.
Thomas, Rijil, et al. “Heart sound segmentation using fractal decomposition.” Engineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th Annual International Conference of the. IEEE, 2016.
Cheng, Xiefeng, et al. “Feature extraction and recognition methods based on phonocardiogram.” Digital Information Processing, Data Mining, and Wireless Communications (DIPDMWC), 2016 3rd International Conference on. IEEE, 2016.
Grzegorczyk, Iga, et al. “PCG classification using a neural network approach.” Computing in Cardiology Conference (CinC), 2016. IEEE, 2016.
Elman, Jeffrey L. “Finding structure in time.” Cognitive science 14.2 (1990): 179–211.
Hochreiter, Sepp, and Jrgen Schmidhuber. “Long short-term memory.” Neural computation 9.8 (1997): 1735–1780.
Graves, Alex, and Jrgen Schmidhuber. “Framewise phoneme classification with bidirectional LSTM and other neural network architectures.” Neural Networks 18.5 (2005): 602–610.
Gers, Felix A., Nicol N. Schraudolph, and Jrgen Schmidhuber. “Learning precise timing with LSTM recurrent networks.” Journal of machine learning research 3.Aug (2002): 115–143.
LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. “Deep learning.” Nature 521.7553 (2015): 436–444.
Kim, Yoon. “Convolutional neural networks for sentence classification.” arXiv preprint arXiv:1408.5882 (2014).
Liu, Chengyu, et al. “An open access database for the evaluation of heart sound algorithms.” Physiological Measurement 37.12 (2016): 2181.
Abadi, Martn, et al. “TensorFlow: A system for large-scale machine learning.” Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI). Savannah, Georgia, USA. 2016.
Maaten, Laurens van der, and Geoffrey Hinton. “Visualizing data using t-SNE.” Journal of Machine Learning Research 9.Nov (2008): 2579–2605.
Acknowledgements
We sincerely thank NVIDIA India for the K40 GPU card that was used in this study.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sujadevi, V.G., Soman, K.P., Vinayakumar, R., Prem Sankar, A.U. (2019). Anomaly Detection in Phonocardiogram Employing Deep Learning. In: Behera, H., Nayak, J., Naik, B., Abraham, A. (eds) Computational Intelligence in Data Mining. Advances in Intelligent Systems and Computing, vol 711. Springer, Singapore. https://doi.org/10.1007/978-981-10-8055-5_47
Download citation
DOI: https://doi.org/10.1007/978-981-10-8055-5_47
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-8054-8
Online ISBN: 978-981-10-8055-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)