Programs
- Amrita’s Fetal Cardiology learning ( AFCL) Module – Fetal Heart Hybrid - Fellowship
- B.Tech. in Artificial Intelligence (AI) and Data Science (Medical Engineering) - Undergraduate
Publication Type : Conference Paper
Publisher : 2017 International Conference on Intelligent Communication and Computational Techniques (ICCT)
Source : 2017 International Conference on Intelligent Communication and Computational Techniques (ICCT) (2017)
Keywords : cardiac diagnosis, cardio-vascular abnormalities, cardio-vascular anomalies, Cardiovascular system, Computer architecture, Deep learning, deep learning techniques, deep models, diagnostic screenings, ECG based cardiac diagnosis, gated recurrent unit, Gated Recurrent Unit (GRU), Heart, heart sounds classification, learning (artificial intelligence), Logic gates, long short term memory, long short-term memory (LSTM), Machine learning, medical signal processing, Microprocessors, PCG classification, Peter Bentley heart sound dataset, Phonocardiography, Phonocardiography (PCG), phonocardiography classification, recurrent neural nets, recurrent neural network, Recurrent neural network (RNN), Recurrent neural networks, remote health diagnostics systems, signal classification
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking, Electronics Communication and Instrumentation Forum (ECIF)
Department : Electronics and Communication, Communication
Verified : Yes
Year : 2017
Abstract : Phonocardiography or PCG plays a vital role in the initial diagnostic screenings of subjects for evaluating the presence of cardio-vascular anomalies. Since it is low-cost and less cumbersome to perform, it is found significant application in remote health diagnostics systems. It is also used to complement the ECG based cardiac diagnosis for detecting cardio-vascular abnormalities. One of the key aspect of PCG is the accurate identification of the heart sounds. In this work we propose to classify the heart sounds by performing various deep learning techniques such as RNN, LSTM and GRU. We used the widely known Peter Bentley heart sound dataset. Our experimental results show Long Short Term Memory (LSTM) provides better accuracy in the identification of heart sounds without the need for any pre-processing of the data.
Cite this Research Publication : V. G. Sujadevi, Dr. Soman K. P., R. Vinayakumar, and Sankar, A. U. P., “Deep models for phonocardiography (PCG) classification”, in 2017 International Conference on Intelligent Communication and Computational Techniques (ICCT), 2017.