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Befina Y., A. Chandrasekar
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- Student, Professor, Computer Science and Engineering, St.Joseph’s College of Engineering, Chennai, Computer Science and Engineering, St.Joseph’s College of Engineering, Chennai, Tamil Nadu, Tamil Nadu, India, India
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Abstract
nHeartbeat sounds play a major role in the detection of various diseases such as heart disease, hyperthyroidism, and high blood pressure. in an early stage. In the proposed method various abnormal and healthy heartbeat audio signals are given as input and the features are extracted using MFCC (Mel-frequency cepstral coefficients). Then, a Deep Learning Approach is applied in which the MFCC audio signals are sent to the CNN (Convolutional Neural Network) + LSTM (Long Short-Term Memory) model to extract and classify the abnormal heartbeat sounds with its associated disease. Thus, a system for classifying abnormal heartbeat sounds is proposed by leading a way to identify and diagnose diseases that are identified using heartbeat sounds in an early stage.
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Keywords: Heartbeat audio signals, Deep Learning, Mel-frequency cepstral coefficients, Convolutional Neural Network, Long Short-Term Memory
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References
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Research & Reviews: A Journal of Embedded System & Applications
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| Volume | 12 | |
| [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | 01 | |
| Received | March 14, 2024 | |
| Accepted | March 30, 2024 | |
| Published | April 4, 2024 |
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