Key result
A 1-Dimensional Convolutional Neural Network deployed on a Raspberry Pi 3B edge device achieved promising recognition accuracies for S1 and S2 heart sounds.
Why the study?
Recognition of first and second heart sounds (S1 and S2) is the critical initial step in detecting cardiac abnormalities, motivating the implementation of automated recognition on edge computing platforms.
A 1-D CNN deployed on an edge computing platform can effectively recognize S1 and S2 heart sounds, enabling real-time PCG analysis.
Edge CNN feasibility for real-time S1/S2 recognition shown; leaves open clinical validation before practice use.
Cardiovascular diseases (CVDs) are often characterized by audio properties of heart sounds. The recognition of first and second heart sounds namely S <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> and S <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> is the first and the foremost step in detecting any cardiac abnormality. This work presents an implementation of optimal 1-Dimensional Convolutional Neural Network (1-D CNN) for S <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> and S <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> recognition on an edge computing platform. The optimal 1-D CNN model trained in the general purpose computing platform is deployed in the Raspberry Pi 3B based edge device for real time recognition of S <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> and S <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> . In addition, to the best of our knowledge, this study is novice and aims for investigation of S <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> and S <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> recognition considering different portions of the segmented S <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> and S <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> signals obtained from the publicly available database as well as experimentally recorded phonocardiogram(PCG) signals. The achieved recognition accuracies are promising for both the databases with different decimation factors of data. The complete setup emulates the real time environment for heart sound recognition, thereby it paves a way for further analysis of the PCG signals for assisting physicians.
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Vakamullu et al. (2022) studied Cardiovascular diseases. 1-Dimensional Convolutional Neural Network (1-D CNN) on edge computing platform was evaluated on S1 and S2 heart sound recognition accuracy. A 1-Dimensional Convolutional Neural Network deployed on a Raspberry Pi 3B edge device achieved promising recognition accuracies for S1 and S2 heart sounds.
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