Key result
The proposed CNN-based inception network model achieved 99.65% accuracy for classifying heart sounds, outperforming hybrid CNN-RNN (91.17%) and LSTM-based RNN (82.32%) models.
Why the study?
During COVID-19, direct patient contact risked spreading infection, highlighting the need for contactless heart sound auscultation and improved deep learning classification of valvular heart problems.
Does a CNN-based inception network model improve the classification accuracy of heart sounds using an ear-contactless stethoscope compared to other machine learning algorithms?
Does a CNN-based inception network model improve the classification accuracy of heart sounds using an ear-contactless stethoscope compared to other machine learning algorithms?
A novel ear-contactless stethoscope combined with an optimized CNN-based inception network model provides highly accurate classification of heart sounds, which may facilitate safe auscultation during infectious outbreaks.
Cardiac-related disorders are rapidly growing throughout the world. Accurate classification of cardiovascular diseases is an important research topic in healthcare. During COVID-19, auscultating heart sounds was challenging as health workers and doctors wear protective clothing, and direct contact with patients can spread the outbreak. Thus, contactless auscultation of heart sound is necessary. In this paper, a low-cost ear contactless stethoscope is designed where auscultation is done with the help of a bluetooth-enabled micro speaker instead of an earpiece. The PCG recordings are further compared with other standard electronic stethoscopes like Littman 3 M. This work is made to improve the performance of deep learning-based classifiers like recurrent neural networks (RNN) and convolutional neural networks (CNN) for different valvular heart problems using tuning of hyperparameters like learning rate of optimizers, dropout rate, and hidden layer. Hyper-parameter tuning is used to optimize the performances of various deep learning models and their learning curves for real-time analysis. The acoustic, time, and frequency domain features are used in this research. The investigation is made on the heart sounds of normal and diseased patients available from the standard data repository to train the software models. The proposed CNN-based inception network model achieved an accuracy of 99.65 ± 0.06% on the test dataset with a sensitivity of 98.8 ± 0.05% and specificity of 98.2 ± 0.19%. The proposed hybrid CNN-RNN architecture attained 91.17 ± 0.03% accuracy on test data after hyperparameter optimization, whereas the LSTM-based RNN model achieved 82.32 ± 0.11% accuracy. Finally, the evaluated results were compared with machine learning algorithms, and the improved CNN-based Inception Net model is the most effective among others.
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Roy et al. (2023) studied Valvular heart problems. CNN-based inception network model vs. Hybrid CNN-RNN architecture and LSTM-based RNN model was evaluated on Accuracy on the test dataset. The proposed CNN-based inception network model achieved 99.65% accuracy for classifying heart sounds, outperforming hybrid CNN-RNN (91.17%) and LSTM-based RNN (82.32%) models.
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