Robust optimization of convolutional neural networks with a uniform experiment design method: a case of phonocardiogram testing in patients with heart diseases

Abstract

Background Heart sound measurement is crucial for analyzing and diagnosing patients with heart diseases. This study employed phonocardiogram signals as the input signal for heart disease analysis due to the accessibility of the respective method. This study referenced preprocessing techniques proposed by other researchers for the conversion of phonocardiogram signals into characteristic images composed using frequency subband. Image recognition was then conducted through the use of convolutional neural networks (CNNs), in order to classify the predicted of phonocardiogram signals as normal or abnormal. However, CNN requires the tuning of multiple hyperparameters, which entails an optimization problem for the hyperparameters in the model. To maximize CNN robustness, the uniform experiment design method and a science-based methodical experiment design were used to optimize CNN hyperparameters in this study. Results An artificial intelligence prediction model was constructed using CNN, and the uniform experiment design method was proposed to acquire hyperparameters for optimal CNN robustness. The results indicate Filters ($${X}_{1}$$ X 1 ), Stride ($${X}_{3}$$ X 3 ), Activation functions ($${X}_{6}$$ X 6 ), and Dropout ($${X}_{7}$$ X 7 ) to be significant factors considerably influencing the ability of CNN to distinguish among heart sound states. Finally, the confirmation experiment was conducted, and the hyperparameter combination for optimal model robustness was Filters ($${X}_{1}$$ X 1 ) = 32, Kernel Size ($${X}_{2})$$ X 2 ) = 3 × 3, Stride ($${X}_{3}$$ X 3 ) = (1,1), Padding ($${X}_{4})$$ X 4 ) as same, Optimizer ($${X}_{5})$$ X 5 ) as the stochastic gradient descent, Activation functions ($${X}_{6}$$ X 6 ) as relu, and Dropout ($${X}_{7}$$ X 7 ) = 0.544. With this combination of parameters, the model had an average prediction accuracy rate of 0.787 and standard deviation of 0. Conclusion In this study, phonocardiogram signals were used for the early prediction of heart diseases. The science-based and methodical uniform experiment design was used for the optimization of CNN hyperparameters to construct a CNN with optimal robustness. The results revealed that the constructed model exhibited robustness and an acceptable accuracy rate. Other literature has failed to address hyperparameter optimization problems in CNN; a method is subsequently proposed for robust CNN optimization, thereby solving this problem.

Deep Learning for Heart Sound Analysis: A Literature Review

“…In the reviewed literature, most studies use CNN models with 2 to 34 convolutional layers [9, 10, 11, 13, 14, 15, 18, 21, 22, 23, 24, 25, 26, 27, 28, 31, 32, 33, 34, 35, 37, 38, 40, 41, 42, 47, 48, 50, 51, 52, 55, 58, 59, 60, 61, 68, 69, 71, 72, 73, 84], which are usually equipped with rectified linear units, batch normalization, dropout and pooling components, and some of the layers are linked by residual connections. Wang et al [76] test 10 different CNN models including GoogleNet, SqueezeNet, DarkNet19, ModileNetv2, Inception-ResNetv2, DenseNet201, Inceptionv3, ResNet101, NasNet-Large, and Xception to compare the performances.…”

“…Since detecting cardiac murmurs is a relatively more straightforward task compared to identifying specific diseases, there has been a notable accumulation of high-quality annotated data in recent years, which has been made available to the public [20, 94]. The abundance of accessible data has led to a surge in DL models research on cardiac murmurs detection [21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56]. Although these models can only discern the presence of cardiac murmurs and cannot provide definitive diagnoses, they still play a crucial role in community-based disease screening.…”

“…At last, 71 original articles were included. These studies can be broadly categorized into several groups: methods (15 papers, including heart sound segmentation [6, 7, 8, 9, 10, 11, 12, 13], noise cancellation [14, 15, 16], algorithm development [17, 18, 19], and database development [20]), cardiac murmurs detection (36 papers [21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56]), valvular heart disease (6 papers [57, 58, 59, 60, 61, 62]), congenital heart disease (4 papers [63, 64, 65, 66]), heart failure (4 papers [67, 68, 69, 70]), coronary artery disease (2 papers [71, 72]), rheumatic heart disease (2 papers [73, 74]), and extracardiac applications (2 papers [75, 76]).…”

“…The abundance of accessible data has led to a surge in DL models research on cardiac murmurs detection[21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56]. Although these models can only discern the presence of cardiac murmurs and cannot provide definitive diagnoses, they still play a crucial role in community-based disease screening.…”

Artificial Intelligence in Internet of Things System for Predicting Water Quality in Aquaculture Fishponds

“…Therefore, Chou et al [71] used experimental design method to find the best hyperparameter combination for a CNN. Ho et al [72] and Ho et al [73] also used experimental design method to explore hyperparameter combinations for deep residual network (ResNet) and CNN models. In summary, this paper explored the applicability of LSTM and GRU deep learning models for predicting DOC and used a Taguchi method to optimize hyperparameters.…”