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8 results for “Phonocardiogram”
Classification of Phonocardiograms with Convolutional Neural Networks-Figure 5. An example of a convolutional layer (Convolutional Neural Networks (LeNet), 2018)
<p>Figure 5 shows an example of a convolutional layer. Layer m-1 includes four feature maps. The m is a hidden layer and it includes two feature maps such as h0 and h1. The neuron outputs at h0 and h1 are calculated from the neuron outputs entering the 2x2 receiving area at the layer below layer m-1. Thus, the weights are W0, and W1 and h,0 and h1 are weight tensors that have three dimensions. The leading dimension indexes the map entries summaries, pointing to the other two neuron outputs. To combine all of them Wijkl , with each pixel of the layer's minus k map, it refers to the weight of the link between neuron outputs (i, j) in the coordinates of the feature map m-1 (Convolutional Neural Networks (LeNet), 2018).</p>
Classification of Phonocardiograms with Convolutional Neural Networks-Figure 4. a) Sparse Connectivity, b) Shared Weights (Convolutional Neural Networks (LeNet), 2018)
<p>A CNNs are biologically inspired variants of a multilayer perceptron. CNNs establish a built-in local correlation by applying a local link model between the neurons of adjacent layers. As shown in Figure 4a, the inputs of the hidden units in the m-layer are obtained from a subset of the units having the built-in areas in the m-1 layer. This ensures that a number of layers arrive consecutively, resulting in a filtration. It can encode 5 features such as a neuron in the m+1 hidden layer. On CNNs, each filter hi is repeated on the entire image surface. These repeated units form a feature map that shares the weight and bias parameters. 3 hidden units of the same feature map are shown in Figure 4b. The weights shown by parallel lines in the figure were also limited as same. To learn such shared parameters, the gradient method can be used with only a small modification to the original parameters. The sum of the inverse gradients of the shared parameters equals the gradient of the shared weights.</p>
Classification of Phonocardiograms with Convolutional Neural Networks-Figure 2. PCGs for sample sound file of 103-1305031931979-B
<p>Heart sounds that provide valuable diagnostic information in clinical examinations are among the most important physiological signals in the human body. However, heart sounds include noise, such as external sounds and lung sounds, caused by signal recording conditions. Noisy heart sound signal negatively affects the diagnosis of the doctor (Denga & Hanb, 2018). Digital filters are often used to filter biomedical signal. Digital filtering is defined as the acquisition of desired frequency values according to the characterization of the desired filter in order to improve the signal according to the intended use (Shenoi, 2005; Thede, 1995). Based on the experience gained from previous studies, an elliptic filter was used in this study (Deperlioglu, 2018; Guraksin et. al., 2009) . The heart sound signal covering the first two steps is given in Figure 2 for sample sound file of 103_1305031931979_B in the PASCAL Btraining data set.</p>
Classification of Phonocardiograms with Convolutional Neural Networks-Figure 1. Block diagram of classification process
<p>Heart and vascular diseases are the first cause of death in the world. The heartbeat, which is the result of the blood pumping of the heart, gives information about the functioning of the heart valves. Thus, features can be obtained that can differentiate between normal and abnormal heart sound signals. With the ECG signal, it is possible to visualize the parts of the heartbeat signal. However, ECG measurement is usually expensive and requires extra time. For this reason, studies for the detection of heart diseases generally focus on the analysis of heart sounds. In this context, it is aimed to make a successful classification using deep learning method in this study. The block diagram of the application is shown in Figure 1. The details of each block in the diagram are explained as follows. For all classification study, MATLAB r2017a software was used.</p>
Classification of Phonocardiograms with Convolutional Neural Networks-Figure 6. Confusion matrix of multilayer feedforward network
<p>Multilayer feedforward network was used for classification with ANN. In this application, the ANN structure and parameters were obtained after a review of previous studies and very much number of trial runs. There is a total of 10 neurons in the hidden layer in ANN. The Bayesian regularization backpropagation was used for learning algorithm and the mean square error function was also used the performance algorithm. 134 samples in the data set were used for training data, 29 samples were used for validation data, and 29 samples were used for testing data. The confusion matrix obtained at the end of the classification was given in Figure 6. As seen from the confusion matrix, the accuracy of classification 82.8% was achieved in the ANN classification. The ANN performed with a sensitivity of 92.40% and a specificity of 88.82%.</p>
Classification of Phonocardiograms with Convolutional Neural Networks-Figure 3. Multilayer feed forward network (Gurney, 2004)
<p>Feedforward network is a non-repeating network with the processing units or nodes in the layer, and all nodes in a layer are linked to the nodes of the previous layers. There are different weights on the connection. There is no feedback loop, the signal input can only flow in one direction. Multilayer feedforward network is feedforward ANN concept with multiple weighted layers as seen figure 3. This network is called hidden layers because it has one or more hidden layers between the input and output layers (Gurney, 2004; Tutorials Point, 2017).</p>
Classification of Phonocardiograms with Convolutional Neural Networks-Figure 7. Confusion matrix of convolutional neural network
<p>The final layer of CNN is the classification layer. This layer uses the possibilities returned by the softmax activation function for each input to mutually assign one of the special classes. The confusion matrix obtained at the end of the classification was given in Figure 7. As seen from the confusion matrix, the accuracy of classification 97.9% was achieved in the CNN classification. The CNN performed with sensitivity of 99.47% and specificity of 98.42%.</p>
Automated Algorithm Based Analysis of Phonocardiograms of Newborns
ClinicalTrials.gov study NCT02105480. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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