Experimental Study on Plasmofludic Disk Resonators Filled with Liquid Crystal

In this project, we want to solve one of the problematic issues of deep learning - database of imbalances. With an astonishing development trend, deep learning is also a candidate technique to solve the ECG biometric topic. Additionally, one of the most powerful aspects of deep learning is analyzing massive amounts of data.

In this study, we have experimented with many types of machine learning techniques as well as efficient deep learning models to solve the ECG identification problem. In this study, we focused on how to improve the performances of different types of deep learning architectures.

Figure 1.1: An ECG signal structure [13].

Convolutional Neural Network

The second one is the recurrent neural network (RNN) commonly used with sequential data with three specific types of RNN cells: traditional RNN cell, gated recurrent units (GRUs) cell and long short-term memory (LSTM) cell. Convolutional layers are the primary component of ConvNet for extracting features from input and being a primarily computational element of a model. In practice, a convolutional layer works as a set of 2D filters in certain conditions and spans the volume of the input data.

The properties of each convolutional layer depend on the three hyperparameters that define the output volume: depth, fill numbers, and pitch. The depth hyperparameters are the number of filters as the dimension of the convolution layer. When we slide the kernel filters on the input matrix, the step is the step of the convolutional layer.

However, the output of the convolutional layer is the result of a linear function, we need an activation function to avoid this phenomenon. Although, the deep convnet with many filters collects a large amount of information, this problem affects the speed of the network optimizer. In the real case, this type of layer is a filter used to smooth the output size of the convolutional neuron and simply extract the outlier information as Figure 2.5.

For classification problems, after using fully connected layers, we need to compress the output between 0 and 1 which are equivalent to a probability distribution.

Figure 2.1: Convolutional layer. a. Input matrix, b. Kernel filter, c. Ouput matrix.

Recurrent Neural Network

In general, an LSTM unit includes an input gate, an output gate, and a forgotten gate [60]. The result of the forgotten gate is a probability value between 0 and 1 for each number in the cell state C(t−1). The first step is to activate the sigmoid input gate II.12 to consider what information to update.

Then, in the second step, a tanh function II.13 is applied to create a potential information vector for updating cell C. Then, the new cell state c(t) of this LSTM unit will be updated from the state of cell (t−1) of the last LSTM cell based on onf(t), i(t) and ec(t). After that, another tanh function is applied to the cell state c(t) and multiplied by o(t) to extract the appropriate content.

In 2014, Kyunghyun Cho [12] proposed a new method to overcome the vanishing gradient problem, which is a disadvantage of the traditional RNN. Furthermore, while LSTM activates a forget gate and an input gate, GRUs combines them into an update gate which is also a sigmoid function. The forget gate function of LSTM is now performed on the reset gate of GRU.

This shows that GRU cells are a great way to eliminate the vanishing gradient problem for RNN.

Figure 2.7: Long Short-term Memory Structure [60]

Related work

For skin lesion segmentation, there are many deep-learning-based techniques that have achieved significant achievements such as and [57]. Besides that, there have been recent works to find out effective features from the skin cancer database. Bhuiyan et al [7] have suggested that Otsus method, Gradient Vector Flow, Color based image segmentation using K-mean Clustering are the beneficial methods for feature extraction approaching skin cancer analysis.

Methodology

For this reason, we need to detect and remove the variant category images. While other metrics uniformly distribute histograms of benign and melanoma clusters, the city block distance (l1−norm) shows that there is a special category that yields almost benign images with circular spots or light orange background in this category (Fig. 3.4a). Almost all images with circular spots and a light orange background fall into variant category 5 and are predicted by three main objects: pumpkin, toilet seat and pick.

In order to understand what are the biased features of category 5, using the discriminative localization method of Zhou et al [61], we accept that the activity of the CNN model is focused on circular patches and dim background as Figure 3.5. We evaluate the efficiency of our model by applying the new training database to the VGG-19 model. Compared with the original training database, the accuracy of the validation test in the training task is higher and the training loss is more stable.

The validation accuracy is 80 percent for the model from original training database, but increases to 84.4 percent for the model from new training database. The model of our new training database also shows the more positive performance than the model of primitive database as table III.1. In this section, we proposed the framework to analyze the training data for skin cancer classification.

After training the data with a powerful deep CNN model such as ImageNet VGG-19 pre-trained and k-means clustering, we can detect and remove invalid images from our database.

ECG signal models for biometrics

In this study, we proposed an evaluation study on the performance of deep learning techniques in ECG identification when they are against the intrapersonal ECG problem. At this time, deep learning studies for ECG classification have been investigated, although, almost of these works are for the non-intrapersonal ECG dataset. Palva et al [46] proposed a deep convolutional neural network with an ECG signal processing framework using R peak normalization and distance measures.

Another CNN model was presented by Zhang et al [59] as the core component of a multi-resolution identification system with segmented ECG signal with autocorrelation and wavelet transform. In fact, QT interval correction has been studied as a method of normalization and feature extraction and [18]. Lugovaya [38] proposed the use of QT interval correction to normalize the ECG signal and concluded that T wave properties could improve the verification performance.

Methodology

This proposed method may be a promising way to expand the ECG database and aims to increase the scope and amount of useful features. Combined with wavelet transform and autocorrelation, their framework can improve the performance of a deep learning model when the wavelet transform interprets the ECG signal in different time-frequency patterns. In this study, we want to develop a DL model for only one ECG pulse that is fast to train and has high accuracy, we proposed a simple CNN model in Section IV.3.b and used QTc.

Israel et al [28] is one of the first studies to interpret the phenomenon of invariance to variable heart rate. Similar to what Labati et al [33] have suggested, QRS complexes are effective components for biometrics. In their study, they extracted QRS complex as the primary feature for their Deep-ECG CNN model to handle the ECG identification.

Although a combination between a flexible ability of P and T waves and a stable QRS complex is a promising feature to advance the biometric ECG performance. In 2004, S.Luo et al [39], after evaluating many of the QTc formulas, concluded that Hodge's QT interval correction formula is the most appropriate method for a large-scale ECG database, which is an important and useful point to achieve an achieved deep learning classifiers. Guided filter (GF) is the popular method in computer vision for many problems such as filtering, artifact analysis and data sampling [24].

The user template guided filter used the recorded ECG guide signal t, which is a template to filter one.

Figure 4.1: Block diagram of a traditional RNN by Salloun [53]

Results

For the non-interpersonal cases, the training consists of one recording of ECG Set S and another of a test set. In this section, we experiment with the behavior of a large number of machine learning models and deep learning architectures to evaluate their efficiency in classifying ECG signals with and without interpersonal database corresponding to ECG Set S and ECG Set A. All the results of deep learning models are comparable to the baseline,classification model using the Euclidean algorithm.

In general, the performances of deep learning methods as well as baseline on ECG classification could not be improved by using the user template guided filter. According to the Table IV.3, the deep learning models showed a significant improvement on their Dataset A classification. It is notable that QTc augmentation is an effective method to improve performance of deep learning models on the intra-personal database classification.

In this section, we proposed a new technique to improve the performance of deep learning models using QTc augmentation. Notably, for the intrapersonal datasets, while the deep learning models without QTc augmentation in Table IV.1 and IV.2 showed the lowest power abilities to classify ECG group A, with QTc augmentation, those become more powerful techniques to solve the identification problem. . In this study, we investigated a framework using deep learning and k-means clustering to analyze and improve the quality of skin cancer data.

This method has shown a good performance when it contributed to the obvious improvement of the activity of deep learning models, even on the specific challenging intrapersonal problem of ECG signals. All in all, this study aims to develop new methods to improve the performance of deep learning on biomedical computing. Although deep learning has achieved many state-of-the-art achievements in several fields, in medical data processing, there are still many challenges such as variant database, noisy features, limitations of database quality and quantity, etc.