Authors: B. Tejaswi Reddy, B. Khyathi Nikhitha, L. SaiAnish Reddy, Dr. G. R. Anil
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The most severe kind of mood disorder, depression, has a significant impact on mental health and causes difficulties in daily living. Electroencephalogram (EEG) signals can detect this mood condition. EEG signal analysis is a tedious, time- consuming, and highly specialized method for manually diagnos- ing depression. Therefore, a fully automated depression detection system developed using EEG signals will be useful to clinicians. In this research work, a CNN based hybrid model was designed and this model resulted with 90percent accuracy, whereas with SVM it resulted in 60.
A major depression has a profound impact on a person’s family, personal connections, and other elements of general health. Many people experience depression also known as mental condition that affects approximately more than 350 million individuals worldwide. Current methods for diagnos- ing depression have clear drawbacks, including patient denial, low sensitivity, subjective biases, and accuracy issues. Depres- sion diagnosis is labor intensive due to all these drawbacks. Recently, machine learning approaches have been employed to diagnose depression using biosignals. The categorization accuracy and the real application circumstances still differ, though.
A. EEG and EMs Based Depression Recognition :
The EEG, or electrophysiological technology, measures how well the brain is functioning. It is produced by the neurological system of the brain. It is frequently used to help diagnose mental diseases including depression. Numerous studies conducted to date have shown that the EEG is a viable tool for detecting depression. Erguzel achieved an accuracy of 89.12 percent while classifying the EEG data obtained from 147 patients with serious depression using a back-propagation neural network (BPNN). Hosseinifard attained 88.6 percent accuracy overall through the use of a genetic algorithm (GA) and a support vector machine (SVM) classifier for classification. Muralidhar employed the SVM classifier to achieve a precision of 88.92 percent. Beta, delta, theta, and asymmetry were used as features in Mahato, and MultiCluster Feature Selection was used for feature selection. With SVM, they achieved the greatest classification accuracy of 88.33 percent. Mahato used linear, non-linear, and a combination of linear and non-linear variables to categorize depression and healthy people, respectively. Combining linear and non-linear features resulted in the highest categorization, 93.3 percentage. Betul Constructed a deep hybrid model utilizing convolutional neural networks(CNN) and long-short term memories (LSTM) architectures. They provided precise classification of the left and right hemisphere EEG data of 99.12 and 97.66 percentages, respectively. Li  used EEG readings and a convolutional neural network (ConvNet) to identify depression. Their technique recognized mild depression with an accuracy of 85.6 percent- age. WHAT WE HAVE DONE: The proposed approach determines depression using EEG signals. The results are more accurate and data loss is reduced compared to existing systems, here we use Convolution neural network (CNN) to learn information about the functional connectivity matrices that are seen in these people.
II. LITERATURE SURVEY
Analyzing physiological signs is a solid way to predict stress, according to earlier study. Sensors linked to the human body are used to gather these signals. Researchers tried to determine stress from analyzing physiological signals through the use of conventional machine learning techniques. Mixed outcomes—between 50 and 90 accuracy—have been obtained. A disadvantage using conventional machine learning techniques is their necessity for custom features. Inaccuracy arises when characteristics are not correctly detected. We created a multilayer perceptron neural network and a convolutional neural network in one dimension (1D) as two deep neural networks to overcome this deficiency. Deep neural networks retrieve characteristics derived from raw data using their tiers instead of requiring manually created features. To complete two tasks, Deep neural networks looked at physiological information from sensors worn on the wrist and chest. We specifically designed every neural network to be examined information from either the chest or wrist-worn multilayer perceptron neural network(1D convolutional neural network) sensors. The initial task involved stress detection via bi- nary classification, where Networks may distinguish between stressful and unstressed conditions. The following test required the networks to distinguish between the initial, anxious, and delighted states using a three-class classification system for emotions. On publicly accessible data gathered during earlier investigations, the networks were trained and put to the test.
IV. MODULES EXPLANATION
V. PROCESS DIAGRAM
VI. ABOUT DATASET
This dataset contains EEG brainwave data that has been extracted statistically using our method
Source: EEG brainwave dataset: mental state Size: 989 columns
To recognize mental states for use in human-machine inter- action. We intend to define discriminative features of EEG- based patterns and develop appropriate classifications for categorizing brainwave patterns based on frequency and activity levels. Based on some mental conditions reported by cognitive- behavioral studies, we determined three conditions in which the head-worn Muse was equipped with four EEG sensors (TP9, AF7, AF8, TP10).
To train and test various techniques, this dataset comprises five individual and one-minute sessions for each state of mind. Alpha, beta, theta, delta, and gamma signals from the EEG headband were used as the suggested collection of characteristics, and selection algorithm and classifier model were tested to examine how well they performed in terms of recognition accuracy and the minimum amount of features needed. The findings demonstrate that conventional classi- fiers like Bayesian Networks, Support Vector Machines, and Random Forests may achieve an overall accuracy of over 87percent using only 44 features from a set of over 2100 characteristic
VII. CONVOLUTIONAL NEURAL NETWORK (CNN) — DEEP LEARNING
VIII. THE ALGORITHM BY FOLLOWING
The suggested system uses changes in EEG signals to diagnose depression. As a result, the results are more accurate and data loss is reduced. Both linear and nonlinear approaches can successfully identify these changes. Convolution neural network is what we’re employing (CNN) The project’s major goal is to use EEG signals to determine whether a person is normal or abnormal, which will make evaluation easier and produce more reliable results. In order to identify people with mild depression, CNN is used to learn information about the functional connectivity matrices that are seen in these people. In contrast to the graph theory now in use, this strategy is novel for recognizing depression.
IX. RESULT ANALYSIS
The bar chart shows the performance comparison of CNN and SVM. It can be seen from the findings that the perfor- mance values are higher for CNN than that of SVM. However, the CNN based model takes longer time for computation and has various issues like overfitting, exploding gradient and class imbalance.
This paper has concentrated on depression detection and prediction by using deep learning algorithms which is a convo- lutional neural network using EEG signal. We can observe that this model performs the conventional classification in terms of results, CNN gives more accuracy that is 92.5 which is better than SVM because it gives only 68.3 as accuracy. Future research will focus on exploring more sophisticated EEG signal analysis applications and studying many methods for signal processing and classification. There are several ap- plications that use the analysis of EEG-based computer-aided techniques, however these are either least or not addressed in the current study for example, researching EEG signals for those with neurological diseases and sleep issues. The scope of current research on the categorization and processing of signals employed in these applications will be attempted to be covered. By compiling a significant dataset of patient data and evaluating it using a range of signal processing and classification approaches, it has been intended to further research on EEG-based depression diagnosis.
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