Download EEG-Based Depression Detection and more Study Guides, Projects, Research Study of Commodities in PDF only on Docsity!
A
MINI PROJECT REPORT
ON
DEPRESSION DETECTION USING EEG
Submitted by MADASU UMA MAHESH 20W91A05C MARILLA DEVENDER 20W91A05B K MANOJ KUMAR 20W91A05A GATLA SNEHA SRI 20W91A056 5 Under the Esteemed Guidance of Mr MOHUMMAD ABDUL WAHED TO Jawaharlal Nehru Technological University, Hyderabad In partial fulfillment of the requirements for award of degree of BACHELOR OF TECHNOLOGY IN COMPUTER SCIENCE AND ENGINEERING DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING MALLA REDDY INSTITUTE OF ENGINEERING AND TECHNOLOGY (UGC AUTONOMOUS) (Sponsored by Malla Reddy Educational society) (Affiliated to JNTU, Hyderabad) Maisammaguda, Dhulapally post, Secunderabad-500014. 2020 - 2024
DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
BONAFIDE CERTIFICATE
This is to certify that this is the Bonafide certificate of a MINI PROJECT Report titled “DEPRESSION DETECTION USING EEG ” is submitted by MADASU UMA MAHESH 20W91A05C0, MARILLA DEVENDER 20W91A05B6, K MANOJ KUMAR 20W91A05A2, GATLA SNEHA SRI 20W91A0565 of B. Tech in the partial fulfillment of the requirements for the degree of Bachelor of Technology in Computer Science and Engineering, Malla Reddy Institute of Engineering & Technology.
Project coordinator Head of the Department
ACKNOWLEDGEMENT
First and foremost, I am grateful to the Principal Dr. P. SRINIVAS , for providing me with all the resources in the college to make my project a success. I thank him for his valuable suggestions at the time of seminars which encouraged me to give my best in the project. I would like to express my gratitude to Dr. ASHWAQUL HASSAN , Head of the Department, Department of Computer Science and Engineering for his support and valuable suggestions during the dissertation work. I offer my sincere gratitude to our mini project guide, Mr MOHUMMAD ABDUL WAHED (Assistant Professor) of Computer Science and Engineering department who has supported me throughout this project with their patience and valuable suggestions. I would also like to thank all the supporting staff of the Dept. of CSE and all other departments who have been helpful directly or indirectly in making the seminar a success. I am extremely grateful to my parents for their blessings and prayers for my completion of seminar. MADASU UMA MAHESH 20W91A05C MARILLA DEVENDER 20W91A05B K MANOJ KUMAR 20W91A05A GATLA SNEHA SRI 20W91A
i
Abstract
Depression is a widespread mental health disorder with a significant global impact on individuals and society. Early and accurate diagnosis of depression is essential for effective treatment and intervention. This abstract presents an overview of research on the utilization of Electroencephalography (EEG) in the detection of depression. Electroencephalography is a non-invasive neuroimaging technique that records the electrical activity of the brain. Over the years, researchers have explored the potential of EEG to provide valuable insights into the neurophysiological underpinnings of depression. This paper reviews the current state of EEG-based depression detection and the promise it holds for improving diagnosis and treatment strategies. Recent studies have demonstrated that individuals with depression exhibit distinctive EEG patterns compared to healthy controls. These patterns often involve alterations in brainwave frequencies, connectivity, and asymmetry, which may serve as biomarkers for depression. By employing advanced signal processing techniques and machine learning algorithms, researchers have made significant strides in classifying depressed individuals with high accuracy using EEG data. Furthermore, EEG-based depression detection has the potential to offer several advantages over traditional assessment methods. It provides an objective and quantifiable measure of brain activity, reducing the subjectivity associated with self-reporting and clinical assessments. This abstract concludes by highlighting the potential of EEG as a valuable tool for depression detection. While challenges such as data variability and interpretational complexity persist, ongoing research in this field offers a promising avenue for developing reliable and accessible diagnostic tools for depression.
iii LIST OF FIGURES Fig No Figure Name Page No
**1. USE CASE DIAGRAM 07
- CLASS DIAGRAM 08
- SEQUENCE DIAGRAM 09
- COLLABRATION DIAGRAM 10
- DOWNLOADING THE 29 - 30** **CORRECT VEERSION
- INSTALLATION OF PYTHON 31 - 32
- VERIFY THE PYTHON INSTALLATION 33
- CHECK HOW PYTHON IDLE WORKS 34
- SCREENSHOTS 41 - 46**
1
1. INTRODUCTION
Depression, a pervasive and debilitating mental health disorder, affects millions of people worldwide, exerting a profound impact on their quality of life and well- being. Early and accurate diagnosis of depression is essential for timely intervention and effective treatment. However, diagnosing depression remains a complex challenge, often relying on subjective self-reporting and clinical assessments. In this context, emerging technologies and neurophysiological approaches have the potential to revolutionize depression detection. One such innovative approach is the utilization of Electroencephalography (EEG) for the detection and characterization of depression. EEG, a non-invasive neuroimaging technique, records the electrical activity of the brain by measuring the voltage fluctuations resulting from the collective neural activity of millions of neurons. The EEG method has gained prominence in recent years as a promising tool for understanding the neurobiological basis of depression and as a means of developing more objective and reliable diagnostic criteria. This introduction provides an overview of the current landscape of depression diagnosis, highlighting the challenges faced by traditional methods, and introduces EEG as an exciting avenue for improving the accuracy and objectivity of depression detection. We will explore the unique features of EEG, the neurophysiological markers associated with depression, and the potential benefits that EEG offers in enhancing our understanding of this complex mental health condition. As we delve into the evolving field of depression detection using EEG, it becomes evident that this innovative approach has the potential to transform the way we diagnose and manage depression. EEG's capacity to capture real-time, neural electrical activity offers a unique window into the depressed brain, providing insights that extend beyond self-reporting and clinical evaluation. The objective, quantifiable nature of EEG data, along with its non-invasive and cost-effective characteristics, makes it an enticing prospect for both researchers and clinicians seeking more accurate, accessible, and early detection methods for depression.
3 2 .2 Proposed System: Depression Detection Using EEG
Advantages:
- Objective Biomarkers: EEG-based depression detection leverages the brain's electrical activity to provide objective biomarkers for depressive states. This improves diagnostic accuracy and reduces subjectivity.
- Early Detection: EEG can detect subtle changes in brain activity as sociated with depression, allowing for early detection and intervention, potentially preventing the progression of the condition.
- Non-Invasive: The use of EEG is non-invasive and does not involve exposure to radiation, making it a safe and comfortable diagnostic tool for patients.
- Data-Driven: Advanced signal processing techniques and machine learning algorithms can analyze EEG data to identify depression with high accuracy, improving the efficiency of diagnosis.
- Remote Monitoring: EEG data can be collected remotely, enabling continuous monitoring of patients' mental health without the need for frequent in-person visits.
- Reduced Stigma: EEG-based diagnosis may reduce the social stigma associated with traditional clinical assessments, as it focuses on neurophysiological markers rather than subjective self-reporting.
- Personalized Treatment: With more accurate diagnosis, healthcare providers can tailor treatment plans to individual patients, improving the effectiveness of interventions.
- Research Potential: EEG data can contribute to a better understanding of the neurophysiological basis of depression, leading to advancements in treatment and prevention.
4
3. ANALYSIS
3.1 Introduction
SYSTEM STUDY FEASIBILITY STUDY
The feasibility of the project is analyzed in this phase and business proposal is put forth with a very general plan for the project and some cost estimates. During system analysis the feasibility study of the proposed system is to be carried out. This is to ensure that the proposed system is not a burden to the company. For feasibility analysis, some understanding of the major requirements for the system is essential.
Three key considerations involved in the feasibility analysis are
ECONOMICAL FEASIBILITY
TECHNICAL FEASIBILITY
SOCIAL FEASIBILITY
ECONOMICAL FEASIBILITY
This study is carried out to check the economic impact that the system will have on the organization. The amount of fund that the company can pour into the research and development of the system is limited. The expenditures must be justified. Thus the developed system as well within the budget and this was achieved because most of the technologies used are freely available. Only the
customized products had to be purchased.
TECHNICAL FEASIBILITY
This study is carried out to check the technical feasibility, that is, the technical requirements of the system. Any system developed must not have a high demand on the available technical resources. This will lead to high demands on the available technical resources. This will lead to high demands being placed on the client. The developed system must have a modest requirement, as only minimal or null changes are required for implementing this system.
6
4.DESIGN
4.1 UML DIAGRAMS :
UML stands for Unified Modeling Language. UML is a standardized general-purpose modeling language in the field of object-oriented software engineering. The standard is managed, and was created by, the Object Management Group. The goal is for UML to become a common language for creating models of object oriented computer software. In its current form UML is comprised of two major components: a Meta-model and a notation. In the future, some form of method or process may also be added to; or associated with, UML. The Unified Modeling Language is a standard language for specifying, Visualization, Constructing and documenting the artifacts of software system, as well as for business modeling and other non-software systems. The UML represents a collection of best engineering practices that have proven successful in the modeling of large and complex systems. The UML is a very important part of developing objects oriented software and the software development process. The UML uses mostly graphical notations to express the design of software projects.
GOALS:
The Primary goals in the design of the UML are as follows:
- Provide users a ready-to-use, expressive visual modeling Language so that they can develop and exchange meaningful models.
- Provide extendibility and specialization mechanisms to extend the core concepts.
- Be independent of particular programming languages and development process.
- Provide a formal basis for understanding the modeling language.
- Encourage the growth of OO tools market.
- Support higher level development concepts such as collaborations, frameworks, patterns and components.
7. Integrate best practices.
7
USE CASE DIAGRAM:
A use case diagram in the Unified Modeling Language (UML) is a type of behavioral diagram defined by and created from a Use-case analysis. Its purpose is to present a graphical overview of the functionality provided by a system in terms of actors, their goals (represented as use cases), and any dependencies between those use cases. The main purpose of a use case diagram is to show what system functions are performed for which actor. Roles of the actors in the system can be depicted.
9
SEQUENCE DIAGRAM:
A sequence diagram in Unified Modeling Language (UML) is a kind of interaction diagram that shows how processes operate with one another and in what order. It is a construct of a Message Sequence Chart. Sequence diagrams are sometimes called event diagrams, event scenarios, and timing diagrams.
10
COLLABRATION DIAGRAM:
Activity diagrams are graphical representations of workflows of stepwise activities and actions with support for choice, iteration and concurrency. In the Unified Modeling Language, activity diagrams can be used to describe the business and operational step-by-step workflows of components in a system. An activity diagram shows the overall flow of control.
12
5.2.2 Advantages of Python :
Let’s see how Python dominates over other languages.
1. Extensive Libraries Python downloads with an extensive library and it contain code for various purposes like regular expressions, documentation-generation, unit-testing, web browsers, threading, databases, CGI, email, image manipulation, and more. So, we don’t have to write the complete code for that manually. 2. Extensible As we have seen earlier, Python can be extended to other languages. You can write some of your code in languages like C++ or C. This comes in handy, especially in projects. 3. Embeddable Complimentary to extensibility, Python is embeddable as well. You can put your Python code in your source code of a different language, like C++. This lets us add scripting capabilities to our code in the other language. 4. Improved Productivity The language’s simplicity and extensive libraries render programmers more productive than languages like Java and C++ do. Also, the fact that you need to write less and get more things done. 5. IOT Opportunities Since Python forms the basis of new platforms like Raspberry Pi, it finds the future bright for the Internet Of Things. This is a way to connect the language with the real world. When working with Java, you may have to create a class to print ‘Hello World’. But in Python, just a print statement will do. It is also quite easy to learn, understand, and code. This is why when people pick up Python, they have a hard time adjusting to other more verbose languages like Java. 7. Readable Because it is not such a verbose language, reading Python is much like reading English. This is the reason why it is so easy to learn, understand, and code. It also does not need curly braces to define blocks, and indentation is mandatory. This further aids the readability of the code.
13
8. Object-Oriented This language supports both the procedural and object-oriented programming paradigms. While functions help us with code reusability, classes and objects let us model the real world. A class allows the encapsulation of data and functions into one. 9. Free and Open-Source Like we said earlier, Python is freely available. But not only can you download Python for free, but you can also download its source code, make changes to it, and even distribute it. It downloads with an extensive collection of libraries to help you with your tasks. 10. Portable When you code your project in a language like C++, you may need to make some changes to it if you want to run it on another platform. But it isn’t the same with Python. Here, you need to code only once , and you can run it anywhere. This is called Write Once Run Anywhere (WORA). However, you need to be careful enough not to include any system-dependent features. 11. Interpreted Lastly, we will say that it is an interpreted language. Since statements are executed one by one, debugging is easier than in compiled languages. Any doubts till now in the advantages of Python? Mention in the comment section.
5.2.3 Advantages of Python Over Other Languages :
1. Less Coding Almost all of the tasks done in Python requires less coding when the same task is done in other languages. Python also has an awesome standard library support, so you don’t have to search for any third-party libraries to get your job done. This is the reason that many people suggest learning Python to beginners. 2. Affordable Python is free therefore individuals, small companies or big organizations can leverage the free available resources to build applications. Python is popular and widely used so it gives you better community support.
