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TITLE PAGE
WEARABLE DEVICES TO PREDICT ANXIETY ATTACKS (1)
BY
---
--/H2013/01430
DEPARTMENT OF ----
SCHOOL OF ---
INSTITUTE OF ---
DECEMBER,2018
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This is to certify that the research work, " wearable devices to predict anxiety attacks (1)" by ---, Reg. No. --/H2007/01430 submitted in partial fulfillment of the requirement award of a Higher National Diploma on --- has been approved.
By
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Supervisor Head of Department.
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External Invigilator
DEDICATION
This project is dedicated to Almighty God for his protection, kindness, strength over my life throughout the period and also to my --- for his financial support and moral care towards me.Also to my mentor --- for her academic advice she often gives to me. May Almighty God shield them from the peril of this world and bless their entire endeavour Amen.
ACKNOWLEDGEMENT
The successful completion of this project work could not have been a reality without the encouragement of my --- and other people. My immensely appreciation goes to my humble and able supervisor mr. --- for his kindness in supervising this project.
My warmest gratitude goes to my parents for their moral, spiritual and financial support throughout my study in this institution.
My appreciation goes to some of my lecturers among whom are Mr. ---, and Dr. ---. I also recognize the support of some of the staff of --- among whom are: The General Manager, Deputy General manager, the internal Auditor Mr. --- and the ---. Finally, my appreciation goes to my elder sister ---, my lovely friends mercy ---, ---, --- and many others who were quite helpful.
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Wearable devices (WD) are starting to increasingly be used for interventions to promote well-being by reducing anxiety attack (AA). Electrocardiogram (ECG) signal is one of the most commonly used biosignals for assessing the cardiovascular system as it significantly reflects the activity of the autonomic nervous system during emotional changes. Little is known about the accuracy of using ECG features for detecting AAs. Moreover, during our literature review, a limited number of studies were found that involve ECG collection using WD for promoting mental well-being. Thus, for the sake of validating the reliability of ECG features for detecting anxiety in wearable devices WD, we screened 1040 articles, and only 22 were considered for our study; specifically 6 on panic, 4 on post-traumatic stress, 4 on generalized anxiety, 3 on social, 3 on mixed, and 2 on obsessive-compulsive anxiety disorder articles. Most experimental studies had controversial results. Upon reviewing each of these papers, it became apparent that the use of ECG features for detecting different types of anxiety is controversial, and the use of ECG-WD is an emerging area of research, with limited evidence suggesting its reliability. This work is on using wearable devices to predict anxiety attack by providing a real time anxiety prediction using smartphone and cloud computing.
TABLE OF CONTENTS
COVER PAGE
TITLE PAGE
APPROVAL PAGE
DEDICATION
ACKNOWELDGEMENT
ABSTRACT
CHAPTER ONE
- INTRODUCTION
- BACKGROUND OF THE PROJECT
- PROBLEM STATEMENT
- AIM AND OBJECTIVE OF THE PROJECT
- SIGNIFICANCE OF THE PROJECT
- SCOPE OF THE PROJECT
- LIMITATION OF THE STUDY
- PROJECT ORGANISATION
CHAPTER TWO
LITERATURE REVIEW
- OVERVIEW OF ANXIETY ATTACK
- REVIEW OF ANXIETY ATTACK
- TYPES OF ANXIETY DISORDERS
- ANXIETY DISORDER SYMPTOMS
- ANXIETY DISORDER CAUSES AND RISK FACTORS
- OVERVIEW OF WEARABLE TECHNOLOGIES
CHAPTER THREE
MATERIALS AND METHODS
- INTRODUCTION
- HARDWARE DESIGN OF ECG PATCHES
- ANDROID APP SOFTWARE DESIGN
- ECG CLASSIFICATION ALGORITHM
- CLOUD SOFTWARE DESIGN
CHAPTER FOUR
4.0 RESULT ANALYSIS
- ALGORITHM PERFORMANCE
- SYSTEM VERIFICATION
CHAPTER FIVE
CONCLUSIONS AND RECOMMENDATION
- CONCLUSION
- RECOMMENDATION
- REFERENCES
CHAPTER ONE
1.0 INTRODUCTION
1.1 BACKGROUND OF THE STUDY
Anxiety attacks (AA) are the most common type of mental illness in the world, affecting 264 million worldwide according to World Health Organization [2017]. Every 10 years, there is an increase of AD by 14.9% [Vos, T, 2016]. Common mental illness such as anxiety and depression are increasing, especially in low- and middle-income countries [Friedrich, 2017]. These staggering statistics have motivated researchers to develop new and improved technologies to promote well-being and to reduce related morbidity, mortality, and health care costs. Moreover, the gold standard for assessing anxiety and other psychiatric disorders in children or adults is the clinical interview [Siegel, 2012]. Attaining an appointment with a mental health consultant is not an easy process that often takes time, and unfortunately by the time an appointment is reached with a psychologist a person’s life can be in danger.
The increase of using wearable devices (WD), such as electrocardiogram (ECG) smart watches and belts, along with mobile apps, have given new opportunities for WDs to influence our decisions and behaviors. Moreover, the recent advances in analyzing ECG signals for WDs for algorithm development [Elgendi, 2013], detecting R peaks [Elgendi, 2013], compression [Elgendi, 2018], and visualization [Elgendi, M. Eventogram, 2016] create a more reliable technology. Wearable devices WDs can be used at any time and in any environment, enabling objective mental status feedback based on the collected ECG data. In general, WD technologies have been already used in health interventions, providing promising results [Elgendi, M. Eventogram, 2011]. However, so far, these interventions have not been adopted for addressing ADs. Thus, integrating wearable ECG sensors (or other sensors) into our clothes, phones, and accessories could help users at risk and could lead to relatively positive health outcomes. Note that in the literature, to our knowledge, there are three measures for assessing autonomic nervous system functioning in emotion: cardiovascular, electrodermal, and respiratory measures [Kreibig, 2010]. However, the focus of this study is investigating the cardiovascular measures, specifically using ECG signals.
Several reviews investigated the correlation of ECG features with different types of AD; however, these studies were often limited to clinic-based measurement, or a healthcare setting. No review has examined the quality of the ECG signal, the measurement setting, the ECG sensor location, or the portability of the device. Moreover, many papers published ECG features that contradicted with other findings on the same topic [Pittig, 2013]. In other words, the ECG features used in some papers to detect the presence or absence of anxiety attack in subjects were reported as not used to detect the presence or absence of anxiety attack in other papers. For these reasons, limited research has been carried out to assess the validity of ECG and its efficacy and robustness in detecting anxiety attack, especially for real-time feedback.
In this work, a wearable ECG patch device was designed to collect single-lead ECG signals and to continuously send the collected ECG data to an Android smartphone via Bluetooth. The Android APP displayed the ECG waveforms in real time and transmitted every 30s ECG data to a cloud server. The cloud server used the improved ECG classification algorithm to predict anxiety attack and pushed the ECG data and classification results to the web browser of a doctor when anxiety attack was detected. Finally, the Android APP displayed the doctor’s diagnosis.
1.2 PROBLEM STATEMENT
Anxiety attacks are group of mental illnesses that cause constant and overwhelming anxiety and fear. The excessive anxiety can make you avoid work, school, family get-togethers, and other social situations that might trigger or worsen your symptoms. Anxiety attacks come in different form such Panic, fear, and uneasiness, Sleep problem, not being able to stay calm and still, Cold, sweaty, numb, or tingling hands or feet, Shortness of breath, breathing faster and more quickly than normal. According to WHO (2017), anxiety attack (AA) are the most common type of mental illness in the world, affecting 264 million worldwide. When anxiety attack is not well managed it can lead to other sicknesses which can even cause heart attack which leads to sudden death. This study was carried out to predict anxiety attack in a victim using wearable devices.
1.3 AIM AND OBJECTIVES OF THE STUDY
The main aim of this work is to predict oncoming anxiety attacks using wearable devices. The objectives of this work are:
- To save life
- To highlight the electrocardiogram features and their challenges,
- To predict anxiety attack using electrocardiogram (ECG) effectively with wearable devices
- To provide a real time anxiety prediction using smartphone and cloud computing.
1.4 SIGNIFICANCE OF THE STUDY
This research work will throw more light on the techniques for controlling anxiety attack with improved technologies in other to promote well-being and to reduce related morbidity, mortality, and health care costs.
1.5 LIMITATION OF STUDY
As we all know that no human effort to achieve a set of goals goes without difficulties, certain constraints were encountered in the course of carrying out this project and they are as follows:-
- Difficulty in information collection: I found it too difficult in laying hands of useful information regarding this work and this course me to visit different libraries and internet for solution.
- Financial Constraint: Insufficient fund tends to impede the efficiency of the researcher in sourcing for the relevant materials, literature or information and in the process of data collection (internet, questionnaire and interview).
- Time Constraint: The researcher will simultaneously engage in this study with other academic work. This consequently will cut down on the time devoted for the research work.
1.6 SCOPE OF THE STUDY
An anxiety attack usually involves a fear of some specific occurrence or problem that could happen. Symptoms include worry, restlessness, and possibly physical symptoms, such as changes in heart rate. There are different types of wearable devices foe medical use. This work is on a real-time telemonitoring systems based on cloud computing for healthcare or for detecting heart diseases. The cloud computing technology is a powerful tool to provide remote data transmission, complex computation, big data processing, and instant diagnosis. However, these cloud based telemonitoring systems still lack an algorithm for automatic AF detection.
1.7 PROJECT ORGANISATION
CHAPTER TWO: The chapter one of this work has been displayed above. The complete chapter two of " wearable devices to predict anxiety attacks (1)" is also available. Order full work to download. Chapter two of " wearable devices to predict anxiety attacks (1)" consists of the literature review. In this chapter all the related work on " wearable devices to predict anxiety attacks (1)" was reviewed.
CHAPTER THREE: The complete chapter three of " wearable devices to predict anxiety attacks (1)" is available. Order full work to download. Chapter three of " wearable devices to predict anxiety attacks (1)" consists of the methodology. In this chapter all the method used in carrying out this work was discussed.
CHAPTER FOUR: The complete chapter four of " wearable devices to predict anxiety attacks (1)" is available. Order full work to download. Chapter four of " wearable devices to predict anxiety attacks (1)" consists of all the test conducted during the work and the result gotten after the whole work
CHAPTER FIVE: The complete chapter five of design and construction of a " wearable devices to predict anxiety attacks (1)" is available. Order full work to download. Chapter five of " wearable devices to predict anxiety attacks (1)" consist of conclusion, recommendation and references.
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