IRJET- Review on Depression Prediction using Different Methods

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 08 | Aug 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 1603
Review on Depression Prediction using Different Methods
Mrunal Kulkarni1, Prof. Arti R.Wadhekar2
1M.Tech. Student (Department of Electronics And Telecommunication Engineering, Deogiri Institute of
Engineering and Management Studies, Aurangabad, Maharashtra, India)
2 Assistant Professor (Department of Electronics And Telecommunication Engineering, Deogiri Institute of
Engineering and Management Studies, Aurangabad, Maharashtra, India)
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Abstract - This Paper is focused on the basic survey of the
methods which are used to predict depression in humans.
Depression is a mental disorder which may lead to suicide if
not cured at early curable stages. So it is very important to
predict depression as soon as possible. Many people in the
world are suffering from depression in their day to day life.
Depression is of different types and can be predicted by
different ways. In this paper, study about all the techniques
which are used to predict depression and their relative study
about techniques, methods, algorithms used to predict
depression is done.
Key Words: BECK’s Inventory,Artificial Intelligence,EEG,
Voice Recognition, Sentiment Analysis, ANN, Python,
MATLAB.
1. INTRODUCTION
Depression is a very serious disorder and it is very
necessary to predict it at very early curable stage. Human
Brain is a most complex part of the body. So it is very
difficult to understandit’scomplexity.Depressionisfoundas
a Mental disorder, so to predict depression isa verycomplex
part. Psychiatrist says that diagnosis and cure of Depression
is done mostly by using Questions and Answers and by
applying various Psychometric tests and theory and by
observing patient’s response to it. But Now a days, research
says that there are also other methods using which we can
predict depression. This methods are DepressionPrediction
using EEG signal Processing, Depression Prediction using
Audio and Visual Analysis, Depression Prediction usingText
analysis which includes sentiment analysis, emoji analysis,
etc. This methods mostly belongs to Artificial Intelligence,
Machine learning algorithms in it. Following are some key
points which everyone should know before learning these
Depression prediction methods.
1.1 Artificial Intelligence (AI)
Artificial Intelligence (AI) is an area of computer science
that emphasizes the creation of intelligent machines that
work and react like humans .A machine with the ability to
perform cognitive functions such as perceiving, learning,
reasoning and solve problems are deemed to hold an
Artificial Intelligence (AI). AI represents simulated
intelligence in machines .It is a subset of Data Science.
Artificial Intelligence exists when a machine has a cognitive
ability .The AI includes human level concerning reasoning,
speech and vision.AI is used to avoid repetitive task. AI can
repeat a task continuously.AI is used in all industries, from
marketingtosupplychain,finance,foodproduct,socialmedia
applications, etc.
1.2 Machine Learning(ML)
Machine Learning (ML) is a data analytics technique that
teaches computers to do what comes naturally to humans
and animals, i.e., learn from experience. ML algorithms use
computational methods to ‘learn’ information directly from
data without relying on a predetermined equation as a
model. The algorithms adaptively improve their
performance as the number of samplesavailableforlearning
increases. ML is used when:
 Hand written rules and equations are too complex
as in face recognition and speech recognition.
 The rules of a task are constantly changing as in
fraud detection from transaction records.
 The nature of data keep changing and program
needs to adapt as in automated trading ,energy
demand forecastingand predictingshoppingtrends.
1.3 Deep Learning
Deep Learning imitates the way our brain works, i.e.,
learn from experiences. It uses concepts of neural networks
to solve complex problems. Deep learning works as follows:
 Deep Learning is based on basic unit of brain called
brain cell or a neuron. Inspired from a neuron, an
artificial neuron or perceptron was developed.
 A biological neuron has dendrites which areusedto
receive inputs.
 Similarly, a perceptron receive multiple inputs,
applies various transformations and functions and
provides an output.
 Just like how our brain contains multiple connected
neurons called neural network, we can also have a
network of artificial neurons called perceptrons to
form a deep neural network.
 An Artificial Neuron or a Perceptron models a
neuron which has a set of inputs, each of which is
assigned some specific weight. The neuron then

computes some function on these weighted inputs
and gives output.
1.4 Artificial Neural Network (ANN)
An Artificial Neural Network (ANN) is a computational
model based on the structure and functions of biological
neural networks. ANN is an information processing model
that is inspired by the way biological nervous systems such
as brain process information.Informationthatflowsthrough
the network affects the structure of ANN because a neural
network changes or learns, in a sense-based on that input
and output.ANN are considered nonlinear statistical data
modelling tools where the complex relationships between
inputs and outputs are modelled or patterns are found.
Advantages of ANN are:
 Actually Learn from observing data sets.
 ANN used as a random function approximationtool.
 ANN takes data samples rather than entiredata sets
to arrive at solutions, which saves both time and
money.
1.5 Electroencephalography
Electroencephalography is a medical imagingtechnique
that reads scalp electrical activity generated by brain
structures. The electroencephalogram (EEG) is defined as
electrical activity of an alternating type recorded from the
scalp surface after being picked up by metal electrodes and
conductive media [1]. The EEG measured directly from the
cortical surfaceiscalledelectrocardiogramwhilewhenusing
depth probes it is called electrogram.EEGmeasuredfrom the
head surface is considered. Thus electroencephalographic
reading is a completely non-invasive procedure that can be
applied repeatedly to patients, normal adults, and children
with virtually no risk or limitation.
2. LITERATURE SURVEY
In this paper, various techniques for measuring
depression were studied. The depressioncanbepredictedby
using Question and Answer (Q & A) Analysis, i.e.
psychometric test, using EEG signal processing, using Audio,
visual and text analysis, using Sentiment analysis, etc. The in
detailed survey of these techniques is as below:
Question and Answer (Q & A) Analysis or psychometric
test using questions are generally referred by psychiatrists.
There are some standard questionnaires used by doctors to
determine the levels of anxiety and depression that a person
is experiencing. One of the most commonly used test is
Hospital Anxiety and Depression Scale (HADS).TheHADSisa
fourteen item scale that generates ordinal data. Seven of the
items relate to anxiety and seven relate to depression. The
item on the questionnaire consists of questions like ‘I feel
tense or wound up’, ‘worrying thoughtsgothroughmymind’,
‘I have lost interests in my appearance ’, etc. Each item on
questionnaire is scored from 0-3 and this means that a
person can score between 0 and 21 for either anxiety or
depression [2]. The other most commonly used test by
psychiatrist isBECK’sDepressionInventory(BDI).Thistestis
used for measuring the severity of depression. In its current
version BDI –II is designed for individuals aged 13 and over,
and is composed of items relating to symptomsofdepression
such as hopelessness and irritability, cognitions such as guilt
or feelings of being punished, as well as physical symptoms
such as fatigue, weight loss, and lack of interest in sex.
Another technique to predict depression is by analyzing
EEGsignal, i.e. using EEG signal processing.Mallikarjun H. M.
and Dr. H. N. Suresh in their paper, “Depression Level
Prediction using EEG signal processing”[1],obtained
Electroencephalogram Gram (EEG)signals from publicly
available database and are processed in MATLAB. It is useful
in classifyingsubjects withthedisordersusingclassifiertools
present in it. For classification purpose, the features are
extracted from frequency bands (alpha, delta and
theta).Initially EEG signals were read using EDF browser
software and the signals were loaded into MATLAB to getlog
Power Spectral DensityfromEEGbands.Theresultsobtained
from MATLAB are fed into neural network pattern
recognition tool and ANFIS tool box which is integrated in
MATLAB. These are powerful tool are used for data
classification.Relevantextractedfeaturesparameterssuchas
mean, standard deviation, entropy are used as inputs to the
ANFIS and nprtool.
Fig -1: EEG signal processing for classification of
disorders
The depression can be predicted by using Text,
Audio and Visual Analysis of the person. Though it is very
complex method and uses various machine learning
algorithms, the research on these methods are proven by
researcher and engineers to predictpersonsmental state, i.e.
it can be used to predict whether the person is depressed or
not using various parameters. Behaviour of a depressed
person shows change in his speaking toneandfeatures(may
be very low voice or very loud), his facial expressions and
head movement when compared toa non-depressedperson.
Shubham Dham and two others in their paper “Depression
Scale recognition using Audio, Visual and Text Analysis”[3] ,
introduced depression recognitionthroughvisual ,audioand
text features using machine learning algorithms like SVM
and neural networks for classification of dataset.GMM
clustering and fisher vectors were calculated on the relative
distance of the facial regions. Facial Regions used in
recording the relative distance of certain points involves
facial expressions like smiling, laughing, and other visible

emotions. Head pose, statistical descriptors on gaze, pose
and blinking rate were also calculated. Verbal responses of
the person coded in the form of text (sentences, words,
negative words) and audio (low level) features hold the
information regarding the behaviour of the person. The
features extracted from that were trained on SVM machine
learning classifier. Resultsfromaudio,fishervectorsand text
features, both individually and combined outperformed the
baseline results on validation dataset. Fisher vectorfeatures
were also classified using Neural Networks.
Different Machine Learning Techniques and
Algorithms [5]: There are different machine learning
techniques and algorithms which are depending on the type
of data used for classification. Some machine learning
techniques are supervised learning, unsupervised machine
learning, semi-supervised learning, reinforcement learning.
Supervised learning concerned with classified data, i.e. data
with label, while unsupervised learning unlabeled data.
Semi-supervised learning techniques concerned with both
labeled and unlabeled data. Reinforcement learning doesn’t
require any data to predict output.
Based on these techniques there are algorithmsusedfor
classification and training purpose. Examples of Supervised
learning algorithms are decisiontrees,rule-basedclassifiers,
naïve bayesian classification, k-nearestneighborsclassifiers,
neural network, support vector machine. Some of the
unsupervised learning algorithms are k-means clustering,
hidden markov model, Gaussian mixture model.
 Naïve Bayesian: To calculate the probabilitythatan
event will occur, given that another event has
already occurred, Bayes Theorem is used. To
calculate the probability of hypothesis(h) being
true, given our prior knowledge(d), we use Bayes’s
Theorem as follows:
P(h|d)= (P(d|h) P(h)) / P(d)
where: P(h|d) = Posterior probability. The
probability of hypothesis h being true, given the
data d, where P(h|d)= P(d1| h) P(d2| h)….P(dn| h)
P(d)
P(d|h) = Likelihood. The probability of data d given
that the hypothesis h was true.
P(h) = Class prior probability. The probability of
hypothesis h being true (irrespective of the data)
P(d) = Predictor prior probability.Probabilityofthe
data (irrespective of the hypothesis) .
This algorithm is called ‘naive’becauseitassumesthatall the
variables are independent of each other, which is a naive
assumption to make in real-world examples.
 Support Vector Machines (SVM): SVM is binary
classification algorithm. Given a set of points of 2
types in N dimensional place, SVM generatesa (N —
1) dimensional hyperplane to separatethosepoints
into 2 groups. Say you have some points of 2 types
in a paper which are linearly separable. SVM will
find a straight line whichseparatesthosepointsinto
2 types and situated as far as possible from all those
points.
Fig-2: SVM Algorithm
3. PROPOSED SYSTEM
From above survey, different techniques to predict
depression are studied. Here we are proposing a system to
predict depression by taking reference of above survey. One
website is to be developed using python language which
consist of some of above techniques to measure depression.
On primary basis, Q & A analysis using BECK’s depression
inventory and EEG signal processing, will be added on the
website. The main aim to develop such website is that
anyone can measure depression including doctors at any
time. First part Q & A analysis can be accessed by any user to
check whether he or she is in depression or not, by giving
answers to the BECK’s depression inventory .From the total
score the user can predict the level of depression. If the level
of depression is high, user can concerntothedoctor.Alsothe
second part, i.e. depression prediction using EEG signal
processing can be used by doctors or psychiatrists. In this
only they have to take EEG signals of the patient by using
Neurosky’s mindwave kit and give it to the system. The
sytem uses preprocessingtechniqueslikefindingalpha,beta,
theta, delta brain waves and finding parameters like psd,log
psd, mean ,median, entropy from that brain waves and
finally all these parameters are given to the classifier
machine algorithm such as Support Vector Machine (SVM)
for training purpose. Aftertraining,thesystemautomatically

detect whether the person is depressedor not,whentheEEG
data of that person is given for testing. All these
programming is done by using Python as it is the best
language for Machine learning and it has more librariesthan
MATLAB. Unlike MATLAB , python isfreeandeasilyportable
on any operating system.
Fig -3: Proposed System
4. CONCLUSION
In this paper, different ways to predict depression have
learned. Among those methods psychometric test(Q & A)
method and EEG signal processing methods can be used
easily and accuracy of these methods is higher as compared
to audio and visual analysis. Audio and Visual Analysis for
depression prediction is also a great research but it is very
complex method and cannot operate easily inreal time.So in
this paper, First two method, i.e. psychometric test and EEG
signal processing are proposed to predict depression by
using machine learning algorithms as a classifiers.
REFERENCES
[1] Mallikarjun H. M. and Dr. H. N. Suresh , “Depression
Level Prediction using EEG signal processing”,
International conference on contemporary computing
and informatics (2014).
[2] Barry A. Edelstein and others, “Assessment of
Depression and Bereavement in older Adults”, in
handbook ofassessmentsinclinical gerontology(second
edition),2010.
[3] Shubham Dham and others, “Depression Scale
recognition using Audio, Visual and Text Analysis”,
www.researchgate.net, September 2017.
[4] H.M. Mallikarjun and P.Manimegalai , “Adaptive Neuro-
Fuzzy Inference System for Farmers Depression Stage
Prediction”,International Journal of Pure and Applied
Mathematics, volume 116 ,no. 24 , 2017.
[5] Mohssen Mohammed and others, “Machine Learning
Algorithms and Applications”, International Standard
Book Number-13: 978-1-4987-0538-7 (Hardback) ©
2017 by Taylor and Francies group.
.
Website
Ways to calculate depression:
 Q & A
 EEG
Q & A
Score and advice
Upload EEG test signal
Using mind
wave kit
Result
Depressed
or not

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