A daptive N euro -fuzzy Inference System for Farmers ... · A daptive N euro -fuzzy Inference System for Farmers Depression Stage Prediction 1H.M. Mallikarjun and 2 P. Manimegalai

Adaptive Neuro-fuzzy Inference System for Farmers

Depression Stage Prediction 1H.M. Mallikarjun and

2P. Manimegalai

1Dept IT., RNSIT, Bengaluru.

Dept ECE, Karpagam Academy of Higher Education, Coimbatore, India.

[email protected]

2Dept. of ECE, Karpagam Academy of Higher Education, Coimbatore, India.

[email protected]

Abstract Depression stage prediction plays important role in social applications and

Brain Computer Interface. Here prediction of the person is done by

collecting the Electroencephalogram (EEG) data samples. Signals are

obtained by using head kit Neurosky’s Mind Wave aid. It gives raw EEG

waves by the non-invasive method which uses only one electrode.

Subject’s electrical EEG bands namely - Alpha, Beta, Delta, Gama and Theta

variations are extracted by asking few questions from the Standard Patient

Health Questionnaire-9 (PHQ -9) questionnaires. Lucid scribe is a software

application that helps to collect data from the Mind Wave kit and the data

thus collected is exported to the excel sheet. Later by finding the average,

minimum and maximum value of each EEG wave, which is in numeric

form, we will create a new data sheet containing them which is trained to

the Neural Network by using the Adaptive Neuro Fuzzy Inference System

(ANFIS). Samples are trained in ANFIS, tested across trained workspace.

Based on this Sample is classified as Depression Stage 0, Depression Stage

1, Depression Stage 2 and Depression Stage 3.

In this work, Fuzzy based evaluation is carried out with the help of

signal processing by taking 47 different Depression States of different age

Samples. Same are extracted by asking questions from the questionnaires.

At the forehead, mindwave kit gives brain waves. As mindwave kit is

wearable with Bluetooth support this work may be used in various

applications. 43 Subject’s samples are trained and 4 are tested in ANFIS.

The Testing error of 7.03x10-4 is viewed in FIS against training workspace.

Key Words:PHQ-9, EEG, MATLAB, ANFIS, REM.

International Journal of Pure and Applied MathematicsVolume 116 No. 24 2017, 119-129ISSN: 1311-8080 (printed version); ISSN: 1314-3395 (on-line version)url: http://www.ijpam.euSpecial Issue ijpam.eu

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1. Introduction

Indian Agriculture and Farmers Depression

Agriculture has experienced significant changes, and agriculturists confront a

wide assortment of stress. Our intent is to investigate the stages of anxiety and

depression symptoms among Indian farmers. On the off chance that you've

romanticized farming as a simple occupation, it's definitely not. farming is

described by high anxiety. A farmer is both supervisor and worker. Debilitated

advantages and restorative leave rely upon a similar individual. Money related

weights, animals ailment, poor gather, environmental change, government

strategies and enactment can wreck agriculturists.

The exact numbers for farmer suicides is little tougher to determine because

farmer deaths are reported as farming accidents or hunting instead of suicides.

The report brings up that a glance at the quantity of suicides for classes of

callings random to cultivating or development, similar to taxpayer supported

organization, private administration, or among understudies, demonstrates

Andhra Pradesh and Maharashtra have announced altogether higher number of

suicides in every classification contrasted with UP and Bihar.[1]

The suicides point to two things: initial, a genuine agrarian emergency formed

by an expansion in development costs and a decrease in horticultural pay, which

is pushing farmers into an obligation trap; and second, the sociological weights

that ranchers confront on account of the divergence between their earning and

those in urban zones. [2] Literature Survey

A review of literature survey is made here to understand the usage of brainwave

signal processing and utilizing it for depression detection techniques. A study of

various research works published in reputed journals is made. The extract of

each literature is depicted for better understanding of the work in this chapter.

Times of India [1] states that "Depression drives most extreme farmers to

suicide, not obligation, discovers Brookings consider" said a paper by an

American research organization in the wake of breaking down suicide-related

data given by the National Crime Records Bureau (NCRB). Suicides because of

bankruptcy or sudden change in financial status represented a normal of 5% of

farmer suicides in Maharashtra and Andhra Pradesh in the vicinity of 2002 and

2013. "In stunning difference, ailment or weakness (mental and physical)

represents around 30% of all (farmer) suicides in Andhra and Maharashtra. In

this way, well being changes at the state level and especially in provincial

ranges are probably going to greatly affect distress and suicides than excusing

institutional advances," said the report.

Times of India [2] says “Farmers’ suicides continue even amid good crop”-

NAGPUR: Despite good output in the fields, farmers' suicides are continuing.

International Journal of Pure and Applied Mathematics Special Issue

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Data from the suicide prone Amravati division of the region shows 319 farmers

have ended their lives in three months since November. Investigations have

categorized 89 of these as due to agrarian crisis. Another 113 deaths have been

attributed to other reasons, while the rest of the cases are still under

investigation. The data coincides with the harvest time, in a year when there

have been no crop losses in general due to natural calamity. The suicide

numbers on year-on-year basis have come down by nearly 8% in 2016. Also,

the cases attributed to farm crisis have nearly halved to 498 in 2016, as

compared to 828 in 2015.

All the five districts under Amravati division, which includes Yavatmal, Akola,

Amravati, Buldhana and Washim, have been declared suicide prone since last

over a decade, when the phenomenon began. Yavatmal has recorded the highest

number of suicides. With 116 ending their life in November alone in the

division, experts earlier thought it may have been due to cash crunch on account

of demonetization. However, the trend continued even after the cash flow has

normalized. In January, 89 farmers suicides were recorded in Amravati division

with 18 in February as yet.

Farmers' suicide in Karnataka [3] Report of the Fact-discovering Team on

Farmers' suicide in Mysore and Chamarajnagar areas. It expresses that When

the Bank specialists issued notice to Siddaraju for recuperation of portion

Siddaraju, unfit to tolerate the depression submitted suicide on nineteenth Dec

2007 by devouring pesticide. Likewise on fifth December by 10.30 in the

morning Manjunath (Hosapura town of Nanjangud Taluk, who had obtained

from ICICI Bank Mysore for buy of a tractor) dead body was found in the water

tank in his property and Manjunath had committed suicide by expending

pesticide.

The Hindu [4] uncovered occurrences portraying it as 50 suicides in 15 days. Is

it falling costs? Is it an overabundance underway? Or, on the other hand are

farmers recently falling into obligation in light of optimistic spending?

Whatever the reason, Karnataka is again confronting the phantom of rising

suicides Krishna, 32, a farmer in Singamaranahalli, around 30 km from Hunsur

in Mysuru area, expended pesticide and kicked the bucket in the main week of

June. The sesame farmer with three sections of land of land couldn't survive the

obligation trap he was in. There is a genuine agrarian emergency with an

expansion in rural expenses and a decrease in income. There is likewise

sociological weight.

There are right around 350 million individuals experiencing depression all

inclusive. It is delegated major if the individual has no less than few of these

indications for two weeks or more. Be that as it may, there are a few sorts of

depressive issue. In the event that you've been determined to have clinical

depression, you might be experiencing difficulty getting the chance to rest.

There's a purpose behind that. There is an unmistakable connection between

absence of rest and depression. Indeed, one of the normal indications of


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depression is a sleeping disorder or a failure to sleep. [5]

The human brain is the most complex piece of the human life structures, in

which Depression is the most pervasive psychological well-being jumble, even

under the least favorable conditions can prompt suicide. A deliberate way to

deal with anticipate the depression level of a patient and diagnosing depression

in the early treatable stage is essential. [6]

Dissecting brain signals s of the patients experiencing the condition of

depression may prompt fascinating perceptions in the flag parameters that is

very not quite the same as a typical control. [7]

Depression is a mental issue that identifies with a condition of misery and

disheartening. It additionally aff ects the passionate and physical condition of a

man. Right now, there are no standard indicative tests for depression that can

create indisputable outcomes and more finished the side effects of depression

are difficult to analyze. Many individuals who are suff ering from depression are

ignorant of their disease. The EEG signs can be utilized to distinguish the

modifications in the mind's electro-substance potential. The present work

depends on the computerized classification of the typical and depression EEG

signals. In this way, signal handling strategies are utilized to separate concealed

data from the EEG signals. [8]

The writing demonstrates that few components, including discourse designs,

voice prosody, eye minute, circulatory strain, heart rate, EEG signals, and

outward appearances can be thought about for distinguishing the seriousness of

depression. EEG is a commonly used medical testing method that detects

electrical activity of the scalp. Critical advances in neuroscience, sensor

advances, and proficient flag preparing calculations have encouraged the move

from clinical-arranged judgments and research to individual human services

applications. It is apparent through the writing that there is promising future for

the regular utilization of EEG for observing and following well-being. Hence,

we propose our framework, which will work progressively condition and

furthermore create significantly exact and solid outcomes.

Five on a very basic level rule sorts of EEG signs are Alpha, Beta, Delta, Theta,

and Gamma, made for the hard and fragile sentiments, as the emotions are

portrayed into hard and sensitive emotions, Every Signal associated with the

state of the emotions like Gamma closeness minimum while learning

ineptitudes, wretchedness and high while reflection. Beta wave will be

accessible while adrenal, uneasiness, stress and it is perfect in perceptive

fixation and Problem disclosing got to from the frontal and parietal piece of the

cerebrum. Alpha banner high while eat up of alcohol, got to from the occipital

and parietal. Theta is high in eager extend got to from the parietal and

Temporal, Delta is high when in stupor like state, in like manner in significant

rest can be gotten to from everywhere.


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2. Methodology

Depression is a champion among the most understood mental issue that, even

under the slightest positive conditions, can provoke suicide. Diagnosing

depression in its underlying treatable stage is fundamental. It may in like

manner provoke diverse issue like sleep disorders and alcohol dependence.

Proposed Module

The Depression Stages prediction is done by collecting the EEG data signal

from the brain by using “Neurosky's Mindwave Mobile Kit”, which gives the

raw EEG waves. It is a non-invasive method which uses only single electrode.

Person is asked with 10 questions from the Standard Patient Health

Questionnaire-9 (PHQ.9) questionnaires and depending upon his depression

Stage, the different EEG waves, namely Alpha, Beta, Theta, Delta and Gamma

waves varies which are taken and saved by Lucid Scribe and are fed to the

neuro-fuzzy classifier. Here, in the proposed module, the EEG signals of

subjects are obtained by interviewing different age group subjects (47 samples)

with different depression Stages. The parameters are extracted from frequency

bands (Alpha, Delta, Beta, Gamma and Theta). Data set of 47 subjects are

prepared that are fed to the classifiers like ANFIS to detect depression stage.

The proposed module is as shown in Figure 1.

Figure 1: Proposed depression stage prediction module

The subjects are asked questions from standard PHQ.9 questionnaires and

collection of EEG signal data from the subjects by using Neurosky's Mind wave

kit. Extraction and exporting the collected parameter from the raw data.

Statistical computation of the required data of each subject and creation of a

new data feature set of all 47 subjects which are to be fed to the ANFIS.

Classification of the set of test data of parameters to identify the depression

Neurosky’s Mindwave kit

Feature extraction

(Based on PHQ.9)

ANFIS

Stage 1

Stage 2

Stage 3

Normal

Brain Signals


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stage of the subject by using neuro-fuzzy classifier.

Neurposky’s Mind Wave Mobile

Neurosky creator out went with the "Mind wave portable" brain top work in

512-HZ of repeat comprising of just sing anode, the crude EEG signal is

transmitted from the instrument by means of Bluetooth to the structure, by the

support of "Lucid Scribe" programming which hold he log of the general time,

EEG data from the instrument is showed up in Figure 2. Waves for every small

scale second is secured with the data sheet of consistently, time, minute and

seconds, by sending out this to the surpass expectations design we can get the

numerical yield.

EEG Signals are recorded with neuro sky's mindwave kit. Information's of

various age accumulate and unmistakable gender are taken. Lucid Scribe

programming gives varying mind wave signals in regards to time. In Lucid

Scribe Brain signs are assembled. The information is masterminded age, gender

, alpha, theta, gamma and delta for max, min and average parameters.

Figure 2: Mind wave kit

Patient Health Questionnaires (PHQ-9)

The PHQ-9 is a multipurpose instrument for diagonizing, screening, measuring

and checking the seriousness of depression. The PHQ-9 wires DSM-IV

despondent trademark criteria with other driving certified depressive side

effects into a smaller self - report instrument. The PHQ-9 is finished by the

patient in minutes and is instantly scored by the clinician. The PHQ-9 can

additionally be facilitated endlessly, which can reflect change or intensifying of

trouble in light of treatment. Figure 3 demonstrates PHQ - 9 test Kannada

design.

PHQ scores < 10 shows Depression Stage 0 (Normal)

PHQ score between 10 to 14 shows Depression Stage 1

PHQ score between 15 to 19 shows Depression Stage 2

PHQ score >= 20 shows Depression Stage 3.

Samples Collection and Processing

Lucid Scribe is the item which is used to gauge and moreover to record the

mind wave plans using the Neurosky's unit that is related with the head. Lucid

Scribe is a product to record particular mind signals, see REM sleep and trigger


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assorted yields when REM sleep is recognized. This record portrays the parts of

Lucid Scribe, how they work and how to utilize them. Clear Scribe can be

utilized to record cerebrum wave traces utilizing an EEG gadget related with the

PC and play back sound documents or trigger another gadget when REM sleep

is seen through eye change. For the recording, the relating module for the EEG

being used must be displayed close-by the fundamental programming. For

instance, to utilize Lucid Scribe with a headset or headband that contains a

Think Gear, similar to the Mindwave, you need to show in any event Lucid

Scribe and the Neurosky's Think Gear EEG module. Figure 4 indicates test

accumulation by utilizing Mobile personality wave pack by asking PHQ-9.

Figure 3: Sample PHQ-9 in kannada format

Figure 4: Farmers EEG sample collection by asking PHQ-9.


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3. Results and Conclusions

In this Project work, the subject is tested with basic questionnaires and their

brain waves are recorded using Neurosky's mindwave kit. The extracted dataset

is then edited to form the training data samples and testing data samples as

shown in Figure 5 and Figure 6 respectively.

Figure 5: Data set table prepared for training

Figure 6: Data samples prepared for testing

MATLAB software plays major role, supporting in processing of mathematical

values. ANFIS is the tool, which is also called as artificial neural network.

Network structure developed with the given input data and hidden layers

formed, learn by itself. In this project work 43 are trained and 4 are tested in

ANFIS. Loaded data is displayed in blue dot in the tool window. Output is on

vertical line represent 0, 1, 2, 3 for the different depression stage, horizontal line

represents 43 the range of training values. Block Generate FIS setting to be

changed, grid partition to sub clustering, along with the change in the parameter

settings like, Range of influence to 0.5, Squash factor of 1.25, accept ration as

0.5, Reject ratio is 0.15. ANFIS model structure is designed inside the tool, can

be view by click on structure in ANFIS information block this is show in Figure

7. Sub clustering is selected on the second block, generate FIS block. Setting to

the Hybrid method in option method in Train FIS block, also change in error

tolerance, and epochs to 3, and click on train now, in test FIS block select plot

against training data refer to the Figure 7, and click on test now. Figure 8 shows

ANFIS Structure.

Figure 7: Loaded data is shown in the chart


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Figure 8: ANFIS structure

Output is projected on the chart red bubble is the output matched with the blue

ring, 0 is the depression Stage 0, 1 is depression Stage 1, 2 is depression Stage 2

and 3 is depression Stage 3 which we expected output of emotion shown in

Figure 9, selected emotions identified successfully by using ANFIS tool.

Depression 0 (0); Depression 1 (1); Depression 2 (2); Depression 3 (3)

Figure 9: Output of Different Mental States Tested

In ANFIS; 43 data samples trained and 4 are tested. 7.03x10-4

testing error is

observed after plotting test FIS against training data. As mindwave kit is

wearable with Bluetooth support this work may be used in various applications.

4. Conclusion

This work contributes the real-time monitoring module to the Indian formers

community. Project will be predicting the mental health of the formers. This

project indirectly helps to identify the vegetation Stage where govt may plan

subsidy/insurance in drought areas. Depression prediction is a challenging task

which place important role in different social applications. 43 Subject’s samples

are trained and 4 are tested in ANFIS. The Testing error of 7.03x10-4

is viewed

in FIS against training workspace.

5. Scope for Future Work

This work may be improved by preparing large data samples and training them

in the fuzzy system. Advanced neural network methods with large data samples

will give improved result. Also use of large number of electrodes will give clear

picture of depression in a person.


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Acknowledgment

The Portion of this work is funded by KSCST under 40th SPP. We thank

KSCST, IISc for the financial support (Project Proposal Ref. No: 40

S_BE_1633). The Authors might want to thank the administration of RNS

Institute of Technology -Principal Dr. M K Venkatesha, Director Dr. H N

Shivashankar for the support. Authors extend thanks to Ms. Spurthi N, M.Tech

4th sem, IE Branch, RNSIT for her continuous support in database preparation.

References

[1] http://timesofindia.indiatimes.com/city/mumbai/Depression-drives-maximum-farmers-to-suicide-not-debt-finds-Brookings-study/articleshow/49263297.cms

[2] http://timesofindia.indiatimes.com/city/nagpur/farmers-suicides-continue-even-amid-good-crop/articleshow/57239423.cms

[3] http://www.pucl.org/Topics/Industries-envirn-resettlement/ 2007/farmer_suicide.html

[4] http://www.thehindu.com/news/national/karnataka/farmer-suicides-in-karnataka/article7438449.ece

[5] Mallikarjun H.M, Suresh H.N., Depression Level Prediction Using EEG Signals Processing, International Conference on Contemporary Computing and Informatics (2014).

[6] Renu Gautam, Shimi S.L, Features Extraction and Depression Level Prediction by Using EEG Signals, International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 04 Issue: 05, May -2017

[7] Puthankattil S.D., Joseph P.K. Analysis of EEG signals using wavelet entropy and approximate entropy: A case study on depression patients. Int. J. Med. Health Biomed. Pharm. Eng 8(7) (2014), 420-424.

[8] Bairy M.G., Niranjan U.C., Puthankattil S.D., Automated classification of depression EEG signals using wavelet entropies and energies, Journal of Mechanics in Medicine and Biology 16(3) (2015), 1-13.


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A daptive N euro -fuzzy Inference System for Farmers ... · A daptive N euro -fuzzy Inference System for Farmers Depression Stage Prediction 1H.M. Mallikarjun and 2 P. Manimegalai