Automated Analysis of Auditory Brainstem Evoked Response using Wavelets And Neural Networks

Dr. V. UDAYASHANKARA PROF. DEPT OF IT,SJCE

MYSORE 

BY

JYOTHI.BASST.PROFF, DEPT.OF E&C,

NIE-IT, MYSORE

GUIDEDBY

1

CONTENTS

• Introduction

• Algorithm

• Implementation

• Results

• Conclusion

2

INTRODUCTION

• What is Brainstem Evoked Response Audiometry(BERA)?

• What is the principle?

• What are its applications?

3

• Brainstem response is Electrical signal evoked in human Brainstem due to presentation of sound such as click or tone.

• Brainstem Evoked Response Audiometry is a screening test to monitor the Hearing loss or Deafness in a patient .

4

BLOCK DIAGRAM

5

CLINICAL APPLICATIONS

• Estimation of threshold

• Investigation of hearing loss

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TYPICAL BERA WAVEFORM

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ORIGIN OF EACH WAVE:

PEAK ORIGIN

I Cochlear nerve

IIDorsal & Ventral cochlear

nucleus

III Superior olivary complex

IV Nucleus of lateral lemniscus

V Inferior colliculus

VI Medial geniculate body

VII Auditory radiation(cortex8

OBJECTIVE :

The aim of this paper is to develop software

classification model to assist the audiologist with

an automated detection of the ABR waveform and

also detection of peaks for identification of

pathologies.9

ALGORITHM:

• Extraction of BERA from recorded EEG

• Identification of peaks

• Classification

10

FLOW CHART

Get the EEG Data

Is a BERA response present

Identification of peaks

Peak detection

Abnormal Normal

no

Classification

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IMPLEMENTATION:

Data Acquisition: EEG data is collected in JSS hospital and data

base is created which consists of 30 normal (neonates)and 25

abnormal patients..

12

EXTRACTION:

• Band pass filtering with cut-off freq. 30-3000 Hz.

• Signal adaptive filtering using complex wavelets

• This algorithm uses Dual tree complex wavelet

transform.13

PEAK IDENTIFICATION

• Gaussian Derivative estimation filter used

Steps :

• Find Zero Crossings and

• Peak Detection using Local Max And Min Value.

• Finally Peak Labeling.

14

FLOW CHART:

15

PARAMETERS USED FOR LABELING PEAKS

16

17

CLASSIFICATION :

• Artificial Neural Network(ANN) is used .

• Diagnosis of hearing disorder is one of the applications of

neural network.

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• ANN is defined by 3 types of parameters:

1. Inter connection pattern layers of neurons.- nonlinear

2. Learning process for updating weights

3. The activation function

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FEED-FORWARD ARCHITECTURE

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BLOCK DIAGRAM

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LEARNING ALGORITHM :

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PATIENT INFORMATION

• Intensity• Ear• peak I• Amplitude of Peak I• peak III• Amplitude of peak III• peak V• Amplitude of peak V• Inter peak (I – III)• Inter peak (III – V)• Inter peak (I – V)

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RESULT:

1. If the output node value is >0.7467 --- Normal

2. Else if output node value is <= 0.7467 --- Abnormal

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TESTS AND RESULTS:

0 100 200 300 400 500 600 700 800-5000

0

5000

10000

0 100 200 300 400 500 600-1

-0.5

0

0.5

1x 10

4 Complex 1-D wavelet

t

(t

)

Fig: Extracted BERA signal25

Fig: Gaussian First Derivative:26

Fig: First deriv. of the Input signal

-15 -10 -5 0 5 10 15-4

-3

-2

-1

0

1

2

3First Derivative

Time

Am

plitu

de

27

Fig: Zero crossing & peak identification28

Fig: Peak Labeled29

GUI in MATLAB :

30

31

GUI in C:

32

33

ANN RESULTS:

Normal Abnormal

Training Number 30 25

Test Number 25 20

Correct Classification

24 18

Rate of classification

96% 90%

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Accuracy of ANN classification

35

CONCLUSION

• This paper demonstrates the feasibility of an algorithm for extraction of clean BAER waveforms and subsequent automatic peak identification in order to perform functional assessment of the brainstem.

• Automated method gives 96% for normal 90% Accuracy for Abnormal patients.

36

REFERENCES:

• [1]A. Jacquin, E. Causevic, E. R. John, and L. S. Prichep, “Optimal denoising of brainstem auditory evoked response (BAER) for automatic peak identi- fication and brainstem assessment,” in Proc. 28th IEEE 

• [2]A. Jacquin, E. Causevic, E.R. John, J. Kovacevic “Adaptive complex wavelet-based filtering of EEG for extraction of evoked potential responses,” ICASSP’04, 2004.

• [3]Automated Analysis of the Auditory Brainstem Response Using Derivative Estimation Wavelets Andrew P. Bradleya, Wayne J. Wilsonb

• [4]Hall III, J., Handbook of auditory evoked responses,Needham Heights, Massachusetts: Allyn and Bacon,1992.

• [5] Mallat, S.G., A Wavelet tour of signal processing,2nd Ed., San Diego: Academic Press, 1999.

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THANK U

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