Research Methodology Proposal Prepared by: Norhasmizawati Ibrahim (813750)

Sentiment Analysis using Voice Analysis – Early Detection of Parkinson’s Disease Presentation Outline 1.Introduction 2.What is the research all about? 3.What is the research question? 4.Why this research is important? 5.How to answer all these questions?

Background of Study Sentiment analysis –Widely used to identify and extract particular information from both live and recorded conversations. –Part of speech analytic – emphasize on emotional states –Different method of scaling been used to analyze the common words that have correlation to negative, positive or natural sentiment Important –to define the attitude of human when they speak or write on certain issues. –Information extracted perhaps deliver meaningful information. –Developing a prevention of disease.

Problem Statements Parkinson’s Disease (PD) refer to nerve cell in the midbrain having slow progression. Often causing symptom like tremor/shaking, slow movement and stiffness. About 1 in 500 people has Parkinson's and 15K – 20K patients. Individual at risk – 60 years and older. Causes – don’t exactly know why people get Parkinson's and how to prevent it. Facts – researches on voice analysis has shown valuable finding to detect Parkinson’s disease at early stage. The lacking of voice database and some research findings not clear since not applied to pathologic yet.

Significant of Study Important to find out –Scope of data set for analyzing –Analyzing voice pattern and classification –Modelling method for early detection –Accuracy of the voice diagnose result Benefitted –Clinical – biomarker for PD disease detection –Individual with PD – to get earlier treatment –Normal individual – early detection

Research Questions (RQ) 1.How to detect voices changes of early stages of PD? 2.How to analyze the various voice pattern? 3.What is the impact of the voice sentiment analysis to related domain?

Research Objective (RO) 1.To identify changes of voice and articulation at early stages of PD patients? 2.To determine the most effective method for early detection of PD? 3.To evaluate the method performance in terms of the accuracy?

Introduction Research on voice analysis is used extensively in order to detect disease that affected to voice disorder. Inspired researchers to determine the most effective indicator for detection. Methods used will be describe later.

Automatic Detection The automatic detection for voice impairment has been carried out in short-term frame basis using Multilayer Perceptron and Learning Vector Quantization. Based on observation done to vocal folds, LVQ is more reliable to parameterized voice disorder detection with 96%. Using artificial techniques, Optimum-Path Forest techniques was introduced which not assuming any shape or separability of classes or feature space[12]. On the other hand, OPF also free of parameter and run training phase faster than others. Even though, if considering standard of deviation, OPF appear to be similar to others.

Classification Features Performed to identify the most important features to differentiate between normal speaker and speakers with early stage of PD. By performing a correlations-based feature selection for acoustic and vocal modelling, the result are promising with rate for both is 88% and 79%. The result expected to improved after performing feature selection [13]. However, besides voice, articulation and prosodic evaluations, other feature like energy pauses and F0 also need to consider. The research found that masses and the compliances of spring as the most important parameters in two- mass vocal fold model [13].

Classification Features Other research, suggest Intrinsic Mode Functions supported by Fuzzy Set Classifier to differentiate. The experiment, 99% of the participant are successfully classified. In 2012, there also research to analyze the characteristic of speech. By recording /ah/ phonation from136 respondent, 16 features have been extracted using student’s t-test. For further, two types of ANN (MLP and RBF) are used for classification. RBF showing good classification compared to MLP.

Conclusion The most effective method will be selected for the implementation Even OPF is provided with more advantages and result outperformed but if considering the standard deviation, it is similar to other techniques. MFCC and SVM will be utilized for this research implementation due to both are non-invasive method, fast, easy to use, less computational intense and affordable for the clinicians.

Research Design Purpose – to provide answer to the method proposed as accurate as possible Type of study –Experimental approach – to identify and determine which method is appropriate for early detection of PD. Method used –Quantitative approach – apply systematic process utilizing data to test the following research questions.

Population and Sampling Important step in conducting a research. Probability sampling – sample is randomly select from the population. Population – healthy and unhealthy people PopulationGenderAgeEthnicTotal HealthyMale46-85Malay, Chinese, Indian30 Female46-85Malay, Chinese, Indian30 UnhealthyMale46-85Malay, Chinese, Indian30 Female46-85Malay, Chinese, Indian30

Data Collection Use primary data Specifically to address the problem in question. Most voice database not classifying ethnic. Voice collected will provide different vocal in terms of vocal fold that typically considered nearly periodic in healthy voices [15].

Decision (normal/abnormal) Features Classification Acoustic Features Extraction Data Analysis / process Voice Signal Processing Capturing voices Flow of research implementation LOGO

Cont... Extraction process – to identify the best parametric representation for voice detection using MFCC Feature extraction of MFCC computation

Cont... SVM classifier – to differentiate each of the voice pattern identified. WEKA toolkit will be use for signal classification. Feature selection will be performed in order to identify healthy and unhealthy voice signal especially to detect PD at early stage.