International Journal of Pure and Applied Mathematics

Size: px

Start display at page:

Download "International Journal of Pure and Applied Mathematics"

Lisa Bell
5 years ago
Views:

International Journal of Pure and Applied Mathematics Volume 118 No. 9 2018, 253-257 ISSN: 1311-8080 (printed version); ISSN: 1314-3395 (on-line version) url: http://www.ijpam.eu ijpam.

1 International Journal of Pure and Applied Mathematics Volume 118 No , ISSN: (printed version); ISSN: (on-line version) url: ijpam.eu CLASSIFICATION OF PARKINSON DISEASE WITH THE VOICE ATTRIBUTES USING FUZZY INFERENCE SYSTEM J.Sujatha Research Scholar, Vels University, Asst. Professor, PG Department of Information Technology, Bhaktavatsalam Memorial College for Women, Chennai Dr. S.P.Rajagopalan Professor, Department of Computer Science, GKM College of Eng & Technology, Chennai 63. Abstract This paper proposes a method to identify the Parkinson disease using the voice features and classifies the patients as healthy or unhealthy using fuzzy inference systems. Voice changes can be noticed at a very early stage before the cells of the brain get affected[1]. Dataset consists of biomedical voice measurement of the patient. Each row in the dataset corresponds to a voice recording of a patient and consists of features like fundamental frequency, amplitude, jitter, Noise to Harmonic ratio, Harmonic to noise ratio, DFA and spread. For the given dataset, statistical features like mean, standard deviation and energy are extracted. Rules are framed using Mamdani FIS and classified. Classification system improves the accuracy minimizing the errors and helps the doctor to make decision about their patients. To test the efficiency of the proposed system, the same dataset is applied for the existing algorithms and the proposed method. The obtained classification accuracy is higher compared to the existing methods. Keywords: Mamdani, laryngeal, articulation, Diaphragm INTRODUCTION Voice is the sound in pronouncing words or sentences to communicate a message verbally. It involves sound waves through the air to spontaneously communicate ideas and express thoughts. Speaking process involves 4 stages - Respiratory stage, Phonation stage, Resonation stage, Articulation stage. Respiratory stage involves breathing and consists of 2 phases inhalation and exhalation. During inhalation, air is taken into the lungs which compress the diaphragm. Diaphragm is muscle separating chest from abdomen. During exhalation, air is released out. In Phonation stage, voice is produced in speaking as the expiratory air stream from lungs goes up through the trachea or wind pipe to the larynx (voice box). In Resonation stage, Voice produced in phonation is amplified by resonators. In Articulation stage, tone produced in larynx is changed to specific sound by the articuslators. Thus voice production involves compression, vibration, amplification and modification.[2] Voice disorder is characterized by decreased loudness, reduced pitch, soft voice, less precise articulation, dysfluency, stuttering of words, vocal tremors, monotone in conversation, hoarse voice quality, rigidity, stiffness in laryngeal and ribcage muscle and rapid burst of speech. The three main symptoms to be detected in the voice are vocal fold tremors, breathiness, weakness and fluctuation of jaw, tongue and lip. PARKINSONS DISEASE Parkinson disease is also called as kampavata in the ancient Indian medical system of are progressive and degenerative and tend to be more common in older Ayurveda and as Shaking Palsy in Western medical literature. London doctor James Parkinson published an essay on Parkinson based on 6 cases he observed around him. A French neurologist Jean Martin Charcot recognized the importance of Parkinson's work and named the disease after him.[3] The symptoms individuals. It is understood that a dopamine deficiency in the brain is the root of the matter. Parkinson s disease is a nervous system disorder affecting the brain and nerves. Nerve cells in the brain that make a chemical dopamine are slowly destroyed in Parkinson s patients. Dopamine acts as a messenger. The brain sends signals to nerves using dopamine. When dopamine-producing cells are destroyed, the messages are not sent resulting in loss of muscle movement thereby causing thinning and weakening of the muscles. All the muscles are affected, including those involved in walking, speaking, swallowing, facial animation, hand coordination, estc. Parkinson s dataset attributes The Parkinson s disease dataset consists of biomedical voice measurement from the patients[4]. Various voice measures are selected and each row corresponds to a voice recording of a patient. The aim of the data is to classify the patients as healthy or PD and evaluate the performance. Change in voice is the simplest cost effective way to identify the disease at a very early stage before it damages the brain cells and 253

2 Algorithm Confusion Matrix Accuracy International Journal of Pure and Applied Mathematics produces visible symptoms. The features used to classify Parkinson s disease using voice detection are fundamental frequency, amplitude, jitter, Noise to Harmonic ratio, Harmonic to noise ratio, DFA and spread. According to Multi Dimensional Voice Program (MDVP) for a normal individual, Jitter <=1.04%, shimmer <= 3.81%, HNR < 20, pitch is 128 Hz (Between Hz) for male, and 225 Hz (Between Hz) for female. This is called thresholds of Pathology[5]. Any score above these thresholds is a cause of concern. These are the vocal attributes that are used to identify Parkinson s disease. Table 1: Parkinson s dataset attributes Name ASCII subject name and recording number Fo(Hz) Fundamental frequency of standard vocal Fhi(Hz) Fundamental frequency of greatest vocal Flo(Hz) Fundamental frequency of lowest vocal Shimmer (db) Amplitude of peak-to-peak in terms of decibels Shimmer:APQ3 Quotient of amplitude perturbation in 3-point. Shimmer:APQ5 Quotient of amplitude perturbation in 5-point. Inconsistency of period-to-period. Jitter(%) Jitter (Abs) Fundamental frequency variation in cycle-to cycle Jitter (relative) Difference between the average period average and consecutive periods. Comparative Perturbation RAP PPQ Perturbation Quotient of Period in 5- point. NHR Ratio of Noise-to-Harmonic HNR Ratio of Harmonic-to- Noise DFA Detrended fluctuation analysis-based on casual walk Spread1, Spread2, PPE Quantify the fundamental frequency in variation. Status Health status of the subject 1- Parkinson's 0- healthy The output is as follows: BAYES NET Zero R /one R RBF `Classifier HMM Naïve Bayes Logistics RBF Network MLP Classifier RBF Classifier SGD SMO IBK LWL Ada Boost EXISTING WORK Related works have been carried out by other authors. In the paper Performance Evaluation of Machine Learning Algorithms in the Classification of Parkinson Disease Using Voice Attributes, the dataset was implemented on different machine learning algorithms[6]. From the obtained confusion matrix, accuracy is calculated Decision Table J48 Random Forest ACC= = 254

International Journal of Pure and Applied Mathematics PROPOSED WORK Fuzzy logic is a logical extension of multivalued logic[5] which conveniently maps input space to output space.

Mean, standard deviation and energy are calculated for the whole dataset. Rules are framed based on the values of this feature.

Accuracy, specificity, sensitivity and mean square error are calculated This algorithm gives a better classification accuracy over the existing algorithm.

3 International Journal of Pure and Applied Mathematics PROPOSED WORK Fuzzy logic is a logical extension of multivalued logic[5] which conveniently maps input space to output space. Fuzzy inference maps input to output using fuzzy logic. Rules are as follows: RESUTS AND DISCUSSION In this paper, the dataset is loaded. Mean, standard deviation and energy are calculated for the whole dataset. Rules are framed based on the values of this feature. Data is tested based on the rules framed and classified as healthy and unhealthy. Accuracy, specificity, sensitivity and mean square error are calculated This algorithm gives a better classification accuracy over the existing algorithm. The result indicates an average accuracy of 95.51% with sensitivity of and specificity of 96%. The mean square error is around 0.04% SCREEN SHOTS If (mean is medium and standard deviation is medium and energy value is medium) then patient is healthy If part is called the antecedent/premise and the then part of the rule is called as consequent/conclusion. There are three input variables namely mean, standard deviation and energy. Output is an entire fuzzy set ie., the status of the patient healthy or not. This set is defuzzified assigning one value to the output. First the antecedent is evaluated by fuzzifying the input and applying fuzzy operators. This result is applied to the consequent called as implication. If the antecedent is true, the consequent is also true. All the antecedents are calculated simultaneously and resolved to single number using logical operators. The consequent specifies a fuzzy set to the output. The implication function modifies fuzzy set to the degree specified by antecedent. Input Fuzzification Input Membership functions Fuzzy Input Rule Evaluation Rules/Inferences Fuzzy output Defuzzification Output Membership functions Output 255

International Journal of Pure and Applied Mathematics CONCLUSION Parkinson disease is hard to diagnose but the voice changes are machine detectable even in the early stages.

This paper proposes a method to identify the Parkinson disease using the voice features and classifies the patients as healthy or unhealthy using fuzzy inference systems.

4 International Journal of Pure and Applied Mathematics CONCLUSION Parkinson disease is hard to diagnose but the voice changes are machine detectable even in the early stages. So voice recording of the patients is collected to predict PD. This paper proposes a method to identify the Parkinson disease using the voice features and classifies the patients as healthy or unhealthy using fuzzy inference systems. This algorithm gives a higher classification accuracy compared to existing algorithms. This would serve as an aid to the doctors to identify patients with Parkinson disease. REFERENCES [1] Kris Tjaden, Speech and Swallowing in Parkinson s Disease, Top Geriatr Rehabil 2008, 24(2) Pages [2] J.Sujatha, Dr. S.P.Rajagopalan "Parkinson Disease and Voice". International Journal of Computer Trends and Technology (IJCTT) V53(1):19-22, November ISSN: [3] Goetz, C. G. (1986), Charcot on Parkinson's disease. Mov. Disord., 1: doi: /mds [4] Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection', Little MA, McSharry PE, Roberts SJ, Costello DAE, Moroz IM. BioMedical Engineering OnLine 2007, 6:23 [5] Jaoao Paulo Teixeir, Vocal Acoustic Analysis Jitter, Shimmer and HNR Parameters, Procedia Technology,Volume 9, 2013, Pages [6] J.Sujatha, Dr. S.P.Rajagopalan Performance Evaluation of Machine Learning Algorithms in the Classification of Parkinson Disease Using Voice Attributes International Journal of Applied Engineering Research ISSN Volume 12, Number 21 (2017) pp [7] Rajesh, M. & Gnanasekar, J.M. Wireless Pers Commun (2017) 97:

5 257

6 258

Performance Evaluation of Machine Learning Algorithms in the Classification of Parkinson Disease Using Voice Attributes

Performance Evaluation of Machine Learning Algorithms in the Classification of Parkinson Disease Using Voice Attributes J. Sujatha Research Scholar, Vels University, Assistant Professor, Post Graduate