Send the link below via email or IMCopy
Present to your audienceStart remote presentation
- Invited audience members will follow you as you navigate and present
- People invited to a presentation do not need a Prezi account
- This link expires 10 minutes after you close the presentation
- A maximum of 30 users can follow your presentation
- Learn more about this feature in our knowledge base article
Do you really want to delete this prezi?
Neither you, nor the coeditors you shared it with will be able to recover it again.
Make your likes visible on Facebook?
You can change this under Settings & Account at any time.
A Study on Telemonitoring of Parkinson’s Dise
Transcript of A Study on Telemonitoring of Parkinson’s Dise
The brains cells that make ”Dopamine” slowly die.
Parkinson's disease most often develops after age 50.
1 million of people in US are living with Parkinson.
A Study on Telemonitoring of Parkinson’s Disease Progression using Data Mining Techniques.
Progression is measured by different rating scales.
Hoehn and Yahr scale, UPDRS, Schawb and England ADL scale
UPDRS is the most prominent
voice signal attributes for classifcation.
Classification methods used
Sireesha Kakumanu Lalitha Pragna Nasina Bryce Cooper
Univeristy of Nebraska Lincoln
16 voice dependent measures
Co-relation among different attributes
age='(75.2-80.1]' sex='(-inf-0.1]' 52 ==>motor_UPDRS='(22.2744-25.72172]'
age='(50.7-55.6]' sex='(-inf-0.1]' 45 ==> motor_UPDRS='(15.37976-18.82708]'
age='(70.3-75.2]' sex='(0.9-inf)' 67 ==> motor_UPDRS='(15.37976-18.82708]'
age='(55.6-60.5]' sex='(0.9-inf)' 112 ==> motor_UPDRS='(11.93244-15.37976]
Less run time
Lot of rules generated
Different minimal support and confidence values
Age and test time influence the UPDRS scores
Speech attributes are used for the calculation of UPDRS
age (40-50) ==> UPDRS
jitter , shimmer ==> UPDRS
Weekly tests at home using Intel At Home Testing Device (AHTD).
sustain “ahh” sound as long and steady as possible.
Total of 5875 signals.( Instances)
22 Attributes (16 biomedical voice measures).
- - Speech disorder
- Tremor of hands, arms, legs
- Slowness of movements
- Stiffness of limbs
- Postural instability
Performed clincally and has drawbacks
The attributes in the dataset Motor-UPDRS and total-UPDRS were weighed as baseline, three months, six months.
Classification based on the Total-UPDRS scores
Stage 1 : Normal : 0-34
Stage 2 : Mild : 35-69
Stage 3 : Slight : 70-104
Stage 4 : Moderate : 105-139
Stage 5 : Severe : 140 - above
Classification trees are complicated
NNge is the optimal method
Classify people based on voice measures
Subjects fall into stages 1 and 2
Association between different attributes
Predict total UPDRS scores for further cases
Depends on Correlation Coefficient.
Model with highest correlation coefficient is effective model.
Predicting Total_UPDRS score based on the attributes.
Prediction model for UPDRS
Telemonitoring Data Attributes
Hierarchical breakdown of the data.
builds a model where the class is known.
use that model to classify new data where the class is unknown.
Joins an inducer algorithm ( in java).
Implemented cross validation using 10 folds as test options
Prediction using decision table model
Predicted Total UPDRS
Prediction of UPDRS helps in identifying the severity of disease.
Helps in diagnosis.
Decision table produces accurate results.
This model can be also further in predicting the scores.
Building and implanting the model was challenging.
Data Mining: Practical Machine Learning Tools and Techniques
Linguistic Instituite Universality & variability.
Modeling and Predicting with the Decision Table Classifier and Visualizer http://techpubs.sgi.com/library/dynaweb_docs/0640/SGI_EndUser/books/MineSet_UGNT/sgi_html/ch09.html
National Institute of Neurological Disorders and Stroke http://www.ninds.nih.gov/disorders/parkinsons_disease/parkinsons_disease.htm
Data mining using voice measurements increased the chance of early diagnosis.
Data mining approaches helped in obtaining useful relations.
But applying approaches in this data set was challenging and interesting.
As further work , the time series approach can be implemented with more than available data.
Jitter:DDP='(-inf-0.016566]' 149 Jitter:RAP='(-inf-0.005519]' 149 ==> total_UPDRS='(21.3976-26.1968]'
Jitter:RAP='(-inf-0.005519]' 149 Jitter:DDP='(-inf-0.016566]' 149 ==> total_UPDRS='(21.3976-26.1968]'
Jitter:DDP='(-inf-0.016566]' 140 Jitter:RAP='(-inf-0.005519]' 140 ==> total_UPDRS='(26.1968-30.996]'
Jitter:RAP='(-inf-0.005519]' 140 Jitter:DDP='(-inf-0.016566]' 140 ==> total_UPDRS='(26.1968-30.996]'
Age and Gender play a vital role in the progression of the disease.
Speech attributes can be used for determination of UPDRS scores.
Attribute values pose a constraint while detrmining the rules.