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parkinsons-telemonitoring

parkinsons-telemonitoring

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Author: Athanasios Tsanas (tsanasthanasis '@' gmail.com) and Max Little (littlem '@' physics.ox.ac.uk) Source: UCI Please cite: Source: The dataset was created by Athanasios Tsanas (tsanasthanasis '@' gmail.com) and Max Little (littlem '@' physics.ox.ac.uk) of the University of Oxford, in collaboration with 10 medical centers in the US and Intel Corporation who developed the telemonitoring device to record the speech signals. The original study used a range of linear and nonlinear regression methods to predict the clinician's Parkinson's disease symptom score on the UPDRS scale. Data Set Information: This dataset is composed of a range of biomedical voice measurements from 42 people with early-stage Parkinson's disease recruited to a six-month trial of a telemonitoring device for remote symptom progression monitoring. The recordings were automatically captured in the patient's homes. Columns in the table contain subject number, subject age, subject gender, time interval from baseline recruitment date, motor UPDRS, total UPDRS, and 16 biomedical voice measures. Each row corresponds to one of 5,875 voice recording from these individuals. The main aim of the data is to predict the motor and total UPDRS scores ('motor_UPDRS' and 'total_UPDRS') from the 16 voice measures. The data is in ASCII CSV format. The rows of the CSV file contain an instance corresponding to one voice recording. There are around 200 recordings per patient, the subject number of the patient is identified in the first column. For further information or to pass on comments, please contact Athanasios Tsanas (tsanasthanasis '@' gmail.com) or Max Little (littlem '@' physics.ox.ac.uk). Further details are contained in the following reference -- if you use this dataset, please cite: Athanasios Tsanas, Max A. Little, Patrick E. McSharry, Lorraine O. Ramig (2009), 'Accurate telemonitoring of Parkinson’s disease progression by non-invasive speech tests', IEEE Transactions on Biomedical Engineering (to appear). Further details about the biomedical voice measures can be found in: Max A. Little, Patrick E. McSharry, Eric J. Hunter, Lorraine O. Ramig (2009), 'Suitability of dysphonia measurements for telemonitoring of Parkinson's disease', IEEE Transactions on Biomedical Engineering, 56(4):1015-1022 Attribute Information: subject# - Integer that uniquely identifies each subject age - Subject age sex - Subject gender '0' - male, '1' - female test_time - Time since recruitment into the trial. The integer part is the number of days since recruitment. motor_UPDRS - Clinician's motor UPDRS score, linearly interpolated total_UPDRS - Clinician's total UPDRS score, linearly interpolated Jitter(%),Jitter(Abs),Jitter:RAP,Jitter:PPQ5,Jitter:DDP - Several measures of variation in fundamental frequency Shimmer,Shimmer(dB),Shimmer:APQ3,Shimmer:APQ5,Shimmer:APQ11,Shimmer:DDA - Several measures of variation in amplitude NHR,HNR - Two measures of ratio of noise to tonal components in the voice RPDE - A nonlinear dynamical complexity measure DFA - Signal fractal scaling exponent PPE - A nonlinear measure of fundamental frequency variation Relevant Papers: Little MA, McSharry PE, Hunter EJ, Ramig LO (2009), 'Suitability of dysphonia measurements for telemonitoring of Parkinson's disease', IEEE Transactions on Biomedical Engineering, 56(4):1015-1022 Little MA, McSharry PE, Roberts SJ, Costello DAE, Moroz IM. 'Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection', BioMedical Engineering OnLine 2007, 6:23 (26 June 2007) Citation Request: If you use this dataset, please cite the following paper: A Tsanas, MA Little, PE McSharry, LO Ramig (2009) 'Accurate telemonitoring of Parkinson’s disease progression by non-invasive speech tests', IEEE Transactions on Biomedical Engineering (to appear).

22 features

Shimmernumeric3581 unique values
0 missing
PPEnumeric4777 unique values
0 missing
DFAnumeric5282 unique values
0 missing
RPDEnumeric5430 unique values
0 missing
HNRnumeric4780 unique values
0 missing
NHRnumeric5532 unique values
0 missing
Shimmer.DDAnumeric4223 unique values
0 missing
Shimmer.APQ11numeric3283 unique values
0 missing
Shimmer.APQ5numeric2850 unique values
0 missing
Shimmer.APQ3numeric2664 unique values
0 missing
Shimmer.dB.numeric852 unique values
0 missing
subject.numeric42 unique values
0 missing
Jitter.DDPnumeric1703 unique values
0 missing
Jitter.PPQ5numeric840 unique values
0 missing
Jitter.RAPnumeric853 unique values
0 missing
Jitter.Abs.numeric4105 unique values
0 missing
Jitter...numeric1305 unique values
0 missing
total_UPDRSnumeric1129 unique values
0 missing
motor_UPDRSnumeric1080 unique values
0 missing
test_timenumeric2442 unique values
0 missing
sexnumeric2 unique values
0 missing
agenumeric23 unique values
0 missing
Show all 22 features

107 properties

NumberOfInstances
5875
Number of instances (rows) of the dataset.
NumberOfFeatures
22
Number of attributes (columns) of the dataset.
NumberOfClasses
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
22
Number of numeric attributes.
NumberOfSymbolicFeatures
0
Number of nominal attributes.
AutoCorrelation
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_DecisionStumpErrRate
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_DecisionStumpKappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_NaiveBayesAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_NaiveBayesErrRate
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_NaiveBayesKappa
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_kNN1NAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_kNN1NErrRate
Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_kNN1NKappa
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
ClassEntropy
Entropy of the target attribute values.
DecisionStumpAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
Percentage of instances belonging to the most frequent class.
MajorityClassSize
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
81.57
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
92.86
Maximum of means among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
7.59
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
53.45
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
21.46
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
11.52
Mean of means among attributes of the numeric type.
MeanMutualInformation
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
2.67
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
4.5
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-1.39
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
0
Minimum of means among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-0.81
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
Percentage of instances belonging to the least frequent class.
MinorityClassSize
Number of instances belonging to the least frequent class.
NaiveBayesAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
0
Number of binary attributes.
PercentageOfBinaryFeatures
0
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
100
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
0
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
-0.49
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
0.02
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
0.08
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
0.01
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
13.91
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
0.14
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
3.1
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
0.07
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
27.58
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
21.35
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
4.39
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
5.25
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

11 tasks

Clustering on parkinsons-telemonitoring
0 runs
Clustering on parkinsons-telemonitoring
0 runs - estimation_procedure: 50 times Clustering
Clustering on parkinsons-telemonitoring
0 runs - estimation_procedure: 50 times Clustering
Clustering on parkinsons-telemonitoring
0 runs - estimation_procedure: 50 times Clustering
Clustering on parkinsons-telemonitoring
0 runs - estimation_procedure: 50 times Clustering
Clustering on parkinsons-telemonitoring
0 runs - estimation_procedure: 50 times Clustering
Clustering on parkinsons-telemonitoring
0 runs - estimation_procedure: 50 times Clustering
Clustering on parkinsons-telemonitoring
0 runs - estimation_procedure: 50 times Clustering
Clustering on parkinsons-telemonitoring
0 runs - estimation_procedure: 50 times Clustering
Clustering on parkinsons-telemonitoring
0 runs - estimation_procedure: 50 times Clustering
Clustering on parkinsons-telemonitoring
0 runs - estimation_procedure: 50 times Clustering
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