# A Study on the Essential and Parkinson's Arm Tremor Classification +
## 5 Results
### Model Training and Prediction  
To amend the inequality between volunteers with ET and PT, as well as for increasing the available number of data and for providing an initial overview of their differential diagnosis and grouping, data augmentation techniques were considered. Data augmentation is often used in conjunction with convolutional neural networks (CNN) and can be used to address both the requirements of the diversity of the training data and the amount of data [57; 58; 59; 60]. In addition to these two, augmented data can also be used to address the class imbalance problem in classification tasks, in our case the essential tremor class. Normally, in the case of many parameters, the learning model should be introduced with a proportional amount of examples to achieve satisfactory performance. To this goal, the data were initially separated into train and test data in order to be handled independently from each other and avoid information leakage from training to testing sets. Then, a sparse autoencoder with hidden size 1200 maximum epochs was trained in the Matlab environment along with a linear transfer function for the decoder. The autoencoder contains an autoencoder network, which consists of an encoder which maps the input to a hidden representation and a decoder which attempts to map this representation back to the original input. Specifically, the data were transferred to a low-level neural network that implements  
Figure 5: Dendrogram displaying patient categorization.  
Principal Component Analysis (PCA) and, in this case, new data were predicted from the trained autoencoder [61]. It must be noted though that although the data augmentation technique aids to counter the limited data number and the class imbalance, it is by no means better than a database composed from real participants.  
As we observe in Table 4, for each volunteer we received a total of 16 measurement subsets, as defined in Section 4.1. Therefore, tremor classification was performed in 16 different cases, concerning the 4 different arm poses and each of the 4 accelerometers, where the resulting acceleration vector was utilized for each case. Table 7 summarizes the number of original data collected, the data resulted from the data augmentation technique, as well as the total training and test data used for the classification procedure. For each of the three ET subjects (minority class) 15 new data were synthesized in order to counter the data imbalance. Each of the 8 PT subjects used for training had 5 new data synthesized while the rest 4 PT subjects used for testing had 3 new data synthesized. Finally, each of the 15 NT subjects who were categorized as training data had 3 new data synthesized and each of the rest 13 subjects sorted as test data had 1 new data synthesized.  
For the training examples, the 12 parameters extracted from the data processing technique were defined as predictors and \(k\) as a response variable for tremor, with \(k=1\) for ET, \(k=2\) for PT and \(k=3\) for NT volunteers. The training examples were a total of 140, with 32 belonging to the ET class, 48 to PT and the remaining 60 as NT cases. The 21 training algorithms were tested via Matlab and the respective training success rates were extracted for each of the measurements. The models with the higher accuracy were stored and were used for predictions.  
For the test data, the tremor prediction rates were extracted for the respective machine learning algorithms selected for each measurement subset. The test data included a total of 58 examples, with the first 16 involving ET, the next 16 PT, while the remaining 26 examples involved NT cases.