## 3 Results
### Statistical Analysis
#### 3.2.1 Assessment of the Quality of the Dataset  
The evaluation of the quality of the dataset was performed through the analysis of the number of subjects that contained each class, the clinical parameters associated with the progression of the disease, and the EEG recording parameters. It was observed that 69.77% of the selected studies (30 articles) used balanced classes, that is, the classes contained a similar number of subjects. The criterion that was chosen to evaluate when the classes were unbalanced was that the difference between each pair of classes was greater than 25% of the class with the largest number of samples. According to this criterion, 16.28% of the studies did not consider balanced classes, and the articles [35; 40; 55; 56; 68; 69] only had a single class. A training set with unbalanced classes can lead to prediction errors and poor data generalization. It should be noted that for this selection, the total number of subjects per class was taken into account in those studies that used a mixture of patients with different diseases. The mean number of subjects for the balanced sets was approximately 23.10 \(\pm\) 16.74 in each class (this result was calculated for the PD patients). It can be appreciated that among the studies with a balanced dataset, only [41] exceeds 50 subjects in one of the classes, whereas the mode was 20 subjects per class (considered in nine articles). In addition, to verify that the classes could be statistically compared, it was convenient to verify that the subjects exhibited the same demographic characteristics. Of the 30 articles that considered balanced classes, only 18 of them specified the mean age of the patients included in the study. The mean of these groups was 63.37 \(\pm\) 4.22 years, which is approximately the age at which this disease usually begins. Of the remaining articles belonging to this group, eight indicated the age range of the subjects, contained in (40-80) years. The articles [43; 51; 53; 60; 62; 66] did not specify the age of the subjects included in the study. The state of the disease is relevant information to evaluate the quality of the dataset because it can influence the performance of the model and therefore affect the classification problem, since patients with PD in more advanced stages of the disease who were monitored without their habitual take of dopaminergic medication may be easier to distinguish from the controls than a group of PD patients in the early stages of the disease who took their dopaminergic medication. Nevertheless, not all articles contained the state of the disease of the patients. From Table 3 it was observed that 32.56% of the selected articles did not provide any information about the disease status or the medication, 13.95% of the studies only indicated the medication, 6.98% only specified the affectation through the HY scale, and 11.63% of the articles provided little information. Therefore, only 34.88% of the selected articles included all the information related to the patients' condition and their medication. For the dopaminergic medication, it was found that 51.16% of the articles did not report the status of the medication during the EEG recording, 13.95% recorded the EEG in the ON state, 16.28% recorded the EEG in the OFF state, and 18.60% recorded the EEG in ON and OFF states. In relation to the EEG recording, the parameters specified in Table 3 were: the number of EEG channels, the EEG recording length, and the test performed. A high density of electrodes has greater spatial resolution and therefore provides more information about the global state of the brain by increasing the data contained in the EEG. The number of electrodes was specified in all articles with a mean value of 43.34 \(\pm\) 62.18 electrodes. As can be appreciated, there was great heterogeneity in the density of electrodes, with a mode of 14 electrodes considered in eight studies.