## 3 E2E Approach
### CNNs and LSTMs  
CNNs are primarily designed for processing images and generally consist of convolutional, pooling, and fully connected layers [31]. Spectrograms, which represent the frequency spectrum of audio signals over time as an image, can be given as input to a CNN to identify patterns that are indicative of PD [32]. An LSTM is a type of Recurrent Neural Network (RNN) designed to capture long-range dependencies. Traditional RNNs usually use only one hidden state maintaining an internal memory, while LSTMs typically consist of an input gate, an output gate, and a forget gate [33]. This allows the LSTM to learn which information is relevant over longer sequences, which are also contained in the audio signals obtained from PD patients [34]. [PERSON] & [PERSON] [35] implement an SVM and LSTM classifier and applied these to five different datasets. The LSTM classifier consistently obtained a better F-score on all datasets (99.0%, 99.0%, 81.0%, 83.0% and 94.0%, respectively) than the SVM classifier (98.0%, 97.0%, 68.0%, 66.0% and 69.0%, respectively). The authors also report a significant difference in performance between men and women PD classification in favor of the masculine gender. [PERSON] et al. [21] introduce an E2E method consisting of two modules. The first module contains a series of time-distributed 2D-CNN blocks to transform the input to time series dynamic features. In the second module, a 1D-CNN block is built to learn the dependencies between these features. The authors preferred this 1D-CNN over an RNN architecture (e.g., LSTM), despite the fact that RNNs have been specifically developed for sequential input. For each single element in a given sequence, RNNs consider not only that specific element, but also all preceding elements in the sequence. However, time dynamic features are often locally correlated, resulting in local motifs that are more easily detected by 1D-CNNs than RNNs. [PERSON] et al. [21] test their method against two databases: a Chinese database collected at the GYENNO SCIENCE Parkinson's Disease Research Center, and PC-GITA, which is a Spanish database originally presented in [36]. On both databases, the proposed method outperforms traditional ML methods such as Support Vector Machines (SVM), K-Nearest Neighbours (KNN), and Decision Tree (DT), and reaches accuracy scores of between 66.67% and 73.33%. The CNN is able to reach an accuracy of 81.6% and 75.3% for sustained vowels and short sentences in Chinese, respectively. On the Spanish database, consisting of vowels, words, and sentences, an accuracy as high as 92% is achieved. To increase the interpretability of their E2E approach, [PERSON] et al. [21] apply a common feature visualization method specifically developed for CNNs, Grad-class activation maps (Grad-CAM) [37], and show that their CNN tends to focus on the lower frequency regions rather than high-frequency regions of the log Mel-spectrogram. They apply the CNN again using low-frequency (ranging from 0 to 32), high-frequency (ranging from 0 to 64), and full-frequency Mel-spectrograms, as input. The best Area Under the Curve (AUC), for both Chinese and Spanish, is obtained when the lower frequency regions were used as input, indicating that lower frequencies are more influential than higher frequencies. The AUC indicates the area under the Receiver Operating Characteristic (ROC) curve of a classification model [38]. The ROC shows the relationship between the True Positive Rate and False Positive Rate of the model at several thresholds. Similar to [PERSON] et al. [21], [PERSON] et al. [23] indicate that not all features are necessary to achieve high performance. The authors introduce a MASS-PCNN, which combines a multi-agent salp swarm (MASS) algorithm with a novel PD classification neural network (PCNN). The MASS algorithm, an updated version of SSA, selects relevant features based on various patterns. These selected features are then input into the PCNN, which includes several convolutional and pooling layers, an inception module, and a squeeze-andexcitation (SE) module.