## 2 Related Works  
Machine learning studies utilizing EMG datasets have predominantly concentrated on movement detection [35; 36; 37], the identification of abnormal signal patterns [38; 39; 40], and the prediction of diseases [23; 41] based on EMG data. Previous studies validating disease prediction models with EMG data have addressed a range of conditions, from musculoskeletal disorders [42], such as osteoarthritis and rheumatoid arthritis, to neurological disorders, including cerebral palsy [43] and Parkinson's disease [44]. These studies have employed predictive models for disease classification using machine learning techniques, including neural networks, linear discriminant analysis, support vector machines, decision trees, and least squares kernel algorithms, with reported accuracy rates ranging from 72% to 98%.  
Using EMG datasets for disease classification presents several challenging issues. First, the time burden associated with collecting EMG data from patients is significant. Consequently, many studies focus on unsupervised machine learning techniques for dimensionality reduction or feature extraction due to the limited size of available datasets. Additionally, there are well-known data processing challenges, such as noise and artifact interference, high dimensionality and nonlinearity of the data, correlations between features in a time-series dataset, and variability in the analysis of results due to different filtering methods. Each of these issues represents a distinct research topic, and only after addressing these challenges can a dataset suitable for model training be constructed.  
EMG raw data undergoes various filtering methods depending on the measurement environment and equipment. Typically, it passes through a bandpass filter to remove artifacts, followed by rectification to convert the signals to positive values, and then a low-pass filter to create the linear envelope [45]. Feature extraction can be categorized as follows: (1) statistical and physical features in the time domain, frequency domain, and time-frequency domain; (2) linearity features using entropy; and (3) dimensionality reduction features using matrix decomposition and neural networks.  
Due to the challenges associated with sensitive signal processing in time-series data, traditional approaches to stroke detection in machine learning have relied on neuroimaging techniques [46], using MRI and CT scans to validate classification model performance. However, these methods reflect spatial rather than temporal characteristics, making early detection modeling more challenging. Furthermore, limitations such as restricted access to data acquisition and the computational costs associated with analysis have emerged as significant constraints. Recently, the potential of EMG data for stroke detection has garnered renewed attention, driven by extensive research on temporal signal processing for early stroke detection and advancements in feature engineering [47]. A recent study reported performance levels ranging from 59% to 80% when statistical features, derived from power spectrum analysis of EMG data from four muscles (bilateral biceps femoris and gastrocnemius muscles), were used to train various machine learning models [48].