###### Abstract  
Background: Predicting a stroke in advance or through early detection of subtle prodormal symptoms is crucial for determining the prognosis of the remaining life. Electromyography (EMG) has the advantage of easy and quick collection of biological data in clinical settings; however, its application in data processing and utilization is somewhat limited. Thus, this study aims to verify how simple signal processing and feature extraction utilize EMG in machine learning (ML)-based prediction models. Methods: EMG data were collected from the legs of 120 healthy individuals and 120 stroke patients during gait. Four statistical features were extracted from 16 EMG signals and trained on seven ML-based models. The accuracy of the validation and test datasets was also examined. Results: The model with the best performance was Random Forest. Among the 16 EMG signals, the average and maximum values of the muscle activities involved in knee extension (i.e., vastus medialis and rectus femoris) contributed significantly to the predictions. Conclusion: The results of this study confirmed that the simple processing and feature extraction of EMG signals effectively contributed to the accuracy of ML-based models. Routine use of EMG data collected in clinical environments is expected to provide benefits in terms of stroke prevention and rehabilitation. electromyography; machine learning; stroke +
Footnote †: journal: Medical  
-predromal symptom [13]. In particular, TIAs have been reported to cause motor issues, such as balance and gait disturbances, along with cognitive, sensory, and fatigue-related symptoms [14; 15]. Nevertheless, 80% of strokes following TIAs can be prevented by prompt intervention [16]. Among the various tools used to assess the functional status of stroke patients, EMG is a commonly employed technique that measures the electrical activity generated by the muscles. It is widely used in clinical settings to assess motor control abilities quickly and accurately. However, challenges remain in accessing and selecting features from unfiltered time-series data, particularly as the digital healthcare industry advances and the use of medical big data are increasingly developed [17]. Although research on signal processing for EMG data has a long history of addressing issues such as noise and artifacts introduced during the data collection process [18; 19], relatively little progress has been observed in practical feature extraction and selection for disease prediction models. Given the large volume of training data required for predictive models, computational efficiency in feature extraction and selection is as crucial as model performance. Recent advancements in the field of machine learning have seen a growing emphasis on the application of EMG. Studies have demonstrated that machine learning models utilizing features extracted from EMG signals [20]--across various domains such as time, frequency, time-frequency, and statistical--can achieve high classification performance for distinguishing diseases [21; 22; 23] and physical movements [24]. Due to the inherent complexity of predicting disease indicators and symptoms or physical movements, enhancing accuracy remains challenging, prompting the exploration of model integration strategies [25; 26]. Moreover, in the context of disease prediction and classification, both sensitivity--reflecting the model's ability to accurately detect the presence of a disease--and specificity--reflecting the model's ability to differentiate between diseases--are critical metrics. Thus, ongoing efforts are dedicated to improving these performance indicators. Nevertheless, the choice of data features for training, given its significant impact on model complexity and performance, remains a pivotal area of study within machine learning. In the development of machine learning models for medical applications such as stroke prediction, an excessive number of data features can lead to several technical challenges.