Recognizing Anxiety Disorder in Healthy Individuals using ECG and Gait Parameters: Machine Learning Approaches

Introduction The prevalence of mental health disorders such as anxiety, depression, and stress is on the rise. Persistent anxiety adversely affects individuals' quality of life and overall productivity. Early detection employing novel methodologies can enhance the effectiveness of intervention strategies. This study utilized cardio-spatiotemporal features to accurately identify anxiety states. Methods In this study, a cohort of 10 healthy participants was continuously monitored for 24 hours to acquire cardiac and locomotor data. Variables such as heart rate, R-R interval, and step-related metrics were recorded. Subsequently, machine learning techniques, including K-Nearest Neighbors, linear discriminant analysis, and support vector machines, were employed to categorize anxiety levels. The performance of these models was assessed using cross-validation methods. Results In the Fine Tree and Boosted Tree methods, the area under the curve (AUC) outputs were 76% and 80%, respectively, while the other algorithms demonstrated significantly lower accuracy. Discussion The findings of this study demonstrated an association between cardio-spatiotemporal features and anxiety states. Furthermore, the application of machine learning techniques provided a robust, balanced approach to classifying anxiety. Conclusion This study used machine learning to classify and diagnose anxiety by analyzing both muscle and heart characteristics together. Results showed that both traits indicate anxiety behaviors, with certain models achieving up to 76% accuracy. Future research should check anxiety levels beforehand and improve data collection to distinguish normal heart rate changes from those related to anxiety.