Sliding Detrended Fluctuation Analysis (SDFA) Implementation with Parameter Optimization

This article presents an implementation methodology for sliding detrended fluctuation analysis. Our solution incorporates detailed inline documentation and parameter customization capabilities through the MDFA configuration file, enabling users to fine-tune analysis parameters for improved outcomes. The core algorithm employs sliding window techniques with polynomial detrending, where window size and polynomial order are configurable parameters. The variable 'x' is designated for EEG signal storage (dataset not uploaded). The implementation features scalable architecture supporting multiple data types through modular preprocessing components. Additional technical resources including API documentation and user manuals are available to facilitate proper integration and usage of the analytical framework. The codebase implements adaptive fluctuation scaling calculations with optional multi-scale analysis capabilities for comprehensive time-series characterization.