A comprehensive toolkit for Digital Signal Processing (DSP) in healthcare applications.
This repository contains a comprehensive toolkit for Digital Signal Processing (DSP) in healthcare applications. It includes traditional DSP methods as well as advanced machine learning (ML) and deep learning (DL) inspired techniques. The toolkit is designed to process a wide range of physiological signals, such as ECG, EEG, PPG, and respiratory signals, with applications in monitoring, anomaly detection, and signal quality assessment.
Features
- Filtering: Traditional filters (e.g., moving average, Gaussian, Butterworth) and advanced ML-inspired filters.
- Transforms: Fourier Transform, DCT, Wavelet Transform, and various fusion methods.
- Time-Domain Analysis: Peak detection, envelope detection, ZCR, and advanced segmentation techniques.
- Advanced Methods: EMD, sparse signal processing, Bayesian optimization, and more.
- Neuro-Signal Processing: EEG band power analysis, ERP detection, cognitive load measurement.
- Respiratory Analysis: Automated respiratory rate calculation, sleep apnea detection, and multi-sensor fusion.
- Signal Quality Assessment: SNR calculation, artifact detection/removal, and adaptive methods.
- Monitoring and Alert Systems: Real-time anomaly detection, multi-parameter monitoring, and alert correlation.
Please read the instruction in the documentation for detailed usage examples and module descriptions.
Reference
