Biomedical signal processing has a wide range of applications in clinical environments, supporting diagnostics, patient monitoring and disease prevention. Biomedical signals are continuous-time measurements acquired from the human body and are often noisy and highly variable. Biomedical signal processing techniques are used to extract meaningful physiological information. These techniques include system modeling, simulation of signals of interest, signal analysis and comparison with reference signals to improve understanding of physiological system behavior.
These signals involve:
- Physical properties, such as temperature and pressure
- Electrical characteristics, such as potential and current
- Biochemical quantities, such as hormones and neurotransmitters
- Physiological metrics, such as heart rate, blood pressure, brain activity and respiratory rate
Signal processing in biomedical applications involves various advanced techniques, including basic statistical analysis, time-domain methods, frequency-domain and spectral analysis, classification methods, and artificial intelligence and machine learning–based approaches.
Our biomedical team provides services in developing biomedical signal processing and data analysis algorithms using MATLAB, Python and AI-based techniques. These services include applications such as:
- EEG and ECG signal processing
- Advanced filtering techniques
- DC cancellation methods
- AI-assisted classification and feature extraction
- Extraction of vital signs from RF signals
- and more