Machine learning in biosignal analysis from wearable devices.

Area of Science:

• Biomedical Engineering
• Data Science
• Health Informatics

Background:

• Wearable bioelectronics enable continuous health monitoring and personalized insights.
• Biosignal data presents challenges due to volume, complexity, noise, and artifacts.
• Machine learning (ML) is vital for processing complex biosignal data and uncovering patterns.

Purpose of the Study:

• To review key ML algorithms for biosignal processing.
• To provide guidelines for selecting appropriate ML models.
• To discuss ML applications in health monitoring and disease prediction.

Main Methods:

• Exploration of ML algorithms for biosignal processing.
• Discussion of data preprocessing techniques.
• Review of ML models including clustering, regression, and classification.
• Examination of evaluation methods for ML-driven analyses.

Main Results:

• Identification of critical factors for ML model selection: data characteristics, processing goals, computational efficiency, and accuracy.
• Overview of ML applications across neurological, cardiovascular, and biochemical biosignals.
• Highlighting the integration of ML with wearable bioelectronics for advanced health monitoring.

Conclusions:

• ML is essential for overcoming challenges in analyzing complex biosignal data from wearables.
• Careful model selection and preprocessing are key to accurate ML-driven biosignal analysis.
• The integration of ML with wearable bioelectronics promises to revolutionize healthcare systems.