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Pulse rhythm

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Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
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A Review and Tutorial on Machine Learning-Enabled Radar-Based Biomedical Monitoring.

Daniel Krauss¹, Lukas Engel², Tabea Ott³

¹Machine Learning and Data Analytics LabFriedrich-Alexander-Universität Erlangen-Nürnberg 91054 Erlangen Germany.

IEEE Open Journal of Engineering in Medicine and Biology

|August 28, 2024

Summary

This summary is machine-generated.

This tutorial explores radar sensing and machine learning (ML) for biomedical monitoring. It details an end-to-end pipeline for analyzing complex radar data, enhancing diagnostics and disease prevention.

Keywords:

Radar biomedical monitoring ethics machine learning medicine

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Area of Science:

Biomedical Engineering
Signal Processing
Machine Learning

Background:

Radar sensing offers contactless, unobtrusive physiological monitoring, overcoming limitations of current methods.
Radar data is complex and requires specialized interpretation for clinical applications.
Integrating radar data acquisition with ML processing is crucial for a complete biomedical monitoring solution.

Purpose of the Study:

To provide a tutorial on radar-based ML applications for biomedical monitoring.
To emphasize a holistic, end-to-end data analysis pipeline for radar sensing.
To address the ethical considerations of radar-based biomedical monitoring.

Main Methods:

Fundamentals of radar and ML theory are explained.
Data acquisition and representation techniques for radar signals are outlined.
Categories of clinical relevance for radar-based monitoring are discussed.

Main Results:

A comprehensive overview of radar sensing and ML for biomedical applications.
Guidance on creating an integrated data analysis pipeline.
Identification of ethical concerns including data privacy, ownership, and algorithmic bias.

Conclusions:

Radar sensing combined with ML presents a powerful approach for advanced biomedical monitoring.
An end-to-end pipeline is essential for effective interpretation of radar data.
Addressing ethical implications is vital for responsible deployment of this technology.