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Special considerations while measuring pulse

Special considerations while measuring pulse

Assessing a patient's pulse is a fundamental skill in healthcare, but certain situations require special attention:

Pulse rhythm

Pulse rhythm

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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Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...

Pulse amplitude and quality

Pulse amplitude and quality

Pulse amplitude is a crucial indicator of cardiac health because it provides valuable insights into the strength of left ventricular contractions and the overall uniformity of blood circulation within the vasculature. The strength of the pulse is directly related to the force with which the heart contracts and the volume of blood being pumped.
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Pulse oximetry, or SpO2, is a non-invasive method for continuously monitoring arterial oxygen saturation (SaO2). This procedure involves attaching a probe or sensor to the patient's fingertip, forehead, earlobe, or nose bridge. The sensor works by detecting changes in oxygen saturation levels through light signals generated by the oximeter and reflected by the pulsing blood under the probe.
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Average SpO2 values are greater than 95%. If the readings fall below 90%, it indicates that...

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Pulse assessment sites are crucial in evaluating a patient's cardiovascular health. By assessing the pulsations of arteries at specific anatomical locations, healthcare professionals can gather valuable information about blood flow, heart rate, and peripheral circulation. Understanding these pulse assessment sites is essential for conducting comprehensive cardiovascular evaluations and monitoring patients' overall health. These sites are strategically chosen due to the accessibility and...

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Related Experiment Video

Updated: Jun 18, 2026

Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression

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Published on: December 10, 2014

A pilot study on low power pulse rate detection based on compressive sampling.

B Y Huang¹, L Wang, B Wang

¹Shenzhen Institute of Advanced Technology, Shenzhen University Town, Nanshan District, Shenzhen, China. by.huang@sub.siat.ac.cn

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|December 8, 2009

Summary

This summary is machine-generated.

Compressive Sampling (CS) enables low-power pulse rate detection using photoplethysmograph (PPG) signals. A CS rate of approximately 3 offers efficient detection with minimal accuracy loss for pervasive healthcare systems.

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Area of Science:

Biomedical Engineering
Signal Processing
Wearable Technology

Background:

Low power consumption is critical for pervasive healthcare systems.
Compressive Sampling (CS) reconstructs signals sampled below the Nyquist rate.
Photoplethysmograph (PPG) signals are used for pulse rate detection.

Purpose of the Study:

To investigate the feasibility of using CS for low-power pulse rate detection.
To evaluate the trade-off between CS rate, power consumption, and accuracy.
To identify efficient algorithms for CS-based PPG signal reconstruction.

Main Methods:

A miniature PPG measurement device was utilized for signal acquisition.
Experiments were conducted to evaluate pulse rate estimation and power consumption.
The Gradient Projection for Sparse Reconstruction (GPSR) algorithm was employed.

Main Results:

CS significantly reduces power consumption by enabling lower PPG sampling rates.
A CS rate of approximately 3 was found to be optimal for low-power detection.
The GPSR algorithm demonstrated high efficiency in retrieving pulse rate from PPG signals.
Averaging estimation mean-square error was less than 5 at the optimal CS rate.

Conclusions:

CS is a feasible technique for achieving low-power pulse rate detection in healthcare systems.
The GPSR algorithm is highly effective for CS-based PPG signal processing.
An approximate CS rate of 3 balances power savings and measurement accuracy effectively.