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Related Concept Videos

Aliasing01:18

Aliasing

Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original signal...
Bandpass Sampling01:17

Bandpass Sampling

In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2. The spectrum...
Upsampling01:22

Upsampling

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...
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next sampling...

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Related Experiment Video

Updated: Jun 27, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Harmonic-Selective Gaussian Filtering for Morphology and Timing Preservation in PPG Signals.

Sarai Dominguez-Hernandez1, Gonzalo Paez1, Moises Padilla1

  • 1Centro de Investigaciones en Optica, A.C., Loma del Bosque 115, Lomas del Campestre, Leon C.P. 37150, Guanajuato, Mexico.

Sensors (Basel, Switzerland)
|June 26, 2026
PubMed
Summary

This study introduces a novel Gaussian filtering method to improve photoplethysmography (PPG) signal analysis. The technique preserves crucial waveform timing for accurate cardiovascular monitoring.

Keywords:
Gaussian filterPPGdiastoledicrotic notchfrequency domainharmonic componentsphotoplethysmographysignal timingsystolewaveform morphologyzero-phase filter

Related Experiment Videos

Last Updated: Jun 27, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Cardiovascular Physiology

Background:

  • Photoplethysmography (PPG) is vital for non-invasive cardiovascular assessment.
  • Traditional noise reduction techniques can distort PPG signal timing and morphology.
  • Accurate identification of physiological events in PPG signals is often hindered by noise.

Purpose of the Study:

  • To develop a frequency-domain Gaussian filtering framework for PPG signal analysis.
  • To selectively extract harmonic components from PPG signals while preserving temporal features.
  • To address limitations of conventional noise-reduction methods in PPG analysis.

Main Methods:

  • A frequency-domain Gaussian filtering framework was proposed.
  • A Gaussian band-reject filter (0 Hz) suppressed DC component and baseline drift.
  • Symmetric Gaussian bandpass filters isolated harmonic components, with adaptable band numbers.
  • The method was validated on simulated and experimental PPG data.

Main Results:

  • The proposed filtering preserved temporal alignment of key PPG events (systolic peak, diastolic decay, dicrotic notch).
  • Zero-phase properties of the filters avoided signal distortion.
  • Reconstruction from filtered harmonics indicated retention of waveform structure.
  • Low-order harmonics contained significant observable waveform information.

Conclusions:

  • Harmonic-based Gaussian filtering offers a promising approach for PPG signal analysis.
  • The method effectively extracts physiologically relevant information from PPG harmonics.
  • Potential applications include enhanced bedside monitoring and cardiovascular dynamics assessment.