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Discrete Fourier Transform

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The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...

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Related Experiment Video

Updated: Jan 18, 2026

Data Acquisition and Analysis In Brainstem Evoked Response Audiometry In Mice

Data Acquisition and Analysis In Brainstem Evoked Response Audiometry In Mice

Published on: May 10, 2019

The Modified Scaled Adaptive Daqrouq Wavelet for Biomedical Non-Stationary Signals Analysis.

Khaled Daqrouq¹, Rania A Alharbey²

¹Department of Electrical and Computer Engineering, Engineering Faculty, King Abdulaziz University, Jeddah 21589, Saudi Arabia.

Sensors (Basel, Switzerland)

|September 13, 2025

Summary

This summary is machine-generated.

A new Modified Scaled Adaptive Daqrouq Wavelet (MSADW) framework offers superior real-time analysis for non-stationary signals. This adaptive wavelet method precisely detects transients and enhances signal quality in biomedical applications.

Keywords:

continuous wavelet transform modified scaled adaptive Daqrouq wavelet

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Area of Science:

Signal Processing
Biomedical Engineering
Wavelet Analysis

Background:

Traditional wavelets (Morlet, Daubechies) face challenges analyzing non-stationary signals.
Need for adaptive methods that adjust to real-time signal characteristics.

Purpose of the Study:

Introduce the Modified Scaled Adaptive Daqrouq Wavelet (MSADW) framework.
Demonstrate MSADW's superiority over conventional wavelets for non-stationary signal analysis.
Enable low-cost, real-time biomedical signal evaluation.

Main Methods:

MSADW utilizes gradient-dependent scale adjustments and instantaneous frequency monitoring (STFT, Hilbert transforms).
Combines Continuous Wavelet Transform (CWT) and Discrete Wavelet Transform (DWT) for feature extraction.
Employs iterative error reduction using gradient descent and genetic algorithms for adaptability.

Main Results:

MSADW achieves high time precision (0.01 s R-peak detection) and sensitivity.
Successfully reconstructs noise-free speech signals with low Mean Squared Error (MSE: 1.17×10-31).
Accurate P/T-wave detection and effective ECG signal segmentation.

Conclusions:

MSADW provides a localized parameterization framework for enhanced biomedical signal evaluation.
Facilitates real-time medical device evaluation, arrhythmia, and ischemic detection.
Advances wavelet analysis for non-stationary and noise-prone signal domains.