Real-Time Fast Fourier Transform-Based Notch Filter for Single-Frequency Noise Cancellation

Slimane, Anis Ben; Zaid, Azza Ouled

doi:10.4103/jmss.jmss_3_20

Cited by 10 publications

(5 citation statements)

References 20 publications

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“…Common frequencydomain techniques involve frequency-selective filtering and spectral subtraction (SS). Frequency-selective filters include low-pass [332]- [334], high-pass [334]- [336], band-pass [337], [338], and notch filters (band-stop) [339], [340]. These filters attenuate noise components that are outside the primary frequency range.…”

Section: A Effects Of Noisementioning

confidence: 99%

On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach

Aburakhia,

Shami,

Karagiannidis

2024

Preprint

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Recent advancements in sensing, measurement, and computing technologies have significantly expanded the potential for signal-based applications, leveraging the synergy between signal processing and Machine Learning (ML) to improve both performance and reliability. This fusion represents a critical point in the evolution of signal-based systems, highlighting the need to bridge the existing knowledge gap between these two interdisciplinary fields. Despite many attempts in the existing literature to bridge this gap, most are limited to specific applications and focus mainly on feature extraction, often assuming extensive prior knowledge in signal processing. This assumption creates a significant obstacle for a wide range of readers. To address these challenges, this paper takes an integrated article approach. It begins with a detailed tutorial on the fundamentals of signal processing, providing the reader with the necessary background knowledge. Following this, it explores the key stages of a standard signal processing-based ML pipeline, offering an in-depth review of feature extraction techniques, their inherent challenges, and solutions. Differing from existing literature, this work offers an application-independent review and introduces a novel classification taxonomy for feature extraction techniques. Furthermore, it aims at linking theoretical concepts with practical applications, and demonstrates this through two specific use cases: a spectral-based method for condition monitoring of rolling bearings and a wavelet energy analysis for epilepsy detection using EEG signals. In addition to theoretical contributions, this work promotes a collaborative research culture by providing a public repository of relevant Python and MATLAB signal processing codes. This effort is intended to support collaborative research efforts and ensure the reproducibility of the results presented.

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Section: A Effects Of Noisementioning

confidence: 99%

On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach

Aburakhia,

Shami,

Karagiannidis

2024

Preprint

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“…The FFT ltering method utilizes the Fourier transform to analyze the frequency spectrum of a signal and remove noises from the signal in the frequency domain. 8,9 The Wavelet transform denoising method utilizes the dual locality and linear decomposition of wavelet transform in both the time and frequency domains to decompose the signal into components representing different times or frequencies. The main noises generally exist in high-frequency wavelet coefficients, and the wavelet coefficients are processed by using a threshold to eliminate noise aer reconstructing the signal.…”

Section: Introductionmentioning

confidence: 99%

The application of generalized S-transform in the denoising of surface plasmon resonance (SPR) spectrum

Junfeng,

Li-hui

2023

Anal. Methods

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“…Frequency domain filtering of PLI is based on Fourier transforms [32][33][34]. The signal is converted from the time into the frequency domain by fast Fourier transform (FFT), and after rejection of noise spectral components it is restored back into the time domain by inverse FFT (IFFT).…”

Section: Introductionmentioning

confidence: 99%

Common-Mode Driven Synchronous Filtering of the Powerline Interference in ECG

2022

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Powerline interference (PLI) is a major disturbing factor in ground-free biopotential acquisition systems. PLI produces both common-mode and differential input voltages. The first is suppressed by a high common-mode rejection ratio of bioamplifiers. However, the differential PLI component evoked by the imbalance of electrode impedances is amplified together with the diagnostic differential biosignal. Therefore, PLI filtering is always demanded and commonly managed by analog or digital band-rejection filters. In electrocardiography (ECG), PLI filters are not ideal, inducing QRS and ST distortions as a transient reaction to steep slopes, or PLI remains when its amplitude varies and PLI frequency deviates from the notch. This study aims to minimize the filter errors in wide deviation ranges of PLI amplitudes and frequencies, introducing a novel biopotential readout circuit with a software PLI demodulator–remodulator concept for synchronous processing of both differential-mode and common-mode signals. A closed-loop digital synchronous filtering (SF) algorithm is designed to subtract a PLI estimation from the differential-mode input in real time. The PLI estimation branch connected to the SF output includes four stages: (i) prefilter and QRS limiter; (ii) quadrature demodulator of the output PLI using a common-mode driven reference; (iii) two servo loops for low-pass filtering and the integration of in-phase and quadrature errors; (iv) quadrature remodulator for synthesis of the estimated PLI using the common-mode signal as a carrier frequency. A simulation study of artificially generated PLI sinusoids with frequency deviations (48–52 Hz, slew rate 0.01–0.1 Hz/s) and amplitude deviations (root mean square (r.m.s.) 50–1000 μV, slew rate 10–200 μV/s) is conducted for the optimization of SF servo loop settings with artificial signals from the CTS-ECG calibration database (10 s, 1 lead) as well as for the SF algorithm test with 40 low-noise recordings from the Physionet PTB Diagnostic ECG database (10 s, 12 leads) and CTS-ECG analytical database (10 s, 8 leads). The statistical study for the PLI frequencies (48–52 Hz, slew rate ≤ 0.1 Hz/s) and amplitudes (≤1000 μV r.m.s., slew rate ≤ 40 μV/s) show that maximal SF errors do not exceed 15 μV for any record and any lead, which satisfies the standard requirements for a peak ringing noise of < 25 μV. The signal-to-noise ratio improvement reaches 57–60 dB. SF is shown to be robust against phase shifts between differential- and common-mode PLI. Although validated for ECG signals, the presented SF algorithm is generalizable to different biopotential acquisition settings via surface electrodes (electroencephalogram, electromyogram, electrooculogram, etc.) and can benefit many diagnostic and therapeutic medical devices.

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Real-Time Fast Fourier Transform-Based Notch Filter for Single-Frequency Noise Cancellation

Cited by 10 publications

References 20 publications

On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach

On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach

The application of generalized S-transform in the denoising of surface plasmon resonance (SPR) spectrum

Common-Mode Driven Synchronous Filtering of the Powerline Interference in ECG

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