Adaptive Noise Cancellation Algorithm Research Articles

Abstract 3685: Improved detection of minimal residual disease in colorectal cancer patients using adaptive noise cancellation algorithm

Abstract Objective: Existing methods for detecting circulating-tumor DNA (ctDNA) through fixed panels exhibit constrained sensitivity, while customized panels are time-consuming and rely on prerequisite tumor mutation profiles. Here, we aimed to introduce an adaptive noise cancellation approach for efficient Minimal Residuals Disease (MRD) detection in colorectal cancer patients employing a fixed panel under both tumor-informed and tumor-naïve situations. Methods: 108 plasma samples were collected from 52 colorectal cancer patients (11 relapse, 41 non-relapse) at various post-surgical timepoints. Comprehensive mutational profiling of plasma samples was conducted using a fixed MRD panel (SHIELDINGTM ULTRA) that encompasses hotspots in 2365 cancer-related genes. Background errors were removed by an adaptive noise cancellation algorithm relying on DNA fragment profiling, normal pool, and white blood cell background and intra-run plasma sampling. Variants were called both with (tumor-informed) and without (tumor-naïve) the present of tumor tissue. Results: Our longitudinal analysis of 108 plasma samples yielded 90.9% sensitivity, 97.6% specificity and 97.6% negative predictive value in the tumor-informed scenario. The ctDNA positive patients exhibited significantly higher risk than those with negative ctDNA status (Hazard Ratio = 87.2, p = 3.5 × 10^(-5)). For landmark analysis, 33 plasma samples (≤7 days post-operation) were assessed, yielding a remarkable performance of 66.7% sensitivity and 100% specificity. Our approach showed robustness under tumor-naïve scenario, illustrating 90.9% sensitivity, 92.7% specificity and 97.4% NPV for the longitudinal analysis. This corresponded to an increased risk of 49 times in the ctDNA positive patients (HR = 49.0, p = 2.2 × 10^(-4)). Notably, the results of tumor-naïve samples closely aligned with those of tumor-informed samples, showing a concordance of 92.6% and substantial agreement (Cohen’s Kappa = 0.691). Conclusion: Our method stands as an ultra-sensitive tool for identifying colorectal patients at high risk of recurrence. By employing an adaptive noise cancellation algorithm, our fixed panel approach demonstrates superiority over existing fixed panels in accuracy and proves to be a cost-effective alternative to personalized panels. Significantly, it extends potential clinical utilization towards tumor-naïve patients, which is not feasible for personalized panels. Citation Format: Hua Bao, Wanxiangfu Tang, Ningyou Li, Min Wu, Jinfeng Zhang, Baihan Zhu, Xue Wu, Yang Shao. Improved detection of minimal residual disease in colorectal cancer patients using adaptive noise cancellation algorithm [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3685.

Research on sound signal filtering and processing delay based on multiple receivers

With the rapid development of modern communication and audio technology, sound signal processing has become more and more important. This paper deeply explores the potential of multi-receiver audio signal processing technology based on practical application scenarios, and studies the effect of beamforming technology and adaptive noise cancellation algorithm on audio signal quality improvement. Experimental results show that with proper technology selection and framework design, the audio signal processing effect in complex environments can be significantly improved. In addition, this paper also predicts the trend of signal filtering and processing in the future and puts forward suggestions for the application of deep learning in this field, the research of adaptive algorithms, the fusion of multi-sensor information, the optimization of computational efficiency, and the establishment of real scene simulation. Overall, sound signal processing has great potential and opportunities in practical applications, which are worthy of further research and exploration.

Tablet-Based Wearable Patch Sensor Design for Continuous Cardiovascular System Monitoring in Postoperative Settings

Meticulous monitoring for cardiovascular systems is important for postoperative patients in postanesthesia or the intensive care unit. The continuous auscultation of heart and lung sounds can provide a valuable information for patient safety. Although numerous research projects have proposed the design of continuous cardiopulmonary monitoring devices, they primarily focused on the auscultation of heart and lung sounds and mostly served as screening tools. However, there is a lack of devices that could continuously display and monitor the derived cardiopulmonary parameters. This study presents a novel approach to address this need by proposing a bedside monitoring system that utilizes a lightweight and wearable patch sensor for continuous cardiovascular system monitoring. The heart and lung sounds were collected using a chest stethoscope and microphones, and a developed adaptive noise cancellation algorithm was implemented to remove the background noise corrupted with those sounds. Additionally, a short-distance ECG signal was acquired using electrodes and a high precision analog front end. A high-speed processing microcontroller was used to allow real-time data acquisition, processing, and display. A dedicated tablet-based software was developed to display the acquired signal waveforms and the processed cardiovascular parameters. A significant contribution of this work is the seamless integration of continuous auscultation and ECG signal acquisition, thereby enabling the real-time monitoring of cardiovascular parameters. The wearability and lightweight design of the system were achieved through the use of rigid–flex PCBs, which ensured patient comfort and ease of use. The system provides a high-quality signal acquisition and real-time monitoring of the cardiovascular parameters, thus proving its potential as a health monitoring tool.

Performance Optimization of Suppression Method for Platform Vibration Noise based on Vector Hydrophone

Objective: Aiming at the vibration control problem existing in the vector hydrophone under platform vibration and noise excitation, two methods, adaptive noise cancellation algorithm and vibration reduction design, are used to suppress vibration and noise. Methods: A simulation model of vibration and noise signals in the side area of the platform is established first, and the simulation model is simulated by adaptive noise cancellation method. Then noise reduction simulation is carried out by seismic mitigation and isolation method. Finally, the simulation results of the two methods are compared to check the effectiveness of the two noise suppression methods. Results: The results show that adaptive noise cancellation algorithm can effectively cancel the line spectra of platform vibration and noise, and the reduction of continuous spectra is about 10 dB; while the vibration reduction method can reduce the whole vibration and noise of the model, and the vibration and noise interference of the vector hydrophone is reduced by about 15 dB. Conclusion: The obtained results can serve as valuable reference for the performance optimization of suppression methos for vector hydrophones under platform’s vibration and noise excitation.

Understanding the nonlinear behavior of EEG with advanced machine learning in artifact elimination

Steady-state Visually Evoked Potential (SSVEP) based Electroencephalogram (EEG) signal is utilized in brain-computer interface paradigms, diagnosis of brain diseases, and measurement of the cognitive status of the human brain. However, various artifacts such as the Electrocardiogram (ECG), Electrooculogram (EOG), and Electromyogram (EMG) are present in the raw EEG signal, which adversely affect the EEG-based appliances. In this research, Adaptive Neuro-fuzzy Interface Systems (ANFIS) and Hilbert-Huang Transform (HHT) are primarily employed to remove the artifacts from EEG signals. This work proposes Adaptive Noise Cancellation (ANC) and ANFIS based methods for canceling EEG artifacts. A mathematical model of EEG with the aforementioned artifacts is determined to accomplish the research goal, and then those artifacts are eliminated based on their mathematical characteristics. ANC, ANFIS, and HHT algorithms are simulated on the MATLAB platform, and their performances are also justified by various error estimation criteria using hardware implementation.

A continuous phase-frequency modulation technique and adaptive pump noise cancellation method of continuous-wave signal over mud telemetry channel

Continuous wave telemetry is one of the critical downhole-to-surface transmission techniques of downhole information for logging while drilling (LWD). This paper aims to present a highly effective modulation technique for a continuous wave telemetry called Continuous Phase-Frequency Keying (CPFK) and an adaptive coherent detection method for the CPFK signal. The modulation equation of the CPFK signal is established according to the acceleration/deceleration time of the motor in a continuous wave generator, the coherent detection method is given for the demodulation of CPFK signal, and then the adaptive mud pump noise cancellation algorithm is proposed for identifying CPFK signals based on probability analysis and PSO (Particle Swarm Optimization) algorithm. The results of simulation analysis show that the proposed CPFK modulation mode is feasible and the adaptive mud pump noise cancellation algorithm can realize the demodulation and decoding of CPFK signals. It has been found from the analysis that pump noise is the main noise affecting the demodulation of CPFK signals, accurate removal of pump noise can effectively improve the frequency band utilization of CPFK signal and reduce the bit error probability of the adaptive mud pump noise cancellation algorithm.

Vital Sign Detection during Large-Scale and Fast Body Movements Based on an Adaptive Noise Cancellation Algorithm Using a Single Doppler Radar Sensor.

The non-contact detection of human vital signs (i.e., respiration rate (RR) and heartbeat rate (HR)) using a continuous-wave (CW) Doppler radar sensor has great potential for intensive care monitoring, home healthcare, etc. However, large-scale and fast random body movement (RBM) has been a bottleneck for vital sign detection using a single CW Doppler radar. To break this dilemma, this study proposed a scheme combining adaptive noise cancellation (ANC) with polynomial fitting, which could retrieve the weak components of both respiration and heartbeat signals that were submerged under serious RBM interference. In addition, the new-type discrete cosine transform (N-DCT) was introduced to improve the detection accuracy. This scheme was first verified using a numerical simulation. Then, experiments utilizing a 10-GHz Doppler radar sensor that was built from general-purpose radio frequency (RF) and communication instruments were also carried out. No extra RF/microwave components and modules were needed, and neither was a printed circuit board nor an integrated-chip design required. The experimental results showed that both the RR and HR could still be extracted during large-scale and fast body movements using only a single Doppler radar sensor because the RBM noises could be greatly eliminated by utilizing the proposed ANC algorithm.

Adaptive Noise Cancellation Algorithms Implemented onto FPGA-Based Electrical Impedance Tomography System

Electrical Impedance Tomography (EIT) as a non-invasive of electrical conductivity imaging method commonly employs the stationary-coefficient based filters (such as FFT) in order to remove the noise signal. In the practical applications, the stationary-coefficient based filters fail to remove the time-varying random noise which leads to the lack of impedance measurement sensitivity. In this paper, the implementation of adaptive noise cancellation (ANC) algorithms which are Least Mean Square (LMS) and Normalized Least Mean Square (NLMS) filters onto Field Programmable Gate Array (FPGA)-based EIT system is proposed in order to eliminate the time-varying random noise signal. The proposed method was evaluated through experimental studies with biomaterial phantom. The reconstructed EIT images with NLMS is better than the images with LMS by amplitude response AR = 12.5%, position error PE = 200%, resolution RES = 33%, and shape deformation SD = 66%. Moreover, the Analog-to-Digital Converter (ADC) performances of power spectral density (PSD) and the effective number of bit ENOB with NLMS is higher than the performances with LMS by SI = 5.7 % and ENOB = 15.4 %. The results showed that implementing ANC algorithms onto FPGA-based EIT system shows significantly more accurate image reconstruction as compared without ANC algorithms implementation.

Performance Analysis of Cascaded Adaptive Algorithms for Noise Cancellation in Speech Signal

Performance Analysis of Cascaded Adaptive Algorithms for Noise Cancellation in Speech Signal

Adaptive Noise Cancellation Based on Time Delay Estimation for Low Frequency Communication

Low frequency communication, taking advantage of the features of low frequency electromagnetic signals in near field, is widely used in through-the-earth (TTE) wireless applications. However, the low frequency non-Gaussian noise severely limits the communication performance. In this paper, an adaptive noise cancellation algorithm based on time delay estimation (ANC-TDE) and maximum correntropy criterion (MCC) is proposed. The explicit time delay estimation algorithm based on MCC (MCC-ETDE) is used to estimate the time-varying delay and the noise correlationship between the primary input and reference inputs. With a reference noise selected and time delay compensated, the non-Gaussian noise is canceled by adaptive filter based on MCC. The proposed algorithm is implemented on field programmable gate array (FPGA) and the performance is evaluated by simulation and experiment. As shown in the results, the ANC-TDE algorithm can reduce the complexity of ANC filter and adaptively compensate the varied time delay between the primary input and reference input. With the time delay compensated, the ANC-TDE algorithm has better performance in non-Gaussian noise environment and is more suitable for real-time systems.

Performance analysis of two stage adaptive FIR Filter Algorithms for PLI and BW artifact cancellation in ECG

Electrocardiogram (ECG) is a measure of the electrical movement of the heart, and is obtained by surface electrodes at standardized locations on the patient’s chest. During acquisition, various artifacts/noises such as power-line interference (PLI), baseline wander (BW), muscle artifacts (MA) and motion artifacts (EM) obscure the ECG. It is important that these artifacts are minimized for the clinicians to make better diagnosis on heart problems. This paper researches the creative idea of adaptive noise cancellation (ANC) using two stage form of adaptive filters. The concept of cascading and its algorithm for real-time application is simulated on MATLAB. The proposed algorithm utilizes two adaptive filters to estimate gradients accurately which results in good adaptation and performance. The objective of the present investigation is to provide solution in order to improve the performance of noise canceller in terms of filter parameters which are obtained with the help of adaptive algorithms. Different kinds of two stage ANC algorithms are used to eliminate artifacts in ECG by considering the noises such as power line interference and baseline wander. The simulation results show that the performance of the two stage ANC is superior to the conventional single stage ANC system in terms of higher signal-to-noise ratio. Two stage adaptive algorithms are applied on real time ECG signals and compared their performance with the conventional single stage adaptive algorithms in terms of parameters Signal-to-Noise Ratio (SNR), Mean Square Error (MSE), Root Mean Square Error (RMSE) and Distortion.

A Novel Adaptive Spectrum Noise Cancellation Approach for Enhancing Heartbeat Rate Monitoring in a Wearable Device

This paper presents a novel approach, adaptive spectrum noise cancellation (ASNC), for motion artifacts removal in photoplethysmography (PPG) signals measured by an optical biosensor to obtain clean PPG waveforms for heartbeat rate calculation. One challenge faced by this optical sensing method is the inevitable noise induced by movement when the user is in motion, especially when the motion frequency is very close to the target heartbeat rate. The proposed ASNC utilizes the onboard accelerometer and gyroscope sensors to detect and remove the artifacts adaptively, thus obtaining accurate heartbeat rate measurement while in motion. The ASNC algorithm makes use of a commonly accepted spectrum analysis approaches in medical digital signal processing, discrete cosine transform, to carry out frequency domain analysis. Results obtained by the proposed ASNC have been compared with the classic algorithms, the adaptive threshold peak detection and adaptive noise cancellation. The mean (standard deviation) absolute error and mean relative error of heartbeat rate calculated by ASNC is 0.33 (0.57) $\text {beats}\cdot \text {min}^{-1}$ and 0.65%, by adaptive threshold peak detection algorithm is 2.29 (2.21) $\text {beats}\cdot \text {min}^{-1}$ and 8.38%, by adaptive noise cancellation algorithm is 1.70 (1.50) $\text {beats}\cdot \text {min}^{-1}$ and 2.02%. While all algorithms performed well with both simulated PPG data and clean PPG data collected from our verity device in situations free of motion artifacts, ASNC provided better accuracy when motion artifacts increase, especially when motion frequency is very close to the heartbeat rate.

A Study of Non-invasive Heart Rate Monitoring System by using FPGA

The aspire of this paper is in the direction of extort the Fetal Electrocardiogram (FECG) indication from the mother’s tummy signal which is an essential signal to investigate the abnormality of the fetal heart by a general practitioner. By using the least-mean-square (LMS) filter the abdomen of the mother’s are extracted. The LMS filter is an adaptive filter, in which the FIR filter coefficients are extracted from mother’s abdomen. To calculate the fetal heart beat, the FECG based signal estimation is wanted over period. The FECG of the mother is taken at the pregnancy of 20 weeks by using this system. While considering the FECG in real time manner, the maternal woman should wear a wide belt with electrodes fitted in large manner. To separate the signals, the adaptive noise canceller algorithm is used. The adaptive noise canceller algorithm is used to analyze the collected information of the electrodes which are fitted in the belt of the woman’s abdomen. This separation is here done based on predefined values by using the LabVIEW tool. The FIR filter is designed and simulated with the help of Xilinx ISE 12.1 and is implanted by using Spartan 3 of FPGA.

A Study of Sensitivity of a Least Mean Square Filter on its Tap Weight Vector Length and Step Size in Adaptively Cancelling Noise in an Electrocardiogram Signal

m (ECG) is a procedure that records the activity of the heart and presents it as an electrical signal. It is an immensely important diagnostic tool since any deviation from the characteristic shape of an ECG may point to a cardiac anomaly, making it important for instrument and processing to be very accurate. Since the nature of noise that could corrupt the ECG is non-stationary, Adaptive Noise Cancellation (ANC) filters are required. In this paper, the Adaptive Noise Cancellation algorithm Least Mean Squares (LMS) has been implemented to reduce the noise in a corrupted ECG signal. The parameter sensitivity of the filter on its result had been studied, how the values of tap weight vector length and step size of the filter influence the quality of the resultant signal of the filter. This has been done through simulations in MATLAB, the noise have been simulated in MATLAB and the ECG signals have been collected from the ECG-ID database at PhysioNet. For various values of tap weight vector length and step size the performance of the LMS filter is analyzed in terms of PRD and MSE and the observations are tabulated. At the end of this study parameter values are suggested that render the most optimum results.

FPGA Implementation of Adaptive Filters using Partial Update Algorithm

Adaptive filtering is a vast area of research in present decade in the field of noise cancellation and acoustic echo cancellation. Adaptive Noise Cancellation algorithms are Least Mean Square (LMS), Recursive Least Square (RLS) and Lattice algorithms. RLS algorithm is used in adaptive filter because it provides better convergence. The adaptive filters may require to have large number of coefficients to model an unknown physical medium with ample accuracy. The number of filter coefficients decides the computational complexity of adaptation algorithms. This reveals that, for longer adaptive filters, the adaptation process becomes highly expensive, precluding economic implementation on digital signal processors. In this paper, partial update RLS (PURLS) algorithm is developed for denoising Electrocardiogram (ECG) and speech signal efficiently by reducing computational load in adaptive filter implementations. Conventional RLS and PURLS filters are designed in the MATLAB/Simulink and ModelSim, then the designs are validated by implementing them in FPGA KIT and design vision of Synopsys tool.

Interference Cancellation in Aircraft Cockpit by Adaptive Filters

This paper investigates on the development and implementation of adaptive noise cancellation (ANC) algorithm meant for mitigating the high level engine noise in the cockpit of an aircraft, which makes the speech signal unintelligible. Adaptive filters configured as interference canceller have the potential application in mitigating the above interference. A comparative study of Gradient based adaptive Infinite Impulse Response (IIR) algorithm and its modified version is performed using MATLAB simulator in terms of converging speed. From the simulation result the best IIR algorithm is used for implementation in Performance Optimized with Enhanced RISC PC (Power PC) 7448.

Bearing fault diagnosis under unknown variable speed via gear noise cancellation and rotational order sideband identification

The interfering vibration signals of a gearbox often represent a challenging issue in rolling bearing fault detection and diagnosis, particularly under unknown variable rotational speed conditions. Though some methods have been proposed to remove the gearbox interfering signals based on their discrete frequency nature, such methods may not work well under unknown variable speed conditions. As such, we propose a new approach to address this issue. The new approach consists of three main steps: (a) adaptive gear interference removal, (b) fault characteristic order (FCO) based fault detection, and (c) rotational-order-sideband (ROS) based fault type identification. For gear interference removal, an enhanced adaptive noise cancellation (ANC) algorithm has been developed in this study. The new ANC algorithm does not require an additional accelerometer to provide reference input. Instead, the reference signal is adaptively constructed from signal maxima and instantaneous dominant meshing multiple (IDMM) trend. Key ANC parameters such as filter length and step size have also been tailored to suit the variable speed conditions, The main advantage of using ROS for fault type diagnosis is that it is insusceptible to confusion caused by the co-existence of bearing and gear rotational frequency peaks in the identification of the bearing fault characteristic frequency in the FCO sub-order region. The effectiveness of the proposed method has been demonstrated using both simulation and experimental data. Our experimental study also indicates that the proposed method is applicable regardless whether the bearing and gear rotational speeds are proportional to each other or not.

Neural network-based adaptive noise cancellation for enhancement of speech auditory brainstem responses

The measurement of the speech-evoked auditory brainstem response (speech ABR) is a promising technique for evaluating auditory function. However, the speech ABR is severely contaminated by background noise related to other brain electrical activity. The most commonly used method to enhance the signal-to-noise ratio (SNR) of the response is coherent averaging, while recently adaptive filtering has also been reported. All of the applied methods are based on linear operations, but since the assumption of linearity may not be valid for neural activity, linear methods may not be adequate. In this paper, we present a new nonlinear adaptive noise cancellation (ANC) based on a multilayer perceptron neural network to enhance the speech ABR and compare its performance with a linear ANC algorithm based on least mean squares adaptive filtering. The effectiveness of the methods is tested using speech ABR data and is based on two different types of SNR measures, the local SNR at the fundamental frequency of the response and the overall SNR. The results show that the nonlinear neural network-based ANC can reduce the required recording time and performs better than the linear ANC especially when the SNR of the recorded speech ABR is low.

Wavelet Based Adaptive Filtering Algorithms for Acoustic Noise Cancellation

This paper prefers Acoustic Noise cancellation (ANC) system using Wavelet based adaptive filtering algorithms. The Acoustic Noise canceller is implemented using adaptive algorithms like LMS (Least Mean Square), NLMS (Normalized Least Mean Square),RLS (Recursive Least Square), and FRLS (Fast Recursive Least Square). The inclusion of wavelet based transformation in ANC reduces the number of samples to be processed and increase the efficiency of the system by minimizing the processing time. The simulation results shows that the wavelet transform based adaptive algorithms produce improvement in SNR (Signal to Noise Ratio) with less execution time compared to conventional adaptive algorithms.

A robust high-order lattice adaptive notch filter and its application to narrowband noise cancellation

Abstract In this paper, we propose a high-order lattice adaptive notch filter (LANF) that can robustly track multiple sinusoids. Unlike the conventional cascade structure, the proposed high-order LANF has robust tracking characteristics regardless of the frequencies of reference sinusoids and initial notch frequencies. The proposed high-order LANF is applied to a narrowband adaptive noise cancellation (ANC) to mitigate the effect of the broadband disturbance in the reference signal. By utilizing the gradient adaptive lattice (GAL) ANC algorithm and approximately combining it with the proposed high-order LANF, a computationally efficient narrowband ANC system is obtained. Experimental results demonstrate the robustness of the proposed high-order LANF and the effectiveness of the obtained narrowband ANC system.