Adaptive Noise Cancellation Technique Research Articles

A new method for the extraction of fetal ECG from the dependent abdominal signals using blind source separation and adaptive noise cancellation techniques.

BackgroundThe electrocardiogram (ECG) is a diagnostic tool that records the electrical activity of the heart, and depicts it as a series of graph-like tracings, or waves. Being able to interpret these details allows diagnosis of a wide range of heart problems. Fetal electrocardiogram (FECG) extraction has an important impact in medical diagnostics during the mother pregnancy period. Since the observed FECG signals are often mixed with the maternal ECG (MECG) and the noise induced by the movement of electrodes or by mother motion, the separation process of the ECG signal sources from the observed data becomes quite complicated. One of its complexity is when the ECG sources are dependent, thus, in this paper we introduce a new approach of blind source separation (BSS) in the noisy context for both independent and dependent ECG signal source. This approach consist in denoising the observed ECG signals using a bilateral total variation (BTV) filter; then minimizing the Kullbak-Leibler divergence between copula densities to separate the FECG signal from the MECG one.ResultsWe present simulation results illustrating the performance of our proposed method. We will consider many examples of independent/dependent source component signals. The results will be compared with those of the classical method called independent component analysis (ICA) under the same conditions. The accuracy of source estimation is evaluated through a criterion, called again the signal-to-noise-ratio (SNR). The first experiment shows that our proposed method gives accurate estimation of sources in the standard case of independent components, with performance around 27 dB in term of SNR. In the second experiment, we show the capability of the proposed algorithm to successfully separate two noisy mixtures of dependent source components - with classical criterion devoted to the independent case - fails, and that our method is able to deal with the dependent case with good performance.ConclusionsIn this work, we focus specifically on the separation of the ECG signal sources taken from skin two electrodes located on a pregnant woman’s body. The ECG separation is interpreted as a noisy linear BSS problem with instantaneous mixtures. Firstly, a denoising step is required to reduce the noise due to motion artifacts using a BTV filter as a very effective one-pass filter for denoising. Then, we use the Kullbak-Leibler divergence between copula densities to separate the fetal heart rate from the mother one, for both independent and dependent cases.

A Low-IF Tag-Based Motion Compensation Technique for Mobile Doppler Radar Life Signs Monitoring

This paper presents low IF techniques for noninvasive detection of vital signs from a mobile short-range Doppler radar platform. Stationary continuous-wave Doppler radar has been used for displacement measurement and vital signs detection. However, on a mobile platform, measurements become challenging due to motion artifacts induced by the platform. In this work, a complete compensated single transceiver radar system for vital signs detection in the presence of platform movement is demonstrated. In earlier related work, motion of the radar module was determined using cameras installed on site. However, practical mobile monitoring applications would preclude the use of such stationary cameras. In this work, an RF tag and a low IF radar architecture with an adaptive noise cancellation technique is employed to extract desired vital signs motion information even in the presence of large platform motion.

Efficient Sideband Noise Cancellation for Co-located Networks Using ANCT

An efficient noise cancellation technique for a co-located network was realized using Adaptive Noise Cancellation Technique (ANCT). The technique was developed as improved feature to the classical application of passive filters. The paper focused on achieving a theoretical perfect cancellation considering three essential parameters: the amplitude, phase and delay characteristics of the signal and noise at both the primary path and the reference path of the co-located system. An experimental test-bed of the ANC Architecture was developed using Matlab-Simulink block design which demonstrated the error signals before and after cancellation.

Impulsive Noise Cancellation of ECG signal based on SSRLS

In this paper, an enhanced adaptive impulsive noise cancellation technique based on State Space Recursive Least Square (SSRLS) algorithm is proposed. The technique is applied to the Electrocardiogram (ECG) signal, where impulsive noise affects the ECG analysis. Due to state space model-dependent recursive parameters, the presented scheme does not require the reference signal and exhibits better impulsive noise cancellation in ECG signal when compared with existing Normalized Least Mean Square (NLMS) and, Recursive Least Square (RLS) techniques. The fastest convergence and excellent tracking characteristics of proposed scheme demonstrated by the simulation results in mean square error (MSE) sense proved it to be the effective solution of impulsive noise cancellation in ECG signals.

FPGA BASED COMPUTER AIDED DIAGNOSIS OF CARDIAC MURMURS AND SOUNDS

The paper presents complete process and steps involved in FPGA based Computer Aided Diagnosis of cardiac abnormalities. Quality of the recorded heart beats is likely to be affected as the signal would be corrupted with noise. To overcome this problem Adaptive Line Enhancers (ALE) are used to eliminate the wide band noise and ALE, an adaptive noise cancellation technique is implemented with Least Mean Square (LMS) algorithm. The computation speed and noise rejection capability of DSPs do not serve the purpose. Since the FPGA is highly flexible, recursive algorithms are implemented in this device. The paper presents all the design steps involved in design of FPGA based phonocardiograph.

Noise control challenges for auscultation on medical evacuation helicopters

A stethoscope is the standard clinical tool used for auscultation. It amplifies chest sounds and enables diagnosis of both circulatory and respiratory systems. However, in many emergency transportation environments, ambient noise levels in excess of 90dB prevent successful auscultation. In this paper, data is collected in a U.S. Army Sikorsky UH-60 helicopter to analyze the inherent noise present during routine aeromedical transport. Data is gathered with a commercially available stethoscope retrofitted with an array of accelerometers and microphones. Peak acoustic noise levels are recorded in excess of 125dB and ambient vibration levels are measured to be near 0.5g RMS. The signal-to-noise ratio is much below 1 for the entire frequency range of interest in auscultation. It is shown using a combination of simulations and experimental data that existing active noise cancellation techniques using a reference microphone would be ineffective in this scenario. However, similar adaptive noise cancellation techniques using a reference accelerometer within the device would be significantly more effective at enabling auscultation.

Improving defect detection of rolling element bearings in the presence of external vibrations using adaptive noise cancellation and multiscale morphology

Identifying the signals of defective rolling element bearings of industrial machines in the presence of external vibrations is a difficult task. Therefore, in this article, attempt has been done to improve the defect detection of rolling element bearings in the presence of external vibrations using adaptive noise cancellation (ANC), self-adaptive noise cancellation (SANC), and mathematical multiscale morphology (MMM). Circular defects (diameter = 400 µm), on either of the races of the test bearings, have been artificially created and external random vibrations have been imparted to the defective test bearings using an electromechanical shaker in the experimentation reported herein. The defective bearing signal-to-external noise (vibration) ratio has been significantly enhanced after the application of ANC, SANC, and MMM. This resulted in the clear identification of defect frequencies in the vibration spectrum. It is essential to mention here that in ANC technique the least mean square (LMS) algorithm with and without signum function has been used. However, in MMM technique, triangular structural elements are utilized during the closing operation and bottom-hat transform (BHat). In comparison to the LMS algorithm without signum function, the LMS algorithm with signum function has been proved more effective and efficient in minimizing the error. Authors noticed that the MMM filter is much effective for noise removal.

A Wide-Bandwidth 2.4 GHz ISM Band Fractional-$N$ PLL With Adaptive Phase Noise Cancellation

A fast-settling adaptive calibration technique is presented that makes phase noise cancelling DeltaSigma fractional-N PLLs practical for the low reference frequencies commonly used in wireless communication systems. The technique is demonstrated as an enabling component of a 2.4 GHz ISM band CMOS PLL IC with a 730 kHz bandwidth, a 12 MHz reference, and an on-chip loop filter. In addition to the adaptive calibration technique, the IC incorporates a dynamic charge pump biasing technique to reduce power dissipation. The worst-case phase noise of the IC is -101 dBc/Hz and -124 dBc/Hz at 100 kHz and 3 MHz offsets, respectively, and the adaptive phase noise cancellation technique has a worst-case settling time of 35 mus . The IC is implemented in 0.18 CMOS technology. It measures 2.2 x 22 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and its core circuitry consumes 20.9 mA from a 1.8 V supply.

Adaptive Noise Cancellation Techniques in Sigma&amp;#x2013;Delta Analog-to-Digital Converters

Adaptive noise cancellation (ANC) techniques that extract a desired signal from background noise have many applications in different engineering disciplines. In ANC, the corrupted signal is passed through a filter that tends to suppress the noise while leaving the original signal unchanged. This paper demonstrates that the adaptive noise cancellation technique can be embedded in the digital signal postprocessing of a sigma-delta analog-to-digital converter and effectively reduces the quantization noise as well as the thermal noise at the output of the converter. The combination of ANC and the noise-shaping technique enable high-resolution analog-to-digital conversion in wideband applications where noise shaping alone cannot provide enough suppression of quantization noise due to the low oversampling ratio.

Rhythm discrimination during uninterrupted CPR using motion artifact reduction system

Due to motion artifact in the ECG caused by chest compressions automatic external defibrillators (AEDs) have difficulty recognizing ventricular fibrillation (VF) during cardiopulmonary resuscitation (CPR). Frequent interruption of CPR is required for artifact-free ECG interpretation, but these interruptions reduce the efficacy of CPR. We developed a motion artifact reduction system (MARS), based on adaptive noise cancellation techniques, for use during CPR. We hypothesized that this system would allow for automated rhythm discrimination during uninterrupted CPR. Thirteen swine underwent CPR during normal sinus rhythm (NSR) and repeated inductions of VF and asystole, using an automated device that uses a load-distributing band to compress the anterior chest. A single ECG lead and the instantaneous compression force signal were sampled during continuous CPR and fed to MARS, which in turn provided a filtered ECG signal in which artifacts that correlated with compression force were suppressed. The filtered and unfiltered ECGs were then fed simultaneously, and in real time, to three pairs of defibrillators with rhythm discrimination functions. During CPR, non-shockable rhythms were correctly classified by the defibrillators in 59 of 63 instances using the raw ECG, and 60 of 63 instances using the MARS-filtered ECG (p=N.S.). During CPR, VF was correctly classified in 35 of 222 attempts using the raw ECG, and in 310 of 318 cases using the MARS-filtered ECG (p<0.001). As control, when CPR was not applied, all rhythms were correctly identified by each defibrillator using either the raw ECG or the filtered ECG. Motion artifact reduction by adaptive noise cancellation allows for recognition of VF during uninterrupted automated CPR, while this is rarely possible based on the raw ECG. Incorporation of this signal processing strategy may obviate the need for interruptions in chest compression and thus enhance CPR efficacy.

Uncorrupted recording of neurons during microstimulation in situ : Cleaning out the artifacts

An intractable limitation in electrophysiology has been the corruption of neural recording when electrical stimulation is applied within the same tissue. Because stimulation pulses corrupt the neural recordings with large artifacts, neural activity that falls within the time course of the artifacts are obscured or corrupted. Using an adaptive noise cancellation technique, we have developed a real-time method for recording neural activity during experimental microstimulation without interfering with the spectrum of the neural recording. We have developed the technique during in vivo extracellular recording in non-human primates while using microstimulation to activate or perturb ongoing behaviors. We can accurately recover action potentials during prolonged stimulation, regardless of the size, shape, or timing between action potentials and stimulation artifacts. Signal detection reliability for recovery of action potentials can achieve better than one error in 104. Analytical and preliminary experimental studies demonstrate the utility of the technique for intracellular recording as well. Electronic development supported by Riverbend Instruments, Inc.; JWG is supported by NEI (R01-EY015870, R03_EY016234 ) and NINDS (U54_NS039407).

Detection of rolling element bearing defects by adaptive filtering

Most of the difficulties encountered when detecting initial bearing defects are due to the presence of noise in the measured signals. In the present paper, we show the effectiveness of using Adaptive Noise Cancellation techniques, in enhancing the vibration signal which is corrupted with noise in the low frequency spectrum. Two algorithms have been used in the paper, the Adaptive Noise Cancellation (ANC) and the Adaptive Self-Tuning filter (AST). The two algorithms have been first of all tested on a computer simulated signals; then, applied on measured signals on a bearing fault simulator. It has been found that adaptive filtering increases the signal to noise ratio for both simulated and measured signals.

Multi-stage adaptive noise cancellation for ultrasonic NDE

Adaptive noise cancellation techniques are ideally suited for reducing spatially varying noise due to the grain structure of material in ultrasonic nondestructive evaluation. Since grain noise is generally uncorrelated, in contrast to the correlated flaw echoes, adaptive filtering algorithms exploit the correlation properties of signals in a C-scan image to enhance the signal-to-noise ratio of the output signal. In this paper, a multi-stage adaptive noise cancellation method is proposed for enhancing flaw detection in ultrasonic signals. The overall method is based on the use of an adaptive least mean square error filter with primary and reference signals derived from two adjacent positions of the transducers. Results of implementing the proposed algorithm on ultrasonic signals obtained from hard-α inclusions in titanium will be presented.

Adaptive noise cancellation in a multimicrophone system for distortion product otoacoustic emission acquisition

This study focuses on adaptive noise cancellation (ANC) techniques for the acquisition of distortion product otoacoustic emissions (DPOAEs). Otoacoustic emissions (OAEs) are very low level sounds produced by the outer hair cells of normal cochleas, spontaneously or in response to sound stimulation as a byproduct of a frequency and threshold sensitivity increasing mechanism. Current OAE recording systems rely on test probe noise attenuation and synchronous ensemble averaging for increasing signal-to-noise ratios (SNRs). The efficiency of an ANC algorithm for noise suppression was investigated using three microphones: one placed in the test ear, one in the nontest ear for internal noise reference; one near the subject's head for external noise reference. The system proposed was tested with simulations, off-line averaging and real-time implementation of the ANC algorithm. Simulation results showed that the technique had a potential noise reduction capability of 24 dB for complex multifrequency noise signals. Off-line results were positive, with a mean SNR improvement of 4.9 dB. Real-time results indicated that the use of an ANC algorithm in combination with standard averaging methods can reduce noise levels by as much as 10 dB beyond that obtained with standard noise reduction methods and probe attenuation alone.

Dual-channel iterative speech enhancement with constraints on an auditory-based spectrum

A new frequency-domain, constrained iterative algorithm is proposed for dual-channel speech enhancement. The dual-channel enhancement scheme is shown to follow the iterative expectation-maximization (EM) algorithm, resulting in a two-step dual-channel Wiener filtering scheme. A new technique for applying constraints during the EM iterations is developed so as to take advantage of the auditory properties of speech perception. An overriding goal is to enhance quality and at least maintain intelligibility of the estimated speech signal. Constraints are applied over time and iteration on mel-cepstral parameters which parametrize an auditory based spectrum. These constraints also adapt to changing speech characteristics over time with the aid of an adaptive boundary detector. Performance is demonstrated in three areas for speech degraded by additive white Gaussian noise, aircraft cockpit noise, and computer cooling-fan noise. First, global objective speech quality measures show improved quality when compared to unconstrained dual-channel Wiener filtering and a traditional LMS-based adaptive noise cancellation technique, over a range of signal-to-noise ratios and cross-talk levels. Second, time waveforms and frame-to-frame quality measures show good improvement, especially in unvoiced and transitional regions of speech. Informal listening tests confirm improvement in duality as measured by objective measures. Finally, objective measures classified over individual phonemes for a subset of sentences from the TIMIT speech database show a consistent and superior improvement in quality. >

Adaptive cancellation technique in processing myoelectric activity of respiratory muscles.

Spectral analysis of myoelectric activity of the chest muscles is a promising diagnostic tool for respiratory diseases. Reduction of the corrupting cardiac activity from the recorded myoelectric activity of the chest muscles is essential because of the spectral overlap of both the signals. The adaptive noise cancellation technique was used to reduce the interfering cardiac activity from the recorded myoelectric activity. The adaptive noise canceller implemented in a transversal structure was found to successfully reduce the corrupting cardiac activity. This paper describes the cancellation of the corrupting cardiac activity from the recorded myoelectric activity using adaptive noise cancellation technique and the characteristic features of spectra of EMG signals.

The detection of transient‐like signals in the presence of a spatially inhomogeneous, nonstationary noise field

AbstractWe will consider the problem of detecting transient‐like signals by means of a spatially distributed array of sensors embedded in an inhomogeneous, nonstationary noise field. If each element of the array has an independent, uncorrupted reference sensor to estimate the noise statistics, then conventional adaptive noise cancellation algorithms can be used to improve the detection process. However, because of practical real‐world constraints, independent, uncorrupted reference sensors for the noise might not be available. Thus, the applicability of conventional adaptive noise cancellation techniques is in question. This paper will discuss the development of a knowledge‐based signal‐processing system that uses Artificial Intelligence (Al) methodologies to adaptively cancel inhomogeneous, nonstationary noise from a distributed array of passive sensors that is constrained to have no noise‐reference sensors.

Use of adaptive digital signal processing to improve speech communication for normally hearing and hearing-impaired subjects.

A two microphone adaptive digital noise cancellation technique was used to improve word-recognition ability of normally hearing and hearing-impaired subjects in the presence of varying amounts of multitalker speech babble noise and speech spectrum noise. Signal-to-noise ratios varied from -8 dB to +12 dB in 4 dB increments. The adaptive noise cancellation technique resulted in reducing both the speech babble and speech spectrum noises 18 to 22 dB. This reduction in noise resulted in average improvements in word recognition, at the poorest signal-to-noise ratios, ranging from 37% to 50% for the normally hearing subjects and 27% to 40% for the hearing-impaired subjects. Improvements in word recognition in the presence of speech babble noise as a result of adaptive filtering were just as large or larger than improvements found in the presence of speech spectrum noise. The amount of improvement of word-recognition scores was most pronounced at the least favorable signal-to-noise ratios.

Acoustic coupling of hearing aids to telephone receivers

Several significant problems are encountered by users of the telephone who wear a hearing aid and attempt to couple the aid acoustically to the telephone receiver. Many of the problems are caused by the inability of the aid wearer to achieve a good acoustic seal between the aid and the telephone receiver. This poor seal causes large losses at low frequencies and permits the intrusion of room noises onto the desired signal from the telephone receiver. Another problem, acoustic feedback between the aid receiver and microphone, is worsened by the resonance conditions present when the telephone handset is brought into proximity. These degradations have been characterized and a variety of solutions have been examined at Bell‐Northern Research. Low‐frequency response can be improved either in the aid or the handset. Attempts to reduce the perceived room noise level have been made using an adaptive noise cancellation technique. The feedback problem has been addressed via a variety of techniques, including use of an acoustic tube coupled to the aid microphone, automatic gain control circuitry, tracking notch filters, etc.

Acoustic noise cancellation techniques

The environment noise that plagues acoustic echo sounder systems is described as a nonstationary, non‐white and non‐isotropic random process. Classical detection and estimation theory do not treat this type of noise and consequently ad hoc procedures have been developed which will reduce the detrimental effects of acoustic noise. For simple monostatic echosondes the noise limits the dynamic range of received echoes by masking low‐level signals and for Doppler echosondes the noise generates spikes in the received power spectrum which cause the variance of the resulting wind estimates to be increased. Several alternative schemes are given for reducing the effect of noise in the spectrum of such power‐spectrum‐based Doppler processors. Results from the implementation of some of these methods are shown. A time domain adaptive noise cancellation (ANC) technique is described which potentially offers a 5–20‐dB improvement in SNR over any power spectrum noise subtraction scheme. The ANC method uses an external reference noise to estimate the noise on the signal which is then subtracted from the primary signal input, Thus the reference noise that is correlated with the primary input noise is effectively cancelled. Being a time domain preprocessor ANC can be employed on any echosonde.