Celia Shahnaz

Denoising of ECG signals based on noise reduction algorithms in EMD and wavelet domains

Abstract This paper presents a new ECG denoising approach based on noise reduction algorithms in empirical mode decomposition (EMD) and discrete wavelet transform (DWT) domains. Unlike the conventional EMD based ECG denoising approaches that neglect a number of initial intrinsic mode functions (IMFs) containing the QRS complex as well as noise, we propose to perform windowing in the EMD domain in order to reduce the noise from the initial IMFs instead of discarding them completely thus preserving the QRS complex and yielding a relatively cleaner ECG signal. The signal thus obtained is transformed in the DWT domain, where an adaptive soft thresholding based noise reduction algorithm is employed considering the advantageous properties of the DWT compared to that of the EMD in preserving the energy in the presence of noise and in reconstructing the original ECG signal with a better time resolution. Extensive simulations are carried out using the MIT-BIH arrythmia database and the performance of the proposed method is evaluated in terms of several standard metrics. The simulation results show that the proposed method is able to reduce noise from the noisy ECG signals more accurately and consistently in comparison to some of the stateof-the-art methods.

Pitch Estimation Based on a Harmonic Sinusoidal Autocorrelation Model and a Time-Domain Matching Scheme

In this paper, a method for the estimation of pitch from noise-corrupted speech observations based on extracting a pitch harmonic and the corresponding harmonic number is proposed. Starting from the harmonic representation of clean speech, a simple yet accurate harmonic sinusoidal autocorrelation (HSAC) model is first derived. By employing this HSAC model expressed in terms of the pitch harmonics of the clean speech, a new autocorrelation-domain least-squares fitting optimization technique is developed to extract a pitch harmonic from the noisy speech. Then, the harmonic number associated with the pitch harmonic is determined by maximizing an objective function formulated as an impulse-train weighted symmetric average magnitude sum function (SAMSF) of the noisy speech. The period of the impulse-train is governed by the estimated pitch harmonic and the maximization of the objective function is carried out through a time-domain matching of periodicity of the impulse-train with that of the SAMSF. An SAMSF-based pitch tracking scheme using dynamic programming is devised to obtain a smoothed pitch contour. In order to demonstrate the efficacy of the proposed method, simulations are conducted by considering naturally spoken speech signals in the presence of white or multi-talker babble noise at different signal-to-noise ratio (SNR) levels. A comprehensive evaluation of the pitch estimation results shows the superiority of the proposed method over some of the state-of-the-art methods under low levels of SNR.

Noisy speech enhancement based on an adaptive threshold and a modified hard thresholding function in wavelet packet domain

This paper proposes a speech enhancement approach, which statistically determines an adaptive threshold using the Teager energy operated WP coefficients of noisy speech. The obtained threshold is employed upon the WP coefficients of the noisy speech by employing a modified hard thresholding function. Extensive simulations in the presence of different noises indicate that this new method is very effective for both white noise and color noise reduction from speech, resulting in enhanced speech with better speech quality. Several standard objective measures and subjective observations show that the proposed method outperforms recent state-of-the-art thresholding based approaches from high to low level SNRs.

A Robust Pitch Estimation Algorithm in Noise

In this paper, we present a robust pitch estimation algorithm for noise-degraded speech. We propose a new circular average magnitude sum function (CAMSF) and a pseudo normalized correlation function (PNCF) both of which exhibit the periodicity at the pitch period of voiced speech. Exploiting the fact that CAMSF produces a peak while PNCF shows a notch, an integrated time-domain function (ITDF) is developed to enhance the pitch-harmonic-notches in presence of noise. Moreover, a frequency-frame relative smoothed noisy spectrum that acts as a harmonic spectral structure enhancer is utilized to accurately acquire a pitch-harmonic (PH) from noisy speech. We argued that employing the PH, pitch information can be effectively extracted through a variable-period impulse-train in conjunction with the proposed ITDF. It has been ascertained that the overall algorithm simulated using the Keele reference database is able to outperform some of the existing methods and well suited for a wide range of signal-to-noise ratios (SNRs) upto-10 dB.

Identification of motor neuron disease using wavelet domain features extracted from EMG signal

Amyotrophic lateral sclerosis (ALS) is a common fatal motor neuron disease that assails the nerve cells in the brain. As the nervous system controls the muscle activity, the electromyography (EMG) signals can be viewed and examined in order to detect the vital features of the ALS disease in individuals. In this paper, the discrete wavelet transform (DWT) based features, which are extracted from a frame of EMG data, are introduced to classify the normal person and the ALS patients. From each frame of EMG data, instead of using a large number of DWT coefficients, the DWT coefficients with higher values as well as their mean and maxima are proposed to be used, which drastically reduces the feature dimension. It is shown that the proposed feature vector offers a high within class compactness and between class separations. For the purpose of classification, the K-nearest neighborhood classifier is employed. In order to demonstrate the classification performance, an EMG database consisted of 5 normal subjects and 5 ALS patients is considered and it is found that the proposed method is capable of distinctly separating the ALS patients from the normal persons.

A semisoft thresholding method based on Teager energy operation on wavelet packet coefficients for enhancing noisy speech

The performance of thresholding-based methods for speech enhancement largely depends upon the estimation of the exact threshold value. In this paper, a new thresholding-based speech enhancement approach, where the threshold is statistically determined using the Teager energy-operated wavelet packet (WP) coefficients of noisy speech, is proposed. The threshold thus obtained is applied to the WP coefficients of the noisy speech by employing a semisoft thresholding function in order to obtain an enhanced speech. A number of simulations were carried out in the presence of white, car, pink, and multi-talker babble noises to evaluate the performance of the proposed method. Standard objective measures as well as subjective evaluations show that the proposed method is capable of outperforming the existing state-of-the-art thresholding-based speech enhancement approaches for noisy speech of high as well as low levels of SNR.

Enhancement of noisy speech based on a custom thresholding function with a statistically determined threshold

Performance of the thresholding based speech enhancement methods largely depend on the estimate of the exact threshold value as well as on the choice of the thresholding function. In this paper, a speech enhancement method is presented, in which a custom thresholding function is proposed and employed upon the Wavelet Packet (WP) coefficients of the noisy speech. The thresholding function is capable of switching between modified hard and semisoft thresholding functions depending on a parameter that decides the signal characteristics under consideration. Here, the threshold is determined based on the statistical modeling of the Teager energy operated WP coefficients of the noisy speech. Extensive simulations indicate that the threshold thus obtained in conjunction with the custom thresholding function is very effective in reduction of not only the white noise but also the color noise from the noisy speech thus resulting in an enhanced speech with better quality and intelligibility. Several standard objective measures and subjective evaluations including informal listening tests show that the proposed method outperforms the recent state-of-the-art thresholding based approaches of noisy speech enhancement from high to low levels of SNR.

Determination of pitch of noisy speech using dominant harmonic frequency

This paper presents a noise robust method for determination of pitch using least-squares estimate of the dominant harmonic frequency (DHF) from observations of speech, represented by a harmonic sine-wave model, heavily corrupted by white noise. The DHF is effectively estimated using the proposed sinusoidal autocorrelation model (SAM) of the noise-free speech signal. The method is based on matching the period of an impulse train, varied as the integer multiple of the estimated DHF, with the enhanced autocorrelation function of the noisy speech emphasized by the average magnitude difference function (AMDF). The simulation results show that the proposed method can detect pitch with higher accuracy even at a signal to noise ratio (SNR) as low as -5 dB.

An automatic bleeding detection scheme in wireless capsule endoscopy based on histogram of an RGB-indexed image

Wireless capsule endoscopy (WCE) is one of the most effective technologies to diagnose gastrointestinal (GI) diseases, such as bleeding in GI tract. Because of long duration of WCE video containing large number images, it is a burden for clinician to detect diseases in real time. In this paper, an automatic bleeding image detection method is proposed utilizing construction of an index image incorporating certain level of information from each plane of RGB color space. Distinguishable color texture feature is developed from index image by histogram. Support vector machine (SVM) classifier is employed to detect bleeding and non-bleeding images from WCE videos. From extensive experimentation on real time WCE video recordings, it is found that the proposed method can accurately detect bleeding images with high sensitivity and specificity.

An ECG signal denoising method based on enhancement algorithms in EMD and Wavelet domains

This paper presents a new method based on enhancement algorithms in Empirical Mode Decomposition (EMD) and Discrete Wavelet Transform (DWT) domains for ECG signal denoising. Unlike the conventional EMD based ECG denoising methods that neglect a number of initial IMFs containing the QRS complex as well as noise, we propose a windowing method in EMD domain to filter out the noise from the initial IMFs without discarding them completely thus preserving the QRS complex. The comparatively cleaner ECG signal thus obtained from the EMD domain is employed to perform an adaptive soft thresholding in the DWT domain considering the advantageous properties of the DWT compared to EMD in preserving the energy and reconstructing the original ECG signal with a better time resolution. The performance of the proposed method is evaluated in terms of standard metrics by performing extensive simulations using the MIT-BIH arrhythmia database. The simulation results show that the proposed method is able to enhance the noisy ECG signals of different levels of SNR more accurately and consistently in comparison to some of the state-of-the-art methods.