Signal Quality Measures Research Articles

Body Conducted Speech Enhancement by Equalization and Signal Fusion

This paper studies body-conducted speech for noise robust speech processing purposes. As body-conducted speech is typically limited in bandwidth, signal processing is required to obtain a signal that is both high in quality and low in noise. We propose an algorithm that first equalizes the body-conducted speech using filters obtained from a pre-defined filter set and subsequently fuses this equalized signal with a noisy conventional microphone signal using an optimal clean speech amplitude and phase estimator. We evaluated the proposed equalization and fusion technique using a combination of a conventional close-talk and a throat microphone. Subjective listening tests show that the proposed method successfully fuses the speech quality of the conventional signal and the noise robustness of the throat microphone signal. The listening tests also indicate that the inclusion of the body-conducted signal can improve single-channel speech enhancement methods, while a calculated set of objective signal quality measures confirm these observations.

CDMA2000 1x 역방향 링크의 신호 품질 측정 알고리즘

In this paper, we propose and implement a signal quality measurement algorithm for CDMA2000 1x terminal. The proposed algorithm is suitable to be implemented in software on a PC-based platform and extract the received signal after carrying out equalization, PN code acquisition and tracking, frequency and phase offset compensation with 4-oversampled input signal. Then, through despreading and demodulation with the extracted signal, the proposed algorithm regenerate the reference signal to be used in measurement. The signal quality is measured using this regenerated signal and the extracted signal.

Electrocardiogram signal quality measures for unsupervised telehealth environments

The use of telehealth paradigms for the remote management of patients suffering from chronic conditions has become more commonplace with the advancement of Internet connectivity and enterprise software systems. To facilitate clinicians in managing large numbers of telehealth patients, and in digesting the vast array of data returned from the remote monitoring environment, decision support systems in various guises are often utilized. The success of decision support systems in interpreting patient conditions from physiological data is dependent largely on the quality of these recorded data. This paper outlines an algorithm to determine the quality of single-lead electrocardiogram (ECG) recordings obtained from telehealth patients. Three hundred short ECG recordings were manually annotated to identify movement artifact, QRS locations and signal quality (discrete quality levels) by a panel of three experts, who then reconciled the annotation as a group to resolve any discrepancies. After applying a published algorithm to remove gross movement artifact, the proposed method was then applied to estimate the remaining ECG signal quality, using a Parzen window supervised statistical classifier model. The three-class classifier model, using a number of time-domain features and evaluated using cross validation, gave an accuracy in classifying signal quality of 78.7% (κ = 0.67) when using fully automated preprocessing algorithms to remove gross motion artifact and detect QRS locations. This is a similar level of accuracy to the reported human inter-scorer agreement when generating the gold standard annotation (accuracy = 70–89.3%, κ = 0.54–0.84). These results indicate that the assessment of the quality of single-lead ECG recordings, acquired in unsupervised telehealth environments, is entirely feasible and may help to promote the acceptance and utility of future decision support systems for remotely managing chronic disease conditions.

Signal quality measures for unsupervised blood pressure measurement

Accurate systolic and diastolic pressure estimation, using automated blood pressure measurement, is difficult to achieve when the transduced signals are contaminated with noise or interference, such as movement artifact. This study presents an algorithm for automated signal quality assessment in blood pressure measurement by determining the feasibility of accurately detecting systolic and diastolic pressures when corrupted with various levels of movement artifact. The performance of the proposed algorithm is compared to a manually annotated reference scoring (RS). Based on visual representations and audible playback of Korotkoff sounds, the creation of the RS involved two experts identifying sections of the recorded sounds and annotating sections of noise contamination. The experts determined the systolic and diastolic pressure in 100 recorded Korotkoff sound recordings, using a simultaneous electrocardiograph as a reference signal. The recorded Korotkoff sounds were acquired from 25 healthy subjects (16 men and 9 women) with a total of four measurements per subject. Two of these measurements contained purposely induced noise artifact caused by subject movement. Morphological changes in the cuff pressure signal and the width of the Korotkoff pulse were extracted features which were believed to be correlated with the noise presence in the recorded Korotkoff sounds. Verification of reliable Korotkoff pulses was also performed using extracted features from the oscillometric waveform as recorded from the inflatable cuff. The time between an identified noise section and a verified Korotkoff pulse was the key feature used to determine the validity of possible systolic and diastolic pressures in noise contaminated Korotkoff sounds. The performance of the algorithm was assessed based on the ability to: verify if a signal was contaminated with any noise; the accuracy, sensitivity and specificity of this noise classification, and the systolic and diastolic pressure differences between the result obtained from the algorithm and the RS. 90% of the actual noise contaminated signals were correctly identified, and a sample-wise accuracy, sensitivity and specificity of 97.0%, 80.61% and 98.16%, respectively, were obtained from 100 pooled signals. The mean systolic and diastolic differences were 0.37 ± 3.31 and 3.10 ± 5.46 mmHg, respectively, when the artifact detection algorithm was utilized, with the algorithm correctly determined if the signal was clean enough to attempt an estimation of systolic or diastolic pressures in 93% of blood pressure measurements.

ESTIMATION OF OTOACOUSTIC EMISION SIGNALS BY USING SYNCHRONEOUS AVERAGING METHOD / OTOAKUSTINĖS EMISIJOS SIGNALŲ ĮVERTINIMAS PANAUDOJANT SINCHRONINĮ VIDURKINIMĄ

The study presents the investigation results of synchro­nous averaging method and its application in estimation of impulse evoked otoacoustic emission signals (IEOAE). The method was analyzed using synthetic and real signals. Synthetic signals were modeled as the mixtures of deterministic compo­nent with noise realizations. Two types of noise were used: normal (Gaussian) and transient impulses dominated (Lapla­cian). Signal to noise ratio was used as the signal quality measure after processing. In order to account varying amplitude of deterministic component in the realizations weighted aver­aging method was investigated. Results show that the perfor­mance of synchronous averaging method is very similar in case of both types of noise Gaussian and Laplacian. Weighted aver­aging method helps to cope with varying deterministic component or noise level in case of nonhomogenous ensembles as is the case in IEOAE signal.

Sensory adaptation in the light of information and energy

AbstractSignal processing in neurons is constrained by internal noise and energy consumption. Neural systems often invest energy in amplifying signals to protect them against internal noise. There is no general expression for the trade-off between energy and information, because it is strongly dependent on the properties of signal, noise and underlying mechanism. We present the first analysis of this energy-information trade-off in a specific system, the R1-6 photoreceptors of the blowfly, Calliphora vicina. These photoreceptors adapt to light level by adjusting their transduction gain (number of light-gated channels opened per photon) and potassium conductance. By combining experimental measures of photoreceptor impedances, transfer functions and signal quality in a basic membrane model we discovered that photoreceptors do not adapt to maximise their sensitivity. Instead, they use a lower sensitivity at which they capture 99% of the information from naturalistic stimuli. This strategy is efficient because it avoids the wasteful amplification of noisy inputs and reduces energy consumption.

Signal quality measures for pulse oximetry through waveform morphology analysis

Pulse oximetry has been extensively used to estimate oxygen saturation in blood, a vital physiological parameter commonly used when monitoring a subject's health status. However, accurate estimation of this parameter is difficult to achieve when the fundamental signal from which it is derived, the photoplethysmograph (PPG), is contaminated with noise artifact induced by movement of the subject or the measurement apparatus. This study presents a novel method for automatic rejection of artifact contaminated pulse oximetry waveforms, based on waveform morphology analysis. The performance of the proposed algorithm is compared to a manually annotated gold standard. The creation of the gold standard involved two experts identifying sections of the PPG signal containing good quality PPG pulses and/or noise, in 104 fingertip PPG signals, using a simultaneous electrocardiograph (ECG) signal as a reference signal. The fingertip PPG signals were each 1 min in duration and were acquired from 13 healthy subjects (10 males and 3 females). Each signal contained approximately 20 s of purposely induced artifact noise from a variety of activities involving subject movement. Some unique waveform morphology features were extracted from the PPG signals, which were believed to be correlated with signal quality. A simple decision-tree classifier was employed to arrive at a classification decision, at a pulse-by-pulse resolution, of whether a pulse was of acceptable quality for use or not. The performance of the algorithm was assessed using Cohen's kappa coefficient (κ), sensitivity, specificity and accuracy measures. A mean κ of 0.64 ± 0.22 was obtained, while the mean sensitivity, specificity and accuracy were 89 ± 10%, 77 ± 19% and 83 ± 11%, respectively. Furthermore, a heart rate estimate, extracted from uncontaminated sections of PPG, as identified by the algorithm, was compared with the heart rate derived from an uncontaminated simultaneous ECG signal. The mean error between both heart rate readings was 0.49 ± 0.66 beats per minute (BPM), in comparison to an error value observed without using the artifact detection algorithm of 7.23 ± 5.78 BPM. These results demonstrate that automated identification of signal artifact in the PPG signal through waveform morphology analysis is achievable. In addition, a clear improvement in the accuracy of the derived heart rate is also evident when such methods are employed.

Remote Detection of a Frequency Jammer Operating in the Down Link of a Cellular System

The authors propose a novel method to evaluate the interference conditions in a cellular network. The method takes advantage of the signal strength and quality measurements available at a Base Station. Measurements from both up-link and down-link are jointly processed to remotely detect the presence of a medium-range frequency jammer and other situations involving abnormal interference levels, as a malfunctioning of the power control algorithm, a bad radio optimization and planning or an outer interference, among others. The proposed method can be adapted to any cellular system. In this paper we focus on its application to GSM/GPRS system. We include some experiments where real cell data recordings were analyzed.

EVM as Generic QOS Trigger for Heterogeneous Wireless Overlay Networks

Fourth Generation (4G) Wireless System will integrate heterogeneous wireless overlay systems i.e. interworking of WLAN/ GSM/ CDMA/ WiMAX/ LTE/ etc with guaranteed Quality of Service (QoS) and Experience (QoE).QoS(E) vary from network to network and is application sensitive. User needs an optimal mobility solution while roaming in Overlaid wireless environment i.e. user could seamlessly transfer his session/ call to a best available network bearing guaranteed Quality of Experience. And If this Seamless transfer of session is executed between two networks having different access standards then it is called Vertical Handover (VHO). Contemporary VHO decision algorithms are based on generic QoS metrics viz. SNR, bandwidth, jitter, BER and delay. In this paper, Error Vector Magnitude (EVM) is proposed to be a generic QoS trigger for VHO execution. EVM is defined as the deviation of inphase/ quadrature (I/Q) values from ideal signal states and thus provides a measure of signal quality. In 4G Interoperable environment, OFDM is the leading Modulation scheme (more prone to multi-path fading). EVM (modulation error) properly characterises the wireless link/ channel for accurate VHO decision. EVM depends on the inherent transmission impairments viz. frequency offset, phase noise, non-linear-impairment, skewness etc. for a given wireless link. Paper provides an insight to the analytical aspect of EVM & measures EVM (%) for key management subframes like association/re-association/disassociation/ probe request/response frames. EVM relation is explored for different possible NAV-Network Allocation Vectors (frame duration). Finally EVM is compared with SNR, BER and investigation concludes EVM as a promising QoS trigger for OFDM based emerging wireless standards.

Use of signal quality measurements to gain efficiency in the analysis of cDNA microarray data

This research provides a new way to measure error in microarray data in order to improve gene expression analysis. Microarray data contains many sources of error. In order to glean information about mRNA expression levels, the true signal must first be segregated from noise. This research focuses on the variation that can be captured at the spot level in cDNA microarray images. Variation at other levels, due to differences at the array, dye, and block levels, can be corrected for by a variety of existing normalization procedures. Two signal quality estimates that capture the reliability of each spot printed on a microarray are described. A parametric estimate of within-spot variance, referred to here as sigma(2)(spot), assumes that pixels follow a normal distribution and are spatially correlated. A non-parametric estimate of error, called the mean square prediction error (MSPE), assumes that spots of high quality possess pixels that are similar to their neighbors. This paper will provide a framework to use either spot quality measure in downstream analysis, specifically as weights in regression models. Using these spot quality estimates as weights can result in greater efficiency, in a statistical sense, when modeling microarray data.

Signal Quality Measurements for cDNA Microarray Data

Concerns about the reliability of expression data from microarrays inspire ongoing research into measurement error in these experiments. Error arises at both the technical level within the laboratory and the experimental level. In this paper, we will focus on estimating the spot-specific error, as there are few currently available models. This paper outlines two different approaches to quantify the reliability of spot-specific intensity estimates. In both cases, the spatial correlation between pixels and its impact on spot quality is accounted for. The first method is a straightforward parametric estimate of within-spot variance that assumes a Gaussian distribution and accounts for spatial correlation via an overdispersion factor. The second method employs a nonparametric quality estimate referred to throughout as the mean square prediction error (MSPE). The MSPE first smoothes a pixel region and then measures the difference between actual pixel values and the smoother. Both methods herein are compared for real and simulated data to assess numerical characteristics and the ability to describe poor spot quality. We conclude that both approaches capture noise in the microarray platform and highlight situations where one method or the other is superior.

Artificial arterial blood pressure artifact models and an evaluation of a robust blood pressure and heart rate estimator

BackgroundWithin the intensive care unit (ICU), arterial blood pressure (ABP) is typically recorded at different (and sometimes uneven) sampling frequencies, and from different sensors, and is often corrupted by different artifacts and noise which are often non-Gaussian, nonlinear and nonstationary. Extracting robust parameters from such signals, and providing confidences in the estimates is therefore difficult and requires an adaptive filtering approach which accounts for artifact types.MethodsUsing a large ICU database, and over 6000 hours of simultaneously acquired electrocardiogram (ECG) and ABP waveforms sampled at 125 Hz from a 437 patient subset, we documented six general types of ABP artifact. We describe a new ABP signal quality index (SQI), based upon the combination of two previously reported signal quality measures weighted together. One index measures morphological normality, and the other degradation due to noise. After extracting a 6084-hour subset of clean data using our SQI, we evaluated a new robust tracking algorithm for estimating blood pressure and heart rate (HR) based upon a Kalman Filter (KF) with an update sequence modified by the KF innovation sequence and the value of the SQI. In order to do this, we have created six novel models of different categories of artifacts that we have identified in our ABP waveform data. These artifact models were then injected into clean ABP waveforms in a controlled manner. Clinical blood pressure (systolic, mean and diastolic) estimates were then made from the ABP waveforms for both clean and corrupted data. The mean absolute error for systolic, mean and diastolic blood pressure was then calculated for different levels of artifact pollution to provide estimates of expected errors given a single value of the SQI.ResultsOur artifact models demonstrate that artifact types have differing effects on systolic, diastolic and mean ABP estimates. We show that, for most artifact types, diastolic ABP estimates are less noise-sensitive than mean ABP estimates, which in turn are more robust than systolic ABP estimates. We also show that our SQI can provide error bounds for both HR and ABP estimates.ConclusionThe KF/SQI-fusion method described in this article was shown to provide an accurate estimate of blood pressure and HR derived from the ABP waveform even in the presence of high levels of persistent noise and artifact, and during extreme bradycardia and tachycardia. Differences in error between artifact types, measurement sensors and the quality of the source signal can be factored into physiological estimation using an unbiased adaptive filter, signal innovation and signal quality measures.

Method and apparatus of determining bad frame indication for speech service in a wireless communication system

A method of operating a receiver to determine a bad frame indication (BFI) of a received speech block includes decoding speech information bits of the received speech block; re-encoding the decoded speech information bits; comparing the received speech information bits with the corresponding decoded and re-encoded speech information bits to calculate a number of erroneous bits; determining a mode information of the received speech block; calculating a signal quality measurement of the received speech block; calculating a block quality metric according to the mode information, wherein the block quality metric is related to both the number of erroneous bits and the signal quality measurement; and setting the BFI of the received speech block according to the block quality metric.

Improving biometric verification with class-independent quality information

Existing approaches to biometric classification with quality measures make a clear distinction between the single-modality applications and the multi-modal scenarios. This study bridges this gap with Q-stack, a stacking-based classifier ensemble, which uses the class-independent signal quality measures and baseline classifier scores in order to improve the accuracy of uni- and multi-modal biometric classification. The seemingly counterintuitive notion of using class-independent quality information for improving class separation by considering quality measures as conditionally relevant classification features. The authors present Q-stack as a generalised framework of classification with quality information is explained, and argue that existing methods of classification with quality measures are its special cases. The authors further demonstrate the application of Q-stack on the task of biometric identity verification using face and fingerprint modalities, and show that the use of the proposed technique allows a systematic reduction of the error rates below those of the baseline classifiers, in scenarios involving single and multiple biometric modalities.

Carrier phase multipath mitigation based on GNSS signal quality measurements

Carrier phase multipath caused by signal reflections in the vicinity of GNSS stations is a ma- jor error source of precise differential positioning. Detection and mitigation of carrier phase multipath errors can be based on signal quality values (e. g. signal-to-noise ratio values) as provided by GNSS re- ceivers. These values are influenced by various fac- tors as e. g.the actual strength of the transmitted sig- nal, space loss, atmospheric effects, and antenna gain pattern. After their removal, deviations from nominal signal quality values mainly contain multipath infor- mation which can be used to calculate carrier phase multipath corrections. The described technique is valid only for observations collected in static mode and sin- gle dominant reflectors. Practical experiences show that corrections based on signal quality observations are able to remove a large portion of the carrier phase multipath errors.

How Variation in Signal Quality Affects Performance

The information coefficient (IC), the correlation between forecasted and realized return, is a popular measure of signal quality. As shown in this article, variation in IC is an important source of active risk, and IC variation has an effect on optimal portfolio structure. Contrary to popular belief, the ability to take short positions in equity portfolios does not necessarily lead to superior performance. Managers who can maintain a stable IC over time will benefit from short extensions, but managers who have an unstable IC may see their performance deteriorate from increased short positions.

A 0.016 mm$^{2}$, 2.4 GHz RF Signal Quality Measurement Macro for RF Test and Diagnosis

Our RF signal-quality measurement macro employs 1) a new window-shifting measurement technique that obtains the power of a high-frequency component on the basis of lower-frequency measurements, and 2) a new stair-like weighted addition technique that decreases error due to harmonic power, phase, and DC offset in measured signals. The macro occupies only 1/10 the area of conventional on-chip spectrum analyzers. Carrier power for 2.4 GHz-Tx was successfully measured with digital testers alone, with an error of only <1 dB, and harmonic-emission exceeding FCC regulations (> -20 dBm) was also successfully detected.

Accuracy of pulse oximetry in assessing heart rate of infants in the neonatal intensive care unit

To determine the accuracy of pulse oximetry measurement of heart rate in the neonatal intensive care unit. Stable preterm infants were monitored with a pulse oximeter and an ECG. The displays of both monitors were captured on video. Heart rate data from both monitors, including measures of signal quality, were extracted and analysed using Bland Altman plots. In 30 infants the mean (SD) difference between heart rate measured by pulse oximetry and electrocardiography was -0.4 (12) beats per minute. Accuracy was maintained when the signal quality or perfusion was low. Pulse oximetry may provide a useful measurement of heart rate in the neonatal intensive care unit. Studies of this technique in the delivery room are indicated.

MMSE Noise Plus Interference Power Estimation in Adaptive OFDM Systems

Noise variance and signal-to-noise ratio are important parameters for adaptive orthogonal frequency-division multiplexing (OFDM) systems since they serve as a standard measure of signal quality. Conventional algorithms assume that the noise statistics remain constant over the OFDM frequency band and, thereby, average the instantaneous noise samples to get a single estimate. In reality, noise is often made up of white Gaussian noise, along with correlated colored noise that unevenly affects the OFDM spectrum. This paper proposes a minimum mean square error (MMSE) filtering technique to estimate the noise power that takes into account the variation of the noise statistics across the OFDM subcarrier index, as well as across OFDM symbols. The proposed method provides many local estimates that allow tracking of the variation of noise statistics in frequency and time. The MMSE filter coefficients are obtained from the mean-squared-error expression, which can be calculated using the noise statistics. Evaluation of the performance with computer simulations shows that the proposed method tracks the local statistics of the noise more efficiently than conventional methods.

Design and validation of a MR-compatible pneumatic manipulandum

The combination of functional MR imaging and novel robotic tools may provide unique opportunities to probe the neural systems underlying motor control and learning. Here, we describe the design and validation of a MR-compatible, 1 degree-of-freedom pneumatic manipulandum along with experiments demonstrating its safety and efficacy. We first validated the robot's ability to apply computer-controlled loads about the wrist, demonstrating that it possesses sufficient bandwidth to simulate torsional spring-like loads during point-to-point flexion movements. Next, we verified the MR-compatibility of the device by imaging a head phantom during robot operation. We observed no systematic differences in two measures of MRI signal quality (signal/noise and field homogeneity) when the robot was introduced into the scanner environment. Likewise, measurements of joint angle and actuator pressure were not adversely affected by scanning. Finally, we verified device efficacy by scanning 20 healthy human subjects performing rapid wrist flexions against a wide range of spring-like loads. We observed a linear relationship between joint torque at peak movement extent and perturbation magnitude, thus demonstrating the robot's ability to simulate spring-like loads in situ. fMRI revealed task-related activation in regions known to contribute to the control of movement including the left primary sensorimotor cortex and right cerebellum.