Specific Signal Processing Algorithms Research Articles

Performance Assessment of Heartbeat Detection Algorithms on Photoplethysmograph and Functional NearInfrared Spectroscopy Signals

With wearable sensors, the acquisition of physiological signals has become affordable and feasible in everyday life. Specifically, Photoplethysmography (PPG), being a low-cost and highly portable technology, has attracted notable interest for measuring and diagnosing cardiac activity, one of the most important physiological and autonomic indicators. In addition to the technological development, several specific signal-processing algorithms have been designed to enable reliable detection of heartbeats and cope with the lower quality of the signals. In this study, we compare three heartbeat detection algorithms: Derivative-Based Detection (DBD), Recursive Combinatorial Optimization (RCO), and Multi-Scale Peak and Trough Detection (MSPTD). In particular, we considered signals from two datasets, namely, the PPG-DALIA dataset (N = 15) and the FANTASIA dataset (N = 20) which differ in terms of signal characteristics (sampling frequency and length) and type of acquisition devices (wearable and medical-grade). The comparison is performed both in terms of heartbeat detection performance and computational workload required to execute the algorithms. Finally, we explore the applicability of these algorithms on the cardiac component obtained from functional Near InfraRed Spectroscopy signals (fNIRS).The results indicate that, while the MSPTD algorithm achieves a higher F1 score in cases that involve body movements, such as cycling (MSPTD: Mean = 74.7, SD = 14.4; DBD: Mean = 54.4, SD = 21.0; DBD + RCO: Mean = 49.5, SD = 22.9) and walking up and down the stairs (MSPTD: Mean = 62.9, SD = 12.2; DBD: Mean = 50.5, SD = 11.9; DBD + RCO: Mean = 45.0, SD = 14.0), for all other activities the three algorithms perform similarly. In terms of computational complexity, the computation time of the MSPTD algorithm appears to grow exponentially with the signal sampling frequency, thus requiring longer computation times in the case of high-sampling frequency signals, where the usage of the DBD and RCO algorithms might be preferable. All three algorithms appear to be appropriate candidates for exploring the applicability of heartbeat detection on fNIRS data.

Endogenous motion of liver correlates to the severity of portal hypertension.

BACKGROUNDDegree of portal hypertension (PH) is the most important prognostic factor for the decompensation of liver cirrhosis and death, therefore adequate care for patients with liver cirrhosis requires timely detection and evaluation of the presence of clinically significant PH (CSPH) and severe PH (SPH). As the most accurate method for the assessment of PH is an invasive direct measurement of hepatic venous pressure gradient (HVPG), the search for non-invasive methods to diagnose these conditions is actively ongoing.AIMTo evaluate the feasibility of parameters of endogenously induced displacements and strain of liver to assess degree of PH.METHODSOf 36 patients with liver cirrhosis and measured HVPG were included in the case-control study. Endogenous motion of the liver was characterized by derived parameters of region average tissue displacement signal (dantero, dretro, dRMS) and results of endogenous tissue strain imaging using specific radiofrequency signal processing algorithm. Average endogenous strain µ and standard deviation σ of strain were assessed in the regions of interest (ROI) (1 cm × 1 cm and 2 cm × 2 cm in size) and different frequency subbands of endogenous motion (0-10 Hz and 10-20 Hz).RESULTSFour parameters showed statistically significant (P < 0.05) correlation with HVPG measurement. The strongest correlation was obtained for the standard deviation of strain (estimated at 0-10 Hz and 2 cm × 2 cm ROI size). Three parameters showed statistically significant differences between patient groups with CSPH, but only dretro showed significant results in SPH analysis. According to ROC analysis area under the curve (AUC) of the σROI[0…10Hz, 2 cm × 2 cm] parameter reached 0.71 (P = 0.036) for the diagnosis of CSPH; with a cut-off value of 1.28 μm/cm providing 73% sensitivity and 70% specificity. AUC for the diagnosis of CSPH for µROI[0…10Hz, 1 cm × 1 cm] was 0.78 (P = 0.0024); with a cut-off value of 3.92 μm/cm providing 73% sensitivity and 80% specificity. Dretro parameter had an AUC of 0.86 (P = 0.0001) for the diagnosis of CSPH and 0.84 (P = 0.0001) for the diagnosis of SPH. A cut-off value of -132.34 μm yielded 100% sensitivity for both conditions, whereas specificity was 80% and 72% for CSPH and SPH respectively.CONCLUSIONThe parameters of endogenously induced displacements and strain of the liver correlated with HVPG and might be used for non-invasive diagnosis of PH.

P32-S Application of decomposition based quantitative EMG (DQEMG) to focal neuropathies

Background EMG signals can be decomposed into their constituent motor unit potential trains and further analyzed using specific signal processing algorithms which provide novel and useful information regarding the temporal and electrophysiological behaviour and morphological attributes of motor units. In low to moderate stages of focal neuropathies (when only focal demyelination or low axonal sensory affection is registered) traditional needle EMG evaluation is usually normal. However, DQEMG provides evidence of motor unit instability in a significant portion of otherwise normal muscles. Material and methods 20 case and 9 control subjects were studied with median and ulnar ENG, quantitative EMG and DQEMG (abductor pollicis brevis, first dorsal interosseus or abductor digiti minimi). Patients with symptoms suggesting focal median or ulnar neuropathies were selected as cases, except for those showing low-median compound muscle action potential amplitudes or needle EMG abnormalities. Control subjects were included when no ENG nor needle EMG abnormalities findings were encountered. Results 75% of cases with any focal demyelinating finding showed instability features in DQEMG, which were also found in 43% of the normal ENG and control groups. Applying more restrictive DQEMG criteria, instability was found in 63% of cases with focal ENG alterations and 43% with normal ENG, while the control group showed no abnormalities. Conclusions Decomposition-based quantitative EMG is a novel and more powerful tool for detecting abnormalities in motor unit potentials, compared with the classical quantitative or semi-quantitative needle EMG analysis.

An Effective Method for Active Noise Control

By introducing an “antinoise” wave through secondary sources the active noise control (ANC) is achieved. Through an electronic system, the secondary sources are connected using a specific signal processing algorithm for the particular cancellation method. In this paper, we propose a method which is combination of both Feed-forward and Feedback active noise control systems called Hybrid active noise control (H-ANC) method. This method uses lower order filters and also reduces distortion generated by the feedback ANC. So this method is relatively more stable than feedback ANC. The Computation requirements for Hybrid ANC would be high and real-time implementations during one cycle. The algorithm generate the inverse waveform of the signal by collecting the noise information and update the filter coefficients effectively.

A Dual-DSP Sonobuoy Signal Processing System

Sonobuoy is used as aviation antisubmarine device to detect submarines, and its wireless communication mechanism would introduce radio interference. The speech signal needs to be identified from the submarine noise in order to facilitate sonar signal processing system to do further processing of the signal. This paper presents a TS-201 based dual-DSP sonobuoy signal processing system, and proposes an algorithm using Cubic Spline Interpolation and Pearson correlation coefficient to identify the speech signal from submarine radiated noise signal. This article describes the specific signal processing algorithm of the system, the hardware and software design of the system. This article uses a large number of data from experiments to test the hardware and software systems separately. The results of tests are analyzed, which indicate that the system function well in identifying speech signal from submarine radiated noise signal to, with a percentage of 98% correct rate.

Signal processing method of a novel polarized array radar seeker

This paper proposes a novel polarized radar seeker based on the polarized antenna array. A fully polarized signal processing method for the proposed radar seeker is studied under the environments with electromagnetic interferences. A dual polarized antenna array is employed to transmit and receive the radar signals. The instantaneous polarization signal processing technique is used to detect and recognize the targets. The direction of arrival(DOA) of the target is measured through the spatial spectrum with high resolution for the polarized array radar seeker system. The fully polarized signal model of the polarized array radar seeker is formulated and a specific signal processing algorithm is expounded. The theoretical research and numerical simulation results demonstrate that the proposed radar seeker has good performances in target detection and electronic warfare. The research results can provide an effective technical approach to develop and research the new generation radar seeker.

Relationship between distortion and working memory for digital noise-reduction processing in hearing aids

Several recent studies have shown a relationship between working memory and the ability of older adults to benefit from specific advanced signal processing algorithms in hearing aids. In this study, we quantify tradeoffs between benefit due to noise reduction and the perceptual costs associated with distortion caused by the noise reduction algorithm. We also investigate the relationship between these tradeoffs and working memory abilities. Speech intelligibility, speech quality and perceived listening effort were measured in a cohort of elderly adults with hearing loss. Test materials were low-context sentences presented in fluctuating noise conditions at several signal-to-noise ratios. Speech stimuli were processed with a binary mask noise-reduction strategy. The amount of distortion produced by the noise reduction algorithm was parametrically varied by manipulating two binary mask parameters, error rate and attenuation rate. Working memory was assessed with a reading span test. Results will be discussed in terms of the extent to which intelligibility, quality and effort ratings are explained by the amount of distortion and/or noise and by working memory ability.[Funded by NIH, Oticon and GN ReSound]

A Decisive Filtering Selection Approach For Improved Performance Active Noise Cancellation System

In this work we present a filtering selection approach for efficient ANC system. Active noise cancellation (ANC) has wide application in next generation human machine interaction to automobile Heating Ventilating and Air Conditioning (HVAC) devices. We compare conventional adaptive filters algorithms LMS, NLMS, VSLMS, VSNLMS, VSLSMS for a predefined input sound file, where various algorithms run and result in standard output and better performance. The wiener filter based on least means squared (LMS) algorithm family is most sought after solution of ANC. This family includes LMS, NLMS, VSLMS, VSNLMS, VFXLMS, FX- sLMS and many more. Some of these are nonlinear algorithm, which provides better solution for nonlinear noisy environment. The components of the ANC systems like microphones and loudspeaker exhibit nonlinearities themselves. The nonlinear transfer function create worse situation. This is a task which is some sort of a prediction of suitable solution to the problems. The Radial Basis Function of Neural Networks (RBF NN) has been known to be suitable for nonlinear function approximation (1). The classical approach to RBF implementation is to fix the number of hidden neurons based on some property of the input data, and estimate the weights connecting the hidden and output neurons using linear least square method. So an efficient novel decisive approach for better performing ANC algorithms has been proposed. I. OVERVIEW Acoustic Noise Cancellation is a method for reducing undesired noise. It is achieved by introducing a canceling anti-noise wave through secondary sources. These secondary sources are interconnected through an electronic system using a specific signal processing algorithm for the particular cancellation scheme. Noise cancellation makes use of the notion of destructive interference. When two sinusoidal waves are superimposed, the resultant waveform depends on the frequency amplitude and relative phase of the two waves. If the original wave and the inverse of the original wave encounter at a junction at the same time, total cancellation occurs. The challenges are to identify the original signal and generate the inverse noise cancellation on inverse wave without delay in all directions where noises interact and superimpose. We meet in our everyday life. Echo phenomena are interesting and entertaining, but their presences in communication networks are undesirable and represent a serious problem. Echo is delayed and degraded version of original signal which travels back to its source after several reflections or because of some other reason. Nature of echo signal can be either acoustic or electrical, and in order to reduce its undesired effect we employ echo cancellers. Design of echo cancellers requires an application of adaptive filter theory. Echo cancellers must work within specific time limits so adaptive algorithms must provide fast convergence of filter parameters. Weve been applying echo cancellers successfully for many years, but we always tend to improve them and increase their efficiency. The wiener filter based least means squared (LMS) algorithm family is most sought after solution of ANC. This family includes LMS , Fx-LMS, VFx-LMS, FsLMS and many more . Then there are Kalman filter algorithms which are basically based on recursive least square algorithm. Some of these are nonlinear algorithm, which provides better solution for non linear noisy environment. The components of the ANC systems like microphones and loudspeaker exhibit nonlinearities themselves. The non linear transfer function of primary and secondary path may itself create worse situation. II. BACKGROUND LITERATURE REVIEW

An acousto-optic beamformer

There is a great variety of beamforming techniques that can be used for localization of sound sources. The differences among them usually lie in the array layout or in the specific signal processing algorithm used to compute the beamforming output. Any beamforming system consists of a finite number of transducers, which makes beamforming methods vulnerable to spatial aliasing above a certain frequency. The present work uses the acousto-optic effect, i.e., the interaction between sound and light, to localize sound sources in a plane. The use of a beam of light as the sensing element is equivalent to a continuous line aperture with an infinite number of microphones. This makes the proposed acousto-optic beamformer immune to spatial aliasing. This unique feature is illustrated by means of simulations and experimental results within the entire audible frequency range. For ease of comparison, the study is supplemented with measurements carried out with a line array of microphones.

Spatialized additive synthesis

In virtual auditory environments, a spatialized sound source is typically simulated in two stages: first a dry monophonic signal is recorded or synthesized, and then spatial attributes (directivity, width and position) are applied by specific signal processing algorithms. In this paper, a unified analysis/spatialization/synthesis system is presented. It is based on the spectral modeling framework that analyses/synthesizes sounds as a combination of time‐varying sinusoidal, noisy and transient contributions. The proposed system takes advantage of this representation to allow intrinsic parametric sound transformations, such as spatial distribution of sinusoids or diffusion of the noisy contribution around the listener. It integrates timbre and spatial parameters at the same level of sound generation, so as to enhance control capability and computational performance.

Ultra sensitive sensor with enhanced dynamic range for high speed detection of multi-color fluorescence radiation

This paper describes design of the new ultra sensitive sensor system for fluorescence detection applications. System comprises two units: optical spectra separation unit and detection unit. Optical unit of the sensor performs spatial spectra separation of signal from the laser excited fluorescence, and resulting spectra is collected in the detection part of the system. Optical part is built using diffraction grating as spectra separation element. Detection part comprises 32-channel photomultiplier tube working in single photon counting mode with our 32-channel amplifier. Using single photon detection technique and specific signal processing algorithms for collected data, the proposed system allows to achieve unique combination of characteristics—high sensitivity, high detection speed and wide linearity dynamic range comparing to existing commercial instruments. DNA sequencing experiments with new sensor as detection device, and using two types of lasers (Ar-ion and Nd-YAG) were carried out, yielding sequencing traces which have quality factor of 20 for read lengths as long as 650 base pairs.

Active noise control: a tutorial review

Active noise control (ANC) is achieved by introducing a cancelling "antinoise" wave through an appropriate array of secondary sources. These secondary sources are interconnected through an electronic system using a specific signal processing algorithm for the particular cancellation scheme. ANC has application to a wide variety of problems in manufacturing, industrial operations, and consumer products. The emphasis of this paper is on the practical aspects of ANC systems in terms of adaptive signal processing and digital signal processing (DSP) implementation for real-world applications. In this paper, the basic adaptive algorithm for ANC is developed and analyzed based on single-channel broad-band feedforward control. This algorithm is then modified for narrow-band feedforward and adaptive feedback control. In turn, these single-channel ANC algorithms are expanded to multiple-channel cases. Various online secondary-path modeling techniques and special adaptive algorithms, such as lattice, frequency-domain, subband, and recursive-least-squares, are also introduced. Applications of these techniques to actual problems are highlighted by several examples.