Electroglottography in Medical Diagnostics of Vocal Tract Pathologies: A Systematic Review.

Electroglottography (EGG) is a technology developed for measuring the vocal fold contact area during human voice production. Although considered subjective and unreliable as a sole diagnostic method, with the correct application of relevant computational methods, it can constitute a most promising non-invasive voice disorder diagnostic tools in a form of a digital vocal tract pathology classifier. The aim of the following study is to gather and evaluate currently existing digital voice quality assessment systems and vocal tract abnormality classification systems that rely on the use of electroglottographic bio-impedance signals. To fully comprehend the findings of this review, first the subject of EGG is introduced. For that, we summarise most relevant existing research on EGG with a particular focus on its application in diagnostics. Then, we move on to the focal point of this work, which is describing and comparing the existing EGG-based digital voice pathology classification systems. With the application of PRISMA model, 13 articles were chosen and analysed in detail. Direct comparison between chosen studies brought us to pivotal conclusions, which have been described in Section 5 of this report. Meanwhile, certain limitations arising from the literature were identified, such as questionable understanding of the nature of EGG bio-impedance signals. The appropriate recommendations for future work were made, including the application of different methods for EGG feature extraction, as well as the need for continuous EGG datasets development containing signals gathered in various conditions and with different equipments.

Automatic Heart Rate Detection during Sleep Using Tracheal Audio Recordings from Wireless Acoustic Sensor.

BACKGROUND: Heart rate is an essential diagnostic parameter indicating a patient's condition. The assessment of heart rate is also a crucial parameter in the diagnostics of various sleep disorders, including sleep apnoea, as well as sleep/wake pattern analysis. It is usually measured using an electrocardiograph (ECG)-a device monitoring the electrical activity of the heart using several electrodes attached to a patient's upper body-or photoplethysmography (PPG). METHODS: The following paper investigates an alternative method for heart rate detection and monitoring that operates on tracheal audio recordings. Datasets for this research were obtained from six participants along with ECG Holter (for validation), as well as from fifty participants undergoing a full night polysomnography testing, during which both heart rate measurements and audio recordings were acquired. RESULTS: The presented method implements a digital filtering and peak detection algorithm applied to audio recordings obtained with a wireless sensor using a contact microphone attached in the suprasternal notch. The system was validated using ECG Holter data, achieving over 92% accuracy. Furthermore, the proposed algorithm was evaluated against whole-night polysomnography-derived HR using Bland-Altman's plots and Pearson's Correlation Coefficient, reaching the average of 0.82 (0.93 maximum) with 0 BPM error tolerance and 0.89 (0.97 maximum) at ±3 BPM. CONCLUSIONS: The results prove that the proposed system serves the purpose of a precise heart rate monitoring tool that can conveniently assess HR during sleep as a part of a home-based sleep disorder diagnostics process.

Matched time-frequency filter for ECG signal enhancement.

A new method for ECG signal enhancement is presented, based on "matched" time-frequency filtering in the Wigner representation. Performance analysis shows that the method is particularly useful for noise removal in clinical ECG.

Motion artifact suppression in the ECG signal by successive modifications in frequency and time.

Ambulatory electrocardiogram signals can be contaminated with various types of noise. Among these, electrode motion 'em' artifacts are considered particularly undesired as they can be mistaken for ectopic beats. Unfortunately, 'em' noise has proved difficult to tackle using ordinary filtering techniques. In this paper, we explore a novel filtering alternative, and show that it could be considered as a potential candidate for dealing with electrode motion artifacts. The proposed system is composed of two simple parts: a frequency filter and a time window, interconnected in series. The two components are designed such that the overall system operates optimally in the mean square error sense. Experimentation on signals obtained from the MIT-BIH database demonstrates the superiority of the above approach over optimal Fourier filtering.

Optimal two-stage filtering of elastograms.

In ultrasound elastography, tissue axial strains are obtained through the differentiation of measured axial displacements. However, during the measurement process, the displacement signals are often contaminated with de-correlation noise caused by changes in the speckle pattern in the tissue. Thus, the application of the gradient operator on the displacement signals results in the presence of amplified noise in the axial strains, which severely obscures the useful information. The use of an effective denoising scheme is therefore imperative. In this paper, a method based on a two-stage consecutive filtering approach is proposed for the accurate estimation of axial strains. The presented method considers a cascaded system of a frequency filter and a time window, which are both designed such that the overall system operates optimally in a mean square error sense. Experimentation on simulated signals shows that the two-stage scheme employed in this study has good potential as a denoising method for ultrasound elastograms.

Filterbank spectral estimators for the analysis of surface EMG signals during isometric contractions.

The analysis of surface electromyogram (EMG) signals during voluntary isometric contractions can yield important information relating to muscle fatigue. These EMG signals are typically processed to extract specific variables such as the Mean Frequency (MNF) and the Median Frequency (MDF) and studies often follow how these parameters change through time. Traditional approaches to estimate the MNF and MDF variables are based on the periodogram, but this method suffers from a high degree of variability due in part the choice of window size, window function and other inherent limitations. In this paper we propose the use of data-adaptive filterbank spectral analysis techniques, namely the Power Spectrum Capon (PSC) and the Amplitude Spectrum Capon (ASC) methods. These new methods are shown to provide significant reductions in MNF and MDF parameter variability over a wide range of data window sizes.

STFT-based denoising of biomechanical impact signals.

We present an advanced denoising method for non-stationary biomechanical signals with the aim of accurately estimating their second derivative (acceleration). The proposed algorithm is based on the short-time Fourier transform (STFT) representation of the signal and its subsequent modification by means of a suitable time-varying filtering function. The application of the method to experimentally acquired biomechanical signals demonstrated that the proposed algorithm is more robust against noise and achieves a more accurate acceleration-peak estimation as compared to commonly used conventional low-pass filtering.

Fractional fourier-based filter for denoising elastograms.

In ultrasound elastography, tissue axial strains are obtained through the differentiation of axial displacements. However, the application of the gradient operator amplifies the noise present in the displacement rendering unreadable axial strains. In this paper a novel denoising scheme based on repeated filtering in consecutive fractional Fourier transform domains is proposed for the accurate estimation of axial strains. The presented method generates a time-varying cutoff threshold that can accommodate the discrete non-stationarities present in the displacement signal. This is achieved by means of a filter circuit which is composed of a small number of ordinary linear low-pass filters and appropriate fractional Fourier transforms. We show that the proposed method can improve the contrast-to-noise ratio (CNR(e)) of the elastogram outperforming conventional low-pass filters.

A simple filter circuit for denoising biomechanical impact signals.

We present a simple scheme for denoising non-stationary biomechanical signals with the aim of accurately estimating their second derivative (acceleration). The method is based on filtering in fractional Fourier domains using well-known low-pass filters in a way that amounts to a time-varying cut-off threshold. The resulting algorithm is linear and its design is facilitated by the relationship between the fractional Fourier transform and joint time-frequency representations. The implemented filter circuit employs only three low-order filters while its efficiency is further supported by the low computational complexity of the fractional Fourier transform. The results demonstrate that the proposed method can denoise the signals effectively and is more robust against noise as compared to conventional low-pass filters.

Estimation of the second derivative of kinematic impact signals using fractional fourier domain filtering.

A new filtering algorithm is proposed for the accurate estimation of the second derivatives of kinematic signals with impacts. The algorithm operates in predetermined consecutive fractional Fourier transform domains and amounts to an overall linear low-pass filter with time-varying cutoff threshold, which can successfully accommodate the impact-induced changes in the frequency content of the signals. The proposed method was applied to experimentally acquired displacement data and the results have demonstrated its promising performance that was found superior to both conventional techniques and recently introduced advanced schemes.

Impact phase kinematics of instep kicking in soccer.

The purpose of this study was to capture the lower limb kinematics before during and after ball impact of soccer kicking by examining the influence of both sampling rate and smoothing procedures. Nine male soccer players performed maximal instep kicks and the three-dimensional leg movements were captured at 1000 Hz. Angular and linear velocities and accelerations were determined using four different processing approaches: processed using a modified version of a time-frequency filtering algorithm (WGN), smoothed by a second-order low-pass Butterworth filter at 200 Hz cut-off (BWF), re-sampled at 250 Hz without smoothing (RSR) and re-sampled at 250 Hz but filtered by the same Butterworth filter at 10 Hz cut-off (RSF). The WGN approach appeared to establish representative kinematics, whereas the other procedures failed to remove noisy oscillation from the baseline of signal (BWF), lost the peaks of rapid changes (RSR) or produced totally distorted movement patterns (RSF). The results indicate that the procedures used by some previous studies may have been insufficient to adequately capture the lower limb motion near ball impact. We propose a new time-frequency filtering technique as a better way to smooth data whose frequency content varies dramatically.