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We investigated whether pitch-synchronous neural activity could be recorded in humans, with a natural vowel and a vowel in which the fundamental frequency was suppressed. Small variations of speech periodicity were detected in the evoked responses using a fine structure spectrograph (FSS). A significant response (P [much less than] 0.001) was measured in all seven normal subjects even when the fundamental frequency was suppressed, and it very accurately tracked the acoustic pitch contour (normalized mean absolute error < 0.57%). Small variations in speech periodicity, which humans can detect, are therefore available to the perceptual system as pitch-synchronous neural firing. These findings suggest that the measurement of pitch-evoked responses may be a viable tool for objective speech audiometry. Index Terms--Bioelectric potentials, biomedical signal processing, speech processing, time-frequency analysis.

We propose the binaural auditory system as a candidate neural system that may be disrupted by exposure to relatively weak LF magnetic fields. Extracellular currents, induced by time-varying magnetic fields, may change the timing of action potentials in the auditory nerve, thereby disrupting sound localization when interaural time differences are very small. Three subjects were exposed to a 1000 Hz magnetic field--with a maximum rate of change of 2.3 T/s at the location of the cochlea--while presenting two identical 1000 Hz tones randomly delayed to the left or right ear by less than 10 ps. The subjects were asked whether the signal was perceived to be displaced to the left or right side of midline. After a total of over 20,000 trials, conducted at different phase angles between the field signal and the tones, there was no clear evidence for a consistent change in performance when the magnetic field was present. This, however, does not rule out an effect at other combinations of magnetic and acoustic frequencies.

This study introduces an improved method to investigate the effects of reverberation using the speech-evoked auditory brainstem response (ABR) that more realistically captures the influence of self- and overlap-masking induced by room reverberation. Speech-evoked ABR was measured under three acoustic scenarios: anechoic, mild reverberation with dominance of early reflections, and severe reverberation with dominance of late reverberation. Responses were significantly weaker and had longer latencies with severe reverberation relative to anechoic and mild reverberation. Although larger responses and shorter latencies were observed with mild reverberation than anechoic, possibly due to early reflections, these reached significance in only one of six ABR response measures. [ABSTRACT FROM AUTHOR]

Objectives: The use of remote sensing technologies such as radar is gaining popularity as a technique for contactless detection of physiological signals and analysis of human motion. This paper presents a methodology for classifying different events in a collection of phase modulated continuous wave radar returns. The primary application of interest is to monitor inmates where the presence of human vital signs amidst different, interferences needs to be identified. Methods: A comprehensive set of features is derived through time and frequency domain analyses of the radar returns. The Bhattacharyya distance is used to preselect the features with highest class separability as the possible candidate features for use in the classification process. The uncorrelated linear discriminant analysis is performed to decorrelate, denoise, and reduce the dimension of the candidate feature set. Linear and quadratic Bayesian classifiers are designed to distinguish breathing, different human motions, and nonhuman motions. The performance of these classifiers is evaluated on a pilot dataset of radar returns that contained different events including breathing, stopped breathing, simple human motions, and movement of fan and water. Results: Our proposed pattern classification system achieved accuracies of up to $93$% in stationary subject detection, $90$% in stop-breathing detection, and $86$% in interference detection. Conclusion: Our proposed radar pattern recognition system was able to accurately distinguish the predefined events amidst interferences. Significance: Besides inmate monitoring and suicide attempt detection, this paper can be extended to other radar applications such as home-based monitoring of elderly people, apnea detection, and home occupancy detection. [ABSTRACT FROM AUTHOR]

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The measurement of noise exposure from communication headsets poses a methodological challenge. Although several standards describe methods for general noise measurements in occupational settings, these are not directly applicable to noise assessments under communication headsets. For measurements under occluded ears, specialized methods have been specified by the International Standards Organization (ISO 11904) such as the microphone in a real ear and manikin techniques. Simpler methods have also been proposed in some national standards such as the use of general purpose artificial ears and simulators in conjunction with single number corrections to convert measurements to the equivalent diffuse field. However, little is known about the measurement agreement between these various methods and the acoustic manikin technique. Twelve experts positioned circum-aural, supra-aural and insert communication headsets on four different measurement setups (Type 1, Type 2, Type 3.3 artificial ears, and acoustic manikin). Fit-refit measurements of four audio communication signals were taken under quiet laboratory conditions. Data were transformed into equivalent diffuse-field sound levels using third-octave procedures. Results indicate that the Type 1 artificial ear is not suited for the measurement of sound exposure under communication headsets, while Type 2 and Type 3.3 artificial ears are in good agreement with the acoustic manikin technique. Single number corrections were found to introduce a large measurement uncertainty, making the use of the third-octave transformation preferable. [ABSTRACT FROM AUTHOR]

Oscillometry is a popular technique for automatic estimation of blood pressure (BP). However, most of the oscillometric algorithms rely on empirical coefficients for systolic and diastolic pressure evaluation that may differ in various patient populations, rendering the technique unreliable. A promising complementary technique for automatic estimation of BP, based on the dependence of pulse transit time (PTT) on cuff pressure (CP) (PTT–CP mapping), has been proposed in the literature. However, a theoretical grounding for this technique and a nonparametric BP estimation approach are still missing. In this paper, we propose a novel coefficient-free BP estimation method based on PTT–CP dependence. PTT is mathematically modeled as a function of arterial lumen area under the cuff. It is then analytically shown that PTT–CP mappings computed from various points on the arterial pulses can be used to directly estimate systolic, diastolic, and mean arterial pressure without empirical coefficients. Analytical results are cross-validated with a pilot investigation on ten healthy subjects where 150 simultaneous electrocardiogram and oscillometric BP recordings are analyzed. The results are encouraging whereby the mean absolute errors of the proposed method in estimating systolic and diastolic pressures are 5.31 and 4.51 mmHg, respectively, relative to the Food and Drug Administration approved Omron monitor. Our work thus shows promise toward providing robust and objective BP estimation in a variety of patients and monitoring situations. [ABSTRACT FROM PUBLISHER]

In this paper, we present a novel feature-based neural network (NN) approach for estimation of blood pressure (BP) from wrist oscillometric measurements. Unlike previous methods that use the raw oscillometric waveform envelope (OMWE) as input to the NN, in this paper, we propose to use features extracted from the envelope. The OMWE is mathematically modeled as a sum of two Gaussian functions. The optimum parameters of this model are found by minimizing the least squares error between the model and the OMWE using the Levenberg–Marquardt algorithm and are used as features. Two separate feed-forward NNs (FFNNs) are then designed to estimate the systolic and diastolic BPs using these features. The FFNNs are trained using the resilient backpropagation learning algorithm and tested on a data set of BP measurements recorded from 85 subjects. The performance is then compared with that of the conventional maximum amplitude algorithm, adaptive neuro-fuzzy inference system, and already published NN-based methods. It is found that the proposed approach achieves lower values of mean absolute error and standard deviation of error in the estimation of BP. In addition, the proposed approach has the following advantages: lower complexity with respect to the design parameters, smaller training data set, and lower computational load. [ABSTRACT FROM PUBLISHER]

Objectives: To study level effects in the encoding of four vowels in normal hearing adults using the envelope and spectral frequency following response (eFFR and sFFR). We hypothesized that F0, F1 and their neighboring harmonics would grow with increasing level. Material and methods: 19 subjects (8F, 11M, 20-35 yrs) were recorded with four 100 ms vowel stimuli with F0=100 Hz (F1: /a/=700 Hz, /e/=600 Hz,/d/=500 Hz,/u/=300 Hz) at four levels (55, 65, 75, and 85 dBA). eFFR and sFFR spectral amplitudes for each vowel and level were analyzed. Results: Significant effects of level exist at F0 in the eFFR and F1 in the sFFR (p<0.01 in all cases). The combined harmonics show effects oflevel (p<0.001 in all cases), as well as significant pairwise comparisons between most levels. Conclusions: Effects of level exist in the encoding of the speech eFFR and sFFR. 'Ihe eFFR shows increasing spectral richness via the growth of the harmonics of F0 but not at F0 itself, while the sFFR shows a pattern of growth at FI and its neighboring harmonics. [ABSTRACT FROM AUTHOR]