Early detection of epileptic seizures based on parameter identification of neural mass model.

Physiologically based models are attractive for seizure detection, as their parameters can be explicitly related to neurological mechanisms. We propose an early seizure detection algorithm based on parameter identification of a neural mass model. The occurrence of a seizure is detected by analysing the time shift of key model parameters.

Development and perceptual assessment of a synthesizer of disordered voices.

A synthesizer is based on a nonlinear wave-shaping model of the glottal area, an algebraic model of the glottal aerodynamics as well as concatenated-tube models of the trachea and vocal tract. Voice disorders are simulated by way of models of vocal frequency jitter and tremor, vocal amplitude shimmer and tremor, as well as pulsatile additive noise. Six experiments have been carried out to assess the synthesizer perceptually.

A method for the analysis of respiratory sinus arrhythmia using continuous wavelet transforms.

A continuous wavelet transform-based method is presented to study the nonstationary strength and phase delay of the respiratory sinus arrhythmia (RSA). The RSA is the cyclic variation of instantaneous heart rate at the breathing frequency. In studies of cardio-respiratory interaction during sleep, paced breathing or postural changes, low respiratory frequencies, and fast changes can occur.

Vocal dysperiodicities estimation by means of adaptive long-term prediction.

An adaptive formulation of the long-term bidirectional linear predictive analysis is proposed in the context of the acoustic assessment of disordered speech. Vocal dysperiodicities are summarized by means of a signal-to-dysperiodicity ratio (SDR) marker. It is shown that performing an adaptive forward and backward long-term linear prediction of each speech sample and retaining the minimal prediction error energy as a cue of vocal dysperiodicity results in an SDR that correlates with the perceived degree of hoarseness.

Estimation of vocal dysperiodicities in disordered connected speech by means of distant-sample bidirectional linear predictive analysis.

The article presents an analysis of vocal dysperiodicities in connected speech produced by dysphonic speakers. The processing is based on a comparison of the present speech fragment with future and past fragments. The size of the dysperiodicity estimate is zero for periodic speech signals.