An inverse technique for excitation design in nerve fascicle activation: a theoretical study

Position selective stimulation of nerves is an important problem in FES systems, but no systematic method exists to determine the spatio-temporal excitation current profile. In this paper, we provide a theoretical background towards solving this problem. The efficacy of the technique is investigated through Monte Carlo experiments based on simplified computer models of a nerve

Bispectral analysis of single channel EEG to estimate macro-sleep-architecture

Estimation of macro-sleep-architecture (MSA) is a critical process in assessing several sleep disorders such as obstructive sleep apnoea, periodic leg movement disorder, upper-airway resistance syndrome, etc. MSA is defined as classification of sleep into three major states: state wake, state REM and state NREM. Existing methods of MSA analysis use six channels of electrophysiological signals (EEG, EOG and EMG). They depend on the manual scoring of overnight data records using the R&K criteria (1968), developed for visual analysis of signals based on morphological features. Manual scoring is cumbersome, subjective and not suitable for portable devices used for community screening of sleep disorders. To address this issue, we propose a fully automated technology for MSA estimation based on a single channel of EEG data. The proposed technology was compared, on a clinical database of 47 patients, with that of an expert human scorer. The average agreement between the human and the proposed technology was found to be 76 ± 7.5% (kappa = 0.51 ± 0.14). The proposed method estimates MSA using simplified instrumentation making it possible to extend EEG/MSA to portable systems as well; method uses low-computation-load bispectrum techniques independent of R&K criteria (1968) making real-time automated analysis a reality. Copyrigh

Detection of REM/NREM snores in obstructive sleep apnoea patients using a machine learning technique

Obstructive sleep apnoea (OSA) is a serious sleep disorder in which patients suffer from frequent upper airway (UA) collapse during sleep.UAmuscle tone in OSA patients is known to vary with rapid eye movement (REM) and non-REM (NREM) sleep states. Information on sleep-state specificUA collapse has clinical importance in sleep studies and treatment. This paper proposes a machine learning technique to label snoring sounds as belonging toREMorNREMsleep in OSA patients. Our method is based on analysing snore and breathing sound recordings from OSA patients acquired with non-contact bedside microphones. The reference standard to diagnose OSA and sleep states was the laboratory-based clinical polysomnography (PSG).We trained multilevel artificial neural networks (hierarchical neural networks) to label sleep intoREMandNREMclasses using snoring and breathing sounds around a given snoring episode of interest. A total of 41 062 snoring episodes were used for training and testing of the model (training to testing sample ratio was 3:1). The training data set was obtained from 12 subjects and the testing data set was from 7. The two data sets were mutually exclusive. Our method achieved a substantial (Cohen's kappa k=0.62) agreement with the reference standard, that is, the PSG-based clinical classification. Overall, our proposed method achieved 90% testing accuracy of labelling individual snores asREMand NREM

Spectral information changes in obtaining heart rate variability from tachometer R-R interval signals

A detailed analysis of the approximations used in the method of transforming the tachometer R-R interval signal to the Heart Rate Variability (HRV) signal is presented. It is shown that the R-R interval signal and the HRV signal, respectively, are very similar to the true jitter and the approximate jitter signals. Using this similarity, the changes in spectral information of the signal in obtaining an evenly sampled signal via the Berger algorithm are detailed

System reconstruction from higher order spectra slices

We consider the problem of system reconstruction from higher order spectra (HOS) slices. We establish that the impulse response of a complex system can be reconstructed up to a scalar and a shift based on any pair of HOS slices, as long as the distance between the two slices satisfies a certain condition. One slice is sufficient for the reconstruction in the case of a real system. We propose a cepstrum-based method for system reconstruction. We also propose a new method for the reconstruction of the system Fourier phase based on the phase of any odd-indexed bispectrum slice. Being able to choose the slices to be used in the reconstruction allows us to avoid bispectrum regions dominated by noise