QUANTIFICATION OF PRETERM INFANT FEEDING COORDINATION: AN ALGORITHMIC APPROACH

Oral feeding competency is a primary requirement for preterm infant hospital release. Currently there is no widely accepted method to objectively measure oral feeding. Feeding consists primarily of the integration of three individual feeding events: sucking, breathing, and swallowing, and the objective of feeding coordination is to minimize aspiration. The purpose of this work was to quantify the infant feeding process from signals obtained during bottle feeding and ultimately develop a measure of feeding coordination. Sucking was measured using a pressure transducer embedded within a modified silicone bottle block. Breathing was measured using a thermistor embedded within nasal cannula, and swallowing was measured through the use of several different piezoelectric sensors. In addition to feeding signals, electrocardiogram (ECG) signals were obtained as an indicator of overall infant behavioral state during feeding. Event detection algorithms for the individual feeding signals were developed and validated, then used for the development of a measurement of feeding coordination. The final suck event detection algorithm was the result of an iterative process that depended on the validity of the signal model. As the model adapted to better represent the data, the accuracy and specificity of the algorithm improved. For the breath signal, however, the primary barrier to effective event detection was significant baseline drift. The frequency components of the baseline drift overlapped significantly with the breath event frequency components, so a time domain solution was developed. Several methods were tested, and it was found that the acceleration vector of the signal provided the most robust representation of the underlying breath signal while minimizing baseline drift. Swallow signal event detection was not possible due to a lack of available data resulting from problems with the consistency of the obtained signal. A robust method was developed for the batch processing of heart rate variability analysis. Finally a method of coordination analysis was developed based on the event detection algorithm outputs. Coordination was measured by determining the percentage of feeding time that consisted of overlapping suck and breath activity

Automatic electrical stimulation of abdominal wall muscles increases tidal volume and cough peak flow in tetraplegia

<p>Paralysis of the respiratory muscles in people with tetraplegia affects their ability to breathe and contributes to respiratory complications. Surface functional electrical stimulation (FES) of abdominal wall muscles can be used to increase tidal volume (V_{T}) and improve cough peak flow (CPF) in tetraplegic subjects who are able to breathe spontaneously.</p> <p>This study aims to evaluate the feasibility and effectiveness of a novel abdominal FES system which generates stimulation automatically, synchronised with the subjects' voluntary breathing activity. Four subjects with complete tetraplegia (C4-C6), breathing spontaneously, were recruited.</p> <p>The automatic stimulation system ensured that consistent stimulation was achieved. We compared spirometry during unassisted and FES-assisted quiet breathing and coughing, and measured the effect of stimulation on end-tidal CO_2 (EtCO_2) during quiet breathing.</p> <p>The system dependably recognised spontaneous respiratory effort, stimulating appropriately, and was well tolerated by patients. Significant increases in V_T during quiet breathing (range 0.05–0.23 L) and in CPF (range 0.04–0.49 L/s) were observed. Respiratory rate during quiet breathing decreased in all subjects when stimulated, whereas minute ventilation increased by 1.05–2.07 L/min. The changes in EtCO_2 were inconclusive.</p> <p>The automatic stimulation system augmented spontaneous breathing and coughing in tetraplegic patients and may provide a potential means of respiratory support for tetraplegic patients with reduced respiratory capacity.</p&gt

Speech and crosstalk detection in multichannel audio

The analysis of scenarios in which a number of microphones record the activity of speakers, such as in a round-table meeting, presents a number of computational challenges. For example, if each participant wears a microphone, speech from both the microphone's wearer (local speech) and from other participants (crosstalk) is received. The recorded audio can be broadly classified in four ways: local speech, crosstalk plus local speech, crosstalk alone and silence. We describe two experiments related to the automatic classification of audio into these four classes. The first experiment attempted to optimize a set of acoustic features for use with a Gaussian mixture model (GMM) classifier. A large set of potential acoustic features were considered, some of which have been employed in previous studies. The best-performing features were found to be kurtosis, "fundamentalness," and cross-correlation metrics. The second experiment used these features to train an ergodic hidden Markov model classifier. Tests performed on a large corpus of recorded meetings show classification accuracies of up to 96%, and automatic speech recognition performance close to that obtained using ground truth segmentation

Multi-Tier Annotations in the Verbmobil Corpus

In very large and diverse scientific projects where as different groups as linguists and engineers with different intentions work on the same signal data or its orthographic transcript and annotate new valuable information, it will not be easy to build a homogeneous corpus. We will describe how this can be achieved, considering the fact that some of these annotations have not been updated properly, or are based on erroneous or deliberately changed versions of the basis transcription. We used an algorithm similar to dynamic programming to detect differences between the transcription on which the annotation depends and the reference transcription for the whole corpus. These differences are automatically mapped on a set of repair operations for the transcriptions such as splitting compound words and merging neighbouring words. On the basis of these operations the correction process in the annotation is carried out. It always depends on the type of the annotation as well as on the position and the nature of the difference, whether a correction can be carried out automatically or has to be fixed manually. Finally we present a investigation in which we exploit the multi-tier annotations of the Verbmobil corpus to find out how breathing is correlated with prosodic-syntactic boundaries and dialog acts. 1