Double-Lumen Endotracheal Tube-Predicting Insertion Depth and Tube Size Based on Patient's Chest X-ray Image Data and 4 Other Body Parameters.

In thoracic surgery, the double lumen endotracheal tube (DLT) is used for differential ventilation of the lung. DLT allows lung collapse on the surgical side that requires access to the thoracic and mediastinal areas. DLT placement for a given patient depends on two settings: a tube of the correct size (or 'size') and to the correct insertion depth (or 'depth'). Incorrect DLT placements cause oxygen desaturation or carbon dioxide retention in the patient, with possible surgical failure. No guideline on these settings is currently available for anesthesiologists, except for the aid by bronchoscopy. In this study, we aimed to predict DLT 'depths' and 'sizes' applied earlier on a group of patients (n = 231) using a computer modeling approach. First, for these patients we retrospectively determined the correlation coefficient (r) of each of the 17 body parameters against 'depth' and 'size'. Those parameters having r > 0.5 and that could be easily obtained or measured were selected. They were, for both DLT settings: (a) sex, (b) height, (c) tracheal diameter (measured from X-ray), and (d) weight. For 'size', a fifth parameter, (e) chest circumference was added. Based on these four or five parameters, we modeled the clinical DLT settings using a Support Vector Machine (SVM). After excluding statistical outliers (±2 SD), 83.5% of the subjects were left for 'depth' in the modeling, and similarly 85.3% for 'size'. SVM predicted 'depths' matched with their clinical values at a r of 0.91, and for 'sizes', at an r of 0.82. The less satisfactory result on 'size' prediction was likely due to the small target choices (n = 4) and the uneven data distribution. Furthermore, SVM outperformed other common models, such as linear regression. In conclusion, this first model for predicting the two DLT key settings gave satisfactory results. Findings would help anesthesiologists in applying DLT procedures more confidently in an evidence-based way.

Strong energy component is more important than spectral selectivity in modeling responses of midbrain auditory neurons to wide-band environmental sounds.

Modeling central auditory neurons in response to complex sounds not only helps understanding neural processing of speech signals but can also provide insights for biomimetics in neuro-engineering. While modeling responses of midbrain auditory neurons to synthetic tones is rather good, modeling those to environmental sounds is less satisfactory. Environmental sounds typically contain a wide range of frequency components, often with strong and transient energy. These stimulus features have not been examined in the conventional approach of auditory modeling centered on spectral selectivity. To this end, we firstly compared responses to an environmental sound of auditory midbrain neurons across 3 subpopulations of neurons with frequency selectivity in the low, middle and high ranges; secondly, we manipulated the sound energy, both in power and in spectrum, and compared across these subpopulations how their modeled responses were affected. The environmental sound was recorded when a rat was drinking from a feeding bottle (called the 'drinking sound'). The sound spectrum was divided into 20 non-overlapping frequency bands (from 0 to 20 kHz, at 1 kHz width) and presented to an artificial neural model built on a committee machine with parallel spectral inputs to simulate the known tonotopic organization of the auditory system. The model was trained to predict empirical response probability profiles of neurons to the repeated sounds. Results showed that model performance depended more on the strong energy components than on the spectral selectivity. Findings were interpreted to reflect general sensitivity to rapidly changing sound intensities at the auditory midbrain and in the cortex.

A new method for adjusting neural response jitter in the STRF obtained by spike-trigger averaging.

Sensitivity of central auditory neurons to frequency modulated (FM) sound is often characterized based on spectro-temporal receptive field (STRF), which is generated by spike-trigger averaging a random stimulus. Due to the inherent property of time variability in neural response, this method erroneously represents the response jitter as stimulus jitter in the STRF. To reveal the trigger features more clearly, we have implemented a method that minimizes this error. Neural spikes from the brainstem of urethane-anesthetized rats were first recorded in response to two sets of FM stimuli: (a) a random FM tone for the generation of STRF and (b) a family of linear FM ramps for the determination of FM 'trigger point'. Based on the first dataset, STRFs were generated using spike-trigger averaging. Individual modulating waveforms were then matched with respect to their mean waveform at time-windows of a systematically varied length. A stable or optimal variance time profile was found at a particular window length. At this optimal window length, we performed delay adjustments. A marked sharpening in the FM bands in the STRF was found. Results were consistent with the FM 'trigger point' as estimated by the linear FM ramps. We concluded that the present approach of adjusting response jitter was effective in delineating FM trigger features in the STRF.

Responses of central auditory neurons modeled with finite impulse response (FIR) neural networks.

To simulate central auditory responses to complex sounds, a computational model was implemented. It consists of a multi-scale classification process, and an artificial neural network composed of two modules of finite impulse response (FIR) neural networks connected to a maximum network. Electrical activities of single auditory neurons were recorded at the rat midbrain in response to a repetitive pseudo-random frequency modulated (FM) sound. The multi-scale classification process divides the training dataset into either strong or weak response using a multiple-scale Gaussian filter that based on response probability. Two modules of FIR neural network are then independently trained to model the two types of responses. This caters for the possible differences in neuronal circuitry and transmission delay. Their outputs are connected to a maximum network to generate the final output. After training, we use a different set of FM responses collected from the same neuron to test the performance of the model. Two criteria are adopted for assessment. One measures the matching of the modeled output to the actual output on a point-to-point basis. Another measures the matching of bulk responses between the two. Results show that the proposed model predicts the responses of central auditory neurons satisfactorily.