Evaluation of canine detection of COVID-19 infected individuals under controlled settings.

Reverse transcription polymerase chain reaction (RT-PCR) is currently the standard diagnostic method to detect symptomatic and asymptomatic individuals infected with Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, RT-PCR results are not immediate and may falsely be negative before an infected individual sheds viral particles in the upper airways where swabs are collected. Infected individuals emit volatile organic compounds in their breath and sweat that are detectable by trained dogs. Here, we evaluate the diagnostic accuracy of dog detection against SARS-CoV-2 infection. Fifteen dogs previously trained at two centres in Australia were presented to axillary sweat specimens collected from known SARS-CoV-2 human cases (n = 100) and non-cases (n = 414). The true infection status of the cases and non-cases were confirmed based on RT-PCR results as well as clinical presentation. Across dogs, the overall diagnostic sensitivity (DSe) was 95.3% (95%CI: 93.1-97.6%) and diagnostic specificity (DSp) was 97.1% (95%CI: 90.7-100.0%). The DSp decreased significantly when non-case specimens were collected over 1 min rather than 20 min (p value = .004). The location of evaluation did not impact the detection performances. The accuracy of detection varied across dogs and experienced dogs revealed a marginally better DSp (p value = .016). The potential and limitations of this alternative detection tool are discussed.

Spike sorting by joint probabilistic modeling of neural spike trains and waveforms.

This paper details a novel probabilistic method for automatic neural spike sorting which uses stochastic point process models of neural spike trains and parameterized action potential waveforms. A novel likelihood model for observed firing times as the aggregation of hidden neural spike trains is derived, as well as an iterative procedure for clustering the data and finding the parameters that maximize the likelihood. The method is executed and evaluated on both a fully labeled semiartificial dataset and a partially labeled real dataset of extracellular electric traces from rat hippocampus. In conditions of relatively high difficulty (i.e., with additive noise and with similar action potential waveform shapes for distinct neurons) the method achieves significant improvements in clustering performance over a baseline waveform-only Gaussian mixture model (GMM) clustering on the semiartificial set (1.98% reduction in error rate) and outperforms both the GMM and a state-of-the-art method on the real dataset (5.04% reduction in false positive + false negative errors). Finally, an empirical study of two free parameters for our method is performed on the semiartificial dataset.

Hemodynamic-impact-based prioritization of ventricular tachycardia alarms.

Ventricular tachycardia (V-tach) is a very serious condition that occurs when the ventricles are driven at high rates. The abnormal excitation pathways make ventricular contraction less synchronous resulting in less effective filling and emptying of the left ventricles. However, almost half of the V-tach alarms declared through processing of patterns observed in electrocardiography are not clinically actionable. The focus of this study is to provide guidance on determining whether a technically-correct V-tach alarm is clinically-actionable by determining its "hemodynamic impact". A supervisory learning approach based on conditional inference trees to determine the hemodynamic impact of a V-tach alarm based on extracted features is described. According to preliminary results on a subset of Multiparameter intelligent monitoring in intensive care II (MIMIC-II) database, true positive rate of more than 90% can be achieved.