Objective: Over the last few decades, there has been significant interest in the automatic analysis of respiratory sounds. However, currently there are no publicly available large databases with which new algorithms can be evaluated and compared. Further developments in the field are dependent on the creation of such databases. Approach: This paper describes a public respiratory sound database, which was compiled for an international competition, the first scientific challenge of the IFMBE’s International Conference on Biomedical and Health Informatics. The database includes 920 recordings acquired from 126 participants and two sets of annotations. One set contains 6898 annotated respiratory cycles, some including crackles, wheezes, or a combination of both, and some with no adventitious respiratory sounds. In the other set, precise locations of 10 775 events of crackles and wheezes were annotated. Main results: The best system that participated in the challenge achieved an average score of 52.5% with the respiratory cycle annotations and an average score of 91.2% with the event annotations. Significance: The creation and public release of this database will be useful to the research community and could bring attention to the respiratory sound classification problem.

Abstract-Functional near-infrared spectroscopy (fNIRS) is an emerging technique for monitoring the concentration changes of oxy-and deoxy-hemoglobin (oxy-Hb and deoxy-Hb) in the brain. An important consideration in fNIRS-based neuroimaging modality is to conduct group-level analysis from a set of time series measured from a group of subjects. We investigate the feasibility of multilevel statistical inference for fNIRS. As a case study, we search for hemodynamic activations in the prefrontal cortex during Stroop interference. Hierarchical general linear model (GLM) is used for making this multilevel analysis. Activation patterns both at the subject and group level are investigated on a comparative basis using various classical and Bayesian inference methods. All methods showed consistent left lateral prefrontal cortex activation for oxy-Hb during interference condition, while the effects were much less pronounced for deoxy-Hb. Our analysis showed that mixed effects or Bayesian models are more convenient for faithful analysis of fNIRS data. We arrived at two important conclusions. First, fNIRS has the capability to identify activations at the group level, and second, the mixed effects or Bayesian model is the appropriate mechanism to pass from subject to group-level inference.Index Terms-General linear model (GLM), near-infrared spectroscopy, statistical inference, Stroop task.

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