Introducing non-linear analysis into sustained speech characterization to improve sleep apnea detection

We present a novel approach for detecting severe obstructive sleep apnea (OSA) cases by introducing non-linear analysis into sustained speech characterization. The proposed scheme was designed for providing additional information into our baseline system, built on top of state-of-the-art cepstral domain modeling techniques, aiming to improve accuracy rates. This new information is lightly correlated with our previous MFCC modeling of sustained speech and uncorrelated with the information in our continuous speech modeling scheme. Tests have been performed to evaluate the improvement for our detection task, based on sustained speech as well as combined with a continuous speech classifier, resulting in a 10% relative reduction in classification for the first and a 33% relative reduction for the fused scheme. Results encourage us to consider the existence of non-linear effects on OSA patients' voices, and to think about tools which could be used to improve short-time analysis.

Improving automatic detection of obstructive sleep apnea through nonlinear analysis of sustained speech

We present a novel approach for the detection of severe obstructive sleep apnea (OSA) based on patients' voices introducing nonlinear measures to describe sustained speech dynamics. Nonlinear features were combined with state-of-the-art speech recognition systems using statistical modeling techniques (Gaussian mixture models, GMMs) over cepstral parameterization (MFCC) for both continuous and sustained speech. Tests were performed on a database including speech records from both severe OSA and control speakers. A 10 % relative reduction in classification error was obtained for sustained speech when combining MFCC-GMM and nonlinear features, and 33 % when fusing nonlinear features with both sustained and continuous MFCC-GMM. Accuracy reached 88.5 % allowing the system to be used in OSA early detection. Tests showed that nonlinear features and MFCCs are lightly correlated on sustained speech, but uncorrelated on continuous speech. Results also suggest the existence of nonlinear effects in OSA patients' voices, which should be found in continuous speech.

Influence of denitrifiers abundance on N2O emissions in long term tillage system under a rainfed legume crop

Current studies about nitrous oxide (N2O) emissions from legume crops have raised considerable doubt, observing a high variability between sites (0.03-7.09 kg N2O–N ha−1 y -1) [1]. This high variability has been associated to climate and soil conditions, legume species and soil management practices (e.g. conservation or conventional tillage). Conservation tillage (i.e. no tillage (NT) and minimum tillage (MT)) has spread during the last decades because promotes several positive effects (increase of soil organic content, reduction of soil erosion and enhancement of carbon (C) sequestration). However, these benefits could be partly counterbalanced by negative effects on the release of N2O emissions. Among processes responsible for N2O production and consumption in soils, denitrification plays an importantrole both in tilled and no-tilled ropping systems [2]. Recently, amplification of functional bacterial genes involved in denitrification is being used to examine denitrifiers abundance and evaluate their influence on N2O emissions. NirK and nirS are functional genes encoding the cytochrome cd1 and copper nitrite reductase, which is the key enzyme regulating the denitrification process.