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search.filters.applied.f.access: openAccess × Subject: Humans × Subject: human × Subject: time series analysis × WGL Institute: PIK ×

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    Sleep apnea-hypopnea quantification by cardiovascular data analysis
    (San Francisco, CA : Public Library of Science (PLoS), 2014) Camargo, S.; Riedl, M.; Anteneodo, C.; Kurths, J.; Penzel, T.; Wessel, N.
    Sleep disorders are a major risk factor for cardiovascular diseases. Sleep apnea is the most common sleep disturbance and its detection relies on a polysomnography, i.e., a combination of several medical examinations performed during a monitored sleep night. In order to detect occurrences of sleep apnea without the need of combined recordings, we focus our efforts on extracting a quantifier related to the events of sleep apnea from a cardiovascular time series, namely systolic blood pressure (SBP). Physiologic time series are generally highly nonstationary and entrap the application of conventional tools that require a stationary condition. In our study, data nonstationarities are uncovered by a segmentation procedure which splits the signal into stationary patches, providing local quantities such as mean and variance of the SBP signal in each stationary patch, as well as its duration L. We analysed the data of 26 apneic diagnosed individuals, divided into hypertensive and normotensive groups, and compared the results with those of a control group. From the segmentation procedure, we identified that the average duration 〈L〉, as well as the average variance 〈σ2〉, are correlated to the apnea-hypoapnea index (AHI), previously obtained by polysomnographic exams. Moreover, our results unveil an oscillatory pattern in apneic subjects, whose amplitude S∗ is also correlated with AHI. All these quantities allow to separate apneic individuals, with an accuracy of at least 79%. Therefore, they provide alternative criteria to detect sleep apnea based on a single time series, the systolic blood pressure.
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    Change in the embedding dimension as an indicator of an approaching transition
    (San Francisco, CA : Public Library of Science (PLoS), 2014) Neuman, Y.; Marwan, N.; Cohen, Y.
    Predicting a transition point in behavioral data should take into account the complexity of the signal being influenced by contextual factors. In this paper, we propose to analyze changes in the embedding dimension as contextual information indicating a proceeding transitive point, called OPtimal Embedding tRANsition Detection (OPERAND). Three texts were processed and translated to time-series of emotional polarity. It was found that changes in the embedding dimension proceeded transition points in the data. These preliminary results encourage further research into changes in the embedding dimension as generic markers of an approaching transition point.
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    Short term associations of ambient nitrogen dioxide with daily total, cardiovascular, and respiratory mortality: multilocation analysis in 398 cities
    (London : BMJ Publ. Group, 2021) Meng, Xia; Liu, Cong; Chen, Renjie; Sera, Francesco; Vicedo-Cabrera, Ana Maria; Milojevic, Ai; Guo, Yuming; Tong, Shilu; Coelho, Micheline de Sousa Zanotti Stagliorio; Saldiva, Paulo Hilario Nascimento; Lavigne, Eric; Correa, Patricia Matus; Ortega, Nicolas Valdes; Osorio, Samuel; Garcia, null; Kyselý, Jan; Urban, Aleš; Orru, Hans; Maasikmets, Marek; Jaakkola, Jouni J. K.; Ryti, Niilo; Huber, Veronika; Schneider, Alexandra; Katsouyanni, Klea; Analitis, Antonis; Hashizume, Masahiro; Honda, Yasushi; Ng, Chris Fook Sheng; Nunes, Baltazar; Teixeira, João Paulo; Holobaca, Iulian Horia; Fratianni, Simona; Kim, Ho; Tobias, Aurelio; Íñiguez, Carmen; Forsberg, Bertil; Åström, Christofer; Ragettli, Martina S.; Guo, Yue-Liang Leon; Pan, Shih-Chun; Li, Shanshan; Bell, Michelle L.; Zanobetti, Antonella; Schwartz, Joel; Wu, Tangchun; Gasparrini, Antonio; Kan, Haidong
    Objective To evaluate the short term associations between nitrogen dioxide (NO2) and total, cardiovascular, and respiratory mortality across multiple countries/regions worldwide, using a uniform analytical protocol. Design Two stage, time series approach, with overdispersed generalised linear models and multilevel meta-analysis. Setting 398 cities in 22 low to high income countries/regions. Main outcome measures Daily deaths from total (62.8 million), cardiovascular (19.7 million), and respiratory (5.5 million) causes between 1973 and 2018. Results On average, a 10 μg/m3 increase in NO2 concentration on lag 1 day (previous day) was associated with 0.46% (95% confidence interval 0.36% to 0.57%), 0.37% (0.22% to 0.51%), and 0.47% (0.21% to 0.72%) increases in total, cardiovascular, and respiratory mortality, respectively. These associations remained robust after adjusting for co-pollutants (particulate matter with aerodynamic diameter ≤10 μm or ≤2.5 μm (PM10 and PM2.5, respectively), ozone, sulfur dioxide, and carbon monoxide). The pooled concentration-response curves for all three causes were almost linear without discernible thresholds. The proportion of deaths attributable to NO2 concentration above the counterfactual zero level was 1.23% (95% confidence interval 0.96% to 1.51%) across the 398 cities. Conclusions This multilocation study provides key evidence on the independent and linear associations between short term exposure to NO2 and increased risk of total, cardiovascular, and respiratory mortality, suggesting that health benefits would be achieved by tightening the guidelines and regulatory limits of NO2.