Techniques for noise removal from EEG, EOG and air flow signals in sleep patients

Noise is present in the wide variety of signals obtained from sleep patients. This noise comes from a number of sources, from presence of extraneous signals to adjustments in signal amplification and shot noise in the circuits used for data collection. The noise needs to be removed in order to maximize the information gained about the patient using both manual and automatic analysis of the signals. Here we evaluate a number of new techniques for removal of that noise, and the associated problem of separating the original signal sources.Comment: 9 pages, 3 figure

Signal processing techniques for phonocardiogram de-noising and analysis

Explores phonocardiogram de-noising techniques, including wavelet de-noising. Optimised wavelet de-noising, wavelet packet de-noising, matching pursuit technique, and averaging. Optimised wavelet de-noising performed slightly better than other methods, and is recommended to be used in conjunction with averaging. Also explores different methods of extracting features from the de-noised phonocardiogram and classifying it. These include phase space diagrams, HT diagrams, instantaneous signal parameter extraction and phase synchronisation between the ECG and PCG; investigations were limited by the quantity and quality of data availableThesis (M.Eng.Sc.) -- University of Adelaide, Dept. of Electrical and Electronic Engineering, 200

Global oceanic diazotroph database version 2 and elevated estimate of global oceanic N 2 fixation

Marine diazotrophs convert dinitrogen (N2) gas into bioavailable nitrogen (N), supporting life in the global ocean. In 2012, the first version of the global oceanic diazotroph database (version 1) was published. Here, we present an updated version of the database (version 2), significantly increasing the number of in situ diazotrophic measurements from 13 565 to 55 286. Data points for N2 fixation rates, diazotrophic cell abundance, and nifH gene copy abundance have increased by 184 %, 86 %, and 809 %, respectively. Version 2 includes two new data sheets for the nifH gene copy abundance of non-cyanobacterial diazotrophs and cell-specific N2 fixation rates. The measurements of N2 fixation rates approximately follow a log-normal distribution in both version 1 and version 2. However, version 2 considerably extends both the left and right tails of the distribution. Consequently, when estimating global oceanic N2 fixation rates using the geometric means of different ocean basins, version 1 and version 2 yield similar rates (43–57 versus 45–63 Tg N yr−1; ranges based on one geometric standard error). In contrast, when using arithmetic means, version 2 suggests a significantly higher rate of 223±30 Tg N yr−1 (mean ± standard error; same hereafter) compared to version 1 (74±7 Tg N yr−1). Specifically, substantial rate increases are estimated for the South Pacific Ocean (88±23 versus 20±2 Tg N yr−1), primarily driven by measurements in the southwestern subtropics, and for the North Atlantic Ocean (40±9 versus 10±2 Tg N yr−1). Moreover, version 2 estimates the N2 fixation rate in the Indian Ocean to be 35±14 Tg N yr−1, which could not be estimated using version 1 due to limited data availability. Furthermore, a comparison of N2 fixation rates obtained through different measurement methods at the same months, locations, and depths reveals that the conventional 15N2 bubble method yields lower rates in 69 % cases compared to the new 15N2 dissolution method. This updated version of the database can facilitate future studies in marine ecology and biogeochemistry. The database is stored at the Figshare repository (https://doi.org/10.6084/m9.figshare.21677687; Shao et al., 2022).Additional Authors: Antonio Bode, Sophie Bonnet, Deborah A. Bronk, Mark V. Brown, Lisa Campbell, Douglas G. Capone, Edward J. Carpenter, Nicolas Cassar, Bonnie X. Chang, Dreux Chappell, Yuh-ling Lee Chen, Matthew J. Church, Francisco M. Cornejo-Castillo, Amália Maria Sacilotto Detoni, Scott C. Doney, Cecile Dupouy, Marta Estrada, Camila Fernandez, Bieito Fernández-Castro, Debany Fonseca-Batista, Rachel A. Foster, Ken Furuya, Nicole Garcia, Kanji Goto, Jesús Gago, Mary R. Gradoville, M. Robert Hamersley, Britt A. Henke, Cora Hörstmann, Amal Jayakumar, Zhibing Jiang, Shuh-Ji Kao, David M. Karl, Leila R. Kittu, Angela N. Knapp, Sanjeev Kumar, Julie LaRoche, Hongbin Liu, Jiaxing Liu, Caroline Lory, Carolin R. Löscher, Emilio Marañón, Matthew M. Mills, Wiebke Mohr, Pia H. Moisander, Claire Mahaffey, Robert Moore, Beatriz Mouriño-Carballido, Margaret R. Mulholland, Shin-ichiro Nakaoka, Joseph A. Needoba, Eric J. Raes, Eyal Rahav, Teodoro Ramírez-Cárdenas, Christian Furbo Reeder, Lasse Riemann, Virginie Riou, Julie C. Robidart, Vedula V. S. S. Sarma, Takuya Sato, Himanshu Saxena, Corday Selden, Justin R. Seymour, Dalin Shi, Takuhei Shiozaki, Arvind Singh, Rachel E. Sipler, Jun Sun, Koji Suzuki, Kazutaka Takahashi, Yehui Tan, Weiyi Tang, Jean-Éric Tremblay, Kendra Turk-Kubo, Zuozhu Wen, Angelicque E. White, Samuel T. Wilson, Takashi Yoshida, Jonathan P. Zehr, Run Zhang, Yao Zhang, and Ya-Wei Lu

Use of Electronic Cigarettes to Aid Long-Term Smoking Cessation in the United States: Prospective Evidence From the PATH Cohort Study.

Electronic cigarettes (e-cigarettes) are the preferred smoking-cessation aid in the United States; however, there is little evidence regarding long-term effectiveness among those who use them. We used the Population Assessment of Tobacco and Health Study to compare long-term abstinence between matched US smokers who tried to quit with and without use of e-cigarettes as a cessation aid. We identified a nationally representative cohort of 2,535 adult US smokers in 2014-2015 (baseline assessment), who, in 2015-2016 (exposure assessment), reported a past-year attempt to quit and the cessation aids used, and reported smoking status in 2016-2017 (outcome assessment; self-reported ≥12 months continuous abstinence). We used propensity-score methods to match each e-cigarette user with similar nonusers. Among US smokers who used e-cigarettes to help quit, 12.9% (95% confidence interval (CI): 9.1%, 16.7%) successfully attained long-term abstinence. However, there was no difference compared with matched non-e-cigarette users (cigarette abstinence difference: 2%; 95% CI: -3%, 7%). Furthermore, fewer e-cigarette users were abstinent from nicotine products in the long term (nicotine abstinence difference: -4%; 95% CI: -7%, -1%); approximately two-thirds of e-cigarette users who successfully quit smoking continued to use e-cigarettes. These results suggest e-cigarettes may not be an effective cessation aid for adult smokers and, instead, may contribute to continuing nicotine dependence

Use of E-cigarettes and Other Tobacco Products and Progression to Daily Cigarette Smoking.

ObjectivesTo identify predictors of becoming a daily cigarette smoker over the course of 4 years.MethodsWe identified 12- to 24-year-olds at wave 1 of the US Population Assessment of Tobacco and Health Study and determined ever use, age at first use, and daily use through wave 4 for 12 tobacco products.ResultsSixty-two percent of 12- to 24-year-olds (95% confidence interval [CI]: 60.1% to 63.2%) tried tobacco, and 30.2% (95% CI: 28.7% to 31.6%) tried ≥5 tobacco products by wave 4. At wave 4, 12% were daily tobacco users, of whom 70% were daily cigarette smokers (95% CI: 67.4% to 73.0%); daily cigarette smoking was 20.8% in 25- to 28-year-olds (95% CI: 18.9% to 22.9%), whereas daily electronic cigarette (e-cigarette) vaping was 3.3% (95% CI: 2.4% to 4.4%). Compared with single product triers, the risk of progressing to daily cigarette smoking was 15 percentage points higher (adjusted risk difference [aRD] 15%; 95% CI: 12% to 18%) among those who tried ≥5 products. In particular, e-cigarette use increased the risk of later daily cigarette smoking by threefold (3% vs 10%; aRD 7%; 95% CI: 6% to 9%). Daily smoking was 6 percentage points lower (aRD -6%; 95% CI: -8% to -4%) for those who experimented after age 18 years.ConclusionsTrying e-cigarettes and multiple other tobacco products before age 18 years is strongly associated with later daily cigarette smoking. The recent large increase in e-cigarette use will likely reverse the decline in cigarette smoking among US young adults