{"id":225,"date":"2020-11-20T13:42:59","date_gmt":"2020-11-20T05:42:59","guid":{"rendered":"http:\/\/pencidesign.com\/soledad\/soledad-hipster\/?p=225"},"modified":"2022-03-08T09:42:20","modified_gmt":"2022-03-08T01:42:20","slug":"paper-result","status":"publish","type":"page","link":"https:\/\/tidis.org\/paper-result\/","title":{"rendered":"\u8ad6\u6587\u7814\u7a76\u6210\u679c"},"content":{"rendered":"\t\t
In many real world oscillatory signals, the fundamental component
\nof a signal f(t) might be weak or does not exist. This makes it difficult to
\nestimate the instantaneous frequency of the signal. Traditionally, researchers
\napply the rectification trick, working with |f(t)| or ReLu(f(t)) instead, to
\nenhance the fundamental component. This raises an interesting question: what
\ntype of nonlinear function g : R \u2192 R has the property that g(f(t)) has a more
\npronounced fundamental frequency? g(t) = |t| and g(t) = ReLu(t) seem to
\nwork well in practice; we propose a variant of g(t) = 1\/(1 \u2212 |t|) and provide a
\ntheoretical guarantee. Several simulated signals and real signals are analyzed
\nto demonstrate the performance of the proposed solution.<\/p>\n<\/div>\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t<\/div>\r\n\t\t<\/div>\r\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
Brain activity recordings outside clinical or laboratory settings using mobile EEG systems have recently gained popular interest allowing for realistic long-term monitoring and eventually leading to identification of possible biomarkers for diseases. The less obtrusive, minimized systems (e.g. single-channel EEG, no ECG reference) have the drawback of artifact contamination with varying intensity that are particularly difficult to identify and remove. We developed brMEGA, the first algorithm for automated detection and removal of cardiogenic artifacts using non-linear time-frequency analysis and machine learning to (1) detect whether and where cardiogenic artifacts exist, and (2) remove those artifacts. We compare our algorithm against visual artifact identification and a previously established approach and validate it in one real and semi-real datasets. We demonstrated that brMEGA successfully identifies and substantially removes cardiogenic artifacts in single-channel EEG recordings. Moreover, recovery of cardiogenic artifacts gives the opportunity for future extraction of heart rate features without ECG measurement.<\/p>\n<\/div>\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t<\/div>\r\n\t\t<\/div>\r\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
Airflow signal encodes rich information about respiratory system. While the gold standard for measuring airflow is to use a spirometer with an occlusive seal, this is not practical for ambulatory monitoring of patients. Advances in sensor technology have made measurement of motion of the thorax and abdomen feasible with small inexpensive devices, but estimation of airflow from these time series is challenging. We propose to use the nonlinear-type time-frequency analysis tool, synchrosqueezing transform, to properly represent the thoracic and abdominal movement signals as the features, which are used to recover the airflow by the locally stationary Gaussian process. We show that, using a dataset that contains respiratory signals under normal sleep conditions, an accurate prediction can be achieved by fitting the proposed model in the feature space both in the intra- and inter-subject setups. We also apply our method to a more challenging case, where subjects under general anesthesia underwent transitions from pressure support to unassisted ventilation to further demonstrate the utility of the proposed method.<\/p>\n<\/div>\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t<\/div>\r\n\t\t<\/div>\r\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
Study objectives: Polysomnography is the gold standard in identifying sleep stages; however, there are discrepancies in how technicians use the standards. Because organizing meetings to evaluate this discrepancy and\/or reach a consensus among multiple sleep centers is time consuming, we developed an artificial intelligence (AI) system to efficiently evaluate the reliability and consistency of sleep scoring, and hence the sleep center quality.<\/p>\n<\/div>\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t<\/div>\r\n\t\t<\/div>\r\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t