Abstract: A method of creating an anesthetic consciousness index with an artificial neural network includes, obtaining physiological signals, including electroencephalographic signals and eye movement signals, from subjects during a physiological signal monitoring process; filtering noise out of the physiological signals by empirical mode decomposition (EMD); calculating sample entropy values of the noise-removed physiological signals; obtaining sample entropy value sets of the physiological signals; repeating the aforesaid steps to effectuate measurement, noise-filtering, and sample entropy value calculation of the subjects' physiological signals and thus obtain a sample entropy value set; and applying an artificial neural network in conducting regression analysis of the sample entropy value set and a set of levels of consciousness measured with a physiological signal monitor during the physiological signal monitoring process, thereby creating the anesthetic consciousness index model for evaluating the level of consci

Abstract: A server includes a logic subsystem and memory. The memory holds instructions that, when executed by the logic subsystem, cause the logic subsystem to determine if a table is wider than a viewing size of a screen of a requesting device, determine if the table is a data table or a layout table, and format an instance of the table prior to sending that instance of the table to the requesting device. The instance of the table sent to the requesting device is formatted in accordance with a determined viewing size of the screen of the requesting device and in accordance with a determined table type.

Abstract: A method for monitoring the depth of anesthesia is provided for detecting the conscious state of one being anesthetized in the recovery phase or induction phase of anesthesia course in order to facilitate an anesthesiologist to predict exactly the dosage of an anesthetic required. At first, an original electroencephalogram (EEG) is taken from one being tested. Then, the original electroencephalogram is analyzed by approximate entropy to obtain its approximate entropy value. Next, the approximate entropy value is multiplied by 1000/17, and the corrected value is assumed as the predicted value of depth of anesthesia. The predicted value of depth of anesthesia represents degree of the conscious state or the depth of anesthesia for the one being tested. The higher the predicted depth of anesthesia value, the more conscious the one being tested is, i.e., in a shallower depth of anesthesia. On the other hand, the lower the predicted depth of anesthesia value, the less conscious the one being tested is, i.e.

Abstract: A server includes a logic subsystem and memory. The memory holds instructions that, when executed by the logic subsystem, cause the logic subsystem to determine if a table is wider than a viewing size of a screen of a requesting device, determine if the table is a data table or a layout table, and format an instance of the table prior to sending that instance of the table to the requesting device. The instance of the table sent to the requesting device is formatted in accordance with a determined viewing size of the screen of the requesting device and in accordance with a determined table type.

Abstract: A method for monitoring the depth of anesthesia is provided for detecting the conscious state of one being anesthetized in the recovery phase or induction phase of anesthesia course in order to facilitate an anesthesiologist to predict exactly the dosage of an anesthetic required. At first, an original electroencephalogram (EEG) is taken from one being tested. Then, the original electroencephalogram is analyzed by approximate entropy to obtain its approximate entropy value. Next, the approximate entropy value is multiplied by 1000/17, and the corrected value is assumed as the predicted value of depth of anesthesia. The predicted value of depth of anesthesia represents degree of the conscious state or the depth of anesthesia for the one being tested. The higher the predicted depth of anesthesia value, the more conscious the one being tested is, i.e., in a shallower depth of anesthesia. On the other hand, the lower the predicted depth of anesthesia value, the less conscious the one being tested is, i.e.

Abstract: A method for monitoring the depth of anesthesia is provided for detecting the conscious state of one being anesthetized in order to facilitate an anesthesiologist to predict exactly the dosage of an anesthetic required. At first, an original electroencephalogram (EEG) is taken from one being tested. Then, the original electroencephalogram is analyzed by approximate entropy to obtain its approximate entropy value. Next, the approximate entropy value is multiplied by 1000/17, and the corrected value is assumed as the predicted value of depth of anesthesia. The predicted value of depth of anesthesia represents degree of the conscious state or the depth of anesthesia for the one being tested. The higher the predicted depth of anesthesia value, the more conscious the one being tested is, i.e., in a shallower depth of anesthesia. On the other hand, the lower the predicted depth of anesthesia value, the less conscious the one being tested is, i.e., in a deeper depth of anesthesia.

Abstract: Described is a technology directed towards providing a single page of data that when rendered (e.g., on a mobile device) displays combined content from multiple endpoints (e.g., corresponding to different messaging participants). Interaction with the page facilitates communication back to each endpoint. Bidirectional communication logic such as a messaging application provides the interactive page as a user interface. Content corresponding to each conversation is displayed in a separate area, which may include an interactive mechanism (e.g., a text input area and send mechanism) for sending a message to the messaging participant associated with that conversation. When content changes (e.g., following a page refresh), a change indication may be provided, such as by displaying a notification banner, and/or by changing the appearance (e.g., color) of changed message content.