Abstract: A data decoding method, performed by a computer device, is provided. The data decoding method includes obtaining coded data corresponding to a current to-be-decoded unit, obtaining a coding mode restriction type corresponding to the current to-be-decoded unit by decoding the coded data, determining a candidate coding mode set corresponding to the current to-be-decoded unit according to the coding mode restriction type, the candidate coding mode set comprising a block copy intra-frame coding mode, selecting a target coding mode from the candidate coding mode set, and decoding the current to-be-decoded unit according to the target coding mode, thereby obtaining corresponding decoded data.

Abstract: A method of controlling intra prediction for decoding or encoding of a video sequence, is performed by at least one processor and includes obtaining intra prediction modes including directional modes respectively corresponding to angular prediction directions, a first amount of one or more of the directional modes being excluded from the intra prediction modes based on a second amount of the intra prediction modes and a third amount of most probable modes (MPMs). The method further includes selecting, as the MPMs, two or more of the intra prediction modes from which the one or more of the directional modes are excluded, and selecting, for decoding the video sequence, one of the intra prediction modes from which the one or more of the directional modes are excluded.

Abstract: A cached information updating method includes receiving an update request, determining an update processing manner according to a number of pieces of cached information to be updated indicated in the update request, and updating the cached information according to the update processing manner, to update differently according to different numbers of pieces of cached information.

Abstract: A method and apparatus include receiving, by a device, medical information associated with a user. Inquiry information is determined based on the medical information associated with the user and a reinforcement learning model. The inquiry information is provided to permit response information to be received. The response information is received based on providing the inquiry information. Diagnosis information is determined based on the medical information and the response information using a machine learning model. The diagnosis information is provided to a set of devices via a network.

Abstract: The lifting mechanism includes a battery, an electric machine, a hydraulic pump, an oil tank, a hydraulic cylinder, a work platform and a proportional valve or switch valve. In the energy-regeneration mode, the hydraulic fluid drives the hydraulic pump to operate as a hydraulic motor, thus in turn driving the electric machine to operate as a generator and charge the battery. In the present application, the hydraulic pump operates to increase a pressure in a hydraulic line between the hydraulic pump and the proportional valve or switch valve before the proportional valve or switch valve is switched from a unidirectional communication position to a bidirectional communication position.

Abstract: A lifting mechanism has a descending mode including an energy-regeneration mode and a non-energy-regeneration mode. The lifting mechanism includes: a battery, an electric machine, a hydraulic pump, an oil tank, a hydraulic cylinder, a work platform and a flow limiting valve. In the energy-regeneration mode, the hydraulic fluid drives the hydraulic pump to operate as a hydraulic motor, thus in turn driving the electric machine to operate as a generator and charge the battery. In the non-energy-regeneration mode, the flow limiting valve limits the maximum of the descending speed of the work platform. In the present application, the flow limiting valve is used to provide throttling resistance to limit the maximum of the descending speed of the hydraulic fluid, thus in turn defining the maximum of the descending speed of the work platform.

Abstract: A method, computer program, and computer system for detecting and classifying atrial fibrillation by receiving data corresponding to an electrocardiogram (ECG) associated with a patient, extracting RR intervals from the received ECG data, determining previous RR intervals for each of the extracted RR intervals and aggregating features associated with the RR intervals and the previous RR intervals. Patterns associated with the aggregated features are classified, and the computer may determine that a sample of the ECG data corresponding to one or more of the classified patterns contains a pattern associated with atrial fibrillation.

Abstract: A method and device for performing electrocardiography (ECG) analysis, the method including receiving ECG data that is from one or more leads, generating an image based on the ECG data, obtaining a feature map based on the image, inputting the feature map to a first neural network, the first neural network configured to generate an output based on the feature map inputted, inputting the output of the first neural network to a second neural network, the second neural network configured to obtain at least one temporal feature of the image based on the output of the first neural network and a previous state of the second neural network, and classifying a signal included in the ECG data based on the at least one temporal feature obtained by the second neural network.

Abstract: A cached information updating method includes receiving an update request, determining an update processing manner according to a number of pieces of cached information to be updated indicated in the update request, and updating the cached information according to the update processing manner, to update differently according to different numbers of pieces of cached information.

Abstract: There is included an apparatus and system including video capture code configured to control a camera of a mobile phone to capture a video of at least a portion of skin, extraction code configured to extract at least one photoplethysmogram (PPG) signal from the video, determination code configured to determine a peripheral oxygen saturation (SpO2) value based on the PPG signal with respect to a blood flow at the portion of skin, and display code configured to control a display of the mobile phone to display the SpO2 value.

Abstract: A method and apparatus include receiving a three dimensional (3D) non-contrast computed tomography (NCCT) image of a head including a hematoma. A plurality of two dimensional (2D) images of the head including the hematoma are generated using the 3D NCCT image of the head including the hematoma. A plurality of 2D hematoma images are generated using a first 2D convolutional neural network (CNN) based on the plurality of 2D images. A 3D region of interest (ROI) that encompasses the hematoma is identified based on the plurality of 2D hematoma images. A plurality of 2D images that correspond to the ROI are generated. A hematoma expansion (HE) prediction score is determined using a second CNN based on the plurality of 2D images that correspond to the ROI. The HE prediction score is provided.

Abstract: There is included an apparatus and system including video capture code configured to control a camera of a mobile phone to capture a video of at least a portion of skin, extraction code configured to extract at least one photoplethysmogram (PPG) signal from the video, determination code configured to determine a peripheral oxygen saturation (SpO2) value based on the PPG signal with respect to a blood flow at the portion of skin, and display code configured to control a display of the mobile phone to display the SpO2 value

Abstract: A method, computer program, and computer system is provided for receiving data corresponding to a tomograph scan associated with a patient, extracting slices from the received tomograph scan data, and determining adjacent slices for each of the extracted slices. The extracted slices and the adjacent slices may be grouped into slabs, and features associated with the slabs may be identified. It may be determined that a slab corresponding to the identified features contains a feature associated with ICH.

Abstract: A method of filtering an electrocardiogram (ECG) signal includes obtaining the ECG signal; applying a first transformation to the ECG signal to generate a transformed ECG signal; filtering the transformed ECG signal to generate a filtered transformed ECG signal; and applying a second transformation to the filtered transformed ECG signal to generate a filtered ECG signal.

Abstract: A method, computer program, and computer system for detecting and classifying atrial fibrillation by receiving data corresponding to an electrocardiogram (ECG) associated with a patient, extracting RR intervals from the received ECG data, determining previous RR intervals for each of the extracted RR intervals and aggregating features associated with the RR intervals and the previous RR intervals. Patterns associated with the aggregated features are classified, and the computer may determine that a sample of the ECG data corresponding to one or more of the classified patterns contains a pattern associated with atrial fibrillation.

Abstract: A method and apparatus include receiving a three dimensional (3D) non-contrast computed tomography (NCCT) image of a head including a hematoma. A plurality of two dimensional (2D) images of the head including the hematoma are generated using the 3D NCCT image of the head including the hematoma. A plurality of 2D hematoma images are generated using a first 2D convolutional neural network (CNN) based on the plurality of 2D images. A 3D region of interest (ROI) that encompasses the hematoma is identified based on the plurality of 2D hematoma images. A plurality of 2D images that correspond to the ROI are generated. A hematoma expansion (HE) prediction score is determined using a second CNN based on the plurality of 2D images that correspond to the ROI. The HE prediction score is provided.

Abstract: A method and device for performing electrocardiography (ECG) analysis, the method including receiving ECG data that is from one or more leads, generating an image based on the ECG data, obtaining a feature map based on the image, inputting the feature map to a first neural network, the first neural network configured to generate an output based on the feature map inputted, inputting the output of the first neural network to a second neural network, the second neural network configured to obtain at least one temporal feature of the image based on the output of the first neural network and a previous state of the second neural network, and classifying a signal included in the ECG data based on the at least one temporal feature obtained by the second neural network.

Abstract: A method and apparatus for performing a biosignal analysis task using a set of models includes receiving an input biosignal. Information that identifies the biosignal analysis task to be performed in association with the input biosignal is received. A waveform model and a digital signal processing (DSP) model are selected. A first type of feature and a second type of feature of the input biosignal are identified. An analysis model is selected, and the biosignal analysis task is performed using the analysis model.

Abstract: A method and apparatus include receiving, by a device, medical information associated with a user. Inquiry information is determined based on the medical information associated with the user and a reinforcement learning model. The inquiry information is provided to permit response information to be received. The response information is received based on providing the inquiry information. Diagnosis information is determined based on the medical information and the response information using a machine learning model. The diagnosis information is provided to a set of devices via a network.

Abstract: The present invention is directed to a method for determining onset of ventricular arrhythmias using bounded-input bounded-output stability of QT interval (QTI) dynamics. The method of the present invention includes two parts. A first part of the method determines the dependence of each QTI on several prior QTIs and RR intervals (RRI). This determination is represented as an autoregressive model with exogenous input (ARX). A second part of the method determines the BIBO stability of the ARX model in the z-domain. The metrics associated with the first and second parts of the method are then used to predict onset of arrhythmia.