Abstract: An electronic device and a method for diagnosing heart state based on electrocardiogram (ECG) are provided. An ECG file is obtained, and the ECG file is in a first file format and includes a plurality of potential traces of a plurality of leads. The ECG file is converted to a second file format to obtain electrocardiogram data corresponding to multiple leads. Each potential trace relative to time in the ECG file is converted to the ECG data of each lead. Integrated ECG data associated with the leads is generated based on the ECG data of the plurality of leads through the zero-padding operation and the stacking operation. A diagnostic result of heart status is generated based on the integrated ECG data and a deep learning model.

Abstract: A construction method of ribosomal RNA database is provided, including the following steps: selecting a source of nucleic acid sequence database; performing normalization and homogenization on species classification rules; using AI technology for normalized classification and correction; selecting the kingdom to which the sequence species belongs; filtering out redundant sequences and sequences with inconsistent lengths; setting a threshold for unknown bases other than A, T, C or G, and excluding unknown bases that exceed the threshold; and excluding sequences with insufficient classification information.

Abstract: A feature selection method is provided, including: inputting a plurality of pieces of training data into a plurality of training models to perform selection in a plurality of features through each training model for obtaining multiple feature pools; sorting the features based on the number of times each feature is selected by the feature pools to obtain a feature ranking; and extracting a plurality of designated features from the features based on the feature ranking.

Abstract: A method of treating a Cisd2 insufficiency-associated disorder. The method includes administering to a subject in need a compound of formula (I). Also disclosed is a method of increasing Cisd2 gene expression.

Abstract: An electronic device and a method for selecting a feature of an electrocardiogram (ECG) are provided. The method includes: obtaining the ECG; performing a first pre-processing on the ECG to generate a first ECG; marking multiple extreme points corresponding to at least one type of wave on the first ECG; calculating a first feature value corresponding to a first feature according to the multiple extreme points of the at least one type of wave, generating a first performance index corresponding to a machine learning model according to the first feature value, and determining whether to select the first feature according to the first performance index; and outputting the first feature in response to selecting the first feature.

Abstract: An electronic device and a method for predicting a blockage of a coronary artery are provided. The method includes: obtaining multiple pieces of electrocardiogram (ECG) data respectively corresponding to a coronary artery set; generating multiple first probabilities corresponding to the multiple pieces of electrocardiogram data respectively according to the multiple pieces of electrocardiogram data and a first phase model, generating a first determined result according to the multiple first probabilities, and selecting a first data subset corresponding to a first probability subset from the multiple pieces of electrocardiogram data in response to each one in the first data subset of the multiple first probabilities being greater than a first threshold; generating multiple second probabilities corresponding to the first data subset according to the first data subset and a second phase model, and generating a second determined result according to the multiple second probabilities.

Abstract: An electronic device and a method for screening features for predicting a physiological state are provided. The method includes: obtaining multiple physiological data corresponding to multiple features; generating multiple first subsets of the multiple features according to the multiple physiological data based on a first model, wherein the multiple first subsets respectively correspond to the multiple physiological data; selecting a first feature from the multiple features according to the multiple first subsets, calculating a first relation index of the first feature and a second feature corresponding to the multiple features, and selecting the second feature as an accompanied feature of the first feature according to the first relation index; and outputting the first feature and the accompanied feature.