Abstract: A data processing method for the machine learning and an electronic device using the same are provided. The data processing method for the machine learning includes the following steps. For a plurality of sources, a source balancing procedure is performed on an original measuring data to obtain a balanced distribution map. For each of the subjects, a personalization scaling procedure is performed on a plurality of detection values to obtain a personalized scaled measuring data. For each of the sources, a source scaling procedure is performed on the detection values to obtain a by-source scaled measuring data. The balanced distribution map, the personalized scaled measuring data and the by-source scaled measuring data are combined to obtain a balanced personalized scaled data and a balanced by-source scaled data. Based on the balanced personalized scaled data and the balanced by-source scaled data, some of the detection items are outputted.

Abstract: Provided is a method for extending peptide linkers between peptides, including the following steps. All combinations of the peptide linkers of a length of n are listed, and a combination of a best peptide linker is generated. After that, a first condition and a second condition are considered to extend the peptide linkers. The first condition is that a cutting point of the peptide linkers is in the middle, and a center of extending the peptide linkers is selected from amino acids of a cutting point of the best peptide linker. The second condition is that amino acids of a N-terminal and amino acids of a C-terminal of the peptide linkers are searched, and the amino acids of the N-terminal and the amino acids of the C-terminal have a higher probability of being cut off.

Abstract: An alarm system an alarm method for a medical emergency are provided. The alarm method includes: obtaining a physiological signal of a user by a sensor of a portable electronic device; receiving the physiological signal from the portable electronic device, determining whether an abnormal event has occurred according to the physiological signal, and transmitting first feedback information corresponding to the physiological signal to a server in response to the abnormal event by the terminal device; and outputting an alarm message according to the first feedback information by the server.

Abstract: A method for analyzing an electrocardiography (ECG) signal is provided. The ECG signal is measured through a sensor. One or more specified waveforms in the ECG signal are detected. Multiple ECG features from each specified waveform are captured. The ECG features are input into multiple candidate models to obtain multiple candidate scores. Each candidate score is compared with a standard value to obtain an ideal score among the candidate scores. One of the candidate models corresponding to the ideal score is selected as a risk prediction model to predict disease risk through the risk prediction model.

Abstract: A device and method for generating article markup information are provided. The method for generating article markup information includes the following. Segmentation processing is performed on an article to generate a segmentation result. Name entity recognition is performed on the segmentation result to generate a first recognition result. Whether the segmentation result includes any word in an expansion list is determined. Expanded entity classification conversion is performed on the first recognition result to generate a second recognition result. The second recognition result and the segmentation result are used as markup information.

Abstract: A device and method for generating article markup information are provided. The method for generating article markup information includes the following. Segmentation processing is performed on an article to generate a segmentation result. Name entity recognition is performed on the segmentation result to generate a first recognition result. Whether the segmentation result includes any word in an expansion list is determined. Expanded entity classification conversion is performed on the first recognition result to generate a second recognition result. The second recognition result and the segmentation result are used as markup information.

Abstract: A method for detecting cardiac arrhythmia based on a photoplethysmographic (PPG) signal is provided. The method includes: receiving a PPG signal and a motion signal corresponding to a motion made by a user; extracting PPG signal segments and motion signal segments corresponding to a time period from the PPG signal and the motion signal, respectively, at every time period; filtering out motion artifact noise in the PPG signal segments according to the PPG signal segments and the motion signal segments, and converting the PPG signal segments and the motion signal segments into PPG spectrum diagrams and motion spectrum diagrams, respectively; obtaining an estimated heart rate according to the PPG spectrum diagrams and the motion spectrum diagrams; and determining whether cardiac arrhythmia is present based on the filtered PPG signal segments and the estimated heart rate.

Abstract: A method for analyzing an electrocardiography (ECG) signal is provided. The ECG signal is measured through a sensor. One or more specified waveforms in the ECG signal are detected. Multiple ECG features from each specified waveform are captured. The ECG features are input into multiple candidate models to obtain multiple candidate scores. Each candidate score is compared with a standard value to obtain an ideal score among the candidate scores. One of the candidate models corresponding to the ideal score is selected as a risk prediction model to predict disease risk through the risk prediction model.

Abstract: The present disclosure provides a heart-rhythm signal-processing method, including the following steps: obtain PPG signals; perform a labeling operation to determine whether inappropriate signal data exist in the PPG signals and label the inappropriate signal data; perform a first separation operation to remove the inappropriate signal data from the PPG signals and separate the PPG signals into continuous signal segments; find peak positions of the continuous signal segments; perform a second separation operation to separate the continuous signal segments into sub-signal segments according to the number of peaks required for interpretation; perform a feature-extraction operation to obtain feature values respectively corresponding to each of the sub-signal segments, wherein the feature values are relative to peak-to-peak intervals (PPI) of the sub-signal segments; perform a judgement operation to judge the feature values using a judgement model and determine whether the heart rhythm of the subject is belong to

Abstract: A method for detecting cardiac arrhythmia based on a photoplethysmographic (PPG) signal is provided. The method includes: receiving a PPG signal and a motion signal corresponding to a motion made by a user; extracting PPG signal segments and motion signal segments corresponding to a time period from the PPG signal and the motion signal, respectively, at every time period; filtering out motion artifact noise in the PPG signal segments according to the PPG signal segments and the motion signal segments, and converting the PPG signal segments and the motion signal segments into PPG spectrum diagrams and motion spectrum diagrams, respectively; obtaining an estimated heart rate according to the PPG spectrum diagrams and the motion spectrum diagrams; and determining whether cardiac arrhythmia is present based on the filtered PPG signal segments and the estimated heart rate.