9 results about "Heart rate analysis" patented technology

The invention discloses an emotion recognition system and method based on a large model, and relates to the field of data analysis, and the method comprises the steps: collecting facial physiological dynamic data, including specific muscle group motion data and photoelectric volume pulse wave signals, of a person in a calm state, and establishing a reference contraction frequency and a reference heart rate; analyzing data characteristics and calculating an amplification threshold and an enhancement threshold; collecting face data of a target person in real time, and extracting real-time contraction frequency, duration of the real-time contraction frequency exceeding a critical value and real-time heart rate; and judging the emotional state by comparing the amplification and enhancement of the real-time data and the reference value. The system comprises a reference establishment module, a threshold calculation module, a real-time monitoring module, a state identification module and the like. According to the method, non-invasive, objective and accurate emotion recognition is realized through multi-modal physiological signal analysis, and the method has the advantages of high real-time performance and high accuracy.

The invention relates to the technical field of medical care, in particular to a cardiovascular disease risk dynamic early warning system based on multi-source data fusion, and the system comprises the steps: obtaining ejection fraction, coronary artery stenosis percentage and calcification integral data of cardiovascular examination of a monitored object, and heart rate data and pulse pressure difference data of periodic signs; determining a physical examination abnormal coefficient according to the difference between the ejection fraction and the standard value and the difference between the calcification integral data and the standard value and the coronary artery stenosis percentage; according to the height and slope difference of the ST wave band and the PR wave band, the heart rate analysis abnormal index is determined by combining the time interval of the adjacent electric activity wave bands; according to the time sequence change correlation of the heart rate data and the pulse pressure difference data, a physical sign abnormal coefficient is determined; determining a dynamic risk score in combination with the physical examination abnormal coefficient and the physical sign abnormal coefficient; and carrying out risk early warning based on the dynamic risk score. According to the invention, fusion analysis of multi-source and multi-dimensional data can be realized, and the dynamic early warning precision of cardiovascular risks is improved.

The application provides a monitoring system, a monitoring device and an atrial fibrillation presentation method. The atrial fibrillation comprehensive view is displayed by obtaining the heart rate analysis result and the atrial fibrillation analysis result according to the heart rate analysis result and the atrial fibrillation analysis result. The atrial fibrillation comprehensive view includes the heart rate information and the target heart rate identifier in the preset atrial fibrillation monitoring period, and the atrial fibrillation information in the preset atrial fibrillation monitoring period. The atrial fibrillation information includes at least one of the atrial fibrillation load, the atrial fibrillation occurrence frequency and the atrial fibrillation duration. The target heart rate identifier is used for identifying the preset target heart rate. Through the joint presentation of the atrial fibrillation and the heart rate, the quantitative analysis result of the atrial fibrillation event and the heart rate can be comprehensively and intuitively presented to the medical staff in a graphical manner, which helps the medical staff to understand the achievement of the heart rate control target of the atrial fibrillation patient and improves the atrial fibrillation management efficiency.

In some embodiments, a computer-implemented method of measuring heart rate analytics using raw photoplethysmography (PPG) data is provided. A computing system determines a plurality of frequency peaks in the raw PPG data using a Fourier analysis. The computing system determines a plurality of selected frequency peaks from the plurality of frequency peaks. For each selected frequency peak of the plurality of selected frequency peaks, the computing system creates filtered PPG data by applying a frequency of the selected frequency peak as a high-pass filter to the raw PPG data; detects diastole peaks and systole valleys in the filtered PPG data; and performs one or more pulse quality checks on the detected diastole peaks and systole valleys. The computing system uses diastole peaks and systole valleys associated with a lowest selected frequency peak that passed the pulse quality checks to determine a heart rate analytic measurement.

The invention relates to a heart rate analysis method, a heart rate analysis device, heart rate analysis equipment, a storage medium and a program product. The method comprises the following steps: fitting an initial high-frequency pulse signal generated by single-lead electrocardio equipment to obtain a fitted pulse signal, finally determining error information according to an electrocardio signal and the fitted pulse signal, and determining heart rate information of a target object based on the error information. In the method, the amplitude information can reflect the intensity of the cardiac electrical activity, and the frequency information can reflect the rhythm change of the cardiac electrical activity, so that the initial high-frequency pulse signal is fitted by combining the two kinds of information at the same time, and the electrocardiosignal can be more comprehensively and accurately simulated; therefore, the heart rate information determined according to the error information is more accurate.

In some embodiments, a computer-implemented method of measuring heart rate analytics using raw photoplethysmography (PPG) data is provided. A computing system determines a plurality of frequency peaks in the raw PPG data using a Fourier analysis. The computing system determines a plurality of selected frequency peaks from the plurality of frequency peaks. For each selected frequency peak of the plurality of selected frequency peaks, the computing system creates filtered PPG data by applying a frequency of the selected frequency peak as a high-pass filter to the raw PPG data; detects diastole peaks and systole valleys in the filtered PPG data; and performs one or more pulse quality checks on the detected diastole peaks and systole valleys. The computing system uses diastole peaks and systole valleys associated with a lowest selected frequency peak that passed the pulse quality checks to determine a heart rate analytic measurement.

The invention discloses a relaxation degree calculation method based on dynamic threshold heart rate analysis and a physiological constraint model. The relaxation degree calculation method comprises the steps that a single-sample PPG signal is collected through an ear clip type sensor; performing cleaning and feature extraction on the signals to obtain heart rate basic data and time domain features; further extracting an RR interval sequence, and performing frequency domain conversion and psychological feature evaluation to obtain frequency domain features and psychological features; performing weighted fusion on the time domain and frequency domain features by using a dynamic weighting and physiological constraint module to obtain weighted features; and finally, based on the multi-sample data stored in the annular buffer area, performing feature consistency check, calculating psychological features meeting consistency conditions, and outputting a final psychological relaxation degree value. According to the method, dynamic threshold heart rate analysis and the physiological constraint model are combined, the accuracy and reliability of relaxation degree evaluation are effectively improved, and the method is suitable for real-time and continuous psychological state monitoring.

An object of the system according to the embodiment is to determine the authenticity of the description content of the salesperson and to check the inconsistency between the content of the contract document and the description content.SOLUTION: A system includes a contract document confirmation unit, a facial expression analysis unit, a heart rate analysis unit, a summarization unit, and an inconsistency confirmation unit. The contract document confirmation unit confirms the contract document. The expression analysis unit analyzes the contents of the contract document confirmed by the contract document confirmation unit. The heart rate analysis part analyzes the expression of the salesman analyzed by the expression analysis part. The summarization part analyzes the heart rate of the salesman analyzed by the heart rate analysis part. The inconsistency checking unit checks the contract document and the description content summarized by the summarizing unit.SELECTED DRAWING: Figure 1

This invention relates to the field of emotion recognition technology, specifically to a method and system for emotion event recognition based on multimodal physiological parameters. The method determines a baseline time window based on the changing trend of the respiratory rate time series; it determines the heart rate analysis interval and blood pressure analysis interval based on the hysteresis response characteristics of the heart rate and blood pressure time series within the baseline time window; the baseline time window, heart rate, and blood pressure analysis intervals constitute the current representation feature; the feature matching degree is determined based on the similarity between the current representation feature and a preset set of historical representation features; the current representation feature is weighted and fused for optimization based on the feature matching degree to determine the optimized representation feature; the optimized representation feature is input into a pre-trained classification model, which outputs a category label corresponding to the emotional event. This makes the representation feature more closely resemble the real emotion-physiological linkage, so that the classification result can take into account the differences in individual physiological characteristics.