9 results about "Fetal electrocardiogram" patented technology

The invention relates to the technical field of medical monitoring and assistance, in particular to a sensor-based intelligent monitoring system for obstetrical and gynecological operations, which comprises a multi-source sensing module, a feature fusion module, a mode recognition module, a collaborative early warning module and a decision output module, wherein the multi-source sensing module is used for outputting an instrument movement track, a uterine cavity pressure waveform, a fetal electrocardiosignal and a surgical field video stream; the feature fusion module is used for generating a multi-dimensional feature matrix; the mode recognition module is used for outputting an instrument risk label and a fetus risk level; the collaborative early warning module is used for generating a multi-level early warning instruction containing the coordinates of the risk part; and the decision output module is used for forming a dynamically updated safe operation boundary diagram and displaying the safe operation boundary diagram on the surgical field video stream in an overlapping manner. According to the invention, through a multi-source sensing and collaborative early warning mechanism, synchronous monitoring and dynamic risk prompting of operation behaviors and physiological states in the obstetrics and gynecology surgery are realized, and the safety management and control capability in the surgery is improved.

The invention discloses a double-fetal monitoring method based on abdominal electric signal state dynamic conversion analysis, which comprises the following steps of: acquiring abdominal electric signal data of a double-fetal pregnant woman containing all age groups and physical conditions in a clinical environment, and constructing a double-fetal monitoring reference data set through preprocessing and signal separation; and designing an abdominal electric signal state dynamic conversion analysis algorithm suitable for double-fetal monitoring. And constructing a state dynamic conversion template of the source signal in state dynamic conversion analysis, and generating a double-tire position marking data set. And obtaining state labeling time sequence data of state dynamic conversion based on the state dynamic conversion template, constructing a double-fetal electrocardiosignal extraction scheme, and calculating the fetal heart rate. And visualizing a state dynamic conversion time sequence chart and marking real-time fetal heart rate and mother heart rate to realize dynamic monitoring of the state of the double fetuses. According to the invention, the limitation that the fetal heart signals are confused and the independent electrocardiosignals of the two fetuses are difficult to accurately extract during the existing monitoring of the two fetuses is solved, and the real-time performance and the reliability of the clinical monitoring of the two fetuses are improved.

The application discloses a display method of body surface fetal electrocardio monitoring information, a portable body surface fetal electrocardio monitoring device and a storage medium. The method comprises the following steps: acquiring an electrocardio signal, wherein the electrocardio signal comprises a fetal electrocardio signal; obtaining corresponding heart rate information based on the electrocardio signal; and displaying signal related information and the heart rate information in linkage on the same screen, wherein the signal related information comprises at least one of the electrocardio signal and quality information of the electrocardio signal. Through the above method, the user can analyze the heart rate information conveniently.

The present application relates to the technical field of biomedical engineering, in particular to a high signal-to-noise ratio fetal electrocardiogram separation acquisition method and device, through preprocessing of mixed maternal electrocardiogram signals, extracting maternal electrocardiogram signals from the preprocessed mixed maternal electrocardiogram signals, quality detection of the maternal electrocardiogram signals to obtain maternal electrocardiogram signals with high accuracy, removing the maternal electrocardiogram signals with high accuracy from the preprocessed mixed maternal electrocardiogram signals to obtain fetal electrocardiogram signals, quality detection of the fetal electrocardiogram signals to obtain fetal electrocardiogram signals with high accuracy, and receiving signals by a DSP processing unit and performing digital conversion to output fetal electrocardiogram signals with high signal-to-noise ratio, thereby solving the problem that fetal electrocardiogram signals are easily disturbed by maternal electrocardiogram signals, resulting in low accuracy of fetal electrocardiogram monitoring results.

The present application relates to the technical field of medical diagnostic monitoring equipment manufacturing, and discloses a wearable fetal monitoring system, which comprises a flexible patch integrating a bioelectricity collection electrode and an acoustic sensing unit and a connected processing unit, the processing unit acquires a composite physiological signal, identifies a maternal pulsation period by using an acoustic component to extract a synchronous phase reference, and configures a dynamic gain window comprising a suppression zone and a detection zone on a time domain axis; a breathing-induced low-frequency envelope is extracted and a polarized charge accumulation parameter is calculated, a compensation charge current with an opposite phase is triggered by the acoustic sensing unit based on the phase reference, and is injected into a collection front end in real time to neutralize baseline drift, the present application eliminates phase distortion caused by interface polarization through physical layer charge offset, and realizes complete retention of low-frequency morphological characteristics of fetal electrocardiogram signals under dynamic working conditions, and improves the determination accuracy of critical symptoms.

This application provides a method, apparatus, device, and readable storage medium for extracting fetal electrocardiogram (ECG) signals, relating to the field of artificial intelligence. The method includes: acquiring a pregnant woman's abdominal ECG signal; determining a pregnant woman's chest ECG signal related to the pregnant woman's ECG component within the abdominal ECG signal; eliminating the pregnant woman's ECG component from the abdominal ECG signal based on the chest ECG signal to obtain a target ECG signal; and enhancing the target ECG signal to extract the fetal ECG signal. This application addresses the problem that existing fetal ECG signal extraction methods and devices cannot accurately acquire relatively weak fetal ECG signals.

The application provides a new type of single-channel-based fetal heart detection algorithm, first uses a single-channel body surface bioelectricity collection device to collect pregnant woman abdominal bioelectricity data, wherein the data contains fetal electrocardiosignal and other mother and baby physiological signals; then uses wavelet transform and singular value decomposition algorithm to reconstruct the pregnant woman reference electrocardiosignal, uses an adaptive filtering algorithm to eliminate most of the interference physiological signals; then uses wavelet transform, singular value decomposition and digital band-pass filter to reconstruct the fetal electrocardiosignal, obtains the fetal electrocardiogram curve, and simultaneously uses time-frequency domain transform algorithm, non-negative matrix decomposition algorithm and power spectral density to obtain accurate fetal heart rate. The algorithm is a single-channel non-invasive passive fetal electrocardiosignal analysis algorithm, is convenient to operate, is reliable in performance, can accurately obtain fetal electrocardio information, and provides a basis for non-invasive fetal heart monitor product design based on body surface bioelectricity signal collection and analysis.

The invention relates to a method for generating a fetal electrocardiogram based on fetal heart M-type ultrasonic simulation, which comprises the following steps: acquiring a fetal heart image by using M-type ultrasonic equipment, and enabling a sampling line to pass through an atrium and a ventricle; displacement curves of the atrium and the ventricular wall are extracted, and noise filtering is carried out; establishing a mapping relation between the displacement curve and the electrocardiosignal based on an electromechanical coupling model; and generating a simulated fetal electrocardiogram through interpolation, filtering and time domain correction. The fetal simulated electrocardiogram generation method is non-invasive and safe, the simulated waveform is highly consistent with the real FECG, the accuracy rate is larger than or equal to 90%, and the fetal simulated electrocardiogram generation method is suitable for real-time monitoring of fetal arrhythmia and cardiac dysfunction.

The application discloses a kind of fetal electrocardiosignal extraction systems based on adversarial deep learning network, method includes: to the original abdominal electrocardiosignal AECG of puerpera is preprocessed noise reduction, the signal after preprocessing is as the input of binary classification signal quality evaluation network, and high-quality abdominal electrocardiosignal is screened out, subsequently screened AECG and the ECG data of corresponding newborn of puerpera are as input, using cyclic generative adversarial network, AECG is mapped to ECG, ECG is mapped to AECG, network learns to judge after discriminator, output reconstructed FECG, subsequently based on generative adversarial network is post-processed noise reduction, finally output pure, accurate FECG waveform.The application is accurately recovered fetal electrocardiosignal from maternal abdominal electrocardiosignal and denoised by using multi-scale convolution kernel, learning the abdominal electrocardiosignal of puerpera and fetal electrocardiosignal.The integrated fusion model proposed in the application solves the problem of low accuracy of reconstructed FECG signal, and can be widely applied to the field of biomedical signal processing and computer detection.