15 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 invention discloses a skin impedance measurement method and system for fetal electrocardiogram monitoring. The method uses a three-axis gyroscope to collect the body posture information of a pregnant woman, and only starts the impedance measurement link when the pregnant woman is in a calm state. An alternating current excitation current source is started, and a preset alternating current is applied to the skin surface of the pregnant woman through the P electrode. The differential signal between the P electrode and the N electrode is obtained through a differential amplifier as the excitation response signal. The excitation response signal is preprocessed to obtain the excitation response signal after filtering out the maternal and fetal bioelectric signals. Then, the discrete Fourier transform is performed on the excitation response signal after filtering out the maternal and fetal bioelectric signals, and the complex results of the real part and the imaginary part of the fundamental frequency are solved to obtain the human skin impedance, thus solving the technical problems existing in the existing impedance measurement method at the skin-electrode interface, such as ignoring the imaginary part information of the excitation response signal and the noise interference caused by using analog switch devices, resulting in a decrease in the accuracy of impedance measurement.

The invention discloses a maternal and fetal monitoring system which comprises an intelligent sensing device composed of a multi-channel flexible electrode and an accelerometer, the electrode is used for collecting maternal abdomen bio-electricity signals, and the accelerometer is used for collecting maternal acceleration signals; the signal processing and analyzing module is used for carrying out continuous wavelet transformation on the bio-electricity signals according to the collected bio-electricity signals to obtain time-frequency characteristics, recognizing the classification of each fragment in the bio-electricity signals by adopting a deep neural network according to the time-frequency characteristics, extracting maternal electrocardio and fetal electrocardio according to the classified fragments, and sending the extracted maternal electrocardio and fetal electrocardio to a server; according to the collected acceleration signal of the parent body, the sleeping posture of the parent body is detected, and an abnormal alarm is given out; the closed-loop regulation and control module is used for evaluating the maternal-fetal state according to the fetal electrocardiogram, the maternal electrocardiogram and the sleeping posture and adaptively generating an intervention scheme; and the audio intervention module is used for playing music according to the audio intervention strategy. The method is high in accuracy and can realize simultaneous monitoring of the maternal body and the fetus.

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 invention provides an abdominal wall electric signal denoising method based on partial resampling single-channel singular value decomposition and correlation analysis. The method comprises the steps of abdominal wall electric signal preprocessing, Pan-Tompkin method maternal R wave recognition, maternal part resampling, single-channel singular value decomposition, correlation denoising and finally obtaining of denoised fetal electrocardiogram. The method has the remarkable advantages that the problem that SVD algorithm residual noise in abdominal wall electric signals with large heart rate variability is large is solved, meanwhile, residual maternal electrocardio is almost all filtered out through a related denoising analysis method, accuracy is high, and the method can be applied to the conditions that the signal-to-noise ratio of collecting equipment is low, and the signal-to-noise ratio of the collecting equipment is high. And the fetal electrocardiogram with the high signal-to-noise ratio can be obtained under the condition that the number of channels is limited.

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.

The invention provides a lead channel self-adaptive independent fetal electrocardiogram extraction method and a training method and system thereof, and the method comprises the following steps: carrying out the coding processing of a multi-lead mixed electrocardiogram signal collected from the abdominal wall of a parent body, and converting each channel signal into a high-dimensional feature representation; evaluating the high-dimensional feature representation, and endowing each feature with confidence through a space-time attention mechanism and uncertainty perception; weighting and fusing the features of the different lead channels based on confidence to generate fused feature representation; the fused feature representation is decoded as an independent fetal electrocardiogram signal. The problems of weak signals, serious interference, defect of collected signals and the like are effectively solved, the accuracy and robustness of fetal electrocardiogram extraction are remarkably improved, and a new technical means is provided for early clinical diagnosis of fetal arrhythmia.

A method of sensing the heart rate and blood oxygen saturation of a maternal patient and a fetal patient can include obtaining maternal electrocardiogram (mECG) data and fetal electrocardiogram (fECG) data with a plurality of electrodes. The method can also include obtaining photosignal data with an optical sensor, the photosignal data including a maternal photosignal component and a fetal photosignal component. Additionally, the method can include applying mECG to the photosignal data to calculate maternal photoplesthograph (mPPG) data, applying the mPPG to the photosignal data to remove the maternal photosignal component from the photosignal data, and applying the fECG to remaining photosignal data to calculate fetal photoplesthograph (fPPG) data.

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 present invention discloses a fetal electrocardiogram monitoring method and system based on a marked interference signal. After a mixed maternal-fetal electrocardiogram signal is collected, the mixed maternal-fetal electrocardiogram signal is filtered and segmented, and then it is detected whether there are first to fourth category abnormalities. R wave recognition is performed on the segmented mixed maternal-fetal electrocardiogram signal to detect whether there is a fifth category abnormality. After a target signal containing a fetal heart rate signal is extracted, a fetal heart rate curve is drawn according to the R wave recognition result of the target signal. According to the first to fifth category abnormality detection results, the data segment marked as abnormal is not displayed in the fetal heart rate curve, or the data segment marked as abnormal is displayed in the fetal heart rate curve in a visual manner of different colors or different transparency color blocks, so that users can be prevented from clinically interpreting unreliable monitoring results, thereby improving the accuracy and reliability of fetal electrocardiogram monitoring.

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.