2605 results about "Intracardiac Electrogram" patented technology

An electrocardiogram device configured to transmit at least one signal responsive to a wearer's cardiac electrical activity can include a disposable portion and a reusable portion configured to mechanically and electrically mate with each other. The disposable portion can include a base having at least one mechanical connector portion, a plurality of cables and corresponding external ECG electrodes, and a first plurality of electrical connectors associated with the plurality of cables. The reusable portion can include a cover having at least one mechanical connector portion, a second plurality of electrical connectors configured to electrically connect with the first plurality of electrical connectors of the disposable portion, and an output connector port configured to transmit at least one signal responsive to one or more signals outputted by the external ECG electrodes of the disposable portion.

The invention relates to the technical field of sleep aiding, in particular to a method and a system for extracting feature information of a sleep state monitoring model. The method comprises the following steps: carrying out multi-mode physiological signal acquisition on a detected object to obtain original sleep monitoring data; performing noise removal and signal enhancement processing on the original sleep monitoring data, and performing signal segmentation to obtain multi-channel sleep signal fragment data; performing feature extraction based on electroencephalogram, eye movement diagram, electromyogram, electrocardiogram and accelerometer signals on the multi-channel sleep signal fragment data to obtain multi-dimensional feature data; and performing ultrasonic feature and acoustic feature extraction on the multichannel sleep signal fragment data to generate acoustic feature data. According to the method, dynamic modeling and trend prediction are carried out on the conversion process of the sleep state, potential sleep disorder events can be accurately recognized, influence evaluation is carried out on the potential sleep disorder events through deep learning, and support is provided for personalized sleep diagnosis.

The invention discloses a multi-label electrocardiogram classification method based on self-supervised pre-training and multi-modal semantic alignment, which belongs to the technical field of artificial intelligence, and comprises the following steps: realizing self-supervised pre-training of unlabeled data through a single-modal contrast enhancement network, generating global and local contrast views by adopting a multi-scale random cutting strategy, and classifying the global and local contrast views in a multi-scale random cutting mode; in combination with a teacher-student network architecture, the potential invariance features of the ECG signals are learned while negative sample dependence is avoided, the problem of annotation data scarcity is effectively relieved, and the feature robustness is improved. A multi-modal fusion mechanism based on label semantic guidance is provided, a time domain signal and a frequency domain time-frequency graph are mapped to a unified semantic space through fine-grained semantic alignment, local feature enhancement and cross-modal complementary information fusion are realized by using a cross attention mechanism, and the problem of semantic difference caused by modal heterogeneity in a traditional method is overcome. A multi-label comparison loss function based on a disease co-occurrence relation is proposed, a category discrimination boundary is dynamically optimized by modeling a label co-occurrence probability, the feature separability of a tail category is improved while the head category discrimination ability is enhanced, and the problem of sample category imbalance in a multi-label scene is remarkably relieved.

The invention relates to the technical field of health monitoring, and discloses a patient health monitoring method and system based on dynamic electrocardiogram analysis, and the method comprises the steps: synchronously obtaining multi-lead dynamic electrocardiogram data continuously collected by a patient within a preset duration, and related physiological parameters of exercise intensity, respiratory rate and body position change; performing dynamic self-adaptive preprocessing on the dynamic electrocardiogram data based on the associated physiological parameters to obtain standardized electrocardiosignals; extracting a multi-dimensional characteristic parameter set from the standardized electrocardiosignal, and sampling according to a preset time window to generate a characteristic parameter sequence with a timestamp; and inputting the characteristic parameter sequence into a dynamic optimization analysis model capable of iteratively updating parameters through real-time physiological parameter feedback, and performing graded evaluation on the health state of the patient to generate an evaluation result. The system corresponds to the method. By adopting the method and the system, the accuracy of dynamic electrocardiogram monitoring and the evaluation refinement level meet the dynamic monitoring requirement of cardiovascular health.

The invention discloses an electrocardiogram classification system and method fusing multi-scale self-adaptive attention, and relates to the technical field of electrocardiogram classification.The electrocardiogram classification method comprises the steps that electrocardiogram data are collected through a data collection module, and a structured data set is output; the data preprocessing module is used for carrying out data preprocessing on the structured data set; the window data segmentation module obtains a standardized windowed electrocardiosignal; the feature extraction module takes the standardized windowed electrocardiosignals as input, performs three stages of multi-scale convolution feature extraction, liquid neural network dynamic modeling and self-adaptive attention mechanism enhancement, and outputs diagnosis results of nine types of heart diseases; and the training and optimizing module is used for optimizing the extraction and classification capability of the model on the electrocardiosignal features, so that the technical effects of full-process technical upgrading from data acquisition, preprocessing, feature extraction to model training optimization and high-precision electrocardiogram automatic classification are achieved.

The invention discloses an anesthesia depth monitoring system and method based on multiple physiological parameters, and the system comprises a data collection unit which is used for obtaining a perfusion index value, an electrocardiogram signal, a brain wave signal, a blood pressure signal of a patient and the individual feature information of the patient; the signal processing unit is electrically connected to the data acquisition unit and used for processing the signals acquired by the data acquisition unit and calculating the perfusion index change rate, the heart rate variability, the anesthesia consciousness index and the blood pressure fluctuation rate; the parameter fusion unit is electrically connected to the signal processing unit and used for combining the parameters calculated by the signal processing unit with the individual feature information of the patient; the individualized correction unit is electrically connected to the parameter fusion unit and is used for adjusting the comprehensive anesthesia depth score generated by the parameter fusion unit; the method has the advantages that multi-dimensional physiological data and individual characteristics can be integrated, precise evaluation and individualized regulation and control of the anesthesia depth are realized, and the anesthesia safety is improved.

The invention provides an electrocardiogram data anomaly recognition method, device and equipment based on deep learning and a storage medium. An original electrocardiogram signal of a patient is collected, and a target electrocardiogram signal is obtained through multi-stage preprocessing. And identifying an abnormal signal in the target electrocardiogram signal by using a deep fusion learning model, and generating a preliminary identification result. And then, the preliminary recognition result is combined with a pre-trained machine learning model, mixed model processing is performed through a stack generalization fusion method, a Bayesian network is constructed based on the detailed information of the patient, a reasoning result with context sensing is obtained, and a target recognition result is finally generated. And generating an abnormal report according to the target identification result, and storing and displaying the abnormal report to the user in a classified manner according to a predefined standard. According to the method, high precision and personalized diagnosis of anomaly detection are ensured, and the reliability and efficiency of electrocardiogram analysis are improved.

The invention provides an intensive care method and system based on intelligent analysis, and the method comprises the steps: collecting multiple physiological data, including electrocardiogram, blood pressure and the like, of a critical patient in real time through a sensor network; cleaning and standardizing the data; calculating the weight of each physiological data by using a dynamic weight evaluation formula; predicting an abnormal trend; calculating a risk score of the patient by using a comprehensive risk assessment model according to the weight and the abnormal trend; when the score exceeds a threshold value, the system automatically gives an early warning and sends information to the medical staff, and provides preliminary treatment suggestions at the same time. The method combines a traditional intensive care technology with a modern sensor network and a data processing technology, and aims to improve the treatment efficiency and level of critical patients.

The invention provides an anesthesia depth monitoring method, a medium and an electronic device, and belongs to the technical field of anesthesia depth monitoring, and the anesthesia depth monitoring method comprises the following steps: collecting an electroencephalogram signal, an electrocardiogram signal and a respiratory activity signal of a patient, and carrying out preprocessing, grouping and feature extraction to realize anesthesia depth monitoring. The method comprises the following steps: firstly, segmenting a signal, calculating the similarity, and classifying subsequences into stable and fluctuating subsequences; and then, determining a step length according to the alternative interval, processing stable and fluctuating subsequences, and meanwhile, filtering, reducing noise and recording an abnormal waveform segment. Then, the signals and the abnormal waveform segments are processed in parallel on the GPU and the CPU, and features are extracted; and finally, through clustering and dimension reduction optimization features and in combination with abnormal waveform segment features, obtaining final anesthesia depth features, and inputting the anesthesia depth features into a monitoring model to obtain a monitoring result. The technical problem that the anesthesia depth monitoring result is not accurate enough due to the fact that interference or small fluctuation is often difficult to process in an existing anesthesia depth monitoring technology is solved.

A blood pump system includes a catheter, a pump housing disposed distal of a distal end of the catheter, a rotor positioned at least partially in the pump housing, a controller, and an electrode coupled a distal region of the blood pump. The electrode can be used to sense electrocardiogram (EKG) signals and transmit the signals to a controller of the blood pump. The operation of the blood pump can be adjusted based on the EKG signal and on cardiac parameters derived from the EKG signal. Further, the controller can determine a need for defibrillation or pacing of the patient's heart based on the signal and can administer treatment with electrical shocks to the heart via the electrode coupled to the blood pump. The use of an electrode with a blood pump already in place in the heart allows for more efficient and safer treatment of serious cardiac conditions.

An electronic device, such as a watch, has a crown assembly having a shaft and a user-rotatable crown. The user-rotatable crown may include a conductive cap that is mechanically and electrically coupled to the shaft and functions as an electrode. The conductive cap may be coupled to the shaft using solder or another conductive attachment mechanism. The shaft may electrically couple the conductive cap to a processing unit of the electronic device. One or more additional electrodes may be positioned on the exterior surface of the electronic device. The conductive cap is operable to be contacted by a finger of a user of the electronic device while another electrode is positioned against skin of the user. The processing unit of the electronic device is operable to determine a biological parameter, such as an electrocardiogram, of the user based on voltages at the electrodes.

The invention provides a catheter tip accurate positioning method and system based on electrocardiogram guidance. The method is applied to a control module of a catheter tip accurate positioning device. The method comprises the following steps: acquiring a standard electrocardiogram corresponding to a to-be-positioned patient, and constructing P-wave baseline features of the patient according to the standard electrocardiogram; when the catheter is inserted into a preset area of a patient, electrocardiogram detection information sent by an electrocardiogram sensing module of the device is obtained and analyzed, and waveform information of P waves is obtained; obtaining first change information and second change information according to the P-wave baseline characteristics and the waveform information so as to obtain first position information corresponding to the catheter; if it is confirmed that the P wave is obviously increased when the catheter is inserted according to the P wave variability information and it is confirmed that the distance between the catheter and the right atrium is smaller than the preset distance according to the first position information, pushing of the catheter is stopped, and accurate positioning of the catheter tip is completed. The accuracy of catheterization is improved through electrocardiogram guidance, and the safety and accuracy of overall operation are improved.

Artificial intelligence enabled disease profiling is described. An electrocardiogram analysis module is configured to derive disease vectors for a plurality of diseases using electrocardiogram training data from both disease-negative and disease-positive individuals. A standardized input is generated, via a data preprocessor of the electrocardiogram analysis module, from an electrocardiogram recorded from an individual. The standardized input is encoded, by a deep learning autoencoder of the electrocardiogram analysis module, into an embedding, the embedding being a lower-dimensional latent space representation of features extracted from the standardized input. At least one disease risk score for the individual is generated, by a statistical modeling algorithm of the electrocardiogram analysis module, for the plurality of diseases based on the embedding and the disease vectors.

The invention discloses a dynamic electrocardiogram early warning system based on artificial intelligence, which relates to the technical field of medical health monitoring, and comprises a model building module, a threshold setting module, an early warning threshold setting module based on the baseline model, a threshold adjusting module, and a dynamic electrocardiogram early warning module based on the real-time activity state of the user. And dynamically adjusting the early warning threshold. By dynamically adjusting the early warning threshold value, daily activity changes of the user can be better adapted, unnecessary false alarm and missing alarm are reduced, the early warning reliability and the user experience are improved, in addition, a dynamic threshold value adjusting mechanism can also capture the influence of user emotion fluctuation on physiological indexes, and the user experience is improved for high-intensity motion and static states. The early warning threshold can be relaxed and tightened respectively, the sensitivity and accuracy of early warning are ensured, and the intelligent level of early warning monitoring is further improved.