121 results about "Cardiac rhythms" patented technology

The invention discloses a multi-module collaborative rapid electrocardiogram automatic diagnosis method and system, and belongs to the technical field of automatic diagnosis. The method comprises the following steps: preprocessing an original ECG signal; the preprocessed ECG signals serve as input, and after the preprocessed ECG signals are sequentially processed by a lightweight convolution sub-module, a deformation convolution sub-module and a non-local attention sub-module, multi-level features containing local details and global time sequence dependency are output; fusing the multi-level features, and sending the fused multi-level features into a classifier to complete prediction of heart rhythm types to obtain prediction probabilities of all categories; a sliding window mode is adopted, the ECG signal fragments with the fixed length are sent to the diagnosis model for reasoning, the diagnosis model continuously outputs diagnosis results, and alarm or diagnosis prompt information is output in time according to the diagnosis results. According to the method, by designing a lightweight network structure, the parameter quantity and calculation complexity of a diagnosis model are effectively reduced, and real-time online ECG signal automatic diagnosis is realized on the premise of ensuring high diagnosis accuracy.

The invention relates to the technical field of physiological parameter monitoring, in particular to a construction method of a cardiology rehabilitation evaluation model, which comprises the following steps: monitoring a systolic pressure peak value, a diastolic pressure valley value and duration time of a postoperative patient, constructing a waveform parameter set, calculating a peak sequence amplitude change rate through a moving average method, and calculating a peak sequence amplitude change rate. Marking a form abnormal period and a structure drift region; dividing an active phase and a passive phase to extract wave crest characteristics; analyzing time sequence difference to generate a three-dimensional rhythm vector; constructing a regression model; according to the method, heart pressure change is continuously monitored, a waveform structure is identified, compression period change is dynamically captured, a structure drift area is determined and positioned by combining an amplitude change rate and a difference value, multi-dimensional heart rhythm characteristics are extracted, day and night differences are analyzed, and multi-day deviation values are integrated to generate a scoring system; the abnormal recognition, rhythm quantification and trend prediction capabilities are improved, and a high-adaptability quantification basis is provided for rehabilitation evaluation.

The disclosed systems and methods provide systems and methods for rhythm recognition and the analysis of ECG signals.

The invention discloses an electrocardiogram waveform real-time anomaly detection and early warning evaluation method and system, and the method comprises the following steps: S1, collecting the signal data of an electrocardiogram, and carrying out the preprocessing; s2, inputting the electrocardiogram segment sequence into an improved convolutional neural network, and performing feature extraction and aggregation; s3, inputting the fusion feature sequence into a graph attention neural network, and extracting a relation between waveform local dependence and fragment state transition; s4, inputting the global feature map into an anomaly evaluation network, and outputting an anomaly judgment result and a confidence score sequence; s5, generating a risk level tag sequence in combination with the individual parameters; s6, inputting the risk level label sequence into an early warning decision network, and generating a multi-level response label and an alarm trigger signal; and S7, controlling an early warning interface to execute alarm, and synchronously writing related data. According to the invention, real-time anomaly detection and multi-stage early warning response of the electrocardiogram waveform are realized, and the timeliness and reliability of abnormal heart rhythm recognition are improved.

Neurophysiologic assessment system includes an ingestion data hub for generating clean data from heart rhythm data, a neuroscience processing unit for generating primary metrics from the clean data, a contextual analysis unit for receiving and analyzing external contextual data, a behavior analysis unit for generating secondary metrics from the clean data, primary metrics, and external contextual data, and a workflow management unit for controlling the ingestion data hub, and neuroscience processing, contextual analysis, and behavior analysis units. Secondary metrics include at least one of emotional wellbeing, energy, mood, and peak immersion value for a given time frame. The external contextual data include at least one of location, proximity of related persons, time of day, day of a week, weather conditions, traffic conditions, measured activity levels, calendar events, biofeedback, survey results and information provided by the participant, tracked usage of software applications, engagement with social media, and sleep patterns.

The invention discloses an intelligent arrhythmia classification device based on deep learning. The intelligent arrhythmia classification device comprises a data set construction module, a data preprocessing module, an intelligent arrhythmia classification network construction training module and an analysis module, the data set construction module is used for constructing a heart rhythm sample data set, the data preprocessing module is used for expanding the data set by applying a wavelet denoising method and randomly sampling the heart beat type data set with more data distribution by utilizing an undersampling principle, and the arrhythmia intelligent classification network construction training module is used for constructing an arrhythmia intelligent classification network based on a DenseNet-201 network. And carrying out model training by adopting an Adam optimizer and a learning rate attenuation strategy, and classifying the arrhythmia data set. And the analysis module explains a model classification result by utilizing a plurality of methods such as HiResCAM, LIME and SHAP. According to the method, the electrocardiogram can be rapidly analyzed, and the basis for model type judgment can be visually displayed.

The invention discloses a thoracic cavity heart sound signal non-contact extraction system and method based on structured light. The system comprises a structured light projection module, an image acquisition module, an image processing and displacement extraction module and a signal processing and trigger point identification module, structured light is projected on the surface of the chest of a human body to form a regular pattern, and a high-speed image acquisition means is utilized to record micro displacement change generated by light spots under cardiac drive. Thoracic cavity vibration information is extracted through an image processing technology, periodic feature points corresponding to a cardiac rhythm are recognized in combination with a time sequence analysis strategy, a thoracic cavity heart sound signal is extracted, rhythm analysis is conducted, and a cardiac trigger point is output to serve as a trigger signal for synchronous control of medical equipment. The technical problems of signal distortion, equipment interference, potential safety hazards, inconvenience in use and the like in a strong electromagnetic environment in a traditional contact mode are solved, and a high-precision synchronous trigger signal is provided for dynamic imaging.

The present invention relates to a smart mattress that primarily uses non-contact sensors to measure the user's physiological data. Through a non-contact sensing method, it is capable of detecting REM sleep stages, monitoring heart rhythm, and measuring vital information such as respiration, as well as recording and storing physiological data such as bed-leaving movements and sleeping position. By connecting to a mobile device or a computer via a control box, all the recorded information can be displayed as a screen output or uploaded to a cloud database for storage. This enables long-term measurement of the user's physiological data or real-time monitoring, thus contributing to targeted patient care.

The invention discloses an arrhythmia recognition method and system based on electrocardio waveform dynamic feature reconstruction, and the method comprises the steps: obtaining an electrocardiogram of a user, extracting the electrocardio potential of each cardiac blog in the electrocardiogram, and calculating the deformation rate vector of each cardiac blog according to the electrocardio potential; according to the deformation rate vector, calculating a heart rhythm transition value between the heart blogs as a heart rhythm transition fingerprint, calculating heart beat form difference energy between the heart blogs, and generating a form fluctuation index of each heart blog according to the heart beat form difference energy; and acquiring a change acceleration of the form fluctuation index between heart blogs, mapping the change acceleration, the form fluctuation index and the cardiac rhythm transition value into a discrete symbol, and inputting the discrete symbol into a diagnosis synthesizer to output an arrhythmia identification result.

The present disclosure provides methods, systems and devices for guiding, a treatment of a patient experiencing sudden cardiac arrest, by a responder and / or a mechanical CPR device, in accordance with a patient specific treatment protocol. This protocol conditionally implements a pulseless electrical activity protocol when a rhythm classification of an electrocardiogram of the patient is a non-shockable cardiac rhythm with electrical activity and a pulse indication is the absence of the pulse of the patient, or an asystole protocol when the rhythm classification of the electrocardiogram of the patient is a non-shockable cardiac rhythm without electrical activity, or a return of spontaneous circulation protocol when the rhythm classification of the electrocardiogram of the patient is the non-shockable cardiac rhythm with electrical activity and the pulse indication is the presence of the pulse of the patient, or a ventricular fibrillation / ventricular tachycardia protocol when the rhythm classification of the electrocardiogram of the patient is a shockable cardiac rhythm.

A self-applicable defibrillator is provided. The defibrillator includes four walls each affixed on one side of a bottom surface. A dividing layer is affixed to each of the four walls creating an electrode enclosure and a circuit enclosure configured to hold circuitry. Access to the circuitry is restricted by the bottom surface and dividing layer. A pair of pads are stored in the electrode enclosure to monitor cardiac rhythm of a user. Each of the pads are placed on the user, by the user, after the user experiences a cardiac precursor. A cover is configured to fit over the electrode enclosure on a side opposite the circuit enclosure, and the cover is opened for the user to access the pads for placement.

Disclosed are various examples and embodiments of systems, devices, components and methods configured to provide enhanced resolution electrographic flow (EGF) spatial maps of a patient's heart, and subsequently to detect at least one location or type of at least one source, rotational phenomenon, or slow electrical conduction zone or area associated with at least one cardiac rhythm disorder within such maps. Data acquired using intracardiac electrodes and / or body surface electrodes are used in conjunction with EGF techniques to generate an enhanced resolution map corresponding to a spatial map. The enhanced resolution map is configured to reveal on a monitor or display to a user the at least one location or type of the at least one source, rotational phenomenon, or slow electrical conduction zone or area associated with at least one cardiac rhythm disorder.

The invention relates to the technical field of medical big data, and provides an arrhythmia detection system based on a graph neural network, and the system comprises an obtaining module which is used for obtaining a plurality of pieces of electrocardiogram lead data; the embedding module is used for acquiring signal features by using a time feature extraction encoder and embedding the signal features to obtain a topological graph; the construction module is used for constructing a time decay matrix and a spatial knowledge guide matrix, and multiplying the time decay matrix, the spatial knowledge guide matrix and the adjacent matrix of the topological graph to obtain a related adjacent matrix of the topological graph; the updating module is used for updating the topological graph by using the related adjacent matrix to obtain a final topological graph, and performing feature extraction on the final topological graph by using a graph neural network to obtain overall feature representation of the final topological graph; and the arrhythmia detection module is used for carrying out arrhythmia detection based on the overall feature representation to obtain an arrhythmia detection result. The system can improve the accuracy of arrhythmia detection.

Described is an AED and method of use, comprising two pads to be placed on a patient, two electrodes on the two pads respectively, two angular motion sensors associated with the two pads respectively, a shock generation circuit to generate doses of defibrillation shocks, and a processor with memory and power supply, the processor configured to analyse angular motion signals from the two angular motion sensors to determine orientations of the two pads relative to one another, analyse electrocardiogram (ECG) signals from the two electrodes to determine shockable cardiac rhythms, automatically select between adult doses and paediatric doses of defibrillation shocks to be delivered by the two electrodes based, at least in part, on the orientations of the two pads.

A system for improving cardiac performance includes a delivery tube and an expandable implant sized for delivery within the delivery tube. The expandable implant is adjustable between an expanded form and a compressed form. In the expanded form, the expandable implant is sized and shaped to extend beyond the delivery tube for compressing a portion of a heart. The expandable implant may take the form of a balloon and may be inflated and deflated during use. A sensor is preferably provided for measuring cardiac rhythm, wherein the expandable implant moves in synchronization with the measured cardiac rhythm. The sensor is preferably coupled to the expandable implant. The delivery tube may be sized for advancement through a thoracic cavity.

A computer-implemented method and system are described. The method comprises the steps of: identifying a region having an electrical activation sequence within a chamber of the heart from an electrograph, the electrical activation sequence characterizing the region as a potential drive of an abnormal heart rhythm; for each sensing location at or substantially around the region, determining an earliest activation electrode site from a primary activation; for each determined earliest activated electrode site: calculating a value for each of a plurality of modifiers associated with the electrode site, the modifiers determined from electrogram data of the site, tissue characteristics of the site, or anatomical characteristics of the site; determining a sorting factor calculated according to the plurality of modifiers; sorting each of the earliest activated electrode sites according to a sorting factor; and outputting data identifying the region, the data changing a significance of each of the determined earliest activated electrode sites depending on the ranking.

The present application belongs to the technical field of intelligent medical equipment, and specifically relates to a cloud-edge collaborative electrocardio intelligent analysis device and method based on a multi-model pipeline. The problem of misjudgment under noise interference caused by the missing of signal quality evaluation links and the dependence on cloud end final determination due to weak autonomous decision-making ability of the edge end is solved. The device comprises an electrocardio signal acquisition unit, a signal quality evaluation unit containing a first special neural network model for real-time evaluation of original electrocardio signal quality, a preprocessing unit for filtering available signals, an analysis processing unit composed of a second and a third special neural network model in cascade, which sequentially performs multi-dimensional feature extraction and multi-label heart rhythm classification and outputs confidence, and a pre-warning decision unit which determines the pre-warning level according to preset rules in combination with the confidence and historical event statistics. According to the evaluation result and the pre-warning level, the data interaction content is dynamically decided, the desensitization health summary is uploaded in a regular state cycle, the encrypted original signal segment is uploaded when the trigger condition is met, and the cloud end model is updated.

A system for sensing multiple local electric voltages from endocardial surface of a heart, includes: an elongate tubular member having a lumen, a proximal end and a distal end; a plurality of flexible splines; an anchor for securably affixing the proximal portions of the splines, where the anchor is securably affixed within the lumen of the elongate tubular member at the distal end of the elongate tubular member; and a metallic tip for securably affixing the distal portions of the splines.

A method of operating a cardiac rhythm management (CRM) system includes recurrently calculating, by a medical device of the CRM system, impedances between a housing electrode included in a housing of the medical device and each of multiple pacing electrodes, wherein the multiple pacing electrodes include a left bundle branch pacing electrode configured for placement in a left bundle branch of a subject; comparing the calculated impedances to one or more specified impedance values; and producing an alert regarding placement of a pacing electrode in response to a calculated impedance corresponding to the pacing electrode differing from the one or more specified impedance values by a predetermined threshold impedance value.

Techniques are disclosed for monitoring a patient for the occurrence of cardiac arrhythmias. A computing system obtains a cardiac electrogram (EGM) strip for a current patient. Additionally, the computing system may apply a first cardiac rhythm classifier (CRC) with a segment of the cardiac EGM strip as input. The first CRC is trained on training cardiac EGM strips from a first population. The first CRC generates first data regarding an aspect of a cardiac rhythm of the current patient. The computing system may also apply a second CRC with the segment of the cardiac EGM strip as input. The second CRC is trained on training cardiac EGM strips from a smaller, second population. The second CRC generates second data regarding the aspect of the cardiac rhythm of the current patient. The computing system may generate output data based on the first and / or second data.

This invention belongs to the field of defibrillation technology and provides an adaptive synchronized defibrillator and defibrillation method for different abnormal heart rhythms, solving the current problem of not being able to achieve adaptive synchronized defibrillation for different abnormal heart rhythms. The system includes a control module, a charging circuit, an energy storage capacitor, a high-voltage detection circuit, a discharge circuit, and a wave detection circuit. The control module controls whether to activate the R-wave detection module, drive circuit, charging circuit, high-voltage detection circuit, and discharge circuit based on different ECG classification results. By comprehensively utilizing the advantages of software and hardware defibrillation, adaptive synchronized defibrillation can be achieved for various abnormal heart rhythms, exhibiting high efficiency, low latency, and high-quality defibrillation performance.

Techniques are disclosed for monitoring a patient for the occurrence of cardiac arrhythmias. A computing system obtains a cardiac electrogram (EGM) strip for a current patient. Additionally, the computing system may apply a first cardiac rhythm classifier (CRC) with a segment of the cardiac EGM strip as input. The first CRC is trained on training cardiac EGM strips from a first population. The first CRC generates first data regarding an aspect of a cardiac rhythm of the current patient. The computing system may also apply a second CRC with the segment of the cardiac EGM strip as input. The second CRC is trained on training cardiac EGM strips from a smaller, second population. The second CRC generates second data regarding the aspect of the cardiac rhythm of the current patient. The computing system may generate output data based on the first and / or second data.

Disclosed are various examples and embodiments of systems, devices, components and methods configured to estimate the action potential wave propagation in a patient's heart, and subsequently to detect at least one location or type of at least one source of, or rotational phenomenon associated with, at least one cardiac rhythm disorder using intracardiac electrodes and a modified multi-frame Horn-Schunck algorithm to generate a map corresponding to a spatial map, the map being configured to reveal on a monitor or display to a user the at least one location of the at least one source of the at least one cardiac rhythm disorder.

Systems and methods for biometric analysis are described. In some embodiments, a system may include a wearable device comprising a plurality of electrodes, a gesture control processing circuit, and an ECG processing circuit. A common electrode may be configured to provide signals to both the gesture control processing circuit and the ECG processing circuit. While the system is in a gesture control state, the system may be configured to obtain first biopotential signals using at least the common electrode and convert the first biopotential signals to first biopotential data for use in gesture control. While the system is in the ECG state, the system may be configured to obtain second biopotential signals using at least the common electrode and convert the second biopotential signals to ECG data for use in analyzing a user's heart rhythms.

Disclosed herein is a system for cardiac rhythm management including an implanted cardiac pacemaker and a biometric finger ring. This system ensures good oxygenation of a person's body extremities. The finger ring includes optical sensors which measure the person's blood oxygenation level. The operation of the implanted cardiac pacemaker is adjusted based on combined analysis of motion data from the pacemaker, motion data from the finger ring, and blood oxygenation level data from the biometric finger ring.

The invention relates to the technical field of medical instruments, in particular to a control method, device and equipment for defibrillation equipment and a medium, based on a preset cardiopulmonary resuscitation circulation mode, a ventilation stage in next cardiopulmonary resuscitation circulation is determined as a ventilation control window, and based on a window control instruction triggered by the ventilation control window, the defibrillation equipment is controlled. And executing the time limit heart rhythm task and the capacitor charging task, judging whether a preset discharging condition is met or not according to a monitored first completion state of the time limit heart rhythm task and a monitored second completion state of the capacitor charging task, and if the preset discharging condition is met, controlling the defibrillation equipment to perform electric shock within preset discharging time. Visibly, whether the preset discharge condition is met or not is determined according to the monitored first completion state of the time limit heart rhythm task and the monitored second completion state of the capacitor charging task, then the defibrillation device is controlled to conduct electric shock within the preset discharge time, accurate cooperative control over the whole cardio-pulmonary resuscitation process is achieved, and the cardio-pulmonary resuscitation efficiency is improved. The compression break time is compressed to a very low level.

Systems and methods are disclosed to evaluate patient response to cardiac rhythm management therapy and remotely reprogram an implantable medical device, including receiving physiologic information of a patient in a first time period responsive to a first cardiac rhythm management therapy, determining a first patient response metric indicative of patient response to the first cardiac rhythm management therapy, generating a reprogramming recommendation for the implantable medical device including a second cardiac rhythm management therapy based on the first patient response metric, and remotely reprogramming the implantable medical device to provide the second cardiac rhythm management therapy to the patient in a second time period.

The invention discloses a portable card type AED device which comprises a device body, the thickness of the device body is smaller than 5 mm, the weight of the device body is smaller than 70 g, and the device body comprises an outer layer, a middle layer, a core layer and a bottom layer which are sequentially laminated and integrated from top to bottom. A transparent flexible solar cell and a hydrophobic nano coating are integrated on the surface of the outer layer, the middle layer is an energy storage unit, and the core layer comprises a pair of pre-folded disposable electrocardio defibrillation electrode plates; a micro control module is integrated on the bottom layer to analyze heart rhythm types in real time and intelligently judge and trigger the high-voltage discharge circuit to execute defibrillation operation. Compared with the prior art, the portable card type AED device and the using method thereof have the advantages that the portable card type AED device is small in size and has the self-power-supply capacity.

A method of operating a cardiac rhythm management (CRM) system includes: cyclically calculating, by a medical device of the CRM system, an impedance between a housing electrode included in a housing of the medical device and each of a plurality of pacing electrodes, where the plurality of pacing electrodes includes a left bundle pacing electrode configured to be placed in a left bundle of a subject; comparing the calculated impedance with one or more specified impedance values; and generating an alert regarding pacing electrode placement in response to the calculated impedance corresponding to the pacing electrode differing from the one or more specified impedance values by a predetermined threshold impedance value.

The invention discloses an electrocardio data quality assessment method, device and equipment and a medium, and relates to the technical field of medical signal processing, and the method comprises the steps: obtaining to-be-assessed electrocardio sequence data; inputting the to-be-assessed electrocardiogram sequence data into a target electrocardiogram data quality assessment model so as to integrate the electrocardiogram signal information of each single time point in the to-be-assessed electrocardiogram sequence data through the target electrocardiogram data quality assessment model to obtain target dense semantic information used for reflecting the heart rhythm and the amplitude change trend; and performing context sensing processing on the target dense semantic information through the target electrocardiogram data quality evaluation model to obtain a corresponding target output sequence, and obtaining a quality evaluation result of the to-be-evaluated electrocardiogram sequence data according to a prediction probability distribution result of the target output sequence. The sparse information of the to-be-evaluated electrocardiogram sequence data is integrated into the dense semantic information through the model, so that the electrocardiogram signal data can be evaluated more efficiently.