16 results about "Electric impedance tomography" patented technology

The present invention describes an artificial intelligence (AI) enabled electroencephalography (EEG) system that integrates Pathway Hierarchical Adaptive Referencing (PHAR) for localized signal detection, large language models (LLMs) for automated EEG reporting, and Internet of Medical Things (IoMT) connectivity for adaptive neuromodulation control. The system can also deliver transcranial electrical stimulation (tES) pulses and function as an electrical impedance tomography (EIT) system. PHAR employs a multi-layered multiplexer hierarchy and adaptive referencing topologies to optimize EEG signal acquisition and spatial resolution. LLM integration enables automated generation of human-readable EEG reports. IoMT connectivity allows closed-loop neuromodulation, where real-time EEG analysis guides the adjustment of stimulation parameters. The system can deliver tES pulses and perform EIT expands its functionality, allowing for targeted neuromodulation and impedance-based brain imaging. This integrated system revolutionizes EEG-based diagnostics, treatment, and research in neurology and neuroscience, offering a comprehensive and versatile tool for understanding and modulating brain function.

The present invention relates to an operating method of a lung monitoring system operated by at least one processor, the operating method comprising the steps of: acquiring a plurality of electrical impedance tomography (EIT) images of the chest of a patient and a plurality of respiratory sound intensity images generated on the basis of respiratory sound signals acquired from the chest of the patient; mapping, on the basis of a plurality of pieces of respiratory interval information acquired by binning a respiratory signal of the patient, an EIT image and a respiratory sound intensity image corresponding to each respiratory interval to each other; and inputting, into a respiratory anomaly prediction model, at least one pair of the EIT image and the respiratory sound intensity image included in each respiratory interval, to acquire a prediction result for respiratory anomalies of the patient.

An AC current of multiple frequencies is setup in a body to be sensed, and amplitudes and phases of AC voltages at many electrodes attached to the body are detected simultaneously. At electrode measurement circuits, each of multiple modulating signals is mixed separately with the electrode signal using non-linear circuit components such as transistors or diodes. The output of this mixed signal is stored as a charge, in a capacitor or a semiconductor device (e.g. a CMOS transistor) that can store charge when in a certain state. Upon assertion of a readout signal, the charge is converted back into voltage and sent out on an analog voltage output line from which it is read using ADCs.

A method for reconstructing the distribution of the electrical properties of at least one material of a body including a cylindrical part containing a fluid, using a neural network and data on electrical values of the body measured beforehand by electrical impedance tomography using electrodes arranged around a periphery of the cylindrical part of the body, each electrode having been excited by a potential of predefined form. The neural network including an autoencoder having a plurality of levels (L1, . . . , LJ) each comprising at least one convolution layer and at least one dropout layer, and an attention module comprising attention gates (AG1, . . . , AGJ-1), each having an associated attention signal (gj).

Electrical impedance tomography (EIT) is a non-destructive and non-radioactive imaging technique used to detect anomalies in materials. Deep learning (DL)-based EIT reconstruction addresses the non-linear, ill-conditioned nature of EIT inverse problems and allows for the conductivity of materials to be reconstructed directly through neural networks (NNs), as opposed to iteratively with conventional inverse reconstruction algorithms. To advance the DL-based reconstruction for EIT, a neural network (NN) architecture is trained with a custom loss function that serves as a surrogate model for the compressed sensing-based EIT reconstruction algorithm. The NN is trained to mimic a compressed sensing algorithm that performs the EIT conductivity reconstruction. The resultant NN is able to accurately capture the electrical properties and characteristics of the sensing domain when trained with limited data of varying quality, and this improves upon other DL models trained with the traditional MSE loss function.

An electrical impedance tomography method for the measurement of a body comprising a cylindrical part containing a fluid, the method comprising arranging a number of electrodes around a periphery of the cylindrical part of the body, simultaneously exciting each of the electrodes, each electrode being excited by a potential of a selected form, measuring the electrical properties of the body using electrodes, and processing the data from measuring step so as to obtain a signed data matrix representative of an image.

An analytical instrument configured to determine an ACT number of a blood sample based at least in part on a sequence of tomograms generated from electrical impedance tomography measurements performed in a test vessel on a fluid sample produced by mixing the blood sample and an activator of coagulation. In some examples, the ACT number is determined by applying image processing to the sequence of tomograms to determine the intensity of a blood clot in the fluid sample as a function of time. In some other examples, the ACT number is determined from time-dependent impedance profiles of the fluid sample measured via different pairs of electrodes of the test vessel, with the sequence of tomograms being used as an aid to interpretation of possible differences between different ones of the time-dependent impedance profiles.

Systems and techniques for creating clinically useful emulations of EIT scans by using audio sensors to capture sounds from the thoracic cavity, particularly but not solely lung sounds, where a neural network-based Audio Encoder is, in an embodiment, trained by simultaneously performing both an EIT scan and an audio recording of a cohort of test patients. The EIT scans are passed through an image encoder / decoder pair, each of which can also be neural network-based, and the image encoder creates an embedding representative of the EIT scan. The frequency characteristics of the audio signals are captured as an intermediate representation and supplied to the audio encoder which is trained to map an embedding of the converted audio to the embedding of the EIT scan from the image encoder. At run time audio of a patient is processed through the frequency conversion and supplied to the now-trained Audio Encoder to generate an embedding. The embedding is supplied to the image decoder which produces the EIT emulation for review by a clinician.

Electrical Impedance Tomography Measurement Method. The invention relates to a method for reconstructing the distribution of electrical properties of at least one material in a body comprising a cylindrical portion containing a fluid, using a neural network and electrical value data of the body previously measured by electrical impedance tomography using electrodes arranged around a periphery of the cylindrical portion of the body, each electrode having been excited by a potential of predefined shape. The neural network comprises: an autoencoder comprising several levels (L1,…,LJ), each having at least one convolution layer and at least one dropout layer, and an attention module comprising attention gates (AG1,…, AGJ-1), each having an associated attention signal (gj). Figure for the abstract: Fig. 2

A system for non-invasively detecting changes in human tissues, the system comprising: an Electrical Impedance Tomography (EIT) sensor capable of acquiring readings associated with a group of human tissues of a patient; and a processing resource configured to: obtain reference state information based on a reference EIT reading obtained at a past time, the reference state information being indicative of a reference state of the group of human tissues; acquire, using the EIT sensor, a current reading; analyze the current EIT reading to determine current state information indicative of a current state of the group of human tissues; compare the reference state information with the current state information to determine a change between the reference state of the group of human tissues and the current state of the group of human tissues; and provide output indicative of the change, or lack thereof, to a user of the system.

Computing device and method using machine learning for pulmonary artery pressure measurement based on electrical impedance tomography. The following steps are repeated n times: (i) inject an alternating electrical current between a pair of electrodes, the pair of electrodes being located on a belt, the belt being positioned around the chest of a person; (ii) determine corresponding voltage values between m pairs of electrodes located on the belt. The determined n*m voltage values are transmitted to the computing device. The computing device executes a machine learning engine, the machine learning engine using a predictive model stored at the computing device for inferring outputs based on inputs. The inputs comprise the determined n*m voltage values. The outputs comprise a matrix of values representative of at least one of: lung perfusion, pulse transit time values and pulmonary artery pressure values.

A device (10) processes and visualizes EIT data (3) of at least one region of the lungs to determine and visualize ventilation delays in the lungs of a living being. The EIT data (3) are obtained from an electrical impedance tomography apparatus (30). The device makes it possible to visualize regional ventilation delays of the lungs or of regions of the lungs in which the delay exceeds a predefined duration (76) in a joint image (900).

An EIT system includes an EIT device configured to capture impedance images of a patient, a display configured to display information to a user, and a memory device. The EIT system is configured to generate a ventilation map of lungs of the patient. The EIT system is also configured to generate a perfusion map of the lungs of the patient. The EIT system is further configured to compare ventilation pixel values of the ventilation map to respective perfusion pixel values of the perfusion map and generate a V̇ / Q̇ map. The pixels in the V̇ / Q̇ map are separated into at least three groups including mostly ventilated pixels, mostly perfused pixels, and balanced pixels. The V̇ / Q̇ map is produced on the display where each of the mostly ventilated pixels, the mostly perfused pixels, and the balanced pixels are graphically distinguished.

A wearable neural stimulation and sensing device includes a headband worn on a head. The surface of the headband has electrode mounting recesses that can flexibly mount a stimulation electrode and a sensing electrode. The stimulation electrode includes a shallow stimulation electrode and a deep stimulation electrode that can respectively perform trigeminal nerve stimulation (TNS) and temporal interference brain stimulation (TIBS) to implement non-invasive electrical stimulation for shallow and deep brain regions. The sensing electrode, used for electrical impedance tomography, monitors the physiological data of brain activity in real time. A control unit adjusts the electrical stimulation parameter of the stimulation electrode and the measurement parameter of the sensing electrode. A processing unit processes the obtained physiological data. The present invention features precise stimulation, compact size, and portability, making it suitable for the treatment and monitoring of neurodegenerative diseases, thereby improving the quality of life of patients.

The electrical impedance tomography EIT comprises a host facilitating installation of other electric devices, a supporting seat fixedly connected to the upper end face of the host, a display fixedly connected to the upper end of the supporting seat, a lead wire bundle connected to one side of the host, and a scanning assembly connected to the end, away from the host, of the lead wire bundle. A buzzer is fixedly connected to the end face of one side of the host, a warning lamp is fixedly connected to the end face of the upper portion of the host, and an AI data analysis module is embedded in the host. Compared with the prior art, the EIT electrode disposable bandage has the advantages that the ultrasonic probe is integrated in the EIT electrode disposable bandage, tissue movement information is synchronously obtained, the problem that the EIT is fuzzy in soft tissue boundary is solved, the identification capacity of diseases such as pleural effusion and pulmonary ulceration is improved, meanwhile, the AI data analysis module is embedded in the host, bedside real-time analysis is achieved, and the diagnosis accuracy is improved. Emergency situations of pneumothorax and pulmonary atelectasis are automatically identified and an alarm is triggered; historical data of patients can be stored, and the evolution trend of illness conditions can be tracked.

The application relates to a sports rehabilitation monitoring system, which comprises a wearable electrical impedance tomography device and a state feedback device, the wearable electrical impedance tomography device comprising a data processing unit and at least two electrode arrays; the at least two electrode arrays are electrically connected with the data processing unit; the at least two electrode arrays are respectively arranged at two ends of a target part of a subject, and are used for acquiring real-time physical sign data of the target part of the subject; the data processing unit sends the physical sign data to the state feedback device in real time; and the state feedback device feeds back muscle information of the target part of the subject in real time based on the physical sign data. The application provides a muscle movement state monitoring mode with less limitation and a sports rehabilitation monitoring system. The muscle movement state monitoring mode in the current sports rehabilitation treatment has the problem of great use limitation.