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WO2024074874A1 - Détection d'anomalies dans des séries temporelles pour des soins de santé - Google Patents

Détection d'anomalies dans des séries temporelles pour des soins de santé Download PDF

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Publication number
WO2024074874A1
WO2024074874A1 PCT/IB2022/059579 IB2022059579W WO2024074874A1 WO 2024074874 A1 WO2024074874 A1 WO 2024074874A1 IB 2022059579 W IB2022059579 W IB 2022059579W WO 2024074874 A1 WO2024074874 A1 WO 2024074874A1
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Prior art keywords
computer
training
infant
machine learning
signal data
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English (en)
Inventor
Gabriel VARIANE
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Pbsf Inc
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Pbsf Inc
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Priority to PCT/IB2022/059579 priority Critical patent/WO2024074874A1/fr
Publication of WO2024074874A1 publication Critical patent/WO2024074874A1/fr
Priority to US19/169,807 priority patent/US20250228456A1/en
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0059—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence
    • A61B5/0075—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence by spectroscopy, i.e. measuring spectra, e.g. Raman spectroscopy, infrared absorption spectroscopy
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/369—Electroencephalography [EEG]
    • A61B5/372—Analysis of electroencephalograms
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/63—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/67—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients

Definitions

  • Detecting anomalies is one of the challenges that could provide meaningful insights for clinical medicine.
  • a unsupervised multivariate time-series anomaly detection may be used, which could be used for detecting anomalies on multiple signals.
  • multiple univariate time series from the same device (or more generally, an entity) forms a multivariate time series.
  • time-series anomaly detection for healthcare can be concluded as the following:
  • Multimodal patient data can be collected when separate monitoring devices are linked to a centrally placed server through a network.
  • the technique retains the benefit of synchronized timing and adds the use of primarily automatic capture, requiring minimum human input to start and stop the recording.
  • a multimodal approach can combine brain monitoring techniques with other vital signs and clinical information to study systemic and cerebral hemodynamics and electrographic findings early after birth, allowing a better understanding of the critically ill infant physiology.
  • time-series signals can be obtained much easier. Analyzing time-series signals to capture anomalies will be more convenient for diagnosing.
  • NE neonatal encephalopathy
  • IVH germinal matrix-intraventricular hemorrhage
  • WI white matter damage
  • the neonatal period is the most susceptible time of human life for seizure development. Seizures are commonly related to acute brain injury, are associated with increased mortality and impairment, and may constitute a neurological emergency, making monitoring and seizure accurate identification critical components of newborn intensive care management. Neonatal seizures occur in 1 to 3 per 1,000 live births, with substantially higher rates reported in premature neonates.
  • seizure detection in the newborn may be considered a challenge. About 80% of neonatal seizures are not correlated with any clinical signs. Moreover, clinically suspected episodes may not show corresponding electrographic evidence of seizures and may be wrongly diagnosed. Previous studies have shown that treating subclinical seizures is associated with reduced seizure burden and better neurological outcomes. Seizure overdiagnosis and treatment is potentially harmful to the developing brain as well.
  • Seizure activity is characterized in aEEG by a sudden change in background activity as an abrupt rise in minimum and maximum aEEG amplitudes correlated with a stereotypical, repeating form such as spikes or sharp waves, often with high amplitude in raw EEG, with a total duration of at least ten seconds.
  • FIG. 1 we demonstrate the neonatal seizure moment.
  • a Brain Monitoring and Neuroprotection strategies for infants at high risk on a large scale is implemented.
  • the system promotes longitudinal training and homogeneity of care by the use of standard internationally validated protocols.
  • the system uses technology and cloud computing to reach remote centers (nationally and internationally), granting specialized assistance and improving quality of care by reducing distances and breaking frontiers.
  • the system concentrates experience in order to analyze a large amount of data and use Al to create earlier diagnostic tools and new treatment algorithms.
  • the system may store EEG in a frequency of 200Hz (ca 17 million data points, daily, per baby), video fragments for the video EEG, and data from other vital signs such as heart rate, pulse oximetry, temperature, blood pressure and regional tissue oxygenation every 5 seconds.
  • the system may utilize multivariate time-series anomaly detection on neonatal seizures detection.
  • FIG. 2 illustrates how data has been transformed from multiple devices to the cloud service for training.
  • Three types of sources have been used for collecting signal: Nearinfrared spectroscopy (NIRS) Device, GE 0mni700 device and EEG. Signals collected cover EEG, heart rate, temperature, pulse oximetry and regional tissue oxygen saturation levels. Those collected signals can be sent to cloud service and then stored for training. Users can manage models on the cloud and export the model for local inference. In the local box the collected signals will be detected with the trained model. Alert will be sent once there're anomalies detected.
  • NIRS Nearinfrared spectroscopy
  • Seizure moment may be defined as a segment of values. This segment should have different pattern with normal segments. Usually, a real seizure could last more than 10 seconds, and it could be inaccurate if we use single timestamp value to judge seizures as normal behaviors like blinking also triggers spike values in EEG signal and a single timestamp within the seizure moment could be normal.
  • seizure detection we learn timeseries representation with contrastive learning and then build classifier with the sparse label. The learning framework could be seen in FIG. 3.
  • a wide variety of machine learning techniques may be used. Examples include different forms of supervised learning, unsupervised learning, and semi-supervised learning such as decision trees, support vector machines (SVMs), regression, Bayesian networks, and genetic algorithms. Deep learning techniques such as neural networks, including convolutional neural networks (CNN), recurrent neural networks (RNN) and long short-term memory networks (LSTM), transformers, attention models, generative adversial networks (GANs) may also be used. For example, various machine learning models may be used to predict whether an infant has an elevated risk of brain injury based on the time series of data of an infant.
  • CNN convolutional neural networks
  • RNN recurrent neural networks
  • LSTM long short-term memory networks
  • GANs generative adversial networks
  • various machine learning models may be used to predict whether an infant has an elevated risk of brain injury based on the time series of data of an infant.
  • the training techniques for a machine learning model may be supervised, semi-supervised, or unsupervised.
  • the machine learning models may be trained with a set of training samples that are labeled.
  • the training samples may be time series of data of various infants who were known to have brain injury or normal infants that serve as control samples.
  • the labels for each training sample may be binary or multi-class. In binary label, the label may be whether an infant had and did not have brain injury. In multi-class labels, labels may be used to indicate which type of brain injury may be resulted.
  • the training samples may be data of other infants.
  • an unsupervised learning technique may be used.
  • the samples used in training are not labeled.
  • Various unsupervised learning technique such as clustering may be used.
  • the training may be semi-supervised with training set having a mix of labeled samples and unlabeled samples.
  • a machine learning model may be associated with an objective function, which generates a metric value that describes the objective goal of the training process.
  • the training may intend to reduce the error rate of the model in predicting whether the infants in the training samples had brain injury.
  • the objective function may monitor the error rate of the machine learning model.
  • Such an objective function may be called a loss function.
  • Other forms of objective functions may also be used, particularly for unsupervised learning models whose error rates are not easily determined due to the lack of labels.
  • the objective function may correspond to the difference between the model’s predicted outcomes and the manually recorded outcomes in the training sets.
  • the error rate may be measured as cross-entropy loss, LI loss (e.g., the sum of absolute differences between the predicted values and the actual value), L2 loss (e.g., the sum of squared distances).
  • LI loss e.g., the sum of absolute differences between the predicted values and the actual value
  • L2 loss e.g., the sum of squared distances.
  • a convolutional layer convolves the input of the layer (e.g., one or more time series) with one or more kernels to generate different types of images that are filtered by the kernels to generate feature maps.
  • Each convolution result may be associated with an activation function.
  • a pair of convolutional layer may be followed by a recurrent layer that includes one or more feedback loop.
  • the feedback may be used to account for spatial relationships of the features in text or temporal relationships of objects.
  • the layers and may be followed in multiple fully connected layers that have nodes connected to each other.
  • the fully connected layers may be used for classification and object detection.
  • one or more custom layers may also be presented for the generation of a specific format of output.
  • Recurrent layers may be used to analyze the temporal relationships of the time series of data.
  • a neural network 300 includes one or more layers 302, 304, and 306, but may or may not include any pooling layer or recurrent layer. If a pooling layer is present, not all convolutional layers are always followed by a pooling layer. A recurrent layer may also be positioned differently at other locations of the CNN. For each convolutional layer, the sizes of kernels (e.g., 3x3, 5x5, 7x7, etc.) and the numbers of kernels allowed to be learned may be different from other convolutional layers.
  • a machine learning model may include certain layers, nodes, kernels and/or coefficients.
  • Training of a neural network may include forward propagation and backpropagation.
  • Each layer in a neural network may include one or more nodes, which may be fully or partially connected to other nodes in adjacent layers. In forward propagation, the neural network performs the computation in the forward direction based on outputs of a preceding layer.
  • the operation of a node may be defined by one or more functions.
  • the functions that define the operation of a node may include various computation operations such as convolution of data with one or more kernels, pooling, recurrent loop in RNN, various gates in LSTM, etc.
  • the functions may also include an activation function that adjusts the weight of the output of the node. Nodes in different layers may be associated with different functions.
  • Each of the functions in the neural network may be associated with different coefficients (e.g. weights and kernel coefficients) that are adjustable during training.
  • some of the nodes in a neural network may also be associated with an activation function that decides the weight of the output of the node in forward propagation.
  • Common activation functions may include step functions, linear functions, sigmoid functions, hyperbolic tangent functions (tanh), and rectified linear unit functions (ReLU).
  • Training may be completed when the objective function has become sufficiently stable (e.g., the machine learning model has converged) or after a predetermined number of rounds for a particular set of training samples.
  • the trained machine learning model can be used for performing prediction or another suitable task for which the model is trained.
  • FIG. 4 is a block diagram illustrating components of an example computing machine that is capable of reading instructions from a computer-readable medium and execute them in a processor.
  • a computer described herein may include a single computing machine shown in FIG. 4, a virtual machine, a distributed computing system that includes multiples nodes of computing machines shown in FIG. 4, or any other suitable arrangement of computing devices.
  • FIG. 4 shows a diagrammatic representation of a computing machine in the example form of a computer system 400 within which instructions 424 (e.g., software, program code, or machine code), which may be stored in a computer- readable medium for causing the machine to perform any one or more of the processes discussed herein may be executed.
  • the computing machine operates as a standalone device or may be connected (e.g., networked) to other machines. In a networked deployment, the machine may operate in the capacity of a server machine or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment.
  • the structure of a computing machine described in FIG. 4 may correspond to any software, hardware, or combined components shown in FIG. 1, including but not limited to, the user device 110, the application publisher server 120, the access control server 130, a node of a blockchain network, and various engines, modules interfaces, terminals, and machines in various figures. While FIG. 4 shows various hardware and software elements, each of the components described in FIG. 1 may include additional or fewer elements.
  • a computing machine may be a personal computer (PC), a tablet PC, a set-top box (STB), a personal digital assistant (PDA), a cellular telephone, a smartphone, a web appliance, a network router, an internet of things (loT) device, a switch or bridge, or any machine capable of executing instructions 424 that specify actions to be taken by that machine.
  • PC personal computer
  • PDA personal digital assistant
  • STB set-top box
  • a cellular telephone a smartphone
  • web appliance a web appliance
  • network router an internet of things (loT) device
  • switch or bridge or any machine capable of executing instructions 424 that specify actions to be taken by that machine.
  • machine shall also be taken to include any collection of machines that individually or jointly execute instructions 424 to perform any one or more of the methodologies discussed herein.
  • the example computer system 400 includes one or more processors (generally, processor 402) (e.g., a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), one or more application-specific integrated circuits (ASICs), one or more radio-frequency integrated circuits (RFICs), or any combination of these), a main memory 404, and a static memory 406, which are configured to communicate with each other via a bus 408.
  • the computer system 400 may further include graphics display unit 410 (e.g., a plasma display panel (PDP), a liquid crystal display (LCD), a projector, or a cathode ray tube (CRT)).
  • processors generally, processor 402
  • processors e.g., a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), one or more application-specific integrated circuits (ASICs), one or more radio-frequency integrated circuits (RFICs), or any combination of these
  • the computer system 400 may also include alphanumeric input device 412 (e.g., a keyboard), a cursor control device 414 (e.g., a mouse, a trackball, a joystick, a motion sensor, or other pointing instrument), a storage unit 416, a signal generation device 418 (e.g., a speaker), and a network interface device 420, which also are configured to communicate via the bus 408.
  • alphanumeric input device 412 e.g., a keyboard
  • a cursor control device 414 e.g., a mouse, a trackball, a joystick, a motion sensor, or other pointing instrument
  • storage unit 416 e.g., a disk drive, or other pointing instrument
  • signal generation device 418 e.g., a speaker
  • network interface device 420 which also are configured to communicate via the bus 408.
  • the storage unit 416 includes a computer-readable medium 422 on which is stored instructions 424 embodying any one or more of the methodologies or functions described herein.
  • the instructions 424 may also reside, completely or at least partially, within the main memory 404 or within the processor 402 (e.g., within a processor’s cache memory) during execution thereof by the computer system 400, the main memory 404 and the processor 402 also constituting computer-readable media.
  • the instructions 424 may be transmitted or received over a network 426 via the network interface device 420.
  • computer-readable medium 422 is shown in an example embodiment to be a single medium, the term “computer-readable medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, or associated caches and servers) able to store instructions (e.g., instructions 424).
  • the computer-readable medium may include any medium that is capable of storing instructions (e.g., instructions 424) for execution by the machine and that cause the machine to perform any one or more of the methodologies disclosed herein.
  • the computer-readable medium may include, but not be limited to, data repositories in the form of solid-state memories, optical media, and magnetic media.
  • the computer-readable medium does not include a transitory medium such as a signal or a carrier wave.
  • smart contract or other Web3 application
  • owners could add an interface to their applications to have control over the applications after being deployed to the blockchain.
  • the application publishers could also apply security technologies to control the applications in real-time. Since the interactions would be vetted and signed by the access control system before the interaction request reaches the application on the blockchain, the access control server can block and prevent malicious or unwanted actions.
  • Engines may constitute either software modules (e.g., code embodied on a computer-readable medium) or hardware modules.
  • a hardware engine is a tangible unit capable of performing certain operations and may be configured or arranged in a certain manner.
  • one or more computer systems e.g., a standalone, client or server computer system
  • one or more hardware engines of a computer system e.g., a processor or a group of processors
  • software e.g., an application or application portion
  • a hardware engine may be implemented mechanically or electronically.
  • a hardware engine may comprise dedicated circuitry or logic that is permanently configured (e.g., as a special-purpose processor, such as a field programmable gate array (FPGA) or an application-specific integrated circuit (ASIC)) to perform certain operations.
  • a hardware engine may also comprise programmable logic or circuitry (e.g., as encompassed within a general -purpose processor or another programmable processor) that is temporarily configured by software to perform certain operations. It will be appreciated that the decision to implement a hardware engine mechanically, in dedicated and permanently configured circuitry, or in temporarily configured circuitry (e.g., configured by software) may be driven by cost and time considerations.
  • processors e.g., processor 402 that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations.
  • processors may constitute processor-implemented engines that operate to perform one or more operations or functions.
  • the engines referred to herein may, in some example embodiments, comprise processor- implemented engines.
  • the performance of certain of the operations may be distributed among the one or more processors, not only residing within a single machine, but deployed across a number of machines.
  • the one or more processors or processor-implemented modules may be located in a single geographic location (e.g., within a home environment, an office environment, or a server farm). In other example embodiments, the one or more processors or processor-implemented modules may be distributed across a number of geographic locations.

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Abstract

Un dispositif informatique peut recevoir des données de signal biologique d'un nourrisson, les données de signal biologique étant mesurées par un ou plusieurs capteurs, notamment un dispositif de spectroscopie dans le proche infrarouge (NIRS), un dispositif d'électroencéphalogramme (EEG) et/ou un dispositif multiparamétrique. Un dispositif informatique peut générer une ou plusieurs séries temporelles des données de signal biologique. Un dispositif informatique peut entrer la ou les séries temporelles dans un modèle d'apprentissage automatique. Un dispositif informatique peut générer une prédiction par le modèle d'apprentissage automatique pour savoir si le nourrisson présente un risque élevé de lésion cérébrale.
PCT/IB2022/059579 2022-10-06 2022-10-06 Détection d'anomalies dans des séries temporelles pour des soins de santé Ceased WO2024074874A1 (fr)

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PCT/IB2022/059579 WO2024074874A1 (fr) 2022-10-06 2022-10-06 Détection d'anomalies dans des séries temporelles pour des soins de santé
US19/169,807 US20250228456A1 (en) 2022-10-06 2025-04-03 Time-Series Anomaly Detection for Healthcare

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PCT/IB2022/059579 WO2024074874A1 (fr) 2022-10-06 2022-10-06 Détection d'anomalies dans des séries temporelles pour des soins de santé

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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170196497A1 (en) * 2016-01-07 2017-07-13 The Trustees Of Dartmouth College System and method for identifying ictal states in a patient
US20190159675A1 (en) * 2016-04-13 2019-05-30 Rajib Sengupta Point-of-care tele monitoring device for neurological disorders and neurovascular diseases and system and method thereof
US20190246989A1 (en) * 2018-02-10 2019-08-15 The Governing Council Of The University Of Toronto System and method for classifying time series data for state identification
WO2021202661A1 (fr) * 2020-04-03 2021-10-07 The Children's Medical Center Corporation Systèmes et dispositifs informatiques configurés pour un apprentissage profond à partir de prévision de crise d'épilepsie non invasive de données de capteur et procédés associés
US20220139543A1 (en) * 2019-02-08 2022-05-05 Nanyang Technological University Method and system for seizure detection

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170196497A1 (en) * 2016-01-07 2017-07-13 The Trustees Of Dartmouth College System and method for identifying ictal states in a patient
US20190159675A1 (en) * 2016-04-13 2019-05-30 Rajib Sengupta Point-of-care tele monitoring device for neurological disorders and neurovascular diseases and system and method thereof
US20190246989A1 (en) * 2018-02-10 2019-08-15 The Governing Council Of The University Of Toronto System and method for classifying time series data for state identification
US20220139543A1 (en) * 2019-02-08 2022-05-05 Nanyang Technological University Method and system for seizure detection
WO2021202661A1 (fr) * 2020-04-03 2021-10-07 The Children's Medical Center Corporation Systèmes et dispositifs informatiques configurés pour un apprentissage profond à partir de prévision de crise d'épilepsie non invasive de données de capteur et procédés associés

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