TWI783343B - A channel information processing system for identifying neonatal epileptic seizures - Google Patents

Abstract

The present application discloses a channel information processing system for identifying neonatal epileptic seizures, which is used in an environment where neonates are combined with high-risk brain diseases using artificial intelligence to assist continuous EEG monitoring. When using the channel information processing system for identifying neonatal epileptic seizures of the present invention for identifying neonatal epileptic seizures, firstly, monitoring the infant’s brainwave signal receiving action is performed by using a continuous EEG (electroencephalography) to detect and acquire infant brainwave signal data; then, signal processing is performed, and signal processing is performed for each physiological signal corresponding to each channel of the plurality of monitored infant brainwave signal data to generate multiple training data for training artificial intelligence deep CNN (convolutional neural networks)). Furthermore, the identification of neonatal epilepsy model generation actions is performed, and the training of the deep CNN will generate an identification neonatal epilepsy model based on the plurality of training data after the signal processing actions, which can be used to diagnose neonatal epilepsy symptoms, to identify the state of continuous epilepsy for more than 10 seconds, and distinguish between non-epilepsy and epilepsy.

Description

A channel information processing system for identifying neonatal epileptic seizures

The present invention relates to an information system and method, and more specifically, relates to an environment identification of neonatal epilepsy applied to a newborn with high-risk brain lesions using artificial intelligence-assisted continuous EEG monitoring The seizure channel information processing system performs signal processing for each physiological signal corresponding to each channel of multiple monitoring infant brain wave signal data to generate a deep convolutional neural network CNN (deep Convolutional neural networks) multiple training data, according to the multiple training data after the signal processing action, training the deep convolutional neural network CNN will generate a newborn epilepsy model, and use the generated newborn epilepsy to identify A model that can discriminate the brainwave data of infants and young children and use it to identify whether newborns have epileptic seizures.

Newborn seizures are caused by abnormal brain discharge, and newborns are more prone to seizures than adults because the inhibitory nerves in the brain are not yet mature. The main cause of convulsions in newborns is Hypoxic Encephalopathy (Hypoxic Encephalopathy), which is caused by unsatisfactory delivery during the birth process, accounting for more than half; Other metabolic diseases and other factors. Large-scale foreign studies have found that there are about 1.5 to 5.5 cases per 1,000 children, and the incidence is not low, and more than one week after birth. The clinical symptoms of convulsions in newborns are not typical compared with adults or even older children, and the younger they are, the more atypical the symptoms are. Most of the convulsions are partial seizures, which must rely on the good observation of parents and the doctor's advice Alertness, with video EEG to provide evidence.

Electroencephalogram (EEG, electroencephalography) patterns can be used to detect the occurrence of epilepsy and detect brain diseases, especially important in newborns. Continuous electroencephalogram (CEEG, continuous EEG) monitoring is increasingly used in neonates with high-risk encephalopathy and provides assessment of brain functional status and seizures on EEG. In the case of newborns with high-risk brain lesions, most children may have seizures on the EEG, but they have no clinical symptoms, so they cannot be detected without continuous EEG monitoring. In addition, findings on continuous EEG monitoring are often It often affects the clinical treatment process. For example, when EEG seizures and EEG status epilepticus are found, drug treatment is required. However, research reports from several foreign children's medical centers have found that about 10-40% of newborns with high-risk brain lesions have experienced EEG seizures on continuous EEG monitoring. In addition, approximately one-third of EEG seizures progress to EEG status epilepticus. According to foreign statistics, it takes an average of 7 hours of brain wave examination to find evidence of epilepsy. Now there are more and more data showing that EEG seizures and EEG status epilepticus can cause serious consequences such as brain development delay in young children, and have a great relationship with neurological prognosis. And according to foreign statistics, 20-30% of children with minor illnesses will have epilepsy in childhood.

At present, in Taiwan, continuous EEG monitoring has gradually been routinely used in neonates with high-risk encephalopathy. The traditional observation is that the condition is often more than 24 to 48 hours (long-term) and requires continuous EEG recording, so there is a need for automatic detection.

So how can it be solved? The current traditional observation is that the situation is often as long as 24 to 48 hours (long-term) and requires continuous EEG recording, which cannot be detected by automation; how can we use artificial intelligence to monitor with multiple records? Infant brain wave signal data, using deep learning algorithm to generate recognition model of neonatal epilepsy by training deep convolutional neural network CNN (Deep Convolutional Neural Network), used to diagnose neonatal epileptic seizure symptoms, identification for more than 10 seconds The state of continuous epilepsy, and to distinguish between non-epilepsy and epilepsy; how to identify various neonatal epileptic seizures with a high accuracy rate, and the identification time is fast, which can save the time and labor costs of manual interpretation; here, All of the above are problems to be solved.

The main purpose of the present invention is to provide a channel information processing system for identifying neonatal epileptic seizures, which is applied in the environment where artificial intelligence is used to assist continuous EEG monitoring in the case of newborns with high-risk brain lesions. In the channel information processing system for identifying neonatal epileptic seizures in the present invention, firstly, monitor the brain wave signal receiving action of infants, and use a continuous electroencephalography (continuous electroencephalography) to detect and obtain brain wave signal data of infants ; Then, the signal processing action will be performed for each physiological signal corresponding to each channel of the multiple monitoring infant brain wave signal data, so as to generate a deep convolutional neural network for training artificial intelligence Multiple training data of CNN (deep convolutional neural networks), wherein, each data of each training data in these multiple training data is corresponding to these multiple monitoring infants and young children Each piece of infant brain wave signal data monitors each physiological signal of each channel in the infant brain wave signal data. Here, all channels are included in the training data, and the training data is marked manually by professional doctors; Identify the neonatal epilepsy model to generate actions. According to the multiple training data after the signal processing actions, the deep convolutional neural network CNN will be trained to generate a neonatal epilepsy model for diagnosing neonatal epilepsy symptoms. Identify 10 The state of continuous epileptic seizures for more than 2 seconds, and distinguish between non-epileptic occurrence and epileptic occurrence. Using the neonatal epilepsy identification model generated by the information processing system of the channel information processing system for identifying neonatal epileptic seizures of the present invention, it is possible to distinguish the brain wave data of infants and thereby identify whether the newborn has epileptic seizures.

Another object of the present invention is to provide a channel information processing system for identifying epileptic seizures in newborns, which is applied in the environment where artificial intelligence is used to assist continuous EEG monitoring in the case of newborns with high-risk brain lesions. It can solve the problem that the traditional one-time observation is often as long as 24 to 48 hours (long-term) and requires continuous EEG recording, and cannot be detected by automation; it can use artificial intelligence to monitor the brain waves of infants and young children with multiple records Signal data, using deep learning algorithms to generate a model for identifying neonatal epilepsy by training a deep convolutional neural network CNN (Deep Convolutional Neural Network), which is used to diagnose the symptoms of neonatal epilepsy and identify epileptic seizures that occur continuously for more than 10 seconds It can distinguish non-epileptic seizures from epileptic seizures; it can identify various neonatal epileptic seizures with a high accuracy rate, and the identification time is fast, which can save the time and labor costs of manual interpretation.

According to the above-mentioned purpose, the present invention provides a channel information processing system for identifying neonatal epileptic seizures. The channel information processing system includes an information processing module, an artificial intelligence deep convolutional neural network (CNN) module, and a database.

Information processing module, the information processing module will receive multiple monitoring infant brain wave signals related to training artificial intelligence convolution neural network CNN, by using a continuous EEG to detect and obtain infant brain wave Signal data; the information processing module will perform signal processing on each physiological signal corresponding to each channel of these multiple monitoring infant brain wave signal data to generate a deep convolutional neural network for training artificial intelligence Multiple training data of CNN (deep convolutional neural networks), wherein each of the multiple training data corresponds to each of the multiple monitoring infant brain wave signal data Monitor each physiological signal of each channel in the brainwave signal data of infants and young children. Here, all channels are included in the training data, and the training data is marked manually by professional doctors; and the information processing module will be processed by the signal The multiple training data after the action are sent to the artificial intelligence deep convolutional neural network (CNN) module.

Here, the data type of infant brain wave signal data:

1) After the analog potential signal is received, it is amplified (amplifier) and converted into an analog signal.

2) The sampling frequency is 125Hz.

3) For each channel, the Y-axis unit is micro-volt, and the X-axis is time.

4) Different reference channels are subtracted from the signal value of each channel, and the double banana montage is used as the reference spectrum.

5) Each channel corresponds to a different brain region, which can be used as a basis for judging abnormalities in clinical brain regions.

6) List all channel numbers and names. With 11 locations (Fp1, C3, O1, T3, Fp2, C4, O2, T4, Fz, Cz, Pz); in addition, with Montage (Fp1-C3, C3-O2, Fp2-C4, C4-O2, Fp1-T3, T3-O1, Fp2-T4, T4-O2, T3-C3, C3-Cz, Cz-C4, C4-T4, Fz-Cz, Cz-Pz).

7) The epilepsy waves of infants and young children are mostly between 0.5-15Hz, periodic (periodic) or non-periodic (non-periodic), and the waveform characteristics change slowly with time, and some present high-frequency spikes (ictal or preictal spikes).

Here, when professional doctors manually label training data, data processing is performed:

1) Manually mark the time zone of epilepsy on the EEG of infants (premature infants (24 weeks to 37 weeks) and full-term neonates (37 weeks to 48 weeks), with the start point and end point of epilepsy.

2) Cut the segments in units of 10 seconds.

3) Each segment overlaps the previous segment by 2-8 seconds in length.

4) Remove the excessive value of the fragment greater than ±500 micro-volt, the high probability is noise.

5) The data is "not" normalized (normalization, take the maximum value of all channels as 1 and the minimum value as 0, and scale the data between 0-1).

6) Each segment is independently marked as epileptic or non-epileptic. If the segment is not full for 10 seconds, the epileptic state is classified as normal, and if it is full for 10 seconds, it is classified as epileptic state.

7) All channels are included in the model training, and 1-2 channels are randomly eliminated during training, and zero values are used instead to increase the tolerance (tolerance).

8) The data will be randomly shifted along the time axis, and the data will be augmented (Augmentation).

9) Randomly arrange the channels in random order to augment the data, and the time between the channels still maintains the synchronization (Synchronization) relationship.

10) The data will be uploaded to the data center.

11) Computing is processed in the computing center.

Artificial intelligence deep convolutional neural network CNN module, the artificial intelligence deep convolutional neural network CNN module uses a deep learning algorithm to process the multiple training data according to the signal processing action of the information processing module, Identify the neonatal epilepsy model to generate actions, and train the deep convolutional neural network CNN to generate a neonatal epilepsy model for diagnosing the symptoms of neonatal epilepsy, identifying the state of continuous epilepsy for more than 10 seconds, and distinguishing non-epileptic occurrence with epilepsy. Using the neonatal epilepsy identification model generated by the information processing system of the channel information processing system for identifying neonatal epileptic seizures of the present invention, it is possible to distinguish the brain wave data of infants and thereby identify whether the newborn has epileptic seizures.

Here, the artificial intelligence deep convolutional neural network (CNN) module using deep learning algorithms can incorporate all channels into the training data, and the training data is marked manually by professional doctors; for this deep learning model, three methods can be used Channel selection: first, select the channel with higher parameters in the model as an important channel; second, train a single channel for identification tasks, and select the one with the highest identification accuracy; and, third, only remove one channel (leave-one out) training model, if the channel has an important contribution, the identification efficiency will decrease.

In terms of the model method for identifying neonatal epilepsy models using artificial intelligence deep convolutional neural network CNN modules using deep learning algorithms:

1) CNN: Multi-layer convolution layers are used to form a dense layer (Dense Block). Many dense layers can be connected by a transition layer (Transition Block), and finally output through a linear layer (Linear Block), and Softmax operation output.

2) The forward transfer of data through each layer can gradually extract important features. In the dense layer, the features will extract important features, and these features will be concatenated in the transfer layer. This superposition effect is better than that of traditional CNNs, which will retain upstream features.

3) Each training result will update the parameters through Back-propagation, thereby correcting the wrongly identified parameters.

4) The CNN layer is a single-channel feature extraction, and feature extraction will be performed on more than one channel during training or identification. The final input and output of the model will redistribute the weights through the attention layer (Attention) to enhance the correlation between channels and time series.

5) Accuracy, Operating Characteristic Curve (ROC), Area Under the Curve (AUC), F1 Scores, Sensitivity, and Specificity are used as model metrics.

In terms of the operation of the estimation model to identify neonatal epilepsy models:

Model role:

1) It is a classification model (Classification) that can distinguish several different types of epilepsy in infants and young children.

2) Forecasting: 10-30 seconds in advance to predict the impending epilepsy.

3) Human-computer interaction interface, manually marked epilepsy areas can be included in the new epilepsy training set, and the model parameters can be modified in real time.

4) It can achieve real-time detection and detection after recording.

5) With the alarm system, automatic reminders can be set for long-term unmanned.

6) The model can adjust sensitivity to increase or decrease demand.

7) The model parameters are few and easy to deploy.

The database, which works together with the information processing module and the artificial intelligence deep convolutional neural network CNN module, can be used by the information processing module and the artificial intelligence deep convolutional neural network CNN module to access the required data /data.

When using the channel information processing system for identifying neonatal epileptic seizures of the present invention, first, monitor the brainwave signal reception of infants and young children, and use a continuous electroencephalography (continuous electroencephalography) to detect and obtain brainwave signals of infants and young children data.

Then, the signal processing operation will be performed on each physiological signal corresponding to each channel of these multiple monitoring infant brain wave signal data to generate a deep convolutional neural network for training artificial intelligence Multiple training data of CNN (deep convolutional neural networks), wherein each of the multiple training data corresponds to each monitoring of the multiple monitoring infant brain wave signal data For each physiological signal of each channel in the infant brain wave signal data, all channels are included in the training data, and the training data is marked manually by professional doctors.

Furthermore, the neonatal epilepsy model is identified to generate actions. According to the multiple training data after the signal processing actions, the deep convolutional neural network CNN will be trained to generate a neonatal epilepsy model for diagnosing neonatal epilepsy symptoms. , to identify the state of continuous epileptic seizures for more than 10 seconds, and to distinguish between non-epileptogenesis and epilepsy.

Using the neonatal epilepsy identification model generated by the information processing system of the channel information processing system for identifying neonatal epileptic seizures of the present invention, it is possible to distinguish the brain wave data of infants and thereby identify whether the newborn has epileptic seizures.

In order to make those familiar with the art understand the purpose, characteristics and effects of the present invention, the present invention is described in detail as follows by the following specific embodiments and in conjunction with the accompanying drawings:

1: Channel information processing system for identifying neonatal epileptic seizures

2: Information processing module

3: Artificial intelligence deep convolutional neural network CNN module

4: Database

5: Electronic device

11: Dense module

12: Transfer Module

13: Transfer Module

14: Linear layer

15: Softmax function

101 102 103: Steps

201 202 203: Steps

1001 1002 1003: steps

Figure 1 is a schematic diagram of a system for illustrating the system architecture and operation of the channel information processing system of the present invention; Figure 2 is a flow chart for illustrating the identification of the present invention using the method in Figure 1 The channel information processing system for neonatal epileptic seizures is used to perform the flow steps of the channel information processing method for identifying neonatal epileptic seizures; Figure 3 is a schematic diagram for illustrating the channel information processing system for identifying neonatal epileptic seizures of the present invention One embodiment, and operation situation; The 4th figure is a schematic diagram, in order to show the situation of 11 positions (location) and installation (Montage) described in the 3rd figure; The 5th figure is a schematic diagram, in order to show explanation The CNN model training method and composition of the artificial intelligence deep convolutional neural network CNN module of the embodiment in the 3rd figure; the 6th figure is a flow chart, in order to show and explain and utilize in the embodiment in the 3rd figure The process of identifying neonatal epilepsy models and detecting neonatal epileptic seizures; Figure 7 is a schematic diagram to show the normal and irregular brain waves during the recording action described in Figure 6; Figure 8 The picture is a schematic diagram for displaying epileptic brain waves and regular waveforms during the recording action described in Figure 6; Figure 9 is a schematic diagram for displaying a CNN described in Figure 3 The validity of the model and the performance of the training data set; Figure 10 is a schematic diagram for showing the validity of another CNN model and the performance of the training data set explained in Figure 3; Fig. 11 is a schematic diagram for illustrating the neonatal epilepsy identification model generated by using the channel information processing system for identifying neonatal epileptic seizures in the embodiment in Fig. 3 to identify infant brain wave data and identify Whether the newborn has epileptic seizures; and Fig. 12 is a flow chart for illustrating the process of identifying neonatal epileptic seizures by using the channel information processing system for identifying neonatal epileptic seizures of the present invention as shown in Fig. 3 A process step of the channel information processing method.

Fig. 1 is a schematic diagram of the system, which is used to illustrate the system architecture and operation of the channel information processing system of the present invention. As shown in FIG. 1 , the channel information processing system 1 for identifying neonatal epileptic seizures includes an information processing module 2 , an artificial intelligence deep convolutional neural network CNN module 3 , and a database 4 .

Information processing module 2, the information processing module 2 will receive a plurality of monitoring infant brain wave signals related to training artificial intelligence convolutional neural network CNN, by using a continuous EEG to detect and obtain infant Brain wave signal data; the information processing module 2 will perform signal processing on each physiological signal corresponding to each channel of these multiple pieces of monitored infant brain wave signal data, so as to generate deep volumes for training artificial intelligence Multiple training data of deep convolutional neural networks CNN (deep convolutional neural networks), wherein each item of each training data in the multiple training data corresponds to the multiple monitoring infant brain wave signal data For each physiological signal of each channel in the monitored infant brain wave signal data, all channels are included in the training data, and the training data is marked manually by professional doctors; and, the information processing module 2 Send the multiple training data after the signal processing action to the artificial intelligence deep convolutional neural network CNN module 3 .

Here, the data type of infant brain wave signal data:

1) After the analog potential signal is received, it is amplified (amplifier) and converted into an analog signal.

2) The sampling frequency is 125Hz.

3) For each channel, the Y-axis unit is micro-volt, and the X-axis is time.

4) Different reference channels are subtracted from the signal value of each channel, and the double banana montage is used as the reference spectrum.

5) Each channel corresponds to a different brain region, which can be used as a basis for judging abnormalities in clinical brain regions.

6) List all channel numbers and names. With 11 locations (Fp1, C3, O1, T3, Fp2, C4, O2, T4, Fz, Cz, Pz); in addition, with Montage (Fp1-C3, C3-O2, Fp2-C4, C4-O2, Fp1-T3, T3-O1, Fp2-T4, T4-O2, T3-C3, C3-Cz, Cz-C4, C4-T4, Fz-Cz, Cz-Pz).

7) The epilepsy waves of infants and young children are mostly between 0.5-15Hz, periodic (periodic) or non-periodic (non-periodic), and the waveform characteristics change slowly with time, and some present high-frequency spikes (ictal or preictal spikes).

Here, when professional doctors manually label training data, data processing is performed:

1) Manually mark the time zone of epilepsy on the EEG of infants (premature infants (24 weeks to 37 weeks) and full-term neonates (37 weeks to 48 weeks), with the start point and end point of epilepsy.

2) Cut the segments in units of 10 seconds.

3) Each segment overlaps the previous segment by 2-8 seconds in length.

4) Remove the excessive value of the fragment greater than ±500 micro-volt, the high probability is noise.

5) The data is "not" normalized (normalization, take the maximum value of all channels as 1 and the minimum value as 0, and scale the data between 0-1).

6) Each segment is independently marked as epileptic or non-epileptic. If the segment is not full for 10 seconds, the epileptic state is classified as normal, and if it is full for 10 seconds, it is classified as epileptic state.

7) All channels are included in the model training, and 1-2 channels are randomly eliminated during training, and zero values are used instead to increase the tolerance (tolerance).

8) The data will be randomly shifted along the time axis, and the data will be augmented (Augmentation).

9) Randomly arrange the channels in random order to augment the data, and the time between the channels still maintains the synchronization (Synchronization) relationship.

10) The data will be uploaded to the data center.

11) Computing is processed in the computing center.

Artificial intelligence deep convolutional neural network CNN module 3, the artificial intelligence deep convolutional neural network CNN module 3 uses a deep learning algorithm to process the multiple transactions according to the signal processing action of the information processing module 2 Training data, identify the neonatal epilepsy model to generate actions, train the deep convolutional neural network CNN to generate the identification model of neonatal epilepsy, used to diagnose the symptoms of neonatal epilepsy, identify the state of continuous epilepsy for more than 10 seconds, and distinguish Non-epileptic versus epileptic occurrence.

Using the neonatal epilepsy identification model generated by the channel information processing system 1 for identifying neonatal epileptic seizures and its method of the present invention, the brain wave data of infants can be identified to identify whether the newborn has epileptic seizures.

Here, the artificial intelligence deep convolutional neural network CNN module 3 using deep learning algorithms can incorporate all channels into the training data, and the training data is marked manually by professional doctors; for this deep learning model, three types can be used Channel selection method: first, select the channel with higher parameters in the model as the important channel; second, train a single channel for the identification task, and select the one with the highest identification accuracy; and, third, eliminate only one channel (leave- one out) training model, if the channel has an important contribution, the identification efficiency will decrease.

In terms of the model method for identifying neonatal epilepsy models using artificial intelligence deep convolutional neural network CNN module 3 using deep learning algorithms:

1) CNN: Multi-layer convolution layers are used to form a dense layer (Dense Block). Many dense layers can be connected by a transition layer (Transition Block), and finally output through a linear layer (Linear Block), and Softmax operation output.

2) The forward transfer of data through each layer can gradually extract important features. In the dense layer, the features will extract important features, and these features will be concatenated in the transfer layer. This superposition effect is better than that of traditional CNNs, which will retain upstream features.

3) Each training result will update the parameters through Back-propagation, thereby correcting the wrongly identified parameters.

4) The CNN layer is a single-channel feature extraction, and feature extraction will be performed on more than one channel during training or identification. The final input and output of the model will redistribute the weights through the attention layer (Attention) to enhance the correlation between channels and time series.

5) Accuracy, Operating Characteristic Curve (ROC), Area Under the Curve (AUC), F1 Scores, Sensitivity, and Specificity are used as model metrics.

In terms of the operation of the estimation model to identify neonatal epilepsy models:

Model role:

1) It is a classification model (Classification) that can distinguish several different types of epilepsy in infants and young children.

2) Forecasting: 10-30 seconds in advance to predict the impending epilepsy.

3) Human-computer interaction interface, manually marked epilepsy areas can be included in the new epilepsy training set, and the model parameters can be modified in real time.

4) It can achieve real-time detection and detection after recording.

5) With the alarm system, automatic reminders can be set for long-term unmanned.

6) The model can adjust sensitivity to increase or decrease demand.

7) The model parameters are few and easy to deploy.

The database 4, the database 4 cooperates with the information processing module 2 and the artificial intelligence deep convolutional neural network CNN module 3 to operate together, and can be used for the information processing module 2 and the artificial intelligence deep convolutional neural network CNN module 3 Access the required data/data.

Depending on the implementation status, the information processing module 2 and/or the artificial intelligence deep convolutional neural network CNN module 3 is composed of at least one of electronic hardware, firmware, and software, and cooperates with the identification of neonatal epileptic seizures The processor (not shown) of the system/device where the channel information processing system 1 is located operates; and the database 4 is located in the storage module of the system/device where the channel information processing system 1 for identifying neonatal epileptic seizures is located (not shown).

FIG. 2 is a flow chart for illustrating the process steps of the channel information processing method for identifying neonatal epileptic seizures by using the channel information processing system for identifying neonatal epileptic seizures of the present invention as shown in FIG. 1 . As shown in Figure 2, firstly, in step 101, the action of monitoring the brainwave signal reception of the infant is carried out, by using a continuous electroencephalography (continuous electroencephalography) to detect and obtain the brainwave signal data of the infant, and proceed Go to step 102.

Here, the information processing module 2 will receive multiple monitoring infant brain wave signals related to the training of the artificial intelligence convolutional neural network (CNN), by using a continuous EEG to detect and obtain infant brain wave signal data .

In step 102, signal processing is performed, and signal processing is performed on each physiological signal corresponding to each channel of the multiple monitoring infant brain wave signal data, so as to generate a deep convolutional neural network for training artificial intelligence A plurality of training data of network CNN (deep convolutional neural networks), wherein each item of each training data in the plurality of training data corresponds to each of the plurality of monitoring infant brain wave signal data For each physiological signal of each channel in the brain wave signal data of a monitored infant, all channels are included in the training data, and the training data is marked manually by professional physicians, and the process goes to step 103 .

Here, the information processing module 2 will perform signal processing on each physiological signal corresponding to each channel of these multiple monitoring infant brain wave signal data to generate a deep convolutional neural network for training artificial intelligence Multiple training data of CNN (deep convolutional neural networks), wherein each of the multiple training data corresponds to each of the multiple monitoring infant brain wave signal data Monitor each physiological signal of each channel in the brainwave signal data of infants. Here, all channels are included in the training data, and the training data is marked manually by professional doctors; and, the information processing module 2 will pass the signal The multiple training data after the actions are processed are sent to the artificial intelligence deep convolutional neural network CNN module 3 .

In step 103, the generation action of identifying the neonatal epilepsy model is carried out. According to the multiple training data after the signal processing action, the deep convolutional neural network CNN will be trained to generate the identification model of neonatal epilepsy for diagnosing neonatal epilepsy Seizure symptoms, identifying the state of continuous epileptic seizures for more than 10 seconds, and distinguishing non-epileptic occurrence from epileptic occurrence.

Here, the artificial intelligence deep convolutional neural network CNN module 3 uses a deep learning algorithm to identify the actions of the neonatal epilepsy model based on the multiple training data after the signal processing actions of the information processing module 2 , training the deep convolutional neural network CNN to generate a newborn epilepsy model for diagnosing the symptoms of neonatal seizures, identifying the state of continuous epilepsy for more than 10 seconds, and distinguishing the situation of non-epilepsy and epilepsy.

Using the neonatal epilepsy identification model generated by the channel information processing system 1 for identifying neonatal epileptic seizures and its method of the present invention, the brain wave data of infants can be identified to identify whether the newborn has epileptic seizures.

Here, the artificial intelligence deep convolutional neural network CNN module 3 using deep learning algorithms can incorporate all channels into the training data, and the training data is marked manually by professional doctors; for this deep learning model, three types can be used Channel selection method: first, select the channel with higher parameters in the model as the important channel; second, train a single channel for the identification task, and select the one with the highest identification accuracy; and, third, eliminate only one channel (leave- one out) training model, if the channel has an important contribution, the identification efficiency will decrease.

FIG. 3 is a schematic diagram for illustrating an embodiment of the channel information processing system for identifying neonatal epileptic seizures and its operation according to the present invention. As shown in Figure 3, the channel information processing system 1 for identifying neonatal epileptic seizures includes an information processing module 2, an artificial intelligence deep convolutional neural network CNN module 3, and a database 4. Channel information processing system for epileptic seizures 1 It is located, for example, in the electronic device 5 of the hospital computing center/data storage center, the electronic device 5 can be, for example, a portable, desktop, server type system, and the electronic device 5 for the information system is responsible for streaming brain waves Data, perform model calculations, store data, and export reports; wherein, the information processing module 2 and/or the artificial intelligence deep convolution neural network CNN module 3 are composed of at least one of electronic hardware, firmware, and software Composed of one of them, it operates in cooperation with the processor of the electronic device 5 where the channel information processing system 1 for identifying neonatal epileptic seizures is located, and the database 4 is located in the electronic device where the channel information processing system 1 for identifying neonatal epileptic seizures is located 5 storage modules.

Information processing module 2, the information processing module 2 will receive a plurality of monitoring infant brain wave signals related to training artificial intelligence convolutional neural network CNN, by using a continuous EEG to detect and obtain infant Brain wave signal data; the information processing module 2 will perform signal processing on each physiological signal corresponding to each channel of these multiple pieces of monitored infant brain wave signal data, so as to generate deep volumes for training artificial intelligence Multiple training data of deep convolutional neural networks CNN (deep convolutional neural networks), wherein each item of each training data in the multiple training data corresponds to the multiple monitoring infant brain wave signal data For each physiological signal of each channel in the monitored infant brain wave signal data, all channels are included in the training data, and the training data is marked manually by professional doctors; and, the information processing module 2 Send the multiple training data after the signal processing action to the artificial intelligence deep convolutional neural network CNN module 3 .

Also, here, for example, the data type of infant brain wave signal data:

1) After the analog potential signal is received, it is amplified (amplifier) and converted into an analog signal.

2) The sampling frequency is 125Hz.

3) For each channel, the Y-axis unit is micro-volt, and the X-axis is time.

4) Different reference channels are subtracted from the signal value of each channel, and the double banana montage is used as the reference spectrum.

5) Each channel corresponds to a different brain region, which can be used as a basis for judging abnormalities in clinical brain regions.

6) List all channel numbers and names. Here, FIG. 4 is a schematic diagram for showing the 11 positions (location) and the installation (Montage) described in FIG. 3 . As shown in Figure 4, 11 locations (location): Fp1, C3, O1, T3, Fp2, C4, O2, T4, Fz, Cz, Pz; and installation (Montage): Fp1-C3, C3-O2 , Fp2-C4, C4-O2, Fp1-T3, T3-O1, Fp2-T4, T4-O2, T3-C3, C3-Cz, Cz-C4, C4-T4, Fz-Cz, Cz-Pz.

7) The epilepsy waves of infants and young children are mostly between 0.5-15Hz, periodic (periodic) or non-periodic (non-periodic), and the waveform characteristics change slowly with time, and some present high-frequency spikes (ictal or preictal spikes).

Furthermore, here, for example, the data types of brain wave signal data of infants include, but are not limited to: (1) After receiving the analog potential signal, it is amplified (amplifier) and converted into an analog signal; (2) The sampling frequency is 125Hz; (3) For each channel, the Y-axis unit is microvolts, and the X-axis is time; (4) The signal value of each channel will be subtracted from a different reference channel (reference channel), in double banana montage (5) Each channel corresponds to a different brain region, which can be used as a basis for judging abnormalities in clinical brain regions; (6) The epileptic waves of infants and young children range from 0.5 to 15 Hz, periodic (periodic) or aperiodic ( non-periodic), the waveform characteristics change slowly over time, and some show high-frequency spikes (ictal or pre-ictal spikes) during or before epileptic seizures.

In this example, the source of data is the Pediatric Intensive Care Unit of Linkou Chang Gung Hospital, and the sampling objects are premature infants (24 weeks to 37 weeks) and full-term newborns (37 weeks to 48 weeks).

When the present invention is actually implemented, the data type of the brain wave signal data of infants is not limited to the data type described in this embodiment, and the data type of other brain wave signal data of infants is the same, similar to that described in this embodiment. Therefore, I will not repeat them here.

In this embodiment, the CNN input of the artificial intelligence deep convolutional neural network CNN module 3 is the EEG physiological signal. For the data structure, the present invention calls each source of the physiological signal a channel. Each channel is single-dimensional data, with voltages (voltage is the way the machine receives signals) as the recording unit, and the amount of data is accumulated over time. Each channel is a single-dimensional data (1 channel × time). Because the present invention has 13 channels, the CNN input is 13 channel×time data (as 13 single-dimensional data). CNN of the present invention uses such data as training. Each piece of data will be presented by trained physicians using software to interpret the graphics and define the period of symptoms. The CNN of the present invention trains single-dimensional data of 13 voltage types rather than the waveforms seen by the naked eye of the technician. Although the judgment method of the CNN is similar to that of the human brain, the data form is different.

Here, when professional doctors manually label training data, data processing is performed:

1) Manually mark the time zone of epilepsy on the EEG of infants (premature infants (24 weeks to 37 weeks) and full-term neonates (37 weeks to 48 weeks), with the start point and end point of epilepsy.

2) Cut the segments in units of 10 seconds.

3) Each segment overlaps the previous segment by 2-8 seconds in length.

4) Remove the excessive value of the fragment greater than ±500 micro-volt, the high probability is noise.

5) The data is "not" normalized (normalization, take the maximum value of all channels as 1 and the minimum value as 0, and scale the data between 0-1).

6) Each segment is independently marked as epileptic or non-epileptic. If the segment is not full for 10 seconds, the epileptic state is classified as normal, and if it is full for 10 seconds, it is classified as epileptic state.

7) All channels are included in the model training, and 1-2 channels are randomly eliminated during training, and zero values are used instead to increase the tolerance (tolerance).

8) The data will be randomly shifted along the time axis, and the data will be augmented (Augmentation).

9) Randomly arrange the channels in random order to augment the data, and the time between the channels still maintains the synchronization (Synchronization) relationship.

10) The data will be uploaded to the data center.

11) Computing is processed in the computing center.

Artificial intelligence deep convolutional neural network CNN module 3, the artificial intelligence deep convolutional neural network CNN module 3 uses a deep learning algorithm to process the multiple transactions according to the signal processing action of the information processing module 2 Training data, identify the neonatal epilepsy model to generate actions, train the deep convolutional neural network CNN to generate the identification model of neonatal epilepsy, used to diagnose the symptoms of neonatal epilepsy, identify the state of continuous epilepsy for more than 10 seconds, and distinguish Non-epileptic versus epileptic occurrence.

For example, the neonatal epilepsy identification model of the artificial intelligence model including the convolutional neural network includes, but is not limited to: (1) It can distinguish several classic types of infantile epilepsy, which is a classification model (Classification model); (2) Human-computer interaction interface, manually marked epilepsy areas can be included in the new epilepsy training set, and the characteristic parameters of the model can be modified in real time; (3) Real-time detection can be achieved, and detection after recording; (4) Cooperate The automatic alarm system can be set to automatically remind long-term unattended users; (4) The model can be adjusted to increase sensitivity or reduce demand through the alarm system, and generate a clinical recommendation report; (5) The model has few parameters and is easy to deploy.

For example, when the data is processed in the computing center, in addition to model identification, a personalized model can also be generated, and then a generalized model can be generated. The generalized model and the computing center will be trained and updated at any time.

In this embodiment, the feature parameters are updated through a back-propagation for each training result, so as to correct incorrectly identified feature parameters.

Figure 4 is a schematic diagram for illustrating the 11 locations (location) and installation (Montage) in Figure 3. As shown in Figure 4, 11 locations (location): Fp1, C3, O1, T3, Fp2, C4, O2, T4, Fz, Cz, Pz; and installation (Montage): Fp1-C3, C3-O2 , Fp2-C4, C4-O2, Fp1-T3, T3-O1, Fp2-T4, T4-O2, T3-C3, C3-Cz, Cz-C4, C4-T4, Fz-Cz, Cz-Pz.

Here, the artificial intelligence deep convolutional neural network CNN module 3 using deep learning algorithms can incorporate all channels into the training data, and the training data is marked manually by professional doctors; for this deep learning model, three types can be used Channel selection method: first, select the channel with higher parameters in the model as the important channel; second, train a single channel for the identification task, and select the one with the highest identification accuracy; and, third, eliminate only one channel (leave- one out) training model, if the channel has an important contribution, the identification efficiency will decrease.

In terms of the model method for identifying neonatal epilepsy models using artificial intelligence deep convolutional neural network CNN module 3 using deep learning algorithms:

1) CNN: Multi-layer convolution layers are used to form a dense layer (Dense Block). Many dense layers can be connected by a transition layer (Transition Block), and finally output through a linear layer (Linear Block), and Softmax operation output.

2) The forward transfer of data through each layer can gradually extract important features. In the dense layer, the features will extract important features, and these features will be concatenated in the transfer layer. This superposition effect is better than that of traditional CNNs, which will retain upstream features.

3) Each training result will update the parameters through Back-propagation, thereby correcting the wrongly identified parameters.

4) The CNN layer is a single-channel feature extraction, and feature extraction will be performed on more than one channel during training or identification. The final input and output of the model will redistribute the weights through the attention layer (Attention) to enhance the correlation between channels and time series.

5) Accuracy, Operating Characteristic Curve (ROC), Area Under the Curve (AUC), F1 Scores, Sensitivity, and Specificity are used as model metrics.

In terms of the operation of the estimation model to identify neonatal epilepsy models:

Model role:

1) It is a classification model (Classification) that can distinguish several different types of epilepsy in infants and young children.

2) Forecasting: 10-30 seconds in advance to predict the impending epilepsy.

3) Human-computer interaction interface, manually marked epilepsy areas can be included in the new epilepsy training set, and the model parameters can be modified in real time.

4) It can achieve real-time detection and detection after recording.

5) With the alarm system, automatic reminders can be set for long-term unmanned.

6) The model can adjust sensitivity to increase or decrease demand.

7) The model parameters are few and easy to deploy.

The database 4, the database 4 cooperates with the information processing module 2 and the artificial intelligence deep convolutional neural network CNN module 3 to operate together, and can be used for the information processing module 2 and the artificial intelligence deep convolutional neural network CNN module 3 Access the required data/data.

In this embodiment, the CNN input of the artificial intelligence deep convolutional neural network CNN module 3 is the EEG physiological signal. For the data structure, the present invention calls each source of the physiological signal a channel. Each channel is single-dimensional data, with voltages (voltage is the way the machine receives signals) as the recording unit, and the amount of data is accumulated over time. Each channel is a single-dimensional data (1 channel × time). Because the present invention has 13 channels, the CNN input is 13 channel×time data (as 13 single-dimensional data). CNN of the present invention uses such data as training. Each piece of data will be presented by trained physicians using software to interpret the graphics and define the period of symptoms. The CNN of the present invention trains single-dimensional data of 13 voltage types rather than the waveforms seen by the naked eye of the technician. Although the judgment method of the CNN is similar to that of the human brain, the data form is different.

FIG. 5 is a schematic diagram for illustrating the CNN model training method and composition of the artificial intelligence deep convolutional neural network CNN module of the embodiment illustrated in FIG. 3 .

As shown in Figure 5, the training method and composition of the CNN model consists of a dense module (dense module) 11, a transfer module (translation module) 12, a transfer module (translation module) 13, and a linear layer (linear block) 14, and the Softmax function (Softmax regression) 15, and the output is data classification (Classification).

Dense module 11 includes quadratic batch normalization (Batch Normalization), linear rectification function ReLU (Rectified Linear Unit), and convolution layer (Convolution); and transfer module 12 and transfer module 13 include batch normalization, Linear rectification function ReLU, convolution layer, and pooling (polling) layer.

For example, the batch normalization algorithm makes the deep neural network training more stable and speeds up the convergence speed; while the linear rectification function ReLU function and its derivatives are easy to calculate, and the amount of calculation is reduced when passing forward and backward. The derivative value of the function is 1, which can solve the gradient disappearance problem to a certain extent, and has faster convergence speed during training; the convolution layer can have n m*p convolution kernels, n, m, and p are integers, for example, Acting on grayscale images, each convolution kernel acts on some channels of the output image of the previous layer to generate multiple output images; and, the pooling layer acts on the output image of the convolution layer, performing q*r The pooling, q, r are integers, generate multiple output images.

In terms of the CNN model training method using artificial intelligence deep convolutional neural network CNN module 3 to generate an estimation model using deep learning algorithms, as shown in Figure 5, the individual physiological signals of 13 channels As input data:

(1) Use multiple convolution layers to form a dense layer (Dense Block), many dense layers can be connected by a transition layer (Transition Block), and finally output through a linear layer (Linear Block), Softmax function (Softmax regression) operation and output.

(2) Forward-propagation of the data can gradually extract important features through each layer. In the dense layer, the features will extract important features. These features will be superimposed on the transfer layer (concatenate). This superposition effect is better than that of traditional CNN. Preserve the upstream features.

(3) Each training result will update the parameters by back-propagation, thereby correcting the wrongly identified parameters.

(4) The CNN layer is a single-channel feature extraction, and feature extraction will be performed on all or more channels during training or identification. The final output of the estimation model will redistribute the weights through the attention layer (Attention) to enhance the correlation between channels and time series.

(5) The accuracy rate (Accuracy), operating characteristic curve (ROC), area under the curve (AUC), F1 Scores, Sensitivity, and Specificity are used as model measurement standards.

Using the neonatal epilepsy identification model generated by the channel information processing system 1 for identifying neonatal epileptic seizures and its method of the present invention, the brain wave data of infants can be identified to identify whether the newborn has epileptic seizures.

FIG. 6 is a flow chart for illustrating the process of detecting neonatal epileptic seizures by utilizing the neonatal epilepsy model of the embodiment in FIG. 3 . As shown in Figure 6, at first, in step 1001, an automatic detection action is performed; the information system of the electronic device 5 performs unmanned long-term automatic detection (also That is, there is no need for a doctor to be on call), and then the interpretation and warning are performed, and the infant is sent to the ICU to identify the newborn with epileptic seizures, and the process goes to step 1002.

In step 1002, the recording operation is performed; 13 channels (including Fp1-T3, T3-O1, Fp2-T4, T4-O2, Fp1-C3, C3-O1, Fp2-C4, C4-O2, T3-C3, C3 -Cz, Cz-C4, C4-T4 and EKG, refer to Fig. 7 and Fig. 8) EEG recording, and then the physician manually interprets the occurrence of epilepsy, and proceeds to step 1003.

In step 1003, data collection or calibration is performed; after data collection or calibration, CNN model learning is performed and sent to the information system of the electronic device 5 .

Fig. 7 is a schematic diagram for illustrating normal and irregular brain waves during the recording action described in Fig. 6 .

Fig. 8 is a schematic diagram for illustrating the situation of displaying epileptic brain waves and regular waveforms during the recording action in Fig. 6 .

FIG. 9 is a schematic diagram for illustrating the validity of a CNN model illustrated in FIG. 3 and the performance of the training data set. As shown in Fig. 9, the neonatal epilepsy model for identifying neonatal epileptic seizures according to the present invention shows the validity of a CNN model and the performance of the training data set.

Fig. 10 is a schematic diagram to show the validity of another CNN model and the performance of the training data set illustrated in Fig. 3 . As shown in Fig. 10, the present invention is used to identify neonatal epilepsy model for identifying neonatal epilepsy, which shows the validity of another CNN model and the performance of the training data set.

Fig. 11 is a schematic diagram for illustrating the neonatal epilepsy identification model generated by using the channel information processing system for identifying neonatal epileptic seizures in the embodiment in Fig. 3 to identify infant brain wave data and identify Whether the newborn has epileptic seizures. As shown in Figure 11, it shows a schematic diagram of epileptic neonatal patients and epileptic brainwaves.

FIG. 12 is a flow chart for illustrating a process step of a channel information processing method for identifying neonatal epileptic seizures by using the channel information processing system for identifying neonatal epileptic seizures as shown in FIG. 3 of the present invention. As shown in Fig. 12, firstly, in step 201, the action of monitoring the brainwave signal reception of the infant is carried out, by using a continuous electroencephalography (continuous electroencephalography) to detect and acquire the brainwave signal data of the infant, and proceed Go to step 202.

Here, the information processing module 2 will receive multiple monitoring infant brain wave signals related to the training of the artificial intelligence convolutional neural network (CNN), by using a continuous EEG to detect and obtain infant brain wave signal data .

In step 202, the signal processing action is performed, and the signal processing is performed on each physiological signal corresponding to each channel of the multiple monitoring infant brain wave signal data, so as to generate a deep convolutional neural network for training artificial intelligence A plurality of training data of network CNN (deep convolutional neural networks), wherein each item of each training data in the plurality of training data corresponds to each of the plurality of monitoring infant brain wave signal data For each physiological signal of each channel in the brainwave signal data of a monitored infant, all channels are included in the training data, and the training data is marked manually by professional physicians, and the process goes to step 203 .

Here, the information processing module 2 will perform signal processing on each physiological signal corresponding to each channel of these multiple monitoring infant brain wave signal data to generate a deep convolutional neural network for training artificial intelligence Multiple training data of CNN (deep convolutional neural networks), wherein each of the multiple training data corresponds to each of the multiple monitoring infant brain wave signal data Monitor each physiological signal of each channel in the brainwave signal data of infants. Here, all channels are included in the training data, and the training data is marked manually by professional doctors; and, the information processing module 2 will pass the signal The multiple training data after the actions are processed are sent to the artificial intelligence deep convolutional neural network CNN module 3 .

In step 203, the action of identifying the neonatal epilepsy model is performed. According to the multiple training data after the signal processing action, the deep convolutional neural network CNN will be trained to generate the identification model of neonatal epilepsy for diagnosing neonatal epilepsy Seizure symptoms, identifying the state of continuous epileptic seizures for more than 10 seconds, and distinguishing non-epileptic occurrence from epileptic occurrence.

Here, the artificial intelligence deep convolutional neural network CNN module 3 uses a deep learning algorithm to identify the actions of the neonatal epilepsy model based on the multiple training data after the signal processing actions of the information processing module 2 , training the deep convolutional neural network CNN to generate a newborn epilepsy model for diagnosing the symptoms of neonatal seizures, identifying the state of continuous epilepsy for more than 10 seconds, and distinguishing the situation of non-epilepsy and epilepsy.

Using the neonatal epilepsy identification model generated by the channel information processing system 1 for identifying neonatal epileptic seizures and its method of the present invention, the brain wave data of infants can be identified to identify whether the newborn has epileptic seizures.

Here, the artificial intelligence deep convolutional neural network CNN module 3 using deep learning algorithms can incorporate all channels into the training data, and the training data is marked manually by professional doctors; for this deep learning model, three types can be used Channel selection method: first, select the channel with higher parameters in the model As an important channel; second, train a single channel for identification tasks, and select the one with the highest identification accuracy; and, third, only remove one channel (leave-one out) training model, if the channel has an important contribution, the identification efficiency will decrease reduce.

Combining the above embodiments, we can obtain a channel information processing system for identifying neonatal epileptic seizures, which is applied in the environment where artificial intelligence is used to assist continuous EEG monitoring in the case of neonates with high-risk brain lesions. In the channel information processing system for identifying neonatal epileptic seizures of the present invention, first, monitor the infant’s brain wave signal receiving action, and use a continuous electroencephalogram to detect and obtain the infant’s brain wave signal data; then, carry out The signal processing action will carry out signal processing on each physiological signal corresponding to each channel of these multiple monitoring infant brain wave signal data, so as to generate multiple data of the deep convolutional neural network CNN used to train artificial intelligence. A piece of training data, wherein, each piece of data in each piece of training data in the multiple pieces of training data corresponds to each piece of data in each piece of monitoring infant brainwave signal data in the multiple pieces of monitoring infant brainwave signal data Each physiological signal of a channel, here, all channels are included in the training data, and the training data is marked manually by professional doctors; and then, identify the actions generated by the neonatal epilepsy model, and according to these multiple actions after signal processing Write training data, train the deep convolutional neural network CNN to generate a model for identifying neonatal epilepsy, which is used to diagnose the symptoms of neonatal epilepsy, identify the state of continuous epilepsy for more than 10 seconds, and distinguish between non-epilepsy and epilepsy Condition. Using the neonatal epilepsy identification model generated by the information processing system of the channel information processing system for identifying neonatal epileptic seizures of the present invention, it is possible to distinguish the brain wave data of infants and thereby identify whether the newborn has epileptic seizures.

The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the scope of the present invention; all other equivalent changes or modifications that do not deviate from the spirit disclosed in the present invention should be included in the following patents within range.

1: Channel information processing system for identifying neonatal epileptic seizures

2: Information processing module

3: Artificial intelligence deep convolutional neural network CNN module

4: Database

Claims (5)

A channel information processing system for identifying neonatal epileptic seizures, which is applied in the environment where artificial intelligence is used to assist continuous EEG monitoring in the case of newborns with high-risk brain lesions, including: information processing module; artificial intelligence depth Convolutional neural network CNN module; and a database, the database cooperates with the information processing module and the artificial intelligence deep convolutional neural network CNN module to work together for the information processing module, the artificial intelligence depth The convolutional neural network CNN module accesses the required data/data; among them, the information processing module will receive multiple monitoring infant brainwave signals related to training artificial intelligence convolutional neural network CNN, by using A continuous electroencephalogram detects and acquires brain wave signal data of infants; the information processing module will perform signal processing on each physiological signal corresponding to each channel of these multiple monitoring brain wave signals of infants and young children, so as to Generate a plurality of training data for training the deep convolutional neural network CNN of artificial intelligence; and, the artificial intelligence deep convolutional neural network CNN module is processed according to the signal from the information processing module Based on these multiple training data, the deep convolutional neural network (CNN) is trained to generate a neonatal epilepsy model for diagnosing neonatal epileptic seizure symptoms. The channel information processing system for identifying neonatal epileptic seizures as described in Claim 1, wherein each item of each training data in the multiple training data corresponds to the multiple monitoring infant brain wave signals Each of the physiological signals of each channel in the brain wave signals of infants and young children is monitored. The channel information processing system for identifying neonatal epileptic seizures as described in Claim 1, wherein the identification model of neonatal epilepsy is used to diagnose the symptoms of neonatal epileptic seizures, to identify the state of epilepsy occurring continuously for more than 10 seconds, and to distinguish Non-epileptic versus epileptic. The channel information processing system for identifying neonatal epileptic seizures as described in Claim 2, wherein all channels are included in the training data, and the annotations of these multiple training data are manually annotated by professional doctors. The channel information processing system for identifying neonatal epileptic seizures as described in claim 1 or claim 2 or request 3 or claim 4, wherein the channel information processing system for identifying neonatal epileptic seizures is located in the hospital computing center/data Storage center electronics.

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