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Driver atrial fibrillation detection method and system based on dual-channel deep neural network

A deep neural network and detection method technology, applied in the field of safe driving, can solve the problems of real-time, accuracy, convenience and low cost, and achieve the effect of improving safety

Pending Publication Date: 2021-10-15
SUN YAT SEN UNIV
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  • Application Information

AI Technical Summary

Problems solved by technology

Existing equipment or technology cannot meet the real-time, accuracy, convenience and low cost requirements of collecting ECG signals and detecting atrial fibrillation during driving

Method used

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  • Driver atrial fibrillation detection method and system based on dual-channel deep neural network
  • Driver atrial fibrillation detection method and system based on dual-channel deep neural network
  • Driver atrial fibrillation detection method and system based on dual-channel deep neural network

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Embodiment Construction

[0045] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. For the step numbers in the following embodiments, it is only set for the convenience of illustration and description, and the order between the steps is not limited in any way. The execution order of each step in the embodiments can be adapted according to the understanding of those skilled in the art sexual adjustment.

[0046] refer to figure 1 , the present invention provides a kind of driver's atrial fibrillation detection method based on dual-channel deep neural network, and the method comprises the following steps:

[0047] Collect the driver's ECG signal based on the sensor installed on the steering wheel;

[0048] Specifically, the steering wheel reference figure 2 , the sensor includes an electrocardiogram sensor and a pressure sensor, and collects the driver's sign information through the electrocardiogram sensor and the ...

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Abstract

The invention discloses a driver atrial fibrillation detection method and system based on a dual-channel deep neural network. The method comprises the steps: collecting the electrocardiogram signals of a driver based on a sensor installed on a steering wheel; preprocessing the electrocardiogram signal to obtain a preprocessed signal; drawing a time-frequency map and a Poincare map according to the preprocessed signal; and by taking the time-frequency map and the Poincare map as input, performing signal classification based on a pre-constructed convolutional neural network, and completing atrial fibrillation detection. The system comprises a signal acquisition module, a signal preprocessing module, a graph drawing module and a signal classification module. By using the method and the device, the safety in a driving environment is improved. The driver atrial fibrillation detection method and system based on the dual-channel deep neural network can be widely applied to the field of safe driving.

Description

technical field [0001] The invention relates to the field of safe driving, in particular to a driver's atrial fibrillation detection method and system based on a dual-channel deep neural network. Background technique [0002] In the process of traffic, if the driver has a sudden myocardial infarction while driving, it will not only endanger his own life, but also cause harm to the passengers in the car. In recent years, traffic accidents caused by sudden cardiovascular diseases are not uncommon. There are gaps in the areas of ECG detection and atrial fibrillation recognition in driving scenarios. Existing equipment or technology cannot simultaneously meet the requirements of real-time, accuracy, convenience and low cost of collecting ECG signals and detecting atrial fibrillation during driving. Contents of the invention [0003] In order to solve the above-mentioned technical problems, the object of the present invention is to provide a driver's atrial fibrillation detect...

Claims

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Application Information

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IPC IPC(8): A61B5/361A61B5/318A61B5/321A61B5/35
CPCA61B5/361A61B5/318A61B5/321A61B5/35
Inventor 王伟黄欣龙黄诺贤彭显为刘睿琦陈健
Owner SUN YAT SEN UNIV
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