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A system and method for reconstruction of central arterial pressure waveform based on cnn-bilstm

A central arterial pressure and waveform reconstruction technology, applied in neural learning methods, cardiac catheterization, neural architecture, etc., can solve problems such as difficulty in meeting blood pressure detection and diagnosis and treatment needs, difficulty in improving accuracy, and difficulty in feature extraction, and improve learning ability. , improve the reconstruction accuracy, and the reconstruction effect is good

Active Publication Date: 2022-07-08
CHONGQING UNIV OF TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

For these large and complex blood pressure features, the traditional artificially designed blood pressure feature extraction method is becoming more and more difficult to meet the needs of modern medical treatment for blood pressure detection and diagnosis; for the traditional non-invasive central arterial pressure reconstruction method, feature extraction is difficult and the reconstruction error is large , difficult to improve the accuracy and other problems

Method used

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  • A system and method for reconstruction of central arterial pressure waveform based on cnn-bilstm
  • A system and method for reconstruction of central arterial pressure waveform based on cnn-bilstm
  • A system and method for reconstruction of central arterial pressure waveform based on cnn-bilstm

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Embodiment 1

[0041] see Figure 1-7 As shown, the present invention is a central arterial pressure waveform reconstruction system based on CNN-BiLSTM, including a data acquisition control module, a radial artery blood pressure measurement module, a finger arterial blood pressure measurement module, a data processing module, and a central arterial pressure calculation module. and data display module;

[0042] Wherein, the data acquisition control module is used to control other modules to perform sequential measurement and processing;

[0043] Wherein, the radial artery blood pressure measurement module is used to collect the pulsatile signal of the radial artery, and filter and amplify it to form a radial artery pressure signal and transmit it to the data processing module;

[0044] The radial artery blood pressure measurement module is composed of at least a piezoelectric sensor, a first lead wire and a first blood pressure signal processing circuit;

[0045] The piezoelectric sensor is...

Embodiment 2

[0075] On the basis of Embodiment 1, a method for using a system for reconstructing central arterial pressure waveform based on CNN-BiLSTM is disclosed, and the method includes the following steps:

[0076] Step 1: After the tester lies down and rests for 5 minutes, start and control the inflation and deflation speed of the air pump in the digital artery blood pressure measurement module and the air pressure in the cuff through the data acquisition control module to complete the measurement of the digital arterial blood pressure;

[0077] The second step: start and control the radial artery blood pressure measurement module through the data acquisition control module, complete the synchronous measurement of the radial artery blood pressure signal, and perform denoising and drift removal processing;

[0078] The third step: start the data processing module, use the digital artery to complete the calibration of the radial artery pressure, and calculate the basic parameters of the...

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Abstract

The invention discloses a system and method for central arterial pressure waveform reconstruction based on CNN-BiLSTM, and relates to the technical field of artificial intelligence and medical device research and development. The invention includes a data acquisition control module, a radial artery pressure measurement module, a fingertip arterial pressure measurement module, a data processing module, a central arterial pressure calculation module and a data display module. The invention does not need to manually extract features, and does not need to establish an intermediate simulation model and its parameter estimation, and automatically establishes an end-to-end reconstruction model of peripheral blood pressure and central arterial pressure, which effectively improves the reconstruction accuracy of the central arterial pressure waveform. The improvement of the neural network structure improves the model's ability to learn waveform features. Compared with other artificial neural networks, this network has a stronger learning ability for blood pressure waveforms and better reconstruction of central arterial pressure.

Description

technical field [0001] The invention belongs to the technical field of artificial intelligence and medical device research and development, and in particular relates to a system and method for central arterial pressure waveform reconstruction based on CNN-BiLSTM. Background technique [0002] Cardiovascular disease is an important cause of human death and disability. In recent years, with the increase in obesity, the risk of cardiovascular disease has been increasing. Accurately grasping the health status of the cardiovascular system of patients can provide immediate assistance to patients, thereby effectively reducing the incidence and mortality of cardiovascular events. As the lateral pressure of blood on the vessel wall of the ascending aortic root, CAP is more closely related to cardiovascular events than peripheral arterial pressure, and is independently related to some cardiovascular events. Therefore, the detection of CAP is of great significance in the prediction, ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): A61B5/022G06N3/04G06N3/08
CPCA61B5/022A61B5/7225A61B5/725A61B5/7203A61B5/7264G06N3/08A61B2560/0223G06N3/044G06N3/045
Inventor 肖汉光刘畅任慧娇黄金锋刘代代张邦林
Owner CHONGQING UNIV OF TECH
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