Cuff-free blood pressure prediction method based on deep neural network model

Abstract

The invention discloses a cuff-free blood pressure prediction method based on a deep neural network model, which comprises the steps of: obtaining a data set comprising pulse wave signals and electrocardiosignals; pre-processing the obtained pulse wave signals and electrocardiosignals to obtain available signals, wherein the preprocessing comprises signal filtering, fixed-length cutting, peak value cutting / trough cutting, resampling and label value obtaining; and inputting the available signals into the deep neural network model to obtain a predicted systolic pressure value and a predicted diastolic pressure value, wherein the deep neural network model comprises a convolution adaptation layer, a residual network with a compression excitation module and a full connection layer which are connected in sequence, the input of the convolution adaptation layer is the available signal, and the output of the full connection layer is the systolic pressure value and the diastolic pressure value. According to the invention, the pulse wave signals and the electrocardiosignals are analyzed, so that the continuous dynamic blood pressure value can be predicted without a cuff.

Description

technical field [0001] The invention belongs to the field of biological signal processing, and in particular relates to a cuffless blood pressure prediction method based on a deep neural network model. Background technique [0002] Hypertension is the main factor leading to most cardiovascular diseases. Blood pressure assessment and dynamic detection are of great significance for timely understanding the incidence of hypertension. In the study of blood pressure estimation, the existing cuff blood pressure measurement cannot obtain continuous blood pressure values. Therefore, the blood pressure continuous estimation technology based on cuffless measurement has become a current technical research hotspot. Numerous studies have demonstrated that ambulatory blood pressure can be effectively predicted using a combined input of plethysmography and electrocardiogram signals. The pulse arrival time (pulse arrival time) is defined as the time delay from the r peak of the ECG signal...

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Problems solved by technology

[0006] In order to solve the problem that the cuffless blood pressure estimation has a low sample size due to the dependence on the time series network, and is easily affected by the overall waveform baseline drift and high-frequency noise, the present invention provides a cuffless blood pressure based on a deep neural network model. Prediction method, stable and reliable prediction of ambulatory blood pressure value through pulse wave signal and ECG signal on a data set containing a large number of people

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