Deep regression heart rate estimation method for ballistocardiogram signals

Abstract

The present invention provides a deep regression heart rate estimation method for ballistocardiogram signals, which is used for solving a technical problem of larger estimation errors in the prior art. The method comprises the following implementation steps: collecting ballistocardiogram signals and heart pulse signals; filtering the ballistocardiogram signals; acquiring a training sample set anda test sample set by using periodic priori knowledge of the ballistocardiogram signals; constructing a heart rate regression estimation network model based on periodicity and amplitude characteristicsof the ballistocardiogram signals; training the heart rate regression estimation network model; and obtaining a heart rate estimated value of the ballistocardiogram signals. The method obtains periodic characteristics and amplitude characteristics of heartbeat signals by using a bidirectional circulation neural network through a supervised learning mode, simultaneously utilizes the periodic characteristics and the amplitude characteristics of the ballistocardiogram signals to estimate heart rate values by a regression network, simplifies steps of heart rate estimation of the ballistocardiogram signals, and effectively reduces the estimation errors of heart rates of the ballistocardiogram signals.

Description

technical field [0001] The invention belongs to the technical field of biomedical information processing, and relates to a method for estimating a heart rate of a ballistocardiogram signal, in particular to a method for estimating a depth regression heart rate of a ballocardiogram signal. Background technique [0002] In recent years, with the improvement of technology and economic level, people pay more and more attention to their own health problems. Changes in heart rhythm beyond the normal range usually indicate the occurrence of a certain disease, such as sudden cardiac death, asphyxia, arrhythmia, etc. Therefore, heart rate monitoring in daily life is of great significance for the early detection and treatment of people's own diseases. [0003] Currently, electrocardiogram (ECG) is widely used in heart rate monitoring clinically, but this requires close contact of electrodes or heart probes with the human body, which brings great inconvenience and psychological pressu...

AI Technical Summary

Problems solved by technology

[0006] The disadvantage of the above two methods is that the estimation error is relatively large. The reasons are as follows: First, although the periodicity of the ballistocardiogram signal is used to overcome the interference of the non-periodic noise signal, it cannot solve the periodicity in the ballistocardiogram signal. The impact of sexual noise on heart rate estimation, and the above two methods do not use more additional information to guide the estimation of heart rate, so the estimation error is large

Second, the method of calculating the spectral components first and then estimating the heart rate is cumbersome, and the rounding operation when obtaining the frequency of the heartbeat signal will increase the estimation error

Images