BCG signal heart rate calculating method and system based on dynamic second difference threshold

Abstract

The invention discloses a BCG signal heart rate calculating method and system based on a dynamic second difference threshold. The method comprises the following steps of performing collecting to obtain BCG signals; according to a preset rule, performing filtering pretreatment on BCG signals to obtain filtered BCG signals; according to a dynamic second difference threshold algorithm, treating the filtered BCG signals, and determining the position of J wave peaks in each signal period to obtain the sequence of J wave peaks; according to the position of the each J wave peak in the sequence of theJ wave peaks, calculating the average length of BCG signal dominant waves; and according to the average length and the sampling rate, calculating the heart rate. The method and the system are high inalgorithm adaptability, and have good noise removing effects on different signals. The method and the system are simple in parameter structure, and low in calculation complexity, the problems that aconventional BCG signal extracting method is long in calculation time and large in delayed time, are solved, and the BCG signal heart rate calculating method and system have high calculation accuracyand are suitable for real-time monitoring of the heart rate.

Description

technical field

[0001] The present invention relates to the field of medical technology, and more specifically, to a method and system for calculating heart rate of BCG signals based on a dynamic second-order difference threshold. Background technique

[0002] Ballistocardiogram (BCG) is a method of recording the body vibration caused by the pumping of the heart. Compared with existing cardiovascular detection methods, ballistocardiogram signal detection has the advantages of non-invasiveness, non-contact, and convenient detection. BCG signal is a kind of human biological signal, which has the characteristics of low frequency and low intensity, and is easily interfered by breathing, body movement, and power frequency noise. As a result, the BCG signal obtained by direct measurement contains too much noise, and accurate physiological information cannot be obtained directly. Feature information, so it is necessary to filter the collected BCG signal to remove noise interferenc...

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