Method for extracting pulse waveform feature points

Abstract

The invention provides a method for extracting pulse waveform feature points, and aims to accurately detect a dicrotic wave and a main pulse wave of a pulse wave. The method comprises the steps that (1), an electrocardiogram (ECG) signal and a pulse signal are synchronously collected; (2), baseline drift, power frequency interference and electromyogram (EMG) interference are removed from the collected ECG signal and the collected pulse signal, so as to eliminate noise; (3), the ECG signal is detected and analyzed, ECG feather information is extracted, and the positions of an R wave and a T wave of the ECG signal are accurately judged; and (4), the positions of the main wave, a dicrotic notch and the dicrotic wave of the pulse signal are determined on the basis of the positions of the R wave and the T wave of the ECG signal. A system employing the method comprises a physiological signal data collecting card used for synchronously collecting the ECG signal and the pulse signal, an ECG detecting module used for detecting the ECG signal of a human body, and a pulse detecting module used for detecting the pulse signal of the human body.

Description

technical field [0001] The invention relates to a method for extracting pulse waveform feature points based on synchronously collected pulse ECG signals. Background technique [0002] At present, the pulse wave detection and waveform feature extraction mainly use the modulus maximum method and differential method of wavelet analysis. The modulus maximum method of wavelet analysis utilizes the ability of wavelet transform to highlight local features, making it a powerful means to detect transient mutations and signal edges, but this method is not ideal for the detection rate of irregular pulse wave signals; Fast, the detection rate of main wave of pulse wave is high, but the extraction of characteristic parameters such as dicrotic pre-wave and dicrotic wave is not very ideal. The useful signal frequency band of the pulse signal is narrow, the signal mutation is not obvious, and each peak has no obvious characteristics. Regular pulse waves under normal conditions usually hav...

AI Technical Summary

Problems solved by technology

The modulus maximum method of wavelet analysis utilizes the ability of wavelet transform to highlight local features, making it a powerful means to detect transient mutations and signal edges, but this method is not ideal for the detection rate of irregular pulse wave signals; Fast, the detection rate of the main wave of the pulse wave is high, but the extraction of characteristic parameters such as the pre-pulse wave and the dicrotic wave is not very ideal

The useful signal frequency band of the pulse signal is narrow, the signal mutation is not obvious, and each peak has no obvious characteristics

Regular pulse waves under normal conditions usually have the largest peak value of the main pulse wave, but when the subject is in a state of fatigue, the amplitudes of the peak, pre-dicrotic wave and dicrotic wave are very close, and even the peak value of the dicrotic wave is higher than In the case of the peak value of the main wave, at the same time, the difference value of the dicrotic wave is relatively close to the difference value of the main wave, which brings great difficulties to the accurate detection of the pulse wave waveform feature points

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