QRS wave group identification method based on differential zero-crossing detection method

Abstract

The invention discloses a QRS wave group identification method based on differential zero-crossing detection method, which comprises the following steps of: filtering an acquired original electrocardiosignal by using a self-adaptive baseline drift filter, and removing direct current bias of the original electrocardiosignal to obtain an electrocardiosignal which fluctuates up and down near a value 0; using a 15Hz Butterworth low-pass filter to filter the electrocardiosignals, filtering out power frequency interference, and restraining myoelectricity interference; performing first-order difference and second-order difference on the filtered electrocardiosignal, analyzing probability distribution of numerical values after the first-order difference, and acquiring a threshold value for detecting an R peak; and sequentially finding out an S valley and a Q valley through second-order difference, calculating the heart rate and the wavelengths of Q, R and S waves according to the sampling frequency, extracting waveform characteristics, and completing the identification of the QRS wave group. According to the invention, the working efficiency can be improved, the calculated amount is small, the real-time performance is high, the requirement on the computing power of the main control chip is extremely low, and the method can be widely applied to household portable electrocardiograph monitors and telemedicine which are relatively low in price.

Description

technical field [0001] The technical field to which the present invention relates, in particular, relates to a method for identifying QRS complexes based on a differential zero-crossing detection method. Background technique [0002] 24-hour electrocardiogram detection is a common method for diagnosing cardiovascular diseases. Due to the transient and paroxysmal nature of some cardiovascular diseases, electrocardiographs must record the patient's ECG data for a long time for diagnosis, so the amount of collected data is very large and difficult to detect. Diagnose and analyze with the naked eye. Medically, the electrocardiogram is divided into P wave, PR interval, QRS wave, ST segment and T wave. Among them, the QRS wave represents the situation when the ventricle is depolarized. The detection of the QRS wave can detect ventricular premature beats, atrial fibrillation, and atrial fibrillation. Flutter, coronary heart disease and other diseases, so it has certain significanc...

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Problems solved by technology

[0002] 24-hour electrocardiogram detection is a common method for diagnosing cardiovascular diseases. Due to the transient and paroxysmal nature of some cardiovascular diseases, electrocardiographs must record the patient's ECG data for a long time for diagnosis, so the amount of collected data is very large and difficult to detect. Visual diagnosis and analysis

[0003] At present, there are wavelet transform, neural network, template matching, Teager energy operator and other methods for QRS waveform detection. Although wavelet transform has the advantage of a small false detection rate, it is difficult to perform on a portable device with weaker performance due to the huge amount of calculation. Real-time detection is achieved in ECG monitors. The method of neural network is difficult to be widely used due to the long training period and the difficulty in distinguishing ECG waveforms that are greatly different from the training set. Template matching is easier to implement, but it is also difficult to distinguish template-related waveforms. For the problem of less sensitive waveforms, the Teager energy operator can distinguish by tracking the instantaneous energy change of the signal, which can effectively detect the R peak, but it is a little struggling to distinguish the Q point and the S point

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