Research on the detection algorithm of electrocardiogram characteristic wave based on energy segmentation and stationary wavelet transform / 生物医学工程学杂志

The detection of electrocardiogram (ECG) characteristic wave is the basis of cardiovascular disease analysis and heart rate variability analysis. In order to solve the problems of low detection accuracy and poor real-time performance of ECG signal in the state of motion, this paper proposes a detection algorithm based on segmentation energy and stationary wavelet transform (SWT). Firstly, the energy of ECG signal is calculated by segmenting, and the energy candidate peak is obtained after moving average to detect QRS complex. Secondly, the QRS amplitude is set to zero and the fifth component of SWT is used to locate P wave and T wave. The experimental results show that compared with other algorithms, the algorithm in this paper has high accuracy in detecting QRS complex in different motion states. It only takes 0.22 s to detect QSR complex of a 30-minute ECG record, and the real-time performance is improved obviously. On the basis of QRS complex detection, the accuracy of P wave and T wave detection is higher than 95%. The results show that this method can improve the efficiency of ECG signal detection, and provide a new method for real-time ECG signal classification and cardiovascular disease diagnosis.

An automatic gain control circuit to improve ECG acquisition

Abstract Introduction Long-term electrocardiogram (ECG) recordings are widely employed to assist the diagnosis of cardiac and sleep disorders. However, variability of ECG amplitude during the recordings hampers the detection of QRS complexes by algorithms. This work presents a simple electronic circuit to automatically normalize the ECG amplitude, improving its sampling by analog to digital converters (ADCs). Methods The proposed circuit consists of an analog divider that normalizes the ECG amplitude using its absolute peak value as reference. The reference value is obtained by means of a full-wave rectifier and a peak voltage detector. The circuit and tasks of its different stages are described. Results Example of the circuit performance for a bradycardia ECG signal (40bpm) is presented; the signal has its amplitude suddenly halved, and later, restored. The signal is automatically normalized after 5 heart beats for the amplitude drop. For the amplitude increase, the signal is promptly normalized. Conclusion The proposed circuit adjusts the ECG amplitude to the input voltage range of ADC, avoiding signal to noise ratio degradation of the sampled waveform in order to allow a better performance of processing algorithms.

QRS detection based on the combination of improved quick fitting of LADT and neural network / 第三军医大学学报

Objective To investigate the methods for EGG data compression and accurate QRS detection. Methods The quick fitting of LADT was improved and the combination of the improved quick fitting of LADT and neural network was used for the detection of the location of the QRS complex. Results Test by the MIT/BIH arrhythmia database revealed high accuracy rate of QRS detection and easy real time application. Conclusion The purposes of accurate detection of QRS with a little time are realized.