Detection of QRS Complex for Diagnosis of Cardiac Diseases Using Digital Signal Processing Techniques

摘要

Quantifying the measured amplitude and time durations is used to drive the interpretation process for the electrocardiograph. The importance in the ECG wave detection is given to identification of all the beats using QRS complex algorithm. The precision in the identification of QRS complexes is of great importance for the reliability of an automated ECG analyzing system and thus, for the diagnosis of cardiac diseases. The purpose of this elaboration is to become familiar with some of the signal processing problems related to ECG signals. This paper is concerned with the detection of QRS complexes in an electrocardiogram (ECG) waveform. The paper also deals with detection of RR wave and calculates its length in seconds. In the proposed algorithm a threshold is set and the crossing points between it and the QRS complexes are determined. This has the advantage of ensuring that the R-peaks are contained between the crossing points provided that these are determined accurately. This paper presents a tool for analysis of ECG signal which is developed using higher level language in order to help research on QRS detection by making analysis process faster and easier. The paper discuses about how to design adaptive digital filters for noise cancellation, signal extraction and signal smoothening. The power spectrum of the ECG signal can provide useful information about the QRS complex. The heart rate can be determined by multiplying together the normalized frequency and the sampling frequency. Since this method is based on thresholding, signal peaks are defined as those of the QRS complex while noise peaks are those of the T waves, muscle noise, etc. Using an adaptive band pass filter the overall sensitivity of the detector improves. A peak is determined when the signal changes direction within a certain time interval. Whenever a new peak is detected, it must be categorized as a noise peak or a signal peak. If the peak level exceeds a set threshold then it is a QRS peak. The effectiveness of the proposed algorithm is tested by using recordings obtained from the MIT-BIH arrhythmia database.