21-06-2012, 04:28 PM
ECG SIGNAL PROCESSING AND HEART RATE FREQUENCY DETECTION METHODS
ECG SIGNAL PROCESSING AND HEART.pdf (Size: 111.14 KB / Downloads: 104)
Introduction
Electrocardiogram (ECG) represents electrical activity of human heart. ECG is composite from
5 waves - P, Q, R, S and T. This signal could be measured by electrodes from human body in typical
engagement. Signals from these electrodes are brought to simple electrical circuits with amplifiers and
analogue – digital converters.
The main problem of digitalized signal is interference with other noisy signals like power
supply network 50 Hz frequency and breathing muscle artefacts. These noisy elements have to be
removed before the signal is used for next data processing like heart rate frequency detection. Digital
filters and signal processing should be designed very effective for next real-time applications in
embedded devices.
Digital signal processing with digital filters
In this part there is described noise elements filtering and baseline wander elimination with digital
filters. The main noise elements are power supply network 50 Hz frequency and breathing muscle
movements. These artefacts have to be removed before the signal is used for next data processing like
heart rate frequency determination.
Heart rate detection algorithms
In this part there are described algorithms for heart rate detection which have been designed in
Matlab. Algorithms can be divided to algorithms based on statistical and differential mathematical
methods.
The first algorithm is focused on statistical signal processing methods like autocorrelation.
Autocorrelation method can be used because the ECG signal is quasi-periodical. Matlab provides very
simple using of autocorrelation method in signal processing which is very useful for this purpose.
The second and third algorithms are detecting heart rate as difference between R waves in ECG.
These waves are filtrated by band pass filters firstly and then the signal energy is computed. The
wave’s peaks are detected by peak detector or signal thresholding.
Autocorrelation of energy signal
The first algorithm computes heart rate frequency using mathematical statistic autocorrelation
function. Autocorrelation function is applied on signal energy. Integrator filter highlights peaks in
autocorrelation function. Peak detector is used for extract peaks from autocorrelation function. In
the Figure 5, autocorrelation function, peaks and computed heart rate frequency are displayed.
Peaks detection in energy signal envelope
The third algorithm uses the integrator filter firstly. Energy signal is smoothed by this filter. Rpeaks
are highlighted as well. Moreover the energy signal envelope is made [9]. The peak detector
is used to find the peaks in the signal envelope [10]. Heart rate frequency is computed from R-R
intervals. If the interval between two R-peaks is lower than the maximal physiological heart rate,
the next R-peak is taken. It prevents failures caused by artefacts in signal from occurring.