Using wavelet transform in biomedical engineering – heart signal analysis

As we mentioned before, wavelet transform is used to analyze short time and non-stationary signals. Since base wavelet function has to parameters – translation and scaling, it is possible to achieve good time and frequency localization. In other words, we can equally analyze slow signal and fast signal structures without losing resolution and so evaluate signal frequency characteristics and time dynamics.

Heart signal analysis is one of the most common problem in biomedical engineering. Practically every part of ECG signal carries some sort of information about heart condition, possible pathologies, and deceases. So equally frequency and timing characteristics of ECG signal is important. As you know standard ECG signal consists of several typical waveforms like P-QRS-T where in P and T waves low frequency component dominates and in QRS mid and high.


The common condition of hear is myocardial ischemia when blood flow through coronary arteries to heart is reduced what prevents receiving enough oxygen. This can damage heart muscle and lead to heart attack. In order to notice this pathology it is we need to analyze S-T segment of ECG waveform. Insignificant changes in signal can indicate ischemia. In order to find the variations in signal it is needed to analyze signal up to 0,05Hz. Additionally QRS complex also carries some information. Due to ischemia there appears slight arrhythmia which has to be detected.

Using standard methods it is really hard to detect signal variations in QRS signal. Usually they are interpreted as noise and artefacts. Using wavelet analysis there bigger chance to detect those variations related to decease.

Using time-frequency analysis by applying wavelet transformation we can detect weak signals that are practically hidden in stronger waves. For instance in case of Ventricular Late Potentials then microvolt level signals appear that normally are not detectable.


By applying wavelet transform with carefully selected scale we can extract late ventricular potentials.


As you can see with scale S=1/16 an anomaly has been detected on second QRS.

Wavelet transform is effective way of detecting and filtering signals when exact shape is unknown. If you would use Fourier transform you would want that expected signal spectrum clearly distinguishes from noise.

Leave a Reply