Autodetection of J Wave Based on Random Forest with Synchrosqueezed Wavelet Transform

Abstract EN

J wave is the bulge generated in the descending slope of the terminal portion of the QRS complex in the electrocardiogram.

The presence of J wave may lead to sudden death.

However, the diagnosis of J wave variation only depends on doctor’s clinical experiences at present and missed diagnosis is easy to occur.

In this paper, a new method is proposed to realize the automatic detection of J wave.

First, the synchrosqueezed wavelet transform is used to obtain the precise time-frequency information of the ECG.

Then, the inverse transformation of SST is computed to get the intrinsic mode function of the ECG.

At last, the time-frequency features and SST-based and the entropy features based on modes are fed to Random forest to realize the automatic detection of J wave.

As the experimental results shown, the proposed method has achieved the highest accuracy, sensitivity, and specificity compared with existing techniques.