Support vector machine-based ECG compression

This paper presents an adaptive, support vector machine-based ECG signal processing and compression method. After a conventional pre-filtering step, the characteristic waves (QRS, P, T) from the ECG signal are localized. The following step contains a regressive model for waveform description in terms of model parameters. The gained information allows an iterative filtering in permanent concordance with the aimed processing manner. The structure of the algorithm allows real-time adaptation to the heart's state. Using these methods for one channel of the MIT-BIH database, the detection rate of QRS complexes is above 99.9%. The negative influence of various noise types, like 50/60 Hz power line, abrupt baseline shift or drift, and low sampling rate was almost completely eliminated. The vector support machine system allow a good balance between compressing and diagnostic performance and the obtained results can form a solid base for better data storage in clinical environment.