Compression of Vital Biosignals of Patient by Enhanced Set Partitioning in Hierarchical Trees Algorithm

Recent developments in medicine lead to enhance the provision of the healthcare services. In some medical areas, such as telesurgery, telediagnosis, and telemedicine, real-time analysis of patient's vital biosignals (such as ECG signals from different leads, blood pressure and SpO2 waveforms) is necessary to support the patient and diagnose one's disease. In these cases, vital signs data of the patient should be transmitted. Therefore, it is imperative that the data be simultaneously compressed with high processing speed, high compression ratio and low error in results. In this paper, wavelet-based compression of patient's vital biosignals using the Enhanced Set Partitioning in Hierarchical Trees (ESPIHT) algorithm is presented. The proposed method is employed on selected records from MGH/MF waveform database. From the obtained results, it is concluded that the proposed technique is efficient to compress multichannel vital biosignal data.