ECG Signal Compression using Discrete Sinc Interpolation
ICISIP '05 Proceedings of the 2005 3rd International Conference on Intelligent Sensing and Information Processing
Optimal zonal wavelet-based ECG data compression for a mobile telecardiology system
IEEE Transactions on Information Technology in Biomedicine
Wavelet-based low-delay ECG compression algorithm for continuous ECG transmission
IEEE Transactions on Information Technology in Biomedicine
Editorial note on bio, medical, and health informatics
IEEE Transactions on Information Technology in Biomedicine - Special section on new and emerging technologies in bioinformatics and bioengineering
Data structure-guided development of electrocardiographic signal characterization and classification
Artificial Intelligence in Medicine
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An ECG signal processing method with quad level vector (QLV) is proposed for the ECG holter system. The ECG processing consists of the compression flow and the classification flow, and the QLV is proposed for both flows to achieve better performance with low-computation complexity. The compression algorithm is performed by usingECGskeleton and theHuffman coding. Unit block size optimization, adaptive threshold adjustment, and 4-bit-wise Huffman coding methods are applied to reduce the processing cost while maintaining the signal quality. The heartbeat segmentation and the R-peak detection methods are employed for the classification algorithm. The performance is evaluated by using the Massachusetts Institute of Technology-Boston's Beth Israel Hospital ArrhythmiaDatabase, and the noise robust test is also performed for the reliability of the algorithm. Its average compression ratio is 16.9:1 with 0.641% percentage root mean square difference value and the encoding rate is 6.4 kbps. The accuracy performance of the R-peak detection is 100% without noise and 95.63% at the worst case with -10-dB SNR noise. The overall processing cost is reduced by 45.3% with the proposed compression techniques.