Twomey Niall, Walsh Noel, Doyle Orla, McGinley Brian, Glavin Martin, Jones Edward, Marnane W P
Department of Electrical and Electronic Engineering, University College Cork, Ireland, Galway.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:634-7. doi: 10.1109/IEMBS.2010.5627261.
This paper describes the performance of beat detection and heart rate variability (HRV) feature extraction on electrocardiogram signals which have been compressed and reconstructed with a lossy compression algorithm. The set partitioning in hierarchical trees (SPIHT) compression algorithm was used with sixteen compression ratios (CR) between 2 and 50 over the records of the MIT/BIH arrhythmia database. Sensitivities and specificities between 99% and 85% were computed for each CR utilised. The extracted HRV features were between 99% and 82% similar to the features extracted from the annotated records. A notable accuracy drop over all features extracted was noted beyond a CR of 30, with falls of 10% accuracy beyond this compression ratio.
本文描述了在使用有损压缩算法进行压缩和重建的心电图信号上进行心跳检测和心率变异性（HRV）特征提取的性能。在麻省理工学院/贝斯以色列女执事医疗中心心律失常数据库的记录上，使用分层树状集合分割（SPIHT）压缩算法，压缩比（CR）在2到50之间，共16种。针对每个使用的压缩比计算了99%到85%之间的灵敏度和特异性。提取的HRV特征与从注释记录中提取的特征相似度在99%到82%之间。在压缩比超过30时，所有提取特征的准确率显著下降，超过此压缩比后准确率下降了10%。
