摘要
心电数据压缩在远程医疗和动态监测方面具有非常重要的意义。心电信号的各个脉动周期具有很强的相关性。基于隐马尔可夫模型(HiddenMarkovModel,HMM)的心电数据压缩方法充分利用ECG信号的相关性对源信息进行处理。实验证明,该方法在高数据压缩比的情况下仍然能够很好地恢复原始数据,计算复杂度相对较小。
ECG data compression is of great importance to the telemedicine and dynamic monitoring.ECG signal has a strong correlation between every pulse period.This method of ECG data compression based on HMM makes full use of ECG signal＇s correlation to process the original information.Experiment verifies that this method can well rebuild the original data while the data compression ratio is high and the computational complexity is lower relatively.
出处
《计算机工程与应用》
CSCD
北大核心
2006年第25期183-184,226,共3页
Computer Engineering and Applications
基金
国家自然科学基金资助项目(编号:60375037)
