使用MMC记忆体的霍尔特记录仪进行前期睡眠呼吸暂停诊断的家庭记录

A. Yilmaz, T. Dundar
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引用次数: 11

摘要

随着高容量存储器的广泛使用,移动设备的长时间数据记录在今天变得普遍。在本研究中,将该技术应用于专门为家庭前期睡眠呼吸暂停分析而设计的家庭记录设备上,然后在医院进行适当的多导睡眠图检查以确定诊断。设计临床重要信号的便携式记录系统和分析相关的呼吸暂停检测算法是本文进行和报道的两项重要且互补的研究。根据我们的目标,这里讨论的Holter设备能够同时记录多个通道,但在本研究中,我们只考虑三个信号,即ECG,呼吸活动和氧饱和度,这些信号在多导睡眠图诊断阶段对呼吸暂停检测都很重要。在呼吸暂停检测部分,我们初步研究了基于心电信号希尔伯特变换的呼吸暂停检测算法。本研究还比较了基于希尔伯特变换的呼吸暂停检测算法所需要的ECG处理的三种QRS检测算法(基于数字滤波器、基于微分和基于高阶统计量)。基于微分的QRS算法在处理有噪声的心电信号方面具有较好的性能,是目前研究的首选算法。
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Home recording for pre-phase sleep apnea diagnosis by Holter recorder using MMC memory
Long time data recordings by ambulatory devices became common today with widespread use of high capacity memories. In this study, this technology is used on home recording device especially designed for the pre-phase sleep apnea analysis at home before having certain diagnosis by proper polysomnographic examination in a hospital. Designing a portable recording system for clinically significant signals and analyzing an associated apnea detection algorithm are two significant and complementary studies carried out and reported in this paper. According to our aim, the Holter device discussed here is capable of recordings from many channels simultaneously but specifically for this study we consider just three signals namely ECG, breathing activity and oxygen saturation which are all significant for apnea detection in polysomnographic diagnostic phase. In apnea detection part we have initially studied the algorithm based on Hilbert Transform of ECG signal. The study also includes the comparison of three selected QRS detection algorithms (digital filter based, differentiation based and higher order statistics based) for ECG processing required for the Hilbert transform based apnea detection algorithm. Differentiation based QRS algorithm is preferred over other two due to its better performance on noisy ECG signals.
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