Institute of Fundamental Technological Research, Polish Academy of Sciences, Pawinskiego 5B, 02-106 Warsaw, Poland.
Jagiellonian University Medical College, Lazarza 16, 31-530 Krakow, Poland.
Comput Methods Programs Biomed. 2019 Dec;182:105052. doi: 10.1016/j.cmpb.2019.105052. Epub 2019 Aug 24.
People suffer from sleep disorders caused by work-related stress, irregular lifestyle or mental health problems. Therefore, development of effective tools to diagnose sleep disorders is important. Recently, to analyze biomedical signals Information Theory is exploited. We propose efficient classification method of sleep anomalies by applying entropy estimating algorithms to encoded ECGs signals coming from patients suffering from Sleep-Related Breathing Disorders (SRBD).
First, ECGs were discretized using the encoding method which captures the biosignals variability. It takes into account oscillations of ECG measurements around signals averages. Next, to estimate entropy of encoded signals Lempel-Ziv complexity algorithm (LZ) which measures patterns generation rate was applied. Then, optimal encoding parameters, which allow distinguishing normal versus abnormal events during sleep with high sensitivity and specificity were determined numerically. Simultaneously, subjects' states were identified using acoustic signal of breathing recorded in the same period during sleep.
Random sequences show normalized LZ close to 1 while for more regular sequences it is closer to 0. Our calculations show that SRBDs have normalized LZ around 0.32 (on average), while control group has complexity around 0.85. The results obtained to public database are similar, i.e. LZ for SRBDs around 0.48 and for control group 0.7. These show that signals within the control group are more random whereas for the SRBD group ECGs are more deterministic. This finding remained valid for both signals acquired during the whole duration of experiment, and when shorter time intervals were considered. Proposed classifier provided sleep disorders diagnostics with a sensitivity of 93.75 and specificity of 73.00%. To validate our method we have considered also different variants as a training and as testing sets. In all cases, the optimal encoding parameter, sensitivity and specificity values were similar to our results above.
Our pilot study suggests that LZ based algorithm could be used as a clinical tool to classify sleep disorders since the LZ complexities for SRBD positives versus healthy individuals show a significant difference. Moreover, normalized LZ complexity changes are related to the snoring level. This study also indicates that LZ technique is able to detect sleep abnormalities in early disorders stage.
由于工作压力、生活方式不规律或心理健康问题等原因，人们会患上睡眠障碍。因此，开发有效的睡眠障碍诊断工具非常重要。最近，信息论被用于分析生物医学信号。我们提出了一种通过将熵估计算法应用于来自患有睡眠呼吸相关障碍（SRBD）患者的编码 ECG 信号来有效分类睡眠异常的方法。
首先，通过捕获生物信号变化的编码方法对 ECG 进行离散化。它考虑了 ECG 测量值围绕信号平均值的波动。接下来，为了估计编码信号的熵，应用了 Lempel-Ziv 复杂度算法（LZ），该算法用于测量模式生成率。然后，通过数值确定允许以高灵敏度和特异性区分睡眠期间正常与异常事件的最佳编码参数。同时，使用睡眠期间同时记录的呼吸声信号来识别受试者的状态。
随机序列的归一化 LZ 接近 1，而更规则的序列更接近 0。我们的计算表明，SRBD 的归一化 LZ 约为 0.32（平均值），而对照组的复杂度约为 0.85。对公共数据库的结果相似，即 SRBD 的 LZ 约为 0.48，对照组为 0.7。这表明对照组的信号更随机，而 SRBD 组的 ECG 更确定。这一发现对于整个实验期间采集的信号以及较短的时间间隔均成立。所提出的分类器提供了睡眠障碍诊断，其灵敏度为 93.75%，特异性为 73.00%。为了验证我们的方法，我们还考虑了不同的变体作为训练集和测试集。在所有情况下，最佳编码参数、灵敏度和特异性值与我们的结果相似。
我们的初步研究表明，基于 LZ 的算法可以用作临床工具来分类睡眠障碍，因为 SRBD 阳性与健康个体的 LZ 复杂度存在显著差异。此外，归一化 LZ 复杂度的变化与打鼾水平有关。这项研究还表明，LZ 技术能够在早期疾病阶段检测到睡眠异常。
