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首页> 外文会议>International Conference on Biomedical Engineering >Performance Analysis of GMM Classifier for Classification of Normal and Abnormal Segments in PPG Signals
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Performance Analysis of GMM Classifier for Classification of Normal and Abnormal Segments in PPG Signals

机译：PPG信号中正常和异常段分类的GMM分类器性能分析

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Photoplethysmography (PPG) is widely used to estimate the blood flow of skin by utilizing the infrared technique. Parameters such as blood pressure, oxygen saturation levels, blood saturation levels and cardiac output levels can be measured easily. As PPG is non-invasive in nature and it has a low production and maintenance cost, it is widely used in clinical practices. The performance analysis of Gaussian Mixture Model (GMM) as a post classifier is utilized in this paper for the classification of normal and abnormal segments in PPG Signals. The main objective of the paper is to identify normal and abnormal PPG Segments of the PPG waveform observed in the long time monitoring of the Physionet Data Base available online for a particular patient. The PPG Signals are sampled at 100 Hz. The PPG data sample length obtained is 1, 44, 000 and it is segmented into equal intervals comprising of 200 samples totally. Therefore the entire data consists of 720 segments. Totally ten different features such as mean, variance, standard deviation, skewness, kurtosis, energy, approximate entropy, peak maximum, maximum slope, and Singular Value Decomposition (SVD) are extracted and normalized. Based on the SVD values, each segment is labeled as normal or abnormal segment. The normalized features are given as inputs to the GMM classifier to classify the normal and abnormal segments in the PPG Signals. The performance metrics analyzed in this work are specificity, sensitivity, accuracy, precision and False Discovery Rate (FDR). Results show that an accuracy of 98.97% is obtained, precision of 100%, nil FDR, specificity of 100% and sensitivity of 97.95% is obtained.
机译：光增性血晶摄影（PPG）广泛用于通过利用红外技术来估计皮肤的血流。可以容易地测量血压，氧饱和水平，血液饱和水平和心脏输出水平的参数。由于PPG本质上是非侵入性的，并且它具有较低的生产和维护成本，因此广泛应用于临床实践。本文利用了高斯混合模型（GMM）作为后分类器的性能分析，用于PPG信号中正常和异常段的分类。本文的主要目的是识别在为特定患者提供的在线可用的物理仪数据库的长期监测中观察到的PPG波形的正常和异常PPG片段。 PPG信号以100Hz采样。获得的PPG数据样本长度为1,44，000，并且它分段为完全包含200个样品的相等间隔。因此，整个数据由720个段组成。完全十种不同的特征，如平均值，方差，标准偏差，偏斜，峰度，能量，近似熵，峰值最大，最大斜率和奇异值分解（SVD）被提取和标准化。基于SVD值，每个段都标记为正常或异常段。归一化特征被给予GMM分类器的输入，以对PPG信号中的正常和异常段进行分类。在本工作中分析的性能指标是特异性，灵敏度，准确性，精度和假发现率（FDR）。结果表明，获得了98.97％的准确性，获得了100％，零FDR，含量为100％，敏感度为97.95％。

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