Performance Analysis of GMM Classifier for Classification of Normal and Abnormal Segments in PPG Signals
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
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Abstract. 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. Keywords: PPG, GMM, SVD, Normal, Abnormal
1 Introduction PPG is a non-invasive technique which has a very low cost and it is an easy maintenance tool for the purpose of monitoring the changes in the blood volume incessantly in the tissues with respect to function of time [1]. It is one of the standard non-invasive techniques for the measurement analysis of oxygen saturation level and has been widely
acknowledged by both European committee for standardization and International Standard Organization (ISO). In various clinical fields like critical care unit, surgical recovery units and in anesthesia, PPG is used efficiently. Due to its portable and non-intrusive nature, the techniques involved with PPG have been a subject of extensive and in-depth research in the recent decades. With drastic changes and improvements emerging in the field of clinical instrumentation, Digital Signal Processing, opto electronics and so on PPG has achieved a versatile spectrum of different applications initiating from vascular assessment, physiological monitoring in a clinical manner and evaluation of autonomic functions [2]. During the measurement process, the PPG signals can be affected easily which paves the way for inaccurate analysis of PPG waveform. Some of the major errors raised in PPG are due to the ambient light source at the photo detector or the peripheral tissues which can
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