Abstract
A photoplethysmography method has recently been widely used to noninvasively measure blood volume changes during a cardiac cycle. Photoplethysmogram (PPG) signals are sensitive to artifacts that negatively impact the accuracy of many important measurements. In this paper, we propose an efficient system for detecting PPG signal artifacts acquired from a fingertip in the public healthcare database named Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) by using 11 features as the input of the random forest algorithm and classified the signals into two classes: acceptable and anomalous. A real‐time algorithm is proposed to identify artifacts by using the method. The efficient Fisher score feature selection algorithm was used to order and select 11 relevant features from 19 available features that represented the PPG signal very effectively. Six machine learning algorithms (random forest, decision tree, Gaussian naïve Bayes, linear support vector machine, artificial neural network, and probabilistic neural network) were applied with the extracted features, and their classification accuracy was measured. Among them, the random forest had the best performance using only 11 out of 19 features with an accuracy of 85.68%. Our proposed method also achieved good sensitivity and specificity value of 86.57% and 85.09%, respectively. The proposed real‐time algorithm can be an easy and convenient way for real‐time PPG signal artifact detection using smartphones and wearable devices.
Highlights
Photoplethysmogram (PPG) provides a myriad of information related to the cardiovascular system
The average result of testing for 10-fold is considered a single accuracy value. This technique is generally used in machine learning to compare and select a model to estimate its performance on new data
We examined the performance of six machine learning methods and proposed the best method for detecting artifacts in the PPG signals in this database
Summary
Photoplethysmogram (PPG) provides a myriad of information related to the cardiovascular system. A reliable process for measuring different types of physiological signals for daily use is becoming increasingly necessary to minimize hospitalization costs and to save time. PPG signals have been used to measure various clinical parameters, such as blood oxygen saturation, respiratory rate, pulse rate, blood glucose, blood pressure, and many more [2, 3]. A PPG is a waveform used to noninvasively calculate the variation in blood volume in a cardiac cycle [19]. PPG measurement devices are small, portable, and easy to use. The signal has a wave-like motion and represents variation of blood volume in the vessel or capillaries at the measurement site [20]. A single PPG signal of a healthy individual has a systolic and diastolic peak with a dicrotic notch [20]. The period from the start of a signal to the systolic peak is called the systolic phase, while the period from the systolic peak to the end of the single signal is called the diastolic phase
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