An Efficient Fingertip Photoplethysmographic Signal Artifact Detection Method: A Machine Learning Approach

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.

Real-Time Cuffless Continuous Blood Pressure Estimation Using 1D Squeeze U-Net Model: A Progress toward mHealth

“…The sampling frequency of the recorded signal using an android back camera is 30 Hz. The sampling frequency of the recorded signals must match the trained model; thus, linear interpolation [ 46 ] is performed with the recorded PPG signals [ 32 ]. Real-time Equiripple FIR bandpass filtering is performed on the red channel of the PPG signal by using the same filter coefficients used in the training phase.…”

“…Next, the segments are checked for artifacts and divided into two classes: acceptable and unacceptable signals. The unacceptable segments with artifacts are removed from the final dataset by using a random forest machine-learning model [ 32 ]. In Reference [ 32 ], real-time artifact detection of PPG signals is shown.…”

“…The unacceptable segments with artifacts are removed from the final dataset by using a random forest machine-learning model [ 32 ]. In Reference [ 32 ], real-time artifact detection of PPG signals is shown. As our system is also in real time, we used the approach.…”

Beat-Based PPG-ABP Cleaning Technique for Blood Pressure Estimation

“…However, this simple labeling ignores the minimum level of morphological quality completely. Both Machine learning and deep-learning techniques [17,18,22,42,43] are employed for data cleaning or classifying signals as acceptable or anomalous. However, most of these approaches rely on manual data labeling based on experts' annotation.…”

“…It is collected and archived without paying attention to its quality. Hence, many techniques were introduced for data cleaning and abnormality/artifact detection [17][18][19][20][21][22][23].…”