Photoplethysmography Signal Wavelet Enhancement and Novel Features Selection for Non-Invasive Cuff-Less Blood Pressure Monitoring

Abstract

In this paper, new features relevant to blood pressure (BP) estimation using photoplethysmography (PPG) are presented. A total of 195 features, including the proposed ones and those already known in the literature, have been calculated on a set composed of 50,000 pulses from 1080 different patients. Three feature selection methods, namely Correlation-based Feature Selection (CFS), RReliefF and Minimum Redundancy Maximum Relevance (MRMR), have then been applied to identify the most significant features for BP estimation. Some of these features have been extracted through a novel PPG signal enhancement method based on the use of the Maximal Overlap Discrete Wavelet Transform (MODWT). As a matter of fact, the enhanced signal leads to a reliable identification of the characteristic points of the PPG signal (e.g., systolic, diastolic and dicrotic notch points) by simple means, obtaining results comparable with those from purposely defined algorithms. For systolic points, mean and std of errors computed as the difference between the locations obtained using a purposely defined already known algorithm and those using the MODWT enhancement are, respectively, 0.0097 s and 0.0202 s; for diastolic points they are, respectively, 0.0441 s and 0.0486 s; for dicrotic notch points they are 0.0458 s and 0.0896 s. Hence, this study leads to the selection of several new features from the MODWT enhanced signal on every single pulse extracted from PPG signals, in addition to features already known in the literature. These features can be employed to train machine learning (ML) models useful for estimating systolic blood pressure (SBP) and diastolic blood pressure (DBP) in a non-invasive way, which is suitable for telemedicine health-care monitoring.

Non-Invasive Blood Pressure Sensing via Machine Learning

“…In a previous work carried out by the authors [29], PPG signals were analyzed to select the most significant features for BP estimation by using several selection algorithms, i.e., RReliefF [30,31], Correlation-based Feature Selection (CFS), and Minimum Redundancy Maximum Relevance (MRMR) [32,33]. That methodology has led to the justification of the application of the Maximal Overlap Discrete Wavelet Transform (MODWT) to enhance the single-site PPG signal and to the selection of new proposed features [29].…”

“…In a previous work carried out by the authors [29], PPG signals were analyzed to select the most significant features for BP estimation by using several selection algorithms, i.e., RReliefF [30,31], Correlation-based Feature Selection (CFS), and Minimum Redundancy Maximum Relevance (MRMR) [32,33]. That methodology has led to the justification of the application of the Maximal Overlap Discrete Wavelet Transform (MODWT) to enhance the single-site PPG signal and to the selection of new proposed features [29]. Following this line of research, in this paper, our focus is on the actual development of ML techniques to find the best algorithm to measure BP, showing the usefulness of the already analyzed features and, in particular, those selected by means of MRMR, including those obtained after the enhancement with MODWT.…”

“…In this work, the MIMIC-III Waveform Database [34][35][36] was used to obtain the dataset for training and validation following the same processing reported in detail in [29] After the processing briefly described above, the features presented in [29] were calculated on the PPG signal. Many features were extracted in the time and frequency domain, others were related to the amplitude of the characteristic points (max slope point, systolic peak, dicrotic notch, inflection point, and diastolic peak), times and durations of characteristic points, areas, non-linear functions (logarithm of positions of dicrotic notch and inflection point), statistics (mean, STD, skewness, percentiles) and first and second derivatives.…”

“…In this paper, it has been exploited to train supervised regression models able to measure BP starting from features extracted from the PPG signal. For training purposes, each observation of the dataset is provided with systolic and diastolic labels obtained from the corresponding ABP signal, which serves as ground truth, as reported in [29].…”

Smart Patch for Non-Invasive Blood Pressure Monitoring in Epileptic Seizure Patients via the Sole

“…ECG signal, and the PPT estimation from the difference (ms) between the systolic peaks of the ECG and PPG signals (second derivative photoplethysmography (SDPPG)) [28] Recent research has shown a growing interest in developing novel approaches for monitoring blood pressure in epilepsy patients during seizures. However, these studies mainly relied on traditional blood pressure monitoring or ECG signals [29] to measure blood pressure [30,31], which can be uncomfortable for the patient and cannot provide continuous monitoring. Several recent research studies have investigated non-invasive methods for continuous blood pressure monitoring, but few have focused specifically on monitoring blood pressure during seizures [32,33].…”