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

被引:7
作者
Attivissimo, Filippo [1 ]
De Palma, Luisa [1 ]
Di Nisio, Attilio [1 ]
Scarpetta, Marco [1 ]
Lanzolla, Anna Maria Lucia [1 ]
机构
[1] Polytech Univ Bari, Dept Elect & Informat Engn, I-70125 Bari, Italy
关键词
blood pressure (BP); hypertension; features selection; maximal overlap discrete wavelet transform (MODWT); photoplethysmography (PPG); telemedicine; TIME; RISK;
D O I
10.3390/s23042321
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
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.
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页数:18
相关论文
共 46 条
  • [11] Ding Chris, 2005, Journal of Bioinformatics and Computational Biology, V3, P185, DOI 10.1142/S0219720005001004
  • [12] The use of photoplethysmography for assessing hypertension
    Elgendi, Mohamed
    Fletcher, Richard
    Liang, Yongbo
    Howard, Newton
    Lovell, Nigel H.
    Abbott, Derek
    Lim, Kenneth
    Ward, Rabab
    [J]. NPJ DIGITAL MEDICINE, 2019, 2 (1)
  • [13] On the Analysis of Fingertip Photoplethysmogram Signals
    Elgendi, Mohamed
    [J]. CURRENT CARDIOLOGY REVIEWS, 2012, 8 (01) : 14 - 25
  • [14] PULSE TRANSIT-TIME AS AN INDICATOR OF ARTERIAL BLOOD-PRESSURE
    GEDDES, LA
    VOELZ, MH
    BABBS, CF
    BOURLAND, JD
    TACKER, WA
    [J]. PSYCHOPHYSIOLOGY, 1981, 18 (01) : 71 - 74
  • [15] PhysioBank, PhysioToolkit, and PhysioNet - Components of a new research resource for complex physiologic signals
    Goldberger, AL
    Amaral, LAN
    Glass, L
    Hausdorff, JM
    Ivanov, PC
    Mark, RG
    Mietus, JE
    Moody, GB
    Peng, CK
    Stanley, HE
    [J]. CIRCULATION, 2000, 101 (23) : E215 - E220
  • [16] Gurumoorthy S., 2020, Epilepsy, DOI [10.5772/intechopen.93180, DOI 10.5772/INTECHOPEN.93180]
  • [17] Continuous Blood Pressure Estimation Using Exclusively Photopletysmography by LSTM-Based Signal-to-Signal Translation
    Harfiya, Latifa Nabila
    Chang, Ching-Chun
    Li, Yung-Hui
    [J]. SENSORS, 2021, 21 (09)
  • [18] Blood Pressure Estimation Using Photoplethysmogram Signal and Its Morphological Features
    Hasanzadeh, Navid
    Ahmadi, Mohammad Mahdi
    Mohammadzade, Hoda
    [J]. IEEE SENSORS JOURNAL, 2020, 20 (08) : 4300 - 4310
  • [19] Generalized Deep Neural Network Model for Cuffless Blood Pressure Estimation with Photoplethysmogram Signal Only
    Hsu, Yan-Cheng
    Li, Yung-Hui
    Chang, Ching-Chun
    Harfiya, Latifa Nabila
    [J]. SENSORS, 2020, 20 (19) : 1 - 18
  • [20] MIMIC-III, a freely accessible critical care database
    Johnson, Alistair E. W.
    Pollard, Tom J.
    Shen, Lu
    Lehman, Li-wei H.
    Feng, Mengling
    Ghassemi, Mohammad
    Moody, Benjamin
    Szolovits, Peter
    Celi, Leo Anthony
    Mark, Roger G.
    [J]. SCIENTIFIC DATA, 2016, 3