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.
引用
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页数:18
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共 46 条
  • [1] A comparative study on mother wavelet selection in ultrasound image denoising
    Adamo, Francesco
    Andria, Gregorio
    Attivissimo, Filippo
    Lanzolla, Anna Maria Lucia
    Spadavecchia, Maurizio
    [J]. MEASUREMENT, 2013, 46 (08) : 2447 - 2456
  • [2] Linear filtering of 2-D wavelet coefficients for denoising ultrasound medical images
    Andria, G.
    Attivissimo, F.
    Cavone, G.
    Giaquinto, N.
    Lanzolla, A. M. L.
    [J]. MEASUREMENT, 2012, 45 (07) : 1792 - 1800
  • [3] Conceptual design of a machine learning-based wearable soft sensor for non-invasive cardiovascular risk assessment
    Arpaia, Pasquale
    Cuocolo, Renato
    Donnarumma, Francesco
    Esposito, Antonio
    Moccaldi, Nicola
    Natalizio, Angela
    Prevete, Roberto
    [J]. MEASUREMENT, 2021, 169
  • [4] A Wearable EEG Instrument for Real-Time Frontal Asymmetry Monitoring in Worker Stress Analysis
    Arpaia, Pasquale
    Moccaldi, Nicola
    Prevete, Roberto
    Sannino, Isabella
    Tedesco, Annarita
    [J]. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2020, 69 (10) : 8335 - 8343
  • [5] Conventional pulse transit times as markers of blood pressure changes in humans
    Block, Robert C.
    Yavarimanesh, Mohammad
    Natarajan, Keerthana
    Carek, Andrew
    Mousavi, Azin
    Chandrasekhar, Anand
    Kim, Chang-Sei
    Zhu, Junxi
    Schifitto, Giovanni
    Mestha, Lalit K.
    Inan, Omer T.
    Hahn, Jin-Oh
    Mukkamala, Ramakrishna
    [J]. SCIENTIFIC REPORTS, 2020, 10 (01)
  • [7] Castaneda Denisse, 2018, Int J Biosens Bioelectron, V4, P195, DOI 10.15406/ijbsbe.2018.04.00125
  • [8] Home Telemonitoring of Vital Signs-Technical Challenges and Future Directions
    Celler, Branko G.
    Sparks, Ross S.
    [J]. IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 2015, 19 (01) : 82 - 91
  • [9] Estimating Blood Pressure from the Photoplethysmogram Signal and Demographic Features Using Machine Learning Techniques
    Chowdhury, Moajjem Hossain
    Shuzan, Md Nazmul Islam
    Chowdhury, Muhammad E. H.
    Mahbub, Zaid B.
    Uddin, M. Monir
    Khandakar, Amith
    Reaz, Mamun Bin Ibne
    [J]. SENSORS, 2020, 20 (11)
  • [10] Characterization of Heart Rate Estimation Using Piezoelectric Plethysmography in Time- and Frequency-domain
    De Palma, Luisa
    Scarpetta, Marco
    Spadavecchia, Maurizio
    [J]. 2020 IEEE INTERNATIONAL SYMPOSIUM ON MEDICAL MEASUREMENTS AND APPLICATIONS (MEMEA), 2020,