Unobtrusive assessment of neonatal sleep state based on heart rate variability retrieved from electrocardiography used for regular patient monitoring

被引:14
作者
Werth, Jan [1 ,2 ]
Long, Xi [1 ,2 ]
Aarts, Ronald M. [1 ,2 ]
Zwartkruis-Pelgrim, Elly [2 ]
Niemarkt, Hendrik [3 ]
Chen, Wei [4 ]
Andriessen, Peter [5 ]
机构
[1] Univ Technol Eindhoven, Dept Elect Engn, NL-5612 AJ Eindhoven, Netherlands
[2] Philips Res, High Tech Campus 34, NL-5656 AE Eindhoven, Netherlands
[3] Maxima Med Ctr, Neonatal Intens Care Unit, De Run 4600, NL-5504 DB Veldhoven, Netherlands
[4] Fudan Univ, Shanghai Key Lab Med Imaging Comp & Comp Assisted, Dept Elect Engn, Ctr Intelligent Med Elect,Sch Informat Sci & Tech, Shanghai 200433, Peoples R China
[5] Maastricht Univ, Fac Hlth Med & Life Sci, Minderbroedersberg 4-6, NL-6211 LK Maastricht, Netherlands
关键词
Active sleep; Quiet sleep; Heart rate variability; Support vector machine; Automated; Separation; PRETERM INFANTS; FETAL; CLASSIFICATION; REDUCTION; PATTERNS; SIGNALS; DESIGN; TIME; TERM; UNIT;
D O I
10.1016/j.earlhumdev.2017.07.004
中图分类号
R71 [妇产科学];
学科分类号
100211 ;
摘要
As an approach of unobtrusive assessment of neonatal sleep state we aimed at an automated sleep state coding based only on heart rate variability obtained from electrocardiography used for regular patient monitoring. We analyzed active and quiet sleep states of preterm infants between 30 and 37 weeks postmenstrual age. To determine the sleep states we used a nonlinear kernel support vector machine for sleep state separation based on known heart rate variability features. We used unweighted and weighted misclassification penalties for the imbalanced distribution between sleep states. The validation was performed with leave-one-out-cross-validation based on the annotations of three independent observers. We analyzed the classifier performance with receiver operating curves leading to a maximum mean value for the area under the curve of 0.87. Using this sleep state separation methods, we show that automated active and quiet sleep state separation based on heart rate variability in preterm infants is feasible.
引用
收藏
页码:104 / 113
页数:10
相关论文
共 67 条
  • [21] DETERMINATION OF SLEEP STATE IN INFANTS USING RESPIRATORY VARIABILITY
    HADDAD, GG
    JENG, HJ
    LAI, TL
    MELLINS, RB
    [J]. PEDIATRIC RESEARCH, 1987, 21 (06) : 556 - 562
  • [22] MACHINE CLASSIFICATION OF INFANT SLEEP STATE USING CARDIORESPIRATORY MEASURES
    HARPER, RM
    SCHECHTMAN, VL
    KLUGE, KA
    [J]. ELECTROENCEPHALOGRAPHY AND CLINICAL NEUROPHYSIOLOGY, 1987, 67 (04): : 379 - 387
  • [23] Sleeping and waking state development in preterm infants
    Holditch-Davis, D
    Scher, M
    Schwartz, T
    Hudson-Barr, D
    [J]. EARLY HUMAN DEVELOPMENT, 2004, 80 (01) : 43 - 64
  • [24] Huang Chin-Mei, 2004, J Nurs Res, V12, P31
  • [25] Interaction between Heart Rate Variability and Respiration in Preterm Infants
    Indic, P.
    Salisbury, E. B.
    Paydarfar, D.
    Brown, E. N.
    Barbieri, R.
    [J]. COMPUTERS IN CARDIOLOGY 2008, VOLS 1 AND 2, 2008, : 57 - +
  • [26] Assessment of cardio-respiratory interactions in preterm infants by bivariate autoregressive modeling and surrogate data analysis
    Indic, Premananda
    Bloch-Salisbury, Elisabeth
    Bednarek, Frank
    Brown, Emery N.
    Paydarfar, David
    Barbieri, Riccardo
    [J]. EARLY HUMAN DEVELOPMENT, 2011, 87 (07) : 477 - 487
  • [27] An automated method for coding sleep states in human infants based on respiratory rate variability
    Isler, Joseph R.
    Thai, Tracy
    Myers, Michael M.
    Fifer, William P.
    [J]. DEVELOPMENTAL PSYCHOBIOLOGY, 2016, 58 (08) : 1108 - 1115
  • [28] Kommers D. R., 2016, J PEDIAT, P1
  • [29] Koolen N., 2017, CLIN NEUROPHYSIOL
  • [30] Non-contact heart rate and heart rate variability measurements: A review
    Kranjec, J.
    Begus, S.
    Gersak, G.
    Drnovsek, J.
    [J]. BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2014, 13 : 102 - 112