Noninvasive blood pressure monitoring from the supraorbital artery using an artificial neural network oscillometric algorithm

Journal of Clinical Monitoring

Volumn 11, Issue 5, 1995, Pages 289-297

  Narus, Scott ^a   Egbert, Timothy ^a   Lee, Tai Kwong ^a   Lu, Jeff ^a   Westenskow, Dwayne ^a  

^a UNIVERSITY OF UTAH HEALTH SCIENCES CENTER   (United States)

Author keywords

Equipment: computer(s); Measurement techniques: oscillometry? auscultation? artificial neural networks; Monitoring: blood pressure; Supraorbital artery

Indexed keywords

ADULT; AGED; ALGORITHM; ARTICLE; AUSCULTATION; BLOOD PRESSURE MONITORING; BRACHIAL ARTERY; CLINICAL TRIAL; COMPUTER MODEL; CONTROLLED CLINICAL TRIAL; CONTROLLED STUDY; DIASTOLIC BLOOD PRESSURE; HUMAN; HUMAN EXPERIMENT; NORMAL HUMAN; OPHTHALMIC ARTERY; ORBIT; OSCILLOMETRY; SYSTOLIC BLOOD PRESSURE;

ADULT; AGED; ALGORITHMS; BLOOD PRESSURE MONITORS; DATA COLLECTION; HUMANS; MIDDLE AGED; NEURAL NETWORKS (COMPUTER); OSCILLOMETRY;

EID: 0029079812     PISSN: 07481977     EISSN: 15732614     Source Type: Journal    
DOI: 10.1007/BF01616986     Document Type: Article

References (18)

1. Oscillometric determination of diastolic, mean and systolic blood pressure—a numerical model
2. Comparison of intra-arterial and automated oscillometric blood pressure measurement methods in postoperative hypertensive patients
3. An assessment of the Dinamap 845
4. Technical aspects of blood pressure measurement
5. Comparison of direct and indirect methods of measuring arterial blood pressure, part III
6. Multilayer feedforward networks are universal approximators
7. Neural-network-based adaptive matched filtering for QRS detection
8. Learning internal representations by error propagation
9. Indirect measurement of mean blood pressure in the anesthetized patient
10. How to develop neural-network applications
11. Neural network training tips and techniques
12. An introduction to computing with neural nets
13. Learning internal representations by error propagation