Non-invasive estimate of blood glucose and blood pressure from a photoplethysmograph by means of machine learning techniques

Artificial Intelligence in Medicine

Volumn 53, Issue 2, 2011, Pages 127-138

  Monte Moreno, Enric ^a  

^a UNIVERSITAT POLITÈCNICA DE CATALUNYA   (Spain)

Author keywords

Blood glucose estimate; Blood pressure estimate; Machine learning; Noninvasive measurement; Photoplethysmography

Indexed keywords

ACTIVITY DETECTION; BLOOD GLUCOSE; BLOOD GLUCOSE LEVEL; CLINICAL USE; CROSS VALIDATION; FUNCTIONAL RELATIONSHIP; GLUCOSE LEVEL; GRADE B; MACHINE LEARNING TECHNIQUES; MACHINE-LEARNING; MULTI-LAYER PERCEPTRON NEURAL NETWORKS; NON-INVASIVE; NONINVASIVE MEASUREMENTS; PHOTOPLETHYSMOGRAPH; PHOTOPLETHYSMOGRAPHY; PRESSURE VALUES; RANDOM FORESTS; WAVE FORMS;

BLOOD PRESSURE; CALIBRATION; DECISION TREES; ESTIMATION; GLUCOSE; LEARNING ALGORITHMS; LEARNING SYSTEMS; NEURAL NETWORKS; SIGNAL PROCESSING;

BLOOD;

GLUCOSE;

ARTICLE; ARTIFICIAL NEURAL NETWORK; BLOOD PRESSURE MEASUREMENT; CALIBRATION; DIASTOLIC BLOOD PRESSURE; GLUCOSE BLOOD LEVEL; MACHINE LEARNING; PLETHYSMOGRAPHY; PRIORITY JOURNAL; SENSOR; SUPPORT VECTOR MACHINE; SYSTOLIC BLOOD PRESSURE; VALIDATION PROCESS; WAVEFORM;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BLOOD GLUCOSE; BLOOD PRESSURE; BLOOD PRESSURE DETERMINATION; HUMANS; HYPERTENSION; LINEAR MODELS; PHOTOPLETHYSMOGRAPHY;

EID: 80052475559     PISSN: 09333657     EISSN: 18732860     Source Type: Journal    
DOI: 10.1016/j.artmed.2011.05.001     Document Type: Article

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