APPARATUS, METHOD AND COMPUTER READABLE STORAGE MEDIUM FOR PREDICTING BLOOD PRESSURE NON-COMPRESSIVELY USING CONVOLUTIONAL NEURAL NETWORK AND LONG-SHORT-TERM MEMORY NETWORK

APPARATUS, METHOD AND COMPUTER READABLE STORAGE MEDIUM FOR PREDICTING BLOOD PRESSURE NON-COMPRESSIVELY USING CONVOLUTIONAL NEURAL NETWORK AND LONG-SHORT-TERM MEMORY NETWORK

An apparatus for predicting blood pressure non-compressively includes a sequence folding layer configured to convert a sequence image of non-pressurized biosignals into an arrayed image; a CNN layer configured to generate a feature map by performing a convolution operation on an arrayed image; a seq...

Ausführliche Beschreibung

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Bibliographische Detailangaben
Hauptverfasser: ZHANG, Geng Jia, SHIN, Si Ho, KIM, Youn Tae, JUNG, Jae Hyo
Format: Patent
Sprache:eng
Schlagworte:
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
DIAGNOSIS
HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA
HUMAN NECESSITIES
HYGIENE
IDENTIFICATION
INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS
MEDICAL OR VETERINARY SCIENCE
PHYSICS
SURGERY
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Beschreibung
Zusammenfassung:An apparatus for predicting blood pressure non-compressively includes a sequence folding layer configured to convert a sequence image of non-pressurized biosignals into an arrayed image; a CNN layer configured to generate a feature map by performing a convolution operation on an arrayed image; a sequence unfolding layer configured to convert the generated feature map into a sequence image; a flatten layer configured to convert the converted sequence image into one-dimensional data; a long-short-term memory network layer configured to extract feature values from the converted one-dimensional data using weights; a fully-connected layer configured to perform image classification using feature values extracted from the long-short memory network layer; and a regression layer configured to predict systolic blood pressure (SBP) and diastolic blood pressure (DBP) for the classified image.