Results 181 to 190 of about 5,458 (233)

Blood Pressure Estimation with Phonocardiogram on CNN-Based Approach [PDF]

Kasidit Kokkhunthod, Khomdet Phapatanaburi, Wongsathon Pathonsuwan, Talit Jumphoo, Patikorn Anchuen, Porntip Nimkuntod, Monthippa Uthansakul, Peerapong Uthansakul   +7 more
openalex   +1 more source

A dual branch feature extraction network for heart sound signal analysis. [PDF]

Sci Rep
Chen H, Gu W.
europepmc   +1 more source

Enhanced heart sound classification using Mel frequency cepstral coefficients and comparative analysis of single vs. ensemble classifier strategies. [PDF]

PLoS One
Hosseinzadeh M, Haider A, Malik MH, Adeli M, Mzoughi O, Gemeay E, Mohammadi M, Alinejad-Rokny H, Khoshvaght P, Porntaveetus T, Rahmani AM.   +10 more
europepmc   +1 more source

Effects of Exercise on Phonocardiogram : (1) Clinical studies on the Phonocardiographic response to exercise test

, 1965
Shoichi Onishi
openalex   +2 more sources

Infrasound project at the University Centre of Aalborg [PDF]

, 1979
Møller, Henrik
core  

Rapid detection and interpretation of heart murmurs using phonocardiograms, transfer learning and explainable artificial intelligence. [PDF]

Health Inf Sci Syst
Özcan F.
europepmc   +1 more source

Prediction of Left Ventricle Pressure Indices Via a Machine Learning Approach Combining ECG, Pulse Oximetry, and Cardiac Sounds: a Preclinical Feasibility Study. [PDF]

J Cardiovasc Transl Res
Fassina L, Muzio FPL, Berboth L, Ötvös J, Faragli A, Alogna A.   +5 more
europepmc   +1 more source

Developing an AI-assisted digital auscultation tool for automatic assessment of the severity of mitral regurgitation: protocol for a cross-sectional, non-interventional study. [PDF]

BMJ Open
Zhang L, Cheng Z, Xu D, Wang Z, Cai S, Hu N, Ma J, Mei X.   +7 more
europepmc   +1 more source

THE FAST FOURIER TRANSFORM AND THE CONTINUOUS WAVELET TRANSFORM ANALYSIS OF THE NORMAL AND PATHOLOGICALS PHONOCARDIOGRAM SIGNALS

, 2002
S.M. Debbal, F. Bereksi‐Reguig
openalex   +1 more source

Heart sound classification based on convolutional neural network with convolutional block attention module. [PDF]

Front Physiol
Huai X, Jiang L, Wang C, Chen P, Li H.
europepmc   +1 more source