Results 201 to 210 of about 29,766 (248)

Federated learning for cardiovascular disease prediction: a systematic review of clinical applications, validation, and translation readiness. [PDF]

Front Cardiovasc Med
Li J, Xiang W, Shang D, Li S, Li Q.
europepmc   +1 more source

Explainable attention-based deep learning for classification and interpretation of heart murmurs using phonocardiograms. [PDF]

Sci Rep
Althaph B, Challa NP.
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Frog vocal sacs-inspired soft acoustic system with continuously tunable resonance for sound emission and stethoscopic sensing. [PDF]

Sci Adv
Liu C, Dong P, Wang J, Deng Z, Luo J, Liu C, Wu J, Tan K, Pan J, Han R, Li Y, Chen L, Qu X, Ma J, Zhou Q, Yan B, Ran Y, Rong D, Jiang J, Li B, Ren TL, Zhou J, Qiao Y.   +22 more
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Advances in Sensor-Based Technologies for Health, Aging, and Intelligent Care. [PDF]

Sensors (Basel)
Liu L, Miguel Cruz A.
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Wheeze detection in real-world pediatric care: AI applied to smartphone lung auscultation. [PDF]

Eur J Pediatr
Pais-Cunha I, Pessoa D, Silva MC, Ferreira-Cardoso H, Rocha B, Paiva RP, de Carvalho P, Fonseca JA, Azevedo I, Jácome C.   +9 more
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A Federated Learning Paradigm for Heart Sound Classification

2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2022
Cardiovascular diseases (CVDs) have been ranked as the leading cause for deaths. The early diagnosis of CVDs is a crucial task in the medical practice. A plethora of efforts were given to the automated auscultation of heart sound, which leverages the power of computer audition to develop a cheap, non-invasive method that can be used at any time and ...
Wanyong Qiu, Kun Qian 0003, Zhihua Wang, Yi Chang 0004, Zhihao Bao, Bin Hu 0001, Björn W. Schuller, Yoshiharu Yamamoto   +7 more
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Segmentation and classification of heart sounds

Canadian Conference on Electrical and Computer Engineering, 2005., 2006
An algorithm for segmentation of heart sounds (HSs) into a single cardiac cycle (Sl-Systole-S2-Diastole) using homomorphic filtering and k-means clustering and a three way classification of heart sounds into normal (N), systolic murmur (S), and diastolic murmur (D), based on neural networks is developed.
C.N. Gupta, R. Palaniappan, S. Rajan, S. Swaminathan, S.M. Krishnan   +4 more
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Cluster analysis and classification of heart sounds

Biomedical Signal Processing and Control, 2009
Acoustic heart signals, generated by the mechanical processes of the cardiac cycle, carry significant information about the underlying functioning of the cardiovascular system. We describe a computational analysis framework for identifying distinct morphologies of heart sounds and classifying them into physiological states.
Guy Amit, Noam Gavriely, Nathan Intrator
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Unsupervised classification of heart sound recordings

2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, 2013
An unsupervised framework for classifying heart sound data is proposed in this paper. Our goal is to cluster unknown heart sound recordings, such that each cluster contains sound recordings belonging to the same heart diseases or normal heart beat category.
Wei-Ho Tsai, Sung-How Su, Cin-Hao Ma
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Phonocardiogram signals classification into normal heart sounds and heart murmur sounds

2016 11th International Conference on Intelligent Systems: Theories and Applications (SITA), 2016
Heart disease is the biggest killer in the world, it is a serious public health problem facing the world today. This problem has not only attracted the attention of doctors and cardiologists, but also that of signal processing specialists who seek to effectively detect this disease by treating cardiac signals.
Fatima Chakir, Abdelilah Jilbab, Chafik Nacir, Ahmed Hammouch   +3 more
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