Audibility of the Fourth Heart Sound

Jordan, Marlon D.

doi:10.1001/archinte.1987.00370040103018

Cited by 20 publications

(1 citation statement)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…5,6 The main challenge in cardiac auscultation is the difficulty of detecting and interpreting subtle acoustic features associated with heart sound abnormalities. Manual classification of heart sounds suffers from high intra-observer variability, [7][8][9][10][11][12][13][14] causing false positive and false negative results. Much work has been done in trying to improve screening accuracy, including efforts to design devices to record heart sounds and automatically classify them.…”

Section: Introductionmentioning

confidence: 99%

Segmental Convolutional Neural Networks for Detection of Cardiac Abnormality With Noisy Heart Sound Recordings

Zhang¹,

Ayyar²,

Chen³

et al. 2016

Preprint

View full text Add to dashboard Cite

Heart diseases constitute a global health burden, and the problem is exacerbated by the error-prone nature of listening to and interpreting heart sounds. This motivates the development of automated classification to screen for abnormal heart sounds. Existing machine learning-based systems achieve accurate classification of heart sound recordings but rely on expert features that have not been thoroughly evaluated on noisy recordings. Here we propose a segmental convolutional neural network architecture that achieves automatic feature learning from noisy heart sound recordings. Our experiments show that our best model, trained on noisy recording segments acquired with an existing hidden semi-markov model-based approach, attains a classification accuracy of 87.5% on the 2016 PhysioNet/CinC Challenge dataset, compared to the 84.6% accuracy of the state-of-the-art statistical classifier trained and evaluated on the same dataset. Our results indicate the potential of using neural network-based methods to increase the accuracy of automated classification of heart sound recordings for improved screening of heart diseases.

show abstract

Section: Introductionmentioning

confidence: 99%

Segmental Convolutional Neural Networks for Detection of Cardiac Abnormality With Noisy Heart Sound Recordings

Zhang¹,

Ayyar²,

Chen³

et al. 2016

Preprint

View full text Add to dashboard Cite

show abstract

Enhanced Auscultation With a New Graphic Display System

Tavel¹

1994

Arch Intern Med

View full text Add to dashboard Cite

The graphic display system can often provide more information than can be obtained by standard auscultation alone, especially in the detection of low-frequency and multiple sounds, and in the accurate timing of intervals. It is often unable to detect soft high-frequency murmurs. Permanent records allow for more objective comparison of the auscultatory findings of various examiners at different times. This system provides an excellent means by which auscultation skills may be taught or enhanced, especially since its speed and portability allow immediate feedback for comparison with auditory perceptions.

show abstract