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Related Concept Videos

Heart Sounds01:15

Heart Sounds

3.1K
Heart sounds are generated by the turbulence in blood flow due to the closing of heart valves. These sounds are best perceived slightly away from the valves, where the blood flow disseminates the sound.
Auscultation is the process of listening to these internal body sounds using a stethoscope. The heart produces four types of sounds, but only two—S1 and S2—can usually be heard with a stethoscope.
S1, also known as the "lub" sound, is caused by the closure of atrioventricular (A-V)...
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Behavioral Determination of Stimulus Pair Discrimination of Auditory Acoustic and Electrical Stimuli Using a Classical Conditioning and Heart-rate Approach
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Heart Sound Segmentation Using Bidirectional LSTMs With Attention.

Tharindu Fernando, Houman Ghaemmaghami, Simon Denman

    IEEE Journal of Biomedical and Health Informatics
    |November 1, 2019
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel framework using recurrent neural networks and attention mechanisms for segmenting phonocardiogram (PCG) signals into heart states. The method achieves state-of-the-art results, outperforming existing techniques for heart sound analysis.

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    Area of Science:

    • Biomedical Signal Processing
    • Artificial Intelligence in Healthcare

    Background:

    • Heart sound segmentation is vital for diagnosing cardiac conditions.
    • Manual segmentation is labor-intensive and prone to inaccuracies.
    • Automated methods are needed for efficient and accurate heart sound analysis.

    Purpose of the Study:

    • To develop a novel framework for segmenting phonocardiogram (PCG) signals into distinct heart states.
    • To leverage temporal information and salient features within PCG signals for improved heart state detection.
    • To provide a more accurate and cost-effective alternative to manual segmentation.

    Main Methods:

    • Utilized recurrent neural networks (RNNs) combined with attention-based learning mechanisms.
    • Exploited the temporal evolution and salient information within PCG signals.
    • Investigated various feature combinations, including envelop, wavelet, and Mel Frequency Cepstral Coefficients (MFCCs).

    Main Results:

    • Achieved state-of-the-art performance on diverse PCG datasets, including human and animal recordings.
    • Demonstrated the effectiveness of RNNs with attention in handling noisy and irregular PCG data.
    • Identified MFCC features and their derivatives as superior to wavelet and envelop features for this task.

    Conclusions:

    • Recurrent neural networks with attention mechanisms provide an effective approach for PCG signal segmentation.
    • MFCC features offer superior performance for heart sound analysis compared to traditional features.
    • The proposed method enhances diagnostic accuracy for conditions like murmurs and ejection clicks and is applicable to other 1D biomedical signals.