Key result
A novel CNN and BiGRU attention-based architecture achieved 90% accuracy in classifying heartbeat sounds into normal and murmurs for elderly people, outperforming baseline deep learning models.
Why the study?
Early detection of cardiovascular diseases via heartbeat sounds can reduce harmful effects, but segmenting, feature-extracting, and predicting heartbeat sounds in elderly people remains challenging.
Does a CNN + BiGRU attention-based architecture improve the classification accuracy of heartbeat sounds in elderly people compared to baseline deep learning models?
Does a CNN + BiGRU attention-based architecture improve the classification accuracy of heartbeat sounds in elderly people compared to baseline deep learning models?
A novel CNN and BiGRU attention-based deep learning architecture achieves 90% accuracy in classifying heartbeat sounds as normal or murmur, offering a potential tool for early cardiovascular disease detection in the elderly.
Answers are grounded in this paper and cite their sources.
May aid automated murmur screening in elderly; leaves open prospective clinical validation before adoption.
Cardiovascular diseases (CVDs) are a significant cause of death worldwide. CVDs can be prevented by diagnosing heartbeat sounds and other conventional techniques early to reduce the harmful effects caused by CVDs. However, it is still challenging to segment, extract features, and predict heartbeat sounds in elderly people. The inception of deep learning (DL) algorithms has helped detect various types of heartbeat sounds at an early stage. Motivated by this, we proposed an intelligent architecture categorizing heartbeat into normal and murmurs for elderly people. We have used a standard heartbeat dataset with heartbeat class labels, i.e., normal and murmur. Furthermore, it is augmented and preprocessed by normalization and standardization to significantly reduce computational power and time. The proposed convolutional neural network and bi-directional gated recurrent unit (CNN + BiGRU) attention-based architecture for the classification of heartbeat sound achieves an accuracy of 90% compared to the baseline approaches. Hence, the proposed novel CNN + BiGRU attention-based architecture is superior to other DL models for heartbeat sound classification.
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Yadav et al. (2023) studied Cardiovascular diseases (heartbeat sound classification). CNN + BiGRU attention-based architecture vs. Baseline deep learning approaches was evaluated on Classification accuracy of heartbeat sounds (normal vs. murmurs). A novel CNN and BiGRU attention-based architecture achieved 90% accuracy in classifying heartbeat sounds into normal and murmurs for elderly people, outperforming baseline deep learning models.
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