CNN and Bidirectional GRU-Based Heartbeat Sound Classification Architecture for Elderly People

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.

Automated summary

Cardiovascular diseases (CVDs) are a significant cause of death worldwide. Deep learning algorithms have been utilized to detect various types of heartbeat sounds at an early stage, improving the accuracy of classification. The proposed CNN + BiGRU attention-based architecture achieves an accuracy of 90% in heartbeat sound classification, outperforming other DL models.