CardioXNet: A Novel Lightweight Deep Learning Framework for Cardiovascular Disease Classification Using Heart Sound Recordings

TL;DR

CardioXNet is a lightweight deep learning framework that accurately classifies multiple cardiovascular conditions from raw heart sound recordings, suitable for use in resource-limited settings.

Contribution

The paper introduces CardioXNet, a novel end-to-end CRNN architecture with parallel CNN pathways and bidirectional LSTMs, achieving high accuracy with fewer parameters for CVD classification.

Findings

Achieves up to 99.60% accuracy on classification tasks.

Outperforms previous state-of-the-art methods on the same dataset.

Suitable for point-of-care screening on low-resource devices.

Abstract

The alarmingly high mortality rate and increasing global prevalence of cardiovascular diseases signify the crucial need for early detection schemes. Phonocardiogram (PCG) signals have been historically applied in this domain owing to its simplicity and cost-effectiveness. In this paper, we propose CardioXNet, a novel lightweight end-to-end CRNN architecture for automatic detection of five classes of cardiac auscultation namely normal, aortic stenosis, mitral stenosis, mitral regurgitation and mitral valve prolapse using raw PCG signal. The process has been automated by the involvement of two learning phases. Three parallel CNN pathways have been implemented in the representation learning phase to learn the coarse and fine-grained features from the PCG and to explore the salient features from variable receptive fields involving 2D-CNN based squeeze-expansion. Thus, in the representation learning phase, the network extracts efficient time-invariant features and converges with great rapidity. In the sequential residual learning phase, with the bidirectional-LSTMs and the skip connection, the network can proficiently extract temporal features without performing any feature extraction on the signal. The obtained results demonstrate that the proposed end-to-end architecture yields outstanding performance in all the evaluation metrics compared to the previous state-of-the-art methods with up to 99.60% accuracy, 99.56% precision, 99.52% recall and 99.68% F1- score on an average while being computationally comparable. This model outperforms the previous works using the same dataset by a considerable margin. The high accuracy metrics on both primary and secondary dataset combined with a significantly low number of parameters and end-to-end prediction approach makes the proposed network suitable for point of care CVD screening in low resource setups using memory constraint mobile devices.