Abstract
The alarmingly high mortality rate and increasing global prevalence of cardiovascular diseases (CVDs) signify the crucial need for early detection schemes. Phonocardiogram(PCG) signals has been historically applied in this domain owing to its simplicity and cost-effectiveness. However, insufficiency of expert physicians and human subjectivity affect the applicability of this technique, especially in the low-resource settings. For resolving this issue, in this paper, we introduce CardioXNet,a novel lightweight 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 namely, representation learning and sequence residual learning. The first phase mainly focuses on automated feature extraction and it has been implemented in a modular way with three parallel CNN pathways i.e., frequency feature extractor (FFE), pattern extractor (PE) and adaptive feature extractor (AFE). 1D-CNN based FFE and PE respectively learn the coarse and fine-grained features from the PCG while AFE explores the salient features from variable receptive fields involving 2D-CNN based squeezeexpansion. Thus, in the representation learning phase, the network extracts efficient time-invariant features and converges with great rapidity. In the sequential residual learning phase,because of the bidirectional-LSTMs and the skip connection, the network can proficiently extract temporal features. 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 99.6% accuracy, 99.6% precision, 99.6% recall and 99.4% F1-score on an average while being computationally comparable.
Abstract (translated)
URL
https://arxiv.org/abs/2010.01392
