This paper presents a fast and cost-effective method for diagnosing cardiac abnormalities with high accuracy and reliability using low-cost systems in clinics. The primary limitation of automatic diagnosing of cardiac diseases is the rarity of correct and acceptable labeled samples, which can be expensive to prepare. To address this issue, two methods are proposed in this work. The first method is a unique Multi-Branch Deep Convolutional Neural Network (MBDCN) architecture inspired by human auditory processing, specifically designed to optimize feature extraction by employing various sizes of convolutional filters and audio signal power spectrum as input. In the second method, called as Long short-term memory-Convolutional Neural (LSCN) model, Additionally, the network architecture includes Long Short-Term Memory (LSTM) network blocks to improve feature extraction in the time domain. The innovative approach of combining multiple parallel branches consisting of the one-dimensional convolutional layers along with LSTM blocks helps in achieving superior results in audio signal processing tasks. The experimental results demonstrate superiority of the proposed methods over the state-of-the-art techniques. The overall classification accuracy of heart sounds with the LSCN network is more than 96%. The efficiency of this network is significant compared to common feature extraction methods such as Mel Frequency Cepstral Coefficients (MFCC) and wavelet transform. Therefore, the proposed method shows promising results in the automatic analysis of heart sounds and has potential applications in the diagnosis and early detection of cardiovascular diseases.

翻译：本文提出了一种快速且经济高效的方法，可在临床环境中利用低成本系统实现高精度、高可靠性的心脏异常诊断。心脏疾病自动诊断的主要局限在于正确且可接受的标注样本稀缺，且制备成本高昂。为解决此问题，本研究提出两种方法。第一种方法是一种独特的多分支深度卷积神经网络架构，其灵感来源于人类听觉处理机制，通过采用不同尺寸的卷积滤波器并以音频信号功率谱作为输入，专门优化特征提取。第二种方法称为长短期记忆-卷积神经网络模型，该网络架构包含长短期记忆网络模块，以改善时域特征提取。通过将多个由一维卷积层与LSTM模块构成的并行分支相结合，这种创新方法在音频信号处理任务中取得了优异效果。实验结果表明，所提方法优于现有先进技术。LSCN网络对心音的整体分类准确率超过96%。相较于梅尔频率倒谱系数和小波变换等传统特征提取方法，该网络的效率显著提升。因此，所提方法在心音自动分析中展现出良好前景，在心血管疾病的诊断与早期检测中具有潜在应用价值。