Background and objectives

Cardiovascular diseases are critical diseases and need to be diagnosed as early as possible. There is a lack of medical professionals in remote areas to diagnose these diseases. Artificial intelligence-based automatic diagnostic tools can help to diagnose cardiac diseases. This work presents an automatic classification method using machine learning to diagnose multiple cardiac diseases from phonocardiogram signals.

Methods

The proposed system involves a convolutional neural network (CNN) model because of its high accuracy and robustness to automatically diagnose the cardiac disorders from the heart sounds. To improve the accuracy in a noisy environment and make the method robust, the proposed method has used data augmentation techniques for training and multi-classification of multiple cardiac diseases.

Results

The model has been validated both heart sound data and augmented data using n-fold cross-validation. Results of all fold have been shown reported in this work. The model has achieved accuracy on the test set up to 98.60% to diagnose multiple cardiac diseases.

Conclusions

The proposed model can be ported to any computing devices like computers, single board computing processors, android handheld devices etc. To make a stand-alone diagnostic tool that may be of help in remote primary health care centres. The proposed method is non-invasive, efficient, robust, and has low time complexity making it suitable for real-time applications.

中文翻译：

---

使用卷积神经网络从PCG信号自动诊断多种心脏病。

背景和目标

心血管疾病是重要疾病，需要尽早诊断。偏远地区缺乏医务人员来诊断这些疾病。基于人工智能的自动诊断工具可以帮助诊断心脏病。这项工作提出了一种自动分类方法，该方法使用机器学习来从心电图信号诊断多种心脏疾病。

方法

拟议的系统涉及卷积神经网络（CNN）模型，因为它具有很高的准确性和鲁棒性，可以根据心音自动诊断心脏疾病。为了提高在嘈杂环境中的准确性并使该方法具有鲁棒性，所提出的方法已将数据增强技术用于多种心脏病的训练和多分类。

结果

该模型已使用n折交叉验证对心音数据和增强数据进行了验证。所有折叠的结果已显示在这项工作中。该模型在诊断多种心脏疾病的测试设置上已达到98.60％的准确性。

结论

可以将建议的模型移植到任何计算设备上，例如计算机，单板计算处理器，Android手持设备等。以制作独立的诊断工具，这可能对远程初级保健中心有所帮助。所提出的方法是非侵入性的，高效的，鲁棒的，并且时间复杂度低，使其适合于实时应用。