INESC TEC, Campus da FEUP, Rua Dr. Roberto Frias, 4200 - 465, Porto, Portugal.
Instituto Superior de Engenharia do Porto, Rua Dr. Bernardino de Almeida, 431, 4200-072, Porto, Portugal.
J Med Syst. 2019 May 6;43(6):168. doi: 10.1007/s10916-019-1286-5.
Cardiovascular disease is the leading cause of death in the world, and its early detection is a key to improving long-term health outcomes. The auscultation of the heart is still an important method in the medical process because it is very simple and cheap. To detect possible heart anomalies at an early stage, an automatic method enabling cardiac health low-cost screening for the general population would be highly valuable. By analyzing the phonocardiogram signals, it is possible to perform cardiac diagnosis and find possible anomalies at an early-term. Therefore, the development of intelligent and automated analysis tools of the phonocardiogram is very relevant. In this work, we use simultaneously collected electrocardiograms and phonocardiograms from the Physionet Challenge database with the main objective of determining whether a phonocardiogram corresponds to a "normal" or "abnormal" physiological state. Our main contribution is the methodological combination of time domain features and frequency domain features of phonocardiogram signals to improve cardiac disease automatic classification. This novel approach is developed using both features. First, the phonocardiogram signals are segmented with an algorithm based on a logistic regression hidden semi-Markov model, which uses electrocardiogram signals as a reference. Then, two groups of features from the time and frequency domain are extracted from the phonocardiogram segments. One group is based on motifs and the other on Mel-frequency cepstral coefficients. After that, we combine these features into a two-dimensional time-frequency heat map representation. Lastly, a binary classifier is applied to both groups of features to learn a model that discriminates between normal and abnormal phonocardiogram signals. In the experiments, three classification algorithms are used: Support Vector Machines, Convolutional Neural Network, and Random Forest. The best results are achieved when both time and Mel-frequency cepstral coefficients features are considered using a Support Vector Machines with a radial kernel.
心血管疾病是世界上的主要死亡原因，早期发现是改善长期健康结果的关键。听诊仍然是医疗过程中的重要方法，因为它非常简单和廉价。为了早期发现可能的心脏异常，对于普通人群来说，一种能够进行心脏健康低成本筛查的自动方法将具有很高的价值。通过分析心音图信号，可以进行心脏诊断并及早发现可能的异常。因此，开发心音图的智能和自动化分析工具非常重要。在这项工作中，我们同时使用 Physionet 挑战赛数据库中的心电图和心音图进行分析，主要目的是确定心音图是否对应于“正常”或“异常”的生理状态。我们的主要贡献是在心音图信号的时频域特征的方法组合，以提高心脏疾病的自动分类。该新方法使用两种特征进行开发。首先，使用基于逻辑回归隐半马尔可夫模型的算法对心音图信号进行分段，该算法使用心电图信号作为参考。然后，从心音图段中提取时频域的两组特征。一组基于模式，另一组基于梅尔频率倒谱系数。之后，我们将这些特征组合成二维时频热图表示。最后，将二进制分类器应用于两组特征，以学习区分正常和异常心音图信号的模型。在实验中，使用了三种分类算法：支持向量机、卷积神经网络和随机森林。当同时考虑使用径向核的支持向量机的时频和梅尔频率倒谱系数特征时，可获得最佳结果。
