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The automatic detection of heart failure using speech signals

机译：使用语音信号自动检测心力衰竭

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Heart failure (HF) is a major global health concern and is increasing in prevalence. It affects the larynx and breathing - thereby the quality of speech. In this article, we propose an approach for the automatic detection of people with HF using the speech signal. The proposed method explores mel-frequency cepstral coefficient (MFCC) features, glottal features, and their combination to distinguish HF from healthy speech. The glottal features were extracted from the voice source signal estimated using glottal inverse filtering. Four machine learning algorithms, namely, support vector machine, Extra Tree, AdaBoost, and feed-forward neural network (FFNN), were trained separately for individual features and their combination. It was observed that the MFCC features yielded higher classification accuracies compared to glottal features. Furthermore, the complementary nature of glottal features was investigated by combining these features with the MFCC features. Our results show that the FFNN classifier trained using a reduced set of glottal + MFCC features achieved the best overall performance in both speaker-dependent and speaker-independent scenarios.
机译：心力衰竭（HF）是一个主要的全球健康问题，普遍存在。它会影响喉部和呼吸 - 从而言论的质量。在本文中，我们提出了一种使用语音信号自动检测HF的人们的方法。该方法探讨了熔融频率谱系齐系数（MFCC）特征，引光特征及其组合，以区分HF免受健康语音。从使用所称逆滤波估计的语音源信号中提取了光学特征。四种机器学习算法，即支持向量机，额外的树，Adaboost和前馈神经网络（FFNN），分别为单独的特征及其组合进行培训。观察到，与引光功能相比，MFCC特征率升高了较高的分类精度。此外，通过将这些特征与MFCC特征组合来研究引擎特征的互补性。我们的研究结果表明，使用减少的光泽+ MFCC功能训练的FFNN分类器实现了扬声器依赖和独立方案中的最佳整体性能。

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