College of Information Science and Engineering, Northeastern University, Shenyang, China.
Shijiazhuang First People's Hospital, Shijiazhuang, China.
Biomed Tech (Berl). 2023 Feb 15;68(3):285-295. doi: 10.1515/bmt-2021-0254. Print 2023 Jun 27.
Heart diseases represent a serious medical condition that can be fatal. Therefore, it is critical to investigate the measures of its early prevention. The Mel-scale frequency cepstral coefficients (MFCC) feature has been widely used in the early diagnosis of heart abnormity and achieved promising results. During feature extraction, the Mel-scale triangular overlapping filter set is applied, which makes the frequency response more in line with the human auditory property. However, the frequency of the heart sound signals has no specific relationship with the human auditory system, which may not be suitable for processing of heart sound signals. To overcome this issue and obtain a more objective feature that can better adapt to practical use, in this work, we propose an equal scale frequency cepstral coefficients (EFCC) feature based on replacing the Mel-scale filter set with a set of equally spaced triangular overlapping filters. We further designed classifiers combining convolutional neural network (CNN), recurrent neural network (RNN) and random forest (RF) layers, which can extract both the spatial and temporal information of the input features. We evaluated the proposed algorithm on our database and the PhysioNet Computational Cardiology (CinC) 2016 Challenge Database. Results from ten-fold cross-validation reveal that the EFCC-based features show considerably better performance and robustness than the MFCC-based features on the task of classifying heart sounds from novel patients. Our algorithm can be further used in wearable medical devices to monitor the heart status of patients in real time with high precision, which is of great clinical importance.
心脏病是一种严重的医学疾病，可能致命。因此，研究其早期预防措施至关重要。梅尔频率倒谱系数 (MFCC) 特征已广泛应用于心脏异常的早期诊断，并取得了良好的效果。在特征提取过程中，应用梅尔三角重叠滤波器组，使频率响应更符合人类听觉特性。然而，心音信号的频率与人类听觉系统没有特定关系，可能不适合处理心音信号。为了克服这个问题，获得更客观的特征，更好地适应实际应用，我们在这项工作中提出了一种基于等比频率倒谱系数 (EFCC) 的特征，该特征用一组等间距的三角重叠滤波器代替梅尔滤波器组。我们进一步设计了结合卷积神经网络 (CNN)、循环神经网络 (RNN) 和随机森林 (RF) 层的分类器，可以提取输入特征的空间和时间信息。我们在自己的数据库和 PhysioNet 计算心脏病学 (CinC) 2016 挑战赛数据库上评估了所提出的算法。十折交叉验证的结果表明，在分类新患者心音的任务中，基于 EFCC 的特征比基于 MFCC 的特征表现出更好的性能和鲁棒性。我们的算法可以进一步用于可穿戴医疗设备，以高精度实时监测患者的心脏状态，这具有重要的临床意义。
