心音图
计算机科学
帕斯卡(单位)
人工智能
模式识别(心理学)
灵敏度(控制系统)
语音识别
二进制数
时域
扩展(谓词逻辑)
频域
信号(编程语言)
二元分类
计算机视觉
支持向量机
电子工程
算术
数学
工程类
程序设计语言
作者
Zhimin Ren,Yuheng Qiao,Yuping Yuan,You Zhou,Yanchun Liang,Xiaohu Shi
标识
DOI:10.1109/bibm55620.2022.9995295
摘要
Automatic heart sound diagnosis plays an important role in the early detection of cardiovascular diseases. Phonocardiogram (PCG) signals are often used in this field f or its low cost and non-invasive advantages. In this paper, we design a new time and time-frequency features integrated CNN (TTFI-CNN) model for heart sound signals detection. In the model, a 1D CNN and a BiLSTM are combined into 1D CRNN module to extract temporal features from the original PCG signal, and a 2D CNN module is applied to capture high-level features from time-frequency domain MFCCs inputs. The outputs of the two modules are recalibrated by attention mechanism to selectively emphasize informative features and suppress less useful ones. To verify the proposed TTFI-CNN model, it is applied to two public datasets with different classification t asks (binary and multiclassification). The TTFI-CNN model has achieved 97.15% accuracy, 97.13% sensitivity, and 97.17% specificity on physionet/cinc database, and obtained 3.31 and 2.61 precision scores on the PASCAL database A and B, respectively. Compared with the previous state-of-the-art methods, the TTFI-CNN performs best on all the above metrics. https://github.com/XxxNnnSssWww/TTFI-CNN.
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