.png){kind=icon}
.png){kind=icon}
Return
Efficient Anomaly Detection Algorithm for Heart Sound Signal
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2024.3465540.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
According to the latest report by the WHO, cardiovascular disease claims approximately 17.9 million lives annually, making it one of the leading causes of mortality. Hence, early screening and detection of cardiovascular diseases are important for their prevention. Heart sound signals contain a wealth of information on cardiac function and health status. Researchers have recently utilized deep learning methods to detect abnormal features in heart sound signals, thereby facilitating disease diagnosis. Currently, existing heart sound datasets suffer from imbalanced data proportions, complex feature types, and low discriminative power between systolic and diastolic murmurs, resulting in the suboptimal performance of deep learning algorithms in detection. Therefore, we propose a heart sound abnormality detection algorithm based on the Swin Transformer architecture. Firstly, we enhance the ability to extract local texture features of heart sound signals by introducing a convolutional embedding module into the positional encoding layer of the backbone network. Second, we augmented the model's capability to extract the frequency-domain features of heart-sound signals by incorporating a discrete convolutional mapping structure. This structure utilizes discrete cosine transformation in conjunction with convolutional projection to acquire feature matrices, thereby improving classification accuracy. Finally, we employed a Focal Loss function to prioritize abnormal heart-sound samples, enhancing the generalization ability of the model and evaluating the proposed algorithm using the PhysionNet/CinC 2016 public dataset. The results demonstrated an Accuracy of 93.4%, a Specificity of 90.4% and a Sensitivity of 95.7%.
Keywords:
Transformers
Feature extraction
Convolutional neural networks
Hidden Markov models
Classification algorithms
Cardiovascular diseases
Heart beat
Abnormality detection
cardiovascular disease
convolution embedding
discrete convolution projection
heart sound signal
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W