Aortic Stenosis Detection by Improved Inception Convolution Network-Enabled Pulse Wave

• Published in: Proceedings - International Conference on Parallel and Distributed Systems pp. 108 - 115
• Main Authors: Yang, Ruotong, Kang, Le, Zhou, Anfu, Cui, Hao, Ma, Huadong
• Format: Conference Proceeding
• Language: English
• Published: IEEE 10.10.2024
• Subjects: Accuracy; Convolution; Feature extraction; Fingers; Sensitivity; Sensor systems; Sensors; Training; Transformers; Valves
• ISSN: 2690-5965
• DOI: 10.1109/ICPADS63350.2024.00024

Aortic stenosis (AS) is one of the most common and severe valvular heart diseases, which can cause sudden cardiac death. Early detection and diagnosis are the most effective ways to prevent the irreversible progression of AS. Existing methods mainly rely on large or complex devices such as echocardiography, 12-leads electrocardiogram, which are marred by too many medical resources like experimental experts. There has been recent research showing promising results using cardiomechanical signals, and the need for a daily, robust, convenient, low-cost, and user-friendly AS detection system has become more critical than ever. In this paper, we propose FinP-AS, an innovative AS detection system that uses photoplethysmogram (PPG) sensors to achieve fast and low-cost detection of AS. However, using simple and cost-effective PPG sensors does not necessarily make the data analysis process straightforward. Firstly, due to the inherent limitations of devices, PPG signals we captured are always fraught with complex noise and exhibit unclear periodic features. To overcome that, we utilize window slicing and embedding strategies on raw PPG signals, which enhance the periodic characteristics of the PPG signal. Additionally, the features of aortic valve activity are significantly attenuated by the time they are transmitted to the finger, which makes AS features more difficult to extract. To tackle the problem, we have refined our asymmetric convolutional network architecture by incorporating depthwise separable convolutions and residual connections, which allows the network to detect subtle features of AS symptoms in the PPG signal from various depths and orientations beneath the subject's skin, while simultaneously reducing the parameter count by 50 \% and easing the training process. An empirical evaluation of the FinP-AS model across nearly 80 subjects demonstrates robust AS detection, with the best accuracy of 94 \%, and sensitivity of \mathbf{9 8 \%}.