DeepPAD: Detection of Peripheral Arterial Disease Using Deep Learning

Peripheral arterial disease (PAD) is a common circulatory disease caused by the deposition of fatty plaque on the arterial wall. Early detection and treatment of PAD are essential in preventing further cardiovascular and health complications. It was recently shown that PAD can be detected using an oscillometric device by characterizing the peripheral arterial system at different externally applied cuff pressures. However, the extraction of the complex relationship between the pattern of the oscillometric waveforms and the presence of PAD remained challenging. This study proposes a novel deep learning approach for the detection of PAD by capturing the peripheral arterial system behavior at different cuff pressures. The periodic pattern of the oscillometric pulses and their variations as a function of cuff pressure were modeled using a deep recurrent neural network based on the bidirectional long short-term memory and attention mechanism. The proposed model was evaluated by analyzing the raw oscillometric pulses as well as statistical features on data collected from 33 individuals (14 PAD and 19 normal). The results show a high accuracy of up to 94.8%, a sensitivity of up to 90.0%, and a specificity of up to 97.4% in detecting PAD. The proposed method provides new opportunities for noninvasive cardiovascular screening and early detection of PAD using the oscillometric principle.

Automated summary

This study proposes a novel deep learning approach for the detection of peripheral arterial disease (PAD) by analyzing the behavior of the peripheral arterial system at different cuff pressures. The proposed model, based on a deep recurrent neural network and attention mechanism, accurately captures the periodic pattern of oscillometric pulses and their variations with cuff pressure. The results demonstrate high accuracy, sensitivity, and specificity in detecting PAD.