Peripheral artery disease diagnosis based on deep learning-enabled analysis of non-invasive arterial pulse waveforms

Sina Masoumi Shahrbabak, Sooho Kim, Byeng Dong Youn, Hao Min Cheng, Chen Huan Chen, Ramakrishna Mukkamala, Jin Oh Hahn*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

This paper intends to investigate the feasibility of peripheral artery disease (PAD) diagnosis based on the analysis of non-invasive arterial pulse waveforms. We generated realistic synthetic arterial blood pressure (BP) and pulse volume recording (PVR) waveform signals pertaining to PAD present at the abdominal aorta with a wide range of severity levels using a mathematical model that simulates arterial blood circulation and arterial BP-PVR relationships. We developed a deep learning (DL)-enabled algorithm that can diagnose PAD by analyzing brachial and tibial PVR waveforms, and evaluated its efficacy in comparison with the same DL-enabled algorithm based on brachial and tibial arterial BP waveforms as well as the ankle-brachial index (ABI). The results suggested that it is possible to detect PAD based on DL-enabled PVR waveform analysis with adequate accuracy, and its detection efficacy is close to when arterial BP is used (positive and negative predictive values at 40 % abdominal aorta occlusion: 0.78 vs 0.89 and 0.85 vs 0.94; area under the ROC curve (AUC): 0.90 vs 0.97). On the other hand, its efficacy in estimating PAD severity level is not as good as when arterial BP is used (r value: 0.77 vs 0.93; Bland-Altman limits of agreement: −32%-+32 % vs −20%-+19 %). In addition, DL-enabled PVR waveform analysis significantly outperformed ABI in both detection and severity estimation. In sum, the findings from this paper suggest the potential of DL-enabled non-invasive arterial pulse waveform analysis as an affordable and non-invasive means for PAD diagnosis.

Original languageEnglish
Article number107813
JournalComputers in Biology and Medicine
Volume168
DOIs
StatePublished - Jan 2024

Keywords

  • Arterial pulse waveform
  • Deep learning
  • Machine learning
  • Peripheral artery disease
  • Pulse volume recording

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