Photoplethysmogram as a source of biomarkers for AI-based diagnosis of heart failure

Abstract

We present our progress on the “Multi-SENSor SysteM and ARTificial intelligence in service of heart failure diagnosis (SensSmart)” project, which was introduced at the last year’s edition of the Workshop [1]. The goal of the SensSmart project is to enable early diagnosis of heart failure, through the development of: 1) a multi-sensor polycardiograph apparatus (PCG) that produces simultaneous acquisition of the subject’s electrocardiogram (ECG), photoplethysmogram (PPG), heart sounds, and heart movements, and 2) AI-assisted analysis of the acquired signals. This presentation is going to focus on the acquisition and processing of PPG signals. PPG is obtained by using a pulse oximeter which illuminates the skin and measures the changes in light absorption, thereby enabling the detection of blood volume changes in the vessels. Our PCG apparatus measures the blood flow through the brachial, radial, and carotid arteries. During each heartbeat, the generated waveform typically exhibits several characteristic points [2]. The magnitudes and time distances between these points are useful indicators of many cardiac conditions, including heart failure [3]. However, the inter-patient variability of the PPG waveform makes it challenging to derive simple rule-based diagnostic procedures. This has led many researchers to turn to statistical or machine learning methods for processing of PPG signals [4]. In this presentation, we give an overview of AI-based signal processing methods for PPG, and present some preliminary results and challenges in extracting features from real-world signals obtained using our PCG.