  Photoplethysmography (PPG) is a highly promising device due to its advantages
in portability, user-friendly operation, and non-invasive capabilities to
measure a wide range of physiological information. Recent advancements in deep
learning have demonstrated remarkable outcomes by leveraging PPG signals for
tasks related to personal health management and other multifaceted
applications. In this review, we systematically reviewed papers that applied
deep learning models to process PPG data between January 1st of 2017 and July
31st of 2023 from Google Scholar, PubMed and Dimensions. Each paper is analyzed
from three key perspectives: tasks, models, and data. We finally extracted 193
papers where different deep learning frameworks were used to process PPG
signals. Based on the tasks addressed in these papers, we categorized them into
two major groups: medical-related, and non-medical-related. The medical-related
tasks were further divided into seven subgroups, including blood pressure
analysis, cardiovascular monitoring and diagnosis, sleep health, mental health,
respiratory monitoring and analysis, blood glucose analysis, as well as others.
The non-medical-related tasks were divided into four subgroups, which encompass
signal processing, biometric identification, electrocardiogram reconstruction,
and human activity recognition. In conclusion, significant progress has been
made in the field of using deep learning methods to process PPG data recently.
This allows for a more thorough exploration and utilization of the information
contained in PPG signals. However, challenges remain, such as limited quantity
and quality of publicly available databases, a lack of effective validation in
real-world scenarios, and concerns about the interpretability, scalability, and
complexity of deep learning models. Moreover, there are still emerging research
areas that require further investigation.
