Photoplethysmographic (PPG) signals offer diagnostic potential beyond heart
rate analysis or blood oxygen level monitoring. In the recent past, research
focused extensively on non-invasive PPG-based approaches to blood pressure (BP)
estimation. These approaches can be subdivided into regression and
classification methods. The latter assign PPG signals to predefined BP
intervals that represent clinically relevant ranges. The former predict
systolic (SBP) and diastolic (DBP) BP as continuous variables and are of
particular interest to the research community. However, the reported accuracies
of BP regression methods vary widely among publications with some authors even
questioning the feasibility of PPG-based BP regression altogether. In our work,
we compare BP regression and classification approaches. We argue that BP
classification might provide diagnostic value that is equivalent to regression
in many clinically relevant scenarios while being similar or even superior in
terms of performance. We compare several established neural architectures using
publicly available PPG data for SBP regression and classification with and
without personalization using subject-specific data. We found that
classification and regression models perform similar before personalization.
However, after personalization, the accuracy of classification based methods
outperformed regression approaches. We conclude that BP classification might be
preferable over BP regression in certain scenarios where a coarser segmentation
of the BP range is sufficient.

本文比较了基于 Photoplethysmographic 信号的血压回归和分类方法，认为在某些场景下，血压分类方法可能比血压回归方法更可取。作者使用公开的 PPG 数据进行一系列分类和回归模型的比较，发现分类模型在个性化后的准确度优于回归方法，因此在某些足够粗略的 BP 范围分割场景下，可以采用 BP 分类方法。

回归还是分类？基于深度神经网络的 PPG 数据 BP 预测在实际应用中的思考

Regression or Classification? Reflection on BP prediction from PPG data  using Deep Neural Networks in the scope of practical applications

Heart rate monitoring using wrist-type photoplethysmographic (PPG) signals
during subjects' intensive exercise is a difficult problem, since the signals
are contaminated by extremely strong motion artifacts caused by subjects' hand
movements. So far few works have studied this problem. In this work, a general
framework, termed TROIKA, is proposed, which consists of signal decomposiTion
for denoising, sparse signal RecOnstructIon for high-resolution spectrum
estimation, and spectral peaK trAcking with verification. The TROIKA framework
has high estimation accuracy and is robust to strong motion artifacts. Many
variants can be straightforwardly derived from this framework. Experimental
results on datasets recorded from 12 subjects during fast running at the peak
speed of 15 km/hour showed that the average absolute error of heart rate
estimation was 2.34 beat per minute (BPM), and the Pearson correlation between
the estimates and the ground-truth of heart rate was 0.992. This framework is
of great values to wearable devices such as smart-watches which use PPG signals
to monitor heart rate for fitness.

本文提出了一个名为 TROIKA 的通用框架，用于清除并估计含有极强动态干扰的 PPT 信号中的心率，并验证了该框架的高度精确性和鲁棒性。实验结果表明，心率估算的平均绝对误差为 2.34 次 / 分钟 (BPM)，估计值与实际心率的皮尔逊相关系数为 0.992。该框架对于像智能手表这样将 PPT 信号用于健身的可穿戴设备具有重大价值。