Continuous photoplethysmography (PPG)-based blood pressure monitoring is
necessary for healthcare and fitness applications. In Artificial Intelligence
(AI), signal classification levels with the machine and deep learning
arrangements need to be explored further. Techniques based on time-frequency
spectra, such as Short-time Fourier Transform (STFT), have been used to address
the challenges of motion artifact correction. Therefore, the proposed study
works with PPG signals of more than 200 patients (650+ signal samples) with
hypertension, using STFT with various Neural Networks (Convolution Neural
Network (CNN), Long Short-Term Memory (LSTM), Bidirectional Long Short-Term
Memory (Bi-LSTM), followed by machine learning classifiers, such as, Support
Vector Machine (SVM) and Random Forest (RF). The classification has been done
for two categories: Prehypertension (normal levels) and Hypertension (includes
Stage I and Stage II). Various performance metrics have been obtained with two
batch sizes of 3 and 16 for the fusion of the neural networks. With precision
and specificity of 100% and recall of 82.1%, the LSTM model provides the best
results among all combinations of Neural Networks. However, the maximum
accuracy of 71.9% is achieved by the LSTM-CNN model. Further stacked Ensemble
method has been used to achieve 100% accuracy for Meta-LSTM-RF, Meta-
LSTM-CNN-RF and Meta- STFT-CNN-SVM.

使用时间频谱技术 (如 STFT) 相结合的神经网络 (如 CNN、LSTM、Bi-LSTM) 以及机器学习分类器 (SVM 和 RF)，本研究在超过 200 名血压高的患者 (650 + 信号样本) 的 PPG 信号上进行分类，通过两个分类等级 (以下包括 1 级和 2 级)，在 LSTM 和 LSTM-CNN 模型中获得 100% 的精确度和特异度以及 82.1% 的召回率，并利用集成方法实现了不同模型之间的最佳结果。

用于高血压程度的光电容积脉搏图信号深度学习分类

Deep Learning Classification of Photoplethysmogram Signal for  Hypertension Levels

Hypertension, a leading contributor to cardiovascular morbidity, underscores
the need for accurate and continuous blood pressure (BP) monitoring.
Photoplethysmography (PPG) presents a promising approach to this end. However,
the precision of BP estimates derived from PPG signals has been the subject of
ongoing debate, necessitating a comprehensive evaluation of their effectiveness
and constraints. We developed a calibration-based Siamese ResNet model for BP
estimation, using a signal input paired with a reference BP reading. We
compared the use of normalized PPG (N-PPG) against the normalized Invasive
Arterial Blood Pressure (N-IABP) signals as input. The N-IABP signals do not
directly present systolic and diastolic values but theoretically provide a more
accurate BP measure than PPG signals since it is a direct pressure sensor
inside the body. Our strategy establishes a critical benchmark for PPG
performance, realistically calibrating expectations for PPG's BP estimation
capabilities. Nonetheless, we compared the performance of our models using
different signal-filtering conditions to evaluate the impact of filtering on
the results. We evaluated our method using the AAMI and the BHS standards
employing the VitalDB dataset. The N-IABP signals meet with AAMI standards for
both Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (DBP), with
errors of 1.29+-6.33mmHg for systolic pressure and 1.17+-5.78mmHg for systolic
and diastolic pressure respectively for the raw N-IABP signal. In contrast,
N-PPG signals, in their best setup, exhibited inferior performance than N-IABP,
presenting 1.49+-11.82mmHg and 0.89+-7.27mmHg for systolic and diastolic
pressure respectively. Our findings highlight the potential and limitations of
employing PPG for BP estimation, showing that these signals contain information
correlated to BP but may not be sufficient for predicting it accurately.

我们开发了一个基于校准的 Siamese ResNet 模型，使用与参考血压读数配对的信号输入来估计血压。我们将归一化 PPG（N-PPG）与归一化侵入性动脉血压（N-IABP）信号作为输入进行比较。使用 VitalDB 数据集评估了我们的方法，结果显示 N-IABP 信号在 AAMI 标准下满足收缩压和舒张压的要求，而 N-PPG 信号则表现出较差的性能。我们的发现显示了使用 PPG 估计血压的潜力和限制。

探索利用光电容抗信号估计血压的局限性

Exploring the limitations of blood pressure estimation using the  photoplethysmography signal

Accurate and continuous blood pressure (BP) monitoring is essential to the
early prevention of cardiovascular diseases. Non-invasive and cuff-less BP
estimation algorithm has gained much attention in recent years. Previous
studies have demonstrated that brain bio-impedance (BIOZ) is a promising
technique for non-invasive intracranial pressure (ICP) monitoring. Clinically,
treatment for patients with traumatic brain injuries (TBI) requires monitoring
the ICP and BP of patients simultaneously. Estimating BP by brain BIOZ directly
can reduce the number of sensors attached to the patients, thus improving their
comfort. To address the issues, in this study, we explore the feasibility of
leveraging brain BIOZ for BP estimation and propose a novel cuff-less BP
estimation approach called BrainZ-BP. Two electrodes are placed on the forehead
and occipital bone of the head in the anterior-posterior direction for brain
BIOZ measurement. Various features including pulse transit time and
morphological features of brain BIOZ are extracted and fed into four regression
models for BP estimation. Results show that the mean absolute error, root mean
square error, and correlation coefficient of random forest regression model are
2.17 mmHg, 3.91 mmHg, and 0.90 for systolic pressure estimation, and are 1.71
mmHg, 3.02 mmHg, and 0.89 for diastolic pressure estimation. The presented
BrainZ-BP can be applied in the brain BIOZ-based ICP monitoring scenario to
monitor BP simultaneously.

研究探讨了通过大脑生物阻抗 (Brain BIOZ) 来估计血压的方法，并提出了一种新的无袖血压估计方法 BrainZ-BP。使用大脑生物阻抗的脉冲传递时间和形态特征，通过四个回归模型估计血压，结果表明该方法在收缩压和舒张压估计上具有很好的表现，适用于基于大脑生物阻抗的颅内压监测场景，实现同时监测血压。