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.

翻译：基于连续光电容积脉搏波（PPG）的血压监测对于医疗保健和健身应用至关重要。在人工智能（AI）领域，基于机器学习和深度学习架构的信号分级分类方法仍需进一步探索。基于时频谱的技术，如短时傅里叶变换（STFT），已被用于应对运动伪影校正的挑战。因此，本研究利用超过200名高血压患者（650多个信号样本）的PPG信号，结合STFT与多种神经网络（卷积神经网络（CNN）、长短期记忆网络（LSTM）、双向长短期记忆网络（Bi-LSTM）），以及机器学习分类器，如支持向量机（SVM）和随机森林（RF），开展分析。分类任务设定为两个类别：高血压前期（正常水平）和高血压（包括I期和II期）。通过采用3和16两种批处理大小对神经网络进行融合，获得了多种性能指标。在所有神经网络组合中，LSTM模型取得了最佳结果，其精确度和特异度达到100%，召回率为82.1%。然而，LSTM-CNN模型实现了最高的准确率，达到71.9%。进一步采用堆叠集成方法后，Meta-LSTM-RF、Meta-LSTM-CNN-RF和Meta-STFT-CNN-SVM模型均实现了100%的准确率。