Shape-preserving preprocessing for human pulse signals based on adaptive parameter determination

Rehabilitation Assessment System for Stroke Patients Based on Fusion-Type Optoelectronic Plethysmography Device and Multi-Modality Fusion Model: Design and Validation

This study aimed to propose a portable and intelligent rehabilitation evaluation system for digital stroke-patient rehabilitation assessment. Specifically, the study designed and developed a fusion device capable of emitting red, green, and infrared lights simultaneously for photoplethysmography (PPG) acquisition. Leveraging the different penetration depths and tissue reflection characteristics of these light wavelengths, the device can provide richer and more comprehensive physiological information. Furthermore, a Multi-Channel Convolutional Neural Network-Long Short-Term Memory-Attention (MCNN-LSTM-Attention) evaluation model was developed. This model, constructed based on multiple convolutional channels, facilitates the feature extraction and fusion of collected multi-modality data. Additionally, it incorporated an attention mechanism module capable of dynamically adjusting the importance weights of input information, thereby enhancing the accuracy of rehabilitation assessment. To validate the effectiveness of the proposed system, sixteen volunteers were recruited for clinical data collection and validation, comprising eight stroke patients and eight healthy subjects. Experimental results demonstrated the system's promising performance metrics (accuracy: 0.9125, precision: 0.8980, recall: 0.8970, F1 score: 0.8949, and loss function: 0.1261). This rehabilitation evaluation system holds the potential for stroke diagnosis and identification, laying a solid foundation for wearable-based stroke risk assessment and stroke rehabilitation assistance.

Photoplethysmography Driven Hypertension Identification: A Pilot Study

To prevent and diagnose hypertension early, there has been a growing demand to identify its states that align with patients. This pilot study aims to research how a non-invasive method using photoplethysmographic (PPG) signals works together with deep learning algorithms. A portable PPG acquisition device (Max30101 photonic sensor) was utilized to (1) capture PPG signals and (2) wirelessly transmit data sets. In contrast to traditional feature engineering machine learning classification schemes, this study preprocessed raw data and applied a deep learning algorithm (LSTM-Attention) directly to extract deeper correlations between these raw datasets. The Long Short-Term Memory (LSTM) model underlying a gate mechanism and memory unit enables it to handle long sequence data more effectively, avoiding gradient disappearance and possessing the ability to solve long-term dependencies. To enhance the correlation between distant sampling points, an attention mechanism was introduced to capture more data change features than a separate LSTM model. A protocol with 15 healthy volunteers and 15 hypertension patients was implemented to obtain these datasets. The processed result demonstrates that the proposed model could present satisfactory performance (accuracy: 0.991; precision: 0.989; recall: 0.993; F1-score: 0.991). The model we proposed also demonstrated superior performance compared to related studies. The outcome indicates the proposed method could effectively diagnose and identify hypertension; thus, a paradigm to cost-effectively screen hypertension could rapidly be established using wearable smart devices.

Wrist pulse signal acquisition and analysis for disease diagnosis: A review

Pulse diagnosis (PD) plays an indispensable role in healthcare in China, India, Korea, and other Orient countries. It requires considerable training and experience to master. The results of pulse diagnosis rely heavily on the practitioner's subjective analysis, which means that the results from different physicians may be inconsistent. To overcome these drawbacks, computational pulse diagnosis (CPD) is used with advanced sensing techniques and analytical methods. Focusing on the main processes of CPD, this paper provides a systematic review of the latest advances in pulse signal acquisition, signal preprocessing, feature extraction, and signal recognition. The most relevant principles and applications are presented along with current progress. Extensive comparisons and analyses are conducted to evaluate the merits of different methods employed in CPD. While much progress has been made, a lack of datasets and benchmarks has limited the development of CPD. To address this gap and facilitate further research, we present a benchmark to evaluate different methods. We conclude with observations of the status and prospects of CPD.

Photoplethysmography-Based Method for Automatic Detection of Premature Ventricular Contractions

This work introduces a method for detection of premature ventricular contractions (PVCs) in photoplethysmogram (PPG). The method relies on 6 features, characterising PPG pulse power, and peak-to-peak intervals. A sliding window approach is applied to extract the features, which are later normalized with respect to an estimated heart rate. Artificial neural network with either linear and non-linear outputs was investigated as a feature classifier. PhysioNet databases, namely, the MIMIC II and the MIMIC, were used for training and testing, respectively. After annotating the PPGs with respect to synchronously recorded electrocardiogram, two main types of PVCs were distinguished: with and without the observable PPG pulse. The obtained sensitivity and specificity values for both considered PVC types were 92.4/99.9% and 93.2/99.9%, respectively. The achieved high classification results form a basis for a reliable PVC detection using a less obtrusive approach than the electrocardiography-based detection methods.