Advancement Of Medical Instrumentation Research Articles

Fiber Bragg Grating Pulse and Systolic Blood Pressure Measurement System Based on Mach-Zehnder Interferometer.

A fiber Bragg grating (FBG) pulse and systolic blood pressure (SBP) measurement system based on the edge-filtering method is proposed. The edge filter is the Mach-Zehnder interferometer (MZI) fabricated by two fiber couplers with a linear slope of 52.45 dBm/nm. The developed system consists of a broadband light source, an edge filter, fiber Bragg gratings (FBGs), a coarse wavelength-division multiplexer (CWDM), and signal-processing circuits based on a field-programmable gate array (FPGA). It can simultaneously measure pulse pulsations of the radial artery in the wrist at three positions: Cun, Guan and Chi. The SBP can be calculated based on the pulse transit time (PTT) principle. The measurement results compared to a standard blood pressure monitor showed the mean absolute error (MAE) and standard deviation (STD) of the SBP were 0.93 ± 3.13 mmHg. The system meets the requirements of the Association for the Advancement of Medical Instrumentation (AAMI) equipment standards. The proposed system can achieve continuous real-time measurement of pulse and SBP and has the advantages of fast detection speed, stable performance, and no compression sensation for subjects. The system has important application value in the fields of human health monitoring and medical device development.

Comparison of the ClearSight™ finger cuff monitor versus invasive arterial blood pressure measurement in elective cardiac surgery patients: a prospective observational study.

To determine the acceptability of the ClearSight™ system (Edwards Lifesciences Corp., Irvine, CA, USA) for continuous blood pressure monitoring during elective cardiac surgery compared with arterial catheterization. We enrolled 30 patients undergoing elective cardiac surgery in a prospective observational study. Blood pressure measurements were recorded every 10 sec intraoperatively. We determined agreement based on the Association for the Advancement of Medical Instrumentation (AAMI) recommendations. Statistical analysis included fixed bias (difference of measurements between methods), percentage error (accuracy between ClearSight measurement and expected measurement from arterial line), and interchangeability (ability to substitute ClearSight monitor without effecting overall outcome of analysis). We used a paired samples t test to compare the time required for placing each monitor. We found fixed bias in the differences between the ClearSight monitor and invasive arterial blood pressure measurement in systolic blood pressure (SBP; mean difference, 8.7; P < 0.001) and diastolic blood pressure (DBP; mean difference, -2.2; P < 0.001), but not in mean arterial pressure (MAP; mean difference, -0.5; P < 0.001). Bland-Altman plots showed that the means of the limits of agreement were greater than 5mm Hg for SBP, DBP, and MAP. The percentage errors for SBP, DBP, and MAP were lower than the cutoff we calculated from the invasive arterial blood pressure measurements. Average interchangeability rates were 38% for SBP, 50% for DBP, and 50% for MAP. Placement of the ClearSight finger cuff was significantly faster compared with arterial catheterization (mean [standard deviation], 1.7 [0.6] min vs 5.6 [4.1] min; P < 0.001). In this prospective observational study, we did not find the ClearSight system to be an acceptable substitute for invasive arterial blood pressure measurement in elective cardiac surgery patients according to AAMI guidelines. Nevertheless, based on statistical standards, there is evidence to suggest otherwise. ClinicalTrials.gov ( NCT05825937 ); first submitted 11 April 2023.

A paralleled CNN and Transformer network for PPG-based cuff-less blood pressure estimation

Cuff-less blood pressure measurement plays a pivotal part in hypertension diagnosis and prevention. By employing deep learning algorithms, blood pressure estimation applying photoplethysmography (PPG) signals has achieved remarkable improvements and attracted growing attention. However, most existing methods utilize the PPG signal emphasized on localized feature learning, often neglecting the global features in the temporal data. To tackle the issue, we propose a novel architecture. It leverages a parallel processing approach by using Convolutional Neural Network for extracting local features and simultaneously employing Transformer to capture global features. The feature fusion block integrates the features using spatial and channel attention. The blood pressure values are regressed by two fully connected layers. To verify the performance of our method, we conduct evaluations on the Medical Information Mart for Intensive Care (MIMIC) database. On the MIMIC database, our algorithm demonstrates exceptional performance. For Diastolic Pressure, Mean Arterial Pressure, and Systolic Pressure, the mean absolute errors are 2.36 mmHg, 2.03 mmHg, and 4.44 mmHg, respectively. This performance aligns with the rigorous criteria required by an Association for the Advancement of Medical Instrumentation (AAMI) and receives a Grade A rating in accordance with the British Hypertension Society (BHS) standard.

Continuous blood pressure monitoring based on transformer encoders and stacked attention gated recurrent units

Continuous blood pressure monitoring (CBPM) is critical to support the accurate prevention and reliable treatment of cardiovascular diseases. To achieve efficient multi-information interaction and further improve the monitoring performance, this research proposes an intelligent model based on transformer encoders and stacked attention gated recurrent units (TE-SAGRU) for CBPM. Long-term multi-source feature sequences with rich information are initially extracted from photoplethysmography (PPG) and electrocardiography (ECG) signals. The paralleled transformer encoders are constructed for different source feature sequences to obtain high-level feature representations and preserve respective long-term independence. The multiple stacked attention gated recurrent units are cross-connected for multi-interactive feature fusion and promoting complementarity effects of multi-source features on CBPM. Comprehensive comparison experiments are carried out to validate the effectiveness of the TE-SAGRU model, using the dataset with 1000 subjects derived from the MIMIC-III database. The continuous monitoring errors of the TE-SAGRU model for systolic blood pressure (SBP) and diastolic blood pressure (DBP) are 3.91 ± 5.65 mmHg and 2.29 ± 3.01 mmHg. The monitored results pass the requirement of the Association for the Advancement of Medical Instrumentation (AAMI) standard and achieve Grade A of the British Hypertension Society (BHS) protocol.

GAN-SkipNet: A Solution for Data Imbalance in Cardiac Arrhythmia Detection Using Electrocardiogram Signals from a Benchmark Dataset

Electrocardiography (ECG) plays a pivotal role in monitoring cardiac health, yet the manual analysis of ECG signals is challenging due to the complex task of identifying and categorizing various waveforms and morphologies within the data. Additionally, ECG datasets often suffer from a significant class imbalance issue, which can lead to inaccuracies in detecting minority class samples. To address these challenges and enhance the effectiveness and efficiency of cardiac arrhythmia detection from imbalanced ECG datasets, this study proposes a novel approach. This research leverages the MIT-BIH arrhythmia dataset, encompassing a total of 109,446 ECG beats distributed across five classes following the Association for the Advancement of Medical Instrumentation (AAMI) standard. Given the dataset’s inherent class imbalance, a 1D generative adversarial network (GAN) model is introduced, incorporating the Bi-LSTM model to synthetically generate the two minority signal classes, which represent a mere 0.73% fusion (F) and 2.54% supraventricular (S) of the data. The generated signals are rigorously evaluated for similarity to real ECG data using three key metrics: mean squared error (MSE), structural similarity index (SSIM), and Pearson correlation coefficient (r). In addition to addressing data imbalance, the work presents three deep learning models tailored for ECG classification: SkipCNN (a convolutional neural network with skip connections), SkipCNN+LSTM, and SkipCNN+LSTM+Attention mechanisms. To further enhance efficiency and accuracy, the test dataset is rigorously assessed using an ensemble model, which consistently outperforms the individual models. The performance evaluation employs standard metrics such as precision, recall, and F1-score, along with their average, macro average, and weighted average counterparts. Notably, the SkipCNN+LSTM model emerges as the most promising, achieving remarkable precision, recall, and F1-scores of 99.3%, which were further elevated to an impressive 99.60% through ensemble techniques. Consequently, with this innovative combination of data balancing techniques, the GAN-SkipNet model not only resolves the challenges posed by imbalanced data but also provides a robust and reliable solution for cardiac arrhythmia detection. This model stands poised for clinical applications, offering the potential to be deployed in hospitals for real-time cardiac arrhythmia detection, thereby benefiting patients and healthcare practitioners alike.

Assessment of 19 Operation Room and Sterile Processing Units in Puerto Rico, 2023: Preliminary Findings using a new ICAR Tool

Background: Infection prevention and control assessments in healthcare settings serve as a primary resource for obtaining data and providing recommendations based on safety, compliance, and quality assurance guidelines. In Puerto Rico (PR), surgical site infections are underreported in the Epi Info platform used by the Puerto Rico Department of Health (PRDOH), mainly due to the complexity of their identification. By focusing on evaluating Operating Rooms/Sterile Processing and Distribution (OR/SPD) units in acute care facilities (ACFs), our goal is to generate new data within the Healthcare-Associated Infection/Antibiotic Resistance (HAI/AR) Program, specifically related to patient management throughout preoperative, intraoperative, and postoperative phases, as well as reprocessing practices. Methods: Nineteen evaluations of ACFs' OR/SPDs were conducted from May through December 2023. Direct observations, file reviews, and personnel assessments were performed using an infection control assessment and response (ICAR) tool developed collaboratively by a team from an acute facility in PR and the HAI/AR Program staff. This ICAR Tool was customized based on guidelines from the certified Board for Sterile Processing and Distribution (CBSPD), the Association of periOperative Registered Nurses (AORN), and the Association for the Advancement of Medical Instrumentation (AAMI), among other regulatory agencies. The Division of Health Quality Promotion (DHQP) reviewed and approved the tool for use in these evaluations. Results: Key findings indicate that 32% of Sterile Processing Department (SPD) units restrict access to dedicated personnel with available manufacturer’s instructions, yet only 36% of SPD personnel are certified in CBSPD and packaging practices. Only 10% of facilities had a water treatment system for sterilization and Immediate Use Steam Sterilization (IUSS) policies. Notably, 84% of endoscopy areas require additional equipment for cultivating endoscopes, and no facility possessed a borescope for visually inspecting endoscope lumens. Tray inspection occurred in 21%, and only 31% of staff knew the Spaulding Classification and Class V Indicators. Conclusion: These data underscore the necessity of evaluating OR/SPD units in ACFs to provide updated recommendations and mitigate the incidence of surgical site infections (SSI). They offer insight into the structural and functional status of OR/SPD units in Puerto Rico, aligning reporting with OR/SPD practices to enhance patient care and minimize infection risks.

A Continuous Non-Invasive Blood Pressure Prediction Method Based on Deep Sparse Residual U-Net Combined with Improved Squeeze and Excitation Skip Connections.

Arterial blood pressure (ABP) serves as a pivotal clinical metric in cardiovascular health assessments, with the precise forecasting of continuous blood pressure assuming a critical role in both preventing and treating cardiovascular diseases. This study proposes a novel continuous non-invasive blood pressure prediction model, DSRUnet, based on deep sparse residual U-net combined with improved SE skip connections, which aim to enhance the accuracy of using photoplethysmography (PPG) signals for continuous blood pressure prediction. The model first introduces a sparse residual connection approach for path contraction and expansion, facilitating richer information fusion and feature expansion to better capture subtle variations in the original PPG signals, thereby enhancing the network's representational capacity and predictive performance and mitigating potential degradation in the network performance. Furthermore, an enhanced SE-GRU module was embedded in the skip connections to model and weight global information using an attention mechanism, capturing the temporal features of the PPG pulse signals through GRU layers to improve the quality of the transferred feature information and reduce redundant feature learning. Finally, a deep supervision mechanism was incorporated into the decoder module to guide the lower-level network to learn effective feature representations, alleviating the problem of gradient vanishing and facilitating effective training of the network. The proposed DSRUnet model was trained and tested on the publicly available UCI-BP dataset, with the average absolute errors for predicting systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean blood pressure (MBP) being 3.36 ± 6.61 mmHg, 2.35 ± 4.54 mmHg, and 2.21 ± 4.36 mmHg, respectively, meeting the standards set by the Association for the Advancement of Medical Instrumentation (AAMI), and achieving Grade A according to the British Hypertension Society (BHS) Standard for SBP and DBP predictions. Through ablation experiments and comparisons with other state-of-the-art methods, the effectiveness of DSRUnet in blood pressure prediction tasks, particularly for SBP, which generally yields poor prediction results, was significantly higher. The experimental results demonstrate that the DSRUnet model can accurately utilize PPG signals for real-time continuous blood pressure prediction and obtain high-quality and high-precision blood pressure prediction waveforms. Due to its non-invasiveness, continuity, and clinical relevance, the model may have significant implications for clinical applications in hospitals and research on wearable devices in daily life.

The deep convolutional networks for the classification of multi-class arrhythmia

An arrhythmia is an irregular heartbeat. Many researchers in the AI field have carried out the automatic classification of arrhythmias, and the issue that has been widely discussed is imbalanced data. A popular technique for overcoming this problem is the synthetic minority oversampling technique (SMOTE) technique. In this paper, the author adds some sampling of data obtained from other datasets into the primary dataset. In this case, the main dataset is the Massachusetts Institute of Technology–Beth Israel Hospital (MIT-BIH) arrhythmia database and an additional dataset from the MIT-BIH supraventricular arrhythmia database. The classification process is carried out with one-dimensional convolutional neural network model (1D-CNN) to perform multiclass and subject-class advancement of medical instrumentation (AAMII) classifications. The results obtained from this study are an accuracy of 99.10% for multiclass and 99.25% for subject-class.

Классификация сигналов ЭКГ на основе цифровой обработки сигналов, технического выбора функций и радиочастотного классификатора

Determining the optimal integration between features and classifiers has a significant effect on the performance of automatic heartbeat diagnostic systems. This importance stands out when dealing with critical applications that contain limited resources devices and require accurate and fast heartbeat classifiers to help the doctor make an accurate and quick diagnosis of heart diseases. Aiming at this task, this paper introduces a novel approach for choosing the optimal features of the ECG signal to be used with the Random Forest (RF) classifier following the inter-patient method for ECG signals division and obeying the instructions of the "Association for the Advancement of Medical Instrumentation (AAMI)." The features were chosen based on the concept of "Mutual Information Ranking (MIR)." The presented framework is comprehensive in terms of performing all the necessary processes efficiently, starting from ECG digital signal processing, segmentation, feature extraction, feature selection, and ending with ECG classification. The results of the experiments demonstrate that features corresponding to the normalized QRS width and the normalized RR intervals are the most influential features in the heartbeat classification. All tests were conducted using real ECG signals taken from the "MIT-BIH" Arrhythmia Database (MIT-BIH-ARR-DB). The suggested scheme attained the following F1-scores: 91.02%, 73.17%, and 98.04% in the classification of the Ventricular Ectopic Beats (V or VEB), Supraventricular Ectopic Beats (S or SVEB), and Normal Beats (N or NB), respectively. The overall accuracy was 96.26%. Despite its relative simplicity and reliance on few features, the proposed approach outperforms most of the reported state-of-the-art.

Costs involved in compliance with new endoscope reprocessing guidelines.

In March 2022, the Association for the Advancement of Medical Instrumentation (AAMI) released the American National Standards Institute (ANSI)/AAMI ST91:2021, their latest update on comprehensive, flexible, and semirigid endoscope reprocessing. These updated standards recommend the sterilization of high-risk endoscopes when possible and provide new recommendations for the precleaning, leak testing, manual cleaning, visual inspection, automated reprocessing, drying, storage, and transport of endoscopes. ANSI/AAMI ST91:2021 was compared with ANSI/AAMI ST91:2015 for major reprocessing differences that result in either time and/or cost increases. Time estimates were captured by explicit recommendation inclusion or taken from the literature. All the costs were estimated using publicly available resources. The updated standards represent a potential 24.3-minute and 52.35 to 67.57 United States dollars increase per procedure in terms of reprocessing time and spending, respectively, not including capital investments. Capital costs per procedure were highly dependent on the procedure volume of the facility. The new AAMI standards recommend several major changes, such as sterilization, for facilities to reprocess and manage endoscopes between uses. As more facilities increase their reprocessing methods to reflect the updated standards, they do so at a cost and introduce several delays. As the reprocessing landscape evolves, facilities should consider their true costs and alternative solutions, such as single-use endoscopes.

Blood Pressure Estimation Based on PPG and ECG Signals Using Knowledge Distillation.

Easy access bio-signals are useful for alleviating the shortcomings and difficulties associated with cuff-based and invasive blood pressure (BP) measurement techniques. This study proposes a deep learning model, trained using knowledge distillation, based on photoplethysmographic (PPG) and electrocardiogram (ECG) signals to estimate systolic and diastolic blood pressures. The estimation model comprises convolutional layers followed by one bidirectional recurrent layer and attention layers. The training approach involves knowledge distillation, where a smaller model (student model) is trained by leveraging information from a larger model (teacher model). The proposed multistage model was evaluated on 1205 subjects from Medical Information Mart for Intensive Care (MIMIC) III database using the Association for the Advancement of Medical Instrumentation (AAMI) and the standards of the British Hypertension Society (BHS). The results revealed that our model performance achieved grade A in estimating both systolic blood pressure (SBP) and diastolic blood pressure (DBP) and met the requirements of the AAMI standard. After training with knowledge distillation (KD), the model achieved a mean absolute error and standard deviation of 2.94 ± 5.61mmHg for SBP and 2.02 ± 3.60mmHg for DBP. Our results demonstrate the benefits of the knowledge distillation training method in reducing the number of parameters and improving the predictive accuracy of the blood pressure regression model.

AI Accelerator with Ultralightweight Time-Period CNN-Based Model for Arrhythmia Classification.

This work proposes a classification system for arrhythmias, aiming to enhance the efficiency of the diagnostic process for cardiologists. The proposed algorithm includes a naive preprocessing procedure for electrocardiography (ECG) data applicable to various ECG databases. Additionally, this work proposes an ultralightweight model for arrhythmia classification based on a convolutional neural network and incorporating R-peak interval features to represent long-term rhythm information, thereby improving the model's classification performance. The proposed model is trained and tested by using the MIT-BIH and NCKU-CBIC databases in accordance with the classification standards of the Association for the Advancement of Medical Instrumentation (AAMI), achieving high accuracies of 98.32% and 97.1%. This work applies the arrhythmia classification algorithm to a web-based system, thus providing a graphical interface. The cloud-based execution of automated artificial intelligence (AI) classification allows cardiologists and patients to view ECG wave conditions instantly, thereby remarkably enhancing the quality of medical examination. This work also designs a customized integrated circuit for the hardware implementation of an AI accelerator. The accelerator utilizes a parallelized processing element array architecture to perform convolution and fully connected layer operations. It introduces proposed hybrid stationary techniques, combining input and weight stationary modes to increase data reuse drastically and reduce hardware execution cycles and power consumption, ultimately achieving high-performance computing. This accelerator is implemented in the form of a chip by using the TSMC 180 nm CMOS process. It exhibits a power consumption of 122 μW, a classification latency of 6.8 ms, and an energy efficiency of 0.83 μJ/classification.

Continuous Patient-Independent Estimation of Respiratory Rate and Blood Pressure Using Robust Spectro-Temporal Features Derived From Photoplethysmogram Only.

Objective: A patient-independent approach for continuous estimation of vital signs using robust spectro-temporal features derived from only photoplethysmogram (PPG) signal. Methods: In the pre-processing stage, we remove baseline shifts and artifacts of the PPG signal using Incremental Merge Segmentation with adaptive thresholding. From the cleaned PPG, we extract multiple parameters independent of individual patient PPG morphology for both Respiration Rate (RR) and Blood Pressure (BP). In addition, we derived a set of novel spectral and statistical features strongly correlated to BP. We proposed robust correlation-based feature selection methods for accurate RR estimates. For fewer computations and accurate measurements of BP, the most significant features are selected using correlation and mutual information measures in the feature engineering part. Finally, RR and BP are estimated using breath counting and a neural network regression model, respectively. Results: The proposed approach outperforms the current state-of-the-art in both RR and BP. The RR algorithm results in mean absolute errors (median, 25th-75th percentiles) of 0.4 (0.1-0.7) for CapnoBase dataset and 0.5(0.3-2.8) for BIDMC dataset without discarding any data window. Similarly, BP approach has been validated on a large dataset derived from MIMIC-II ([Formula: see text]1700 records) which has errors (mean absolute, standard deviation) of 5.0(6.3) and 3.0(4.0) for systolic and diastolic BP, respectively. The results meet the American Association for the Advancement of Medical Instrumentation (AAMI) and British Hypertension Society (BHS) Class A criteria. Conclusion: By using robust features and feature selection methods, we alleviated patient dependency to have reliable estimates of vitals.

Continuous non-invasive arterial blood pressure monitoring with photoplethysmography via SE-MSResUNet network

ObjectiveArterial blood pressure (ABP) waveforms include physiological parameters closely related to BP values, while only a few studies predict BP values through the monitoring ABP waveforms. Current ABP waveforms monitoring methods are limited by its high computing cost and information redundancy of multi-channel input signals. The aim of our work is to propose a new end-to-end method for monitoring the ABP waveforms using single-channel photoplethysmography (PPG) signals by constructing a lightweight Multi-Scale Residual U-Net fused with Squeeze-and-Excitation module (SE-MSResUNet) model. MethodsFirstly, the PPG and ABP signals are preprocessed with denoising, segmentation, and eliminating phase difference. Subsequently, the SE-MSResUNet model is constructed and trained using training data to achieve the mapping relationship between PPG and ABP signals. Then, the BP values are extracted from the monitored ABP waveforms. Finally, the performance of the SE-MSResUNet in ABP waveform monitoring and BP estimation is investigated. ResultsThe ABP waveforms monitored by our proposed SE-MSResUNet network with PPG signals only are highly consistent with the reference ABP waveforms. The MAE ± SD of estimated diastolic blood pressure (DBP), mean arterial pressure (MAP) and systolic blood pressure (SBP) compared with reference values are 2.16 ± 3.75, 2.29 ± 3.72, and 3.88 ± 6.17 mmHg, respectively. The proposed method meets the Association for the Advancement of Medical Instrumentation (AAMI) standard and reach A level of the British Hypertension Society (BHS) standard. ConclusionsOur proposed method outperforms current state-of-the-art methods for non-invasive blood pressure monitoring from PPG signals. The algorithm provides a potential possibility for continuous non-invasive blood pressure monitoring through portable wearable devices.

Development of continuous cuffless blood pressure prediction platform using enhanced 1-D SENet–LSTM

In this study, we propose a blood pressure estimation that employs a combined 1-D squeeze and excitation network (SENet-LSTM) architecture. Combining photoplethysmography (PPG) and electrocardiogram (ECG) signals can provide more helpful feature information. The original signals were processed by removing the noise and artifacts. By introducing SE block, the ability to learn features is enhanced, and an end-to-end approach is used to realize the automatic learning process. Additionally, the algorithm has the ability to recover arterial blood pressure (ABP) waveforms from blood pressure readings. The results of this study met the Grade A standard for diastolic blood pressure (DBP), systolic blood pressure (SBP), and mean arterial pressure (MAP) set by the British Hypertension Society (BHS) and were also in accordance with the Association for the Advancement of Medical Instrumentation (AAMI) standard. According to the DBP and SBP ranges, blood pressure is divided into the following categories: normal blood pressure, prehypertension, and hypertension. For the classification of Hypertension, Normotension, and Prehypertension using SBP, we achieved accuracy of 94% and F1 scores of 0.92, 0.94, and 0.85. For the results obtained using DBP classification the overall accuracy was 91%, with F1 scores of 0.85, 0.98, and 0.78. When compared to other studies, the classifier results generated by SBP and DBP estimates based on the 1-D SENet-LSTM method improved accuracy by 2% and 11%, respectively.

RNN Based Deep Learning Approach for ECG Beat Classification

The electrical patterns of the heart, captured through an electrocardiogram (ECG/EKG), serve as a diagnostic tool to identify potential issues such as heart attacks, irregular heart rhythms, heart failure, and arrhythmia, which manifests as irregularities in the heartbeat's rhythm. Deep Learning (DL) architectures have been successfully employed for arrhythmia detection and classification and offered superior performance to traditional shallow Machine Learning (ML) approaches. This paper introduces a novel approach utilizing deep learning techniques, specifically a Bidirectional Gated Recurrent Unit (Bi-GRU) model a variant of RNN, to classify ECG arrhythmia beats into distinct categories. The Bi-GRU model is employed in this work due to its capability to capture temporal dependencies in ECG signals, enabling a nuanced understanding of beat sequences for precise classification. Leveraging the MIT-BIH arrhythmia database, a comprehensive dataset containing annotated ECG signals, this study explores the efficacy of deep learning in accurately categorizing beats in to five super classes as per the standard of Association for the Advancement of Medical Instrumentation (AAMI). Evaluation metrics encompassing accuracy (Acc), specificity (Spe), sensitivity (Sen), positive predictive value (Ppv), and F1-score are utilized to assess the model performance in distinguishing between diverse arrhythmia classes.

Single-point curved fiber optic pulse sensor for physiological signal prediction based on the genetic algorithm-support vector regression model

For hypertensive patients, a real-time human physiological signal monitoring system helps to track blood pressure status and provides valuable data to clinicians for early diagnosis and timely intervention. Optical fiber pulse sensing has superior conditions such as resistance to electromagnetic interference and richness of measured pulse characteristics. In this study, a monitoring system based on a single-point curved fiber pulse sensor (CFPS) was used to collect the pulse wave signal of a human radial artery. The features of the pulse wave signal were used to estimate the pulse wave transit time (PTT) and Blood Pressure—systolic blood pressure (SBP) and diastolic blood pressure (DBP)—based on support vector regression (SVR) optimized by a genetic algorithm (GA) (GA-SVR model). The results show that the root mean square error (RMSE) of SBP, DBP and PTT were 1.571 mmHg, 3.250 mmHg and 5.719 ms, respectively, and the results met the Advancement of Medical Instrumentation (AAMI) criteria. Therefore, a real-time pulse wave monitoring system based on a single-point CFPS can well predict human physiological signals such as PTT, SBP and DBP.

Non-Invasive Blood Pressure Sensing via Machine Learning.

In this paper, a machine learning (ML) approach to estimate blood pressure (BP) using photoplethysmography (PPG) is presented. The final aim of this paper was to develop ML methods for estimating blood pressure (BP) in a non-invasive way that is suitable in a telemedicine health-care monitoring context. The training of regression models useful for estimating systolic blood pressure (SBP) and diastolic blood pressure (DBP) was conducted using new extracted features from PPG signals processed using the Maximal Overlap Discrete Wavelet Transform (MODWT). As a matter of fact, the interest was on the use of the most significant features obtained by the Minimum Redundancy Maximum Relevance (MRMR) selection algorithm to train eXtreme Gradient Boost (XGBoost) and Neural Network (NN) models. This aim was satisfactorily achieved by also comparing it with works in the literature; in fact, it was found that XGBoost models are more accurate than NN models in both systolic and diastolic blood pressure measurements, obtaining a Root Mean Square Error (RMSE) for SBP and DBP, respectively, of 5.67 mmHg and 3.95 mmHg. For SBP measurement, this result is an improvement compared to that reported in the literature. Furthermore, the trained XGBoost regression model fulfills the requirements of the Association for the Advancement of Medical Instrumentation (AAMI) as well as grade A of the British Hypertension Society (BHS) standard.

Investigation of the barrier performance of disposable isolation gowns

Recent epidemics and pandemics highlighted the need for effective personal protective equipment, including isolation gowns. The most critical property of an isolation gown is its ability to keep liquids and viruses from passing through the gown. Liquid and viral barrier penetration can be measured using laboratory test methods. Association for the Advancement of Medical Instrumentation (AAMI) PB70 standard defines isolation gown barrier performance levels and requirements. In this study, 22 disposable isolation gown models from6manufacturers were tested for liquid and viral penetration resistance. Standard test methods were used to evaluate water and viral penetration. Test results were evaluated using AAMI PB70 barrier performance criteria for 4 protection levels. Seven of the 22 tested gown models did not pass liquid and viral penetration testing based on AAMI PB70 at the level claimed by the manufacturer. The majority of these failures occurred at the seam and/or tie attachment areas. The study findings underscore the need for improved processes surrounding activities such as premarket testing and postmarket evaluation of gowns according to standardized test methods by third-party laboratories. This study also supports the recent Food and Drug Administration guidance document that clarified the characteristics of isolation gowns considered to be class II and subject to Food and Drug Administration premarket review. Infection preventionists, hospital purchasers, and safety professionals should seek isolation gowns demonstrating conformance to industry standards from manufacturers.

Continuous blood pressure monitoring using photoplethysmography and electrocardiogram signals by random forest feature selection and GWO-GBRT prediction model

Blood pressure (BP) is an important indicator of a person's cardiovascular status, but accurate, continuous BP monitoring remains a challenge. To improve the monitoring performance of continuous BP using electrocardiogram (ECG) and photoplethysmography (PPG) signals, a continuous BP monitoring method based on random forest feature selection (RFFS) and a gray wolf optimization-gradient boosting regression tree (GWO-GBRT) prediction model is developed in this study. First, features in time, frequency, and time–frequency domains are extracted from PPG and ECG signals to supply comprehensive characteristics for BP monitoring. Then, the RFFS method is employed to select sensitive features highly correlated with BP from the candidate feature sets, which reduced redundant information and improved monitoring efficiency. Next, a hybrid prediction model of the gray wolf optimization (GWO) technique and gradient boosting regression tree (GBRT) algorithm is established to learn the dependency relationship between BP and sensitive features. And a new fitness function of GWO is designed to balance the monitoring accuracy and consistency. Finally, ablation and comparative experiments demonstrate the effectiveness and advancement of the proposed method, using the ECG and PPG signals of 150 people downloaded from the MIMIC-III database. The mean absolute error (MAE) and standard deviation (STD) of the proposed method in predicting systolic blood pressure were 2.91 mmHg and 3.97 mmHg, and those of diastolic blood pressure were 1.71 mmHg and 2.35 mmHg. Its monitoring accuracy has surpassed the Association for Advancement of Medical Instrumentation (AAMI) standard and reached the “A” level standard of the British Hypertension Society (BHS) protocol.