Validation of oscillometric ratio and maximum gradient methods for non-invasive blood pressure measurement with intra-arterial blood pressure measurements as reference.

Most non-invasive blood pressure (BP) measurements are carried out using instruments which implement either the Ratio or the Maximum Gradient oscillometric method, mostly during cuff deflation, but more rarely during cuff inflation. Yet, there is little published literature on the relative advantages and accuracy of these two methods. In this study of 40 lightly sedated individuals aged 64.1 ± 9.6 years, we evaluate and compare the performance of the oscillometric ratio (K) and gradient (Grad) methods for the non-invasive estimation of mean pressure, SBP and DBP with reference to invasive intra-arterial values. There was no significant difference between intra-arterial estimates of mean pressure made via Korotkoff sounds (MP-OWE) or the gradient method (MP-Grad). However, 17.7% of MP-OWE and 15% of MP-Grad were in error by more than 10 mmHg. SBP-K and SBP-Grad underestimated SBP by 14 and 18 mmHg, whilst accurately estimating DBP with mean errors of 0.4 ±â€Š5.0 and 1.7 ±â€Š6.1 mmHg, respectively. Relative to the reference standard SBP-K, SBP-Grad and DBP-Grad were estimated with a mean error of -4.5 ±â€Š6.6 and 1.4 ±â€Š5.6 mmHg, respectively, noting that using the full range of recommended ratios introduces errors of 12 and 7 mmHg in SBP and DBP, respectively. We also show that it is possible to find ratios which minimize the root mean square error (RMSE) and the mean error for any particular individual cohort. We developed linear models for estimating SBP and SBP-K from a range of demographic and non-invasive OWE variables with resulting mean errors of 0.15 ±â€Š5.6 and 0.3 ±â€Š5.7 mmHg, acceptable according to the Universal standard.

Evaluation of the oscillometric method for noninvasive blood pressure measurement during cuff deflation and cuff inflation with reference to intra-arterial blood pressure.

There is little quantitative clinical data available to support blood pressure measurement accuracy during cuff inflation. In this study of 35 male and 5 female lightly anaesthetized subjects aged 64.1 ±â€Š9.6 years, we evaluate and compare the performance of both the oscillometric ratio and gradient methods during cuff deflation and cuff inflation with reference to intra-arterial measurements. We show that the oscillometric waveform envelopes (OWE), which are key to both methods, exhibit significant variability in both shape and smoothness leading to at least 15% error in the determination of mean pressure (MP). We confirm the observation from our previous studies that K1 Korotkoff sounds underestimate systolic blood pressure (SBP) and note that this underestimation is increased during cuff inflation. The estimation of diastolic blood pressure (DBP) is generally accurate for both the ratio and the gradient method, with the latter showing a significant increase during inflation. Since the gradient method estimates SBP and DBP from points of maximum gradient on each OWE recorded, it may offer significant benefits over the ratio method. However, we have shown that the ratio method can be optimized for any data set to achieve either a minimum mean error (ME) of close to 0 mmHg or minimum root mean square error (RMSE) with standard deviation (SD) of <5.0 mmHg. We conclude that whilst cuff inflation may offer some advantages, these are neither significant nor substantial, leaving as the only benefit, the potential for more rapid measurement and less patient discomfort.

Measuring blood pressure from Korotkoff sounds as the brachial cuff inflates on average provides higher values than when the cuff deflates.

Objectives. In this study, we test the hypothesis that if, as demonstrated in a previous study, brachial arteries exhibit hysteresis as the occluding cuff is deflated and fail to open until cuff pressure (CP) is well below true intra-arterial blood pressure (IAPB), estimating systolic (SBP) and diastolic blood pressure (DBP) from the presence of Korotkoff sounds (KS) as CP increases may eliminate these errors and give more accurate estimates of SBP and DBP relative to IABP readings.Approach. In 62 subjects of varying ages (45.1 ± 19.8, range 20.6-75.8 years), including 44 men (45.3 ± 19.4, range 20.6-75.8 years) and 18 women (44.4 ± 21.4, range 20.9-75.3 years), we sequentially recorded SBP and DBP both during cuff inflation and cuff deflation using KS.Results. There was a significant (p< 0.0001) increase in SBP from 122.8 ± 13.2 to 127.6 ± 13.0 mmHg and a significant (p= 0.0001) increase in DBP from 70.0 ± 9.0 to 77.5 ± 9.7 mmHg. Of the 62 subjects, 51 showed a positive increase in SBP (0-14 mmHg) and 11 subjects showed a reduction (-0.3 to -7 mmHg). The average differences for SBP and DBP estimates derived as the cuff inflates and those derived as the cuff deflates were 4.8 ± 4.6 mmHg and 2.5 ± 4.6 mmHg, not dissimilar to the differences reported between IABP and non-invasive blood pressure measurements. Although we could not develop multiparameter linear or non-linear models to explain this phenomenon we have clearly demonstrated through ANOVA tests that both body mass index (BMI) and pulse wave velocity are implicated, supporting the hypothesis that the phenomenon is associated with age, higher BMI and stiffer arteries.Significance. The implications of this study are that brachial sphygmomanometry carried out during cuff inflation could be more accurate than measurements carried out as the cuff deflates. Further research is required to validate these results with IAPB measurements.

Comparison of cuff inflation and cuff deflation brachial sphygmomanometry with intra-arterial blood pressure as reference.

Conventional sphygmomanometry with cuff deflation is used to calibrate all noninvasive BP (NIBP) instruments and the International Standard makes no mention of calibrating methods specifically for NIBP instruments, which estimate systolic and diastolic pressure during cuff inflation rather than cuff deflation. There is however increasing interest in inflation-based NIBP (iNIBP) instruments on the basis of shorter measurement time, reduction in maximal inflation pressure and improvement in patient comfort and outcomes. However, we have previously demonstrated that SBP estimates based on the occurrence of the first K1 Korotkoff sounds during cuff deflation can underestimate intra-arterial SBP (IA-SBP) by an average of 14 ±â€Š10 mmHg. In this study, we compare the dynamics of intra-arterial blood pressure (IABP) measurements with sequential measurement of Korotkoff sounds during both cuff inflation and cuff deflation in the same individual. In 40 individuals aged 64.1 ±â€Š9.6 years (range 36-86 years), the overall dynamic responses below the cuff were similar, but the underestimation error was significantly larger during inflation than deflation, increasing from 14 ±â€Š10 to 19 ±â€Š12 mmHg ( P  < 0.0001). No statistical models were found which could compensate for this error as were found for cuff deflation. The statistically significant BP differences between inflation and deflation protocols reported in this study suggest different behaviour of the arterial and venous vasculature between arterial opening and closing which warrant further investigation, particularly for iNIBP devices reporting estimates during cuff inflation. In addition, measuring Korotkoff sounds during cuff inflation represents significant technical difficulties because of increasing pump motor noise.

Accurate detection of Korotkoff sounds reveals large discrepancy between intra-arterial systolic pressure and simultaneous noninvasive measurement of blood pressure with brachial cuff sphygmomanometry.

Cardiovascular disease is the number 1 cause of death globally, with elevated blood pressure (BP) being the single largest risk factor. Hence, BP is an important physiological parameter used as an indicator of cardiovascular health. Noninvasive cuff-based automated monitoring is now the dominant method for BP measurement and irrespective of whether the oscillometric or the auscultatory method is used, all are calibrated according to the Universal Standard (ISO 81060-2:2019), which requires two trained operators to listen to Korotkoff K1 sounds for SBP and K4/K5 sounds for DBP. Hence, Korotkoff sounds are fundamental to the calibration of all NIBP devices. In this study of 40 lightly sedated patients, aged 64.1 ±â€Š9.6 years, we compare SBP and DBP recorded directly by intra-arterial fluid filled catheters to values recorded from the onset (SBP-K) and cessation (DBP-K) of Korotkoff sounds. We demonstrate that whilst DBP-K measurements are in good agreement, with a mean difference of -0.3 ±â€Š5.2 mmHg, SBP-K underestimates true intra-arterial SBP (IA-SBP) by an average of 14 ±â€Š9.6 mmHg. The underestimation arises from delays in the re-opening of the brachial artery following deflation of the brachial cuff to below SBP. The reasons for this delay are not known but appear related to the difference between SBP and the pressure under the cuff as blood first begins to flow, as the cuff deflates. Linear models are presented that can correct the underestimation in SBP resulting in estimates with a mean difference of 0.2 ±â€Š7.1 mmHg with respect to intra-arterial SBP.

Assessing the Generalizability of a Deep Learning-based Automated Atrial Fibrillation Algorithm.

Automated detection of atrial fibrillation (AF) from electrocardiogram (ECG) traces remains a challenging task and is crucial for telemonitoring of patients after stroke. This study aimed to quantify the generalizability of a deep learning (DL)-based automated ECG classification algorithm. We first developed a novel hybrid DL (HDL) model using the PhysioNet/CinC Challenge 2017 (CinC2017) dataset (publicly available) that can classify the ECG recordings as one of four classes: normal sinus rhythm (NSR), AF, other rhythms (OR), and too noisy (TN) recordings. The (pre)trained HDL was then used to classify 636 ECG samples collected by our research team using a handheld ECG device, CONTEC PM10 Portable ECG Monitor, from 102 (age: 68 ± 15 years, 74 male) outpatients of the Eastern Heart Clinic and inpatients in the Cardiology ward of Prince of Wales Hospital, Sydney, Australia. The proposed HDL model achieved average test F1-score of 0.892 for NSR, AF, and OR, relative to the reference values, on the CinC2017 dataset. The HDL model also achieved an average F1-score of 0.722 (AF: 0.905, NSR: 0.791, OR: 0.471 and TN: 0.342) on the dataset created by our research team. After retraining the HDL model on this dataset using a 5-fold cross validation method, the average F1-score increased to 0.961. We finally conclude that the generalizability of the HDL-based algorithm developed for AF detection from short-term single-lead ECG traces is acceptable. However, the accuracy of the pre-trained DL model was significantly improved by retraining the model parameters on the new dataset of ECG traces.

New Perspectives on Non-invasive Blood Pressure Measurement.

Noninvasive blood pressure (NIBP) devices are calibrated against validated auscultation sphygmomanometers using Korotkoff sounds. This study aimed to investigate the timing of Korotkoff sounds in relation to pulse appearance in the brachial artery and values of intra-arterial blood pressure. Experiments were carried out on 15 participants, (14 males, 64.3 ± 10.4 years; one female, 86 yo), undergoing coronary angiography. A conventional occluding cuff, with a microphone for Korotkoff sounds, was placed on the upper arm (on the brachial artery). Intra-arterial blood pressure (IABP) was measured below the cuff with a fluid-filled catheter inserted via the radial artery and an external transducer. Finger photoplethysmography was used to measure brachial pulse wave velocity (PWV). Korotkoff sounds were processed electronically and custom algorithms identified the cuff pressure (CP) at which the first and last Korotkoff sounds were heard. PWV and max slope of the IABP pressure pulse were recorded to estimate arterial stiffness. The brachial artery closed at a CP of 132.0 ± 17.1 mmHg. Systolic and diastolic blood pressure (SBP and DBP) were 147.6 ± 14.3 and 72.7 ± 10.1 mmHg; mean pressure (MP, 100.1 ± 10.4 mmHg) was similar to MP derived from the peak of the oscillogram (98.5 ± 13.6 mmHg). Difference between IABP and CP recorded at first and last occurrence of Korotkoff sounds were, SBP: 19.0 ± 8.3 (range 2-29) mmHg, DBP: 4.0 ± 4.3 (range 2-12) mmHg. SBP derived from the onset of Korotkoff sounds can underestimate IABP by up to 19 mmHg. Since Korotkoff sounds are the recommended method mandated by the universal standard for the validation of blood pressure measuring devices, these errors are propagated through to all NIBP measurement devices irrespective of whether they use auscultatory or oscillometric methods.

Revisiting Transfer Learning Method for Tuberculosis Diagnosis.

Transfer learning (TL) has been proven to be a good strategy for solving domain-specific problems in many deep learning (DL) applications. Typically, in TL, a pre-trained DL model is used as a feature extractor and the extracted features are then fed to a newly trained classifier as the model head. In this study, we propose a new ensemble approach of transfer learning that uses multiple neural network classifiers at once in the model head. We compared the classification results of the proposed ensemble approach with the direct approach of several popular models, namely VGG-16, ResNet-50, and MobileNet, on two publicly available tuberculosis datasets, i.e., Montgomery County (MC) and Shenzhen (SZ) datasets. Moreover, we also compared the results when a fully pre-trained DL model was used for feature extraction versus the cases in which the features were obtained from a middle layer of the pre-trained DL model. Several metrics derived from confusion matrix results were used, namely the accuracy (ACC), sensitivity (SNS), specificity (SPC), precision (PRC), and F1-score. We concluded that the proposed ensemble approach outperformed the direct approach. Best result was achieved by ResNet-50 when the features were extracted from a middle layer with an accuracy of 91.2698% on MC dataset.Clinical Relevance- The proposed ensemble approach could increase the detection accuracy of 7-8% for Montgomery County dataset and 4-5% for Shenzhen dataset.

Machine and Deep Learning for Tuberculosis Detection on Chest X-Rays: Systematic Literature Review.

BACKGROUND: Tuberculosis (TB) was the leading infectious cause of mortality globally prior to COVID-19 and chest radiography has an important role in the detection, and subsequent diagnosis, of patients with this disease. The conventional experts reading has substantial within- and between-observer variability, indicating poor reliability of human readers. Substantial efforts have been made in utilizing various artificial intelligence-based algorithms to address the limitations of human reading of chest radiographs for diagnosing TB. OBJECTIVE: This systematic literature review (SLR) aims to assess the performance of machine learning (ML) and deep learning (DL) in the detection of TB using chest radiography (chest x-ray [CXR]). METHODS: In conducting and reporting the SLR, we followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A total of 309 records were identified from Scopus, PubMed, and IEEE (Institute of Electrical and Electronics Engineers) databases. We independently screened, reviewed, and assessed all available records and included 47 studies that met the inclusion criteria in this SLR. We also performed the risk of bias assessment using Quality Assessment of Diagnostic Accuracy Studies version 2 (QUADAS-2) and meta-analysis of 10 included studies that provided confusion matrix results. RESULTS: Various CXR data sets have been used in the included studies, with 2 of the most popular ones being Montgomery County (n=29) and Shenzhen (n=36) data sets. DL (n=34) was more commonly used than ML (n=7) in the included studies. Most studies used human radiologist's report as the reference standard. Support vector machine (n=5), k-nearest neighbors (n=3), and random forest (n=2) were the most popular ML approaches. Meanwhile, convolutional neural networks were the most commonly used DL techniques, with the 4 most popular applications being ResNet-50 (n=11), VGG-16 (n=8), VGG-19 (n=7), and AlexNet (n=6). Four performance metrics were popularly used, namely, accuracy (n=35), area under the curve (AUC; n=34), sensitivity (n=27), and specificity (n=23). In terms of the performance results, ML showed higher accuracy (mean ~93.71%) and sensitivity (mean ~92.55%), while on average DL models achieved better AUC (mean ~92.12%) and specificity (mean ~91.54%). Based on data from 10 studies that provided confusion matrix results, we estimated the pooled sensitivity and specificity of ML and DL methods to be 0.9857 (95% CI 0.9477-1.00) and 0.9805 (95% CI 0.9255-1.00), respectively. From the risk of bias assessment, 17 studies were regarded as having unclear risks for the reference standard aspect and 6 studies were regarded as having unclear risks for the flow and timing aspect. Only 2 included studies had built applications based on the proposed solutions. CONCLUSIONS: Findings from this SLR confirm the high potential of both ML and DL for TB detection using CXR. Future studies need to pay a close attention on 2 aspects of risk of bias, namely, the reference standard and the flow and timing aspects. TRIAL REGISTRATION: PROSPERO CRD42021277155; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=277155.

A Comparison of Haemodynamic Responses between Head-Up Tilt and Lower Body Negative Pressure.

Orthostatic intolerance (OI), a disorder of the autonomic nervous system, it is the development of symptoms when standing upright which are relieved when reclining. Head-up tilt (HUT) table test is a common test for assessing orthostatic tolerance. However, HUT is limited with low sensitivity and specificity. Another approach to stimulate the changing direction and value of the gravity field vector is the lower body negative pressure (LBNP) chamber. The aims of the study is to evaluate the physiological responses of healthy subjects on HUT and LBNP, and examine the relations of two tests. A total of 19 subjects were recruited. A validated wearable device, Sotera Visi Mobile was use to collect physiological signals simultaneously throughout the experiment procedures. Each subject went through a baseline supine rest, 70o of HUT test, another round of baseline supine rest, followed by activation of LBNP test. Three level of suction were applied, i.e. -30 mmHg, -40 mmHg, and -50 mmHg. In this pilot study, healthy subjects showed significantly increased of heart rate, and decreased of systolic blood pressure and diastolic blood pressure, in both HUT and LBNP tests. Although both tests are capable of stimulating a decreased blood volume in the central circulation, but the physiological responses behaved differently and shown only very week correlation. This suggesting that a combination of LBNP test with HUT test might work the best in orthostatic intolerance assessment.

Artificial Intelligence Based Blood Pressure Estimation From Auscultatory and Oscillometric Waveforms: A Methodological Review.

Cardiovascular disease is known as the number one cause of death globally, with elevated blood pressure (BP) being the single largest risk factor. Hence, BP is an important physiological parameter used as an indicator of cardiovascular health. The use of automated non-invasive blood pressure (NIBP) measurement devices is growing, as they can be used without expertise and BP measurement can be performed by patients at home. Non-invasive cuff-based monitoring is the dominant method for BP measurement. While the oscillometric technique is most common, some automated NIBP measurement methods have been developed based on the auscultatory technique. By utilizing (relatively) large BP data annotated by experts, models can be trained using machine learning and statistical concepts to develop novel NIBP estimation algorithms. Amongst artificial intelligence (AI) techniques, deep learning has received increasing attention in different fields due to its strength in data classification and feature extraction problems. This paper reviews AI-based BP estimation methods with a focus on recent advances in deep learning-based approaches within the field. Various architectures and methodologies proposed todate are discussed to clarify their strengths and weaknesses. Based on the literature reviewed, deep learning brings plausible benefits to the field of BP estimation. We also discuss some limitations which can hinder the widespread adoption of deep learning in the field and suggest frameworks to overcome these challenges.

Measuring blood pressure from Korotkoff sounds as the brachial cuff inflates on average provides higher values than when the cuff deflates.

Objective. In this study, we test the hypothesis that if, as demonstrated in a previous study, brachial arteries exhibit hysteresis as the occluding cuff is deflated and fail to open until cuff pressure (CP) is well below true intra-arterial blood pressure (IABP). Approach Estimating systolic (SBP) and diastolic blood pressure (DBP) from the presence of Korotkoff sounds as CPincreasesmay eliminate these errors and give more accurate estimates of SBP relative to IABP readings.Main Results.In 63 subjects of varying age 45.4 ± 19.9 years (range 21-76 years), including 44 men (45.2 ± 19.5, range 21-76 years) and 19 women (45.6 ± 21.4, range 21-75 years), there was a significant (p< 0.0001) increase in SBP from 124.4 ± 15.7 to 129.2 ± 16.3 mmHg and a significant (p< 0.0001) increase in DBP from 70.2 ± 10.7 to 73.6 ± 11.5 mmHg. Of the 63 subjects, 59 showed a positive increase in SBP (1-19 mmHg) and 5 subjects showed a reduction (-5 to -1 mmHg). The average differences for SBP estimates derived as the cuff inflates and estimates derived as the cuff deflates were 4.9 ± 4.7 mmHg, not dissimilar to the differences observed between IABP and NIBP measurements. Although we could not develop multiparameter linear or nonlinear models to explain this phenomenon we have clearly demonstrated through analysis of variance test that both body mass index (BMI) and pulse wave velocity are implicated, supporting the hypothesis that the phenomenon is associated with age, higher BMI and stiffer arteries.Significance. The implications of this study are potentially profound requiring the implementation of a new paradigm for NIBP measurement and a revision of the international standards for their calibration.

Human Motion Intent Description Based on Bumpless Switching Mechanism for Rehabilitation Robot.

This paper aims to improve the performance of an electromyography (EMG) decoder based on a switching mechanism in controlling a rehabilitation robot for assisting human-robot cooperation arm movements. For a complex arm movement, the major difficulty of the EMG decoder modeling is to decode EMG signals with high accuracy in real-time. Our recent study presented a switching mechanism for carving up a complex task into simple subtasks and trained different submodels with low nonlinearity. However, it was observed that a "bump" behavior of decoder output (i.e., the discontinuity) occurred during the switching between two submodels. The bumps might cause unexpected impacts on the affected limb and thus potentially injure patients. To improve this undesired transient behavior on decoder outputs, we attempt to maintain the continuity of the outputs during the switching between multiple submodels. A bumpless switching mechanism is proposed by parameterizing submodels with all shared states and applied in the construction of the EMG decoder. Numerical simulation and real-time experiments demonstrated that the bumpless decoder shows high estimation accuracy in both offline and online EMG decoding. Furthermore, the outputs achieved by the proposed bumpless decoder in both testing and verification phases are significantly smoother than the ones obtained by a multimodel decoder without a bumpless switching mechanism. Therefore, the bumpless switching approach can be used to provide a smooth and accurate motion intent prediction from multi-channel EMG signals. Indeed, the method can actually prevent participants from being exposed to the risk of unpredictable loads.

A Novel Automated Blood Pressure Estimation Algorithm Using Sequences of Korotkoff Sounds.

The use of automated non-invasive blood pressure (NIBP) measurement devices is growing, as they can be used without expertise, and BP measurement can be performed by patients at home. Non-invasive cuff-based monitoring is the dominant method for BP measurement. While the oscillometric technique is most common, a few automated NIBP measurement methods have been developed based on the auscultatory technique. Amongst artificial intelligence (AI) techniques, deep learning has received increasing attention in different fields due to its strength in data classification, and feature extraction problems. This paper proposes a novel automated AI-based technique for NIBP estimation from auscultatory waveforms (AWs) based on converting the NIBP estimation problem to a sequence-to-sequence classification problem. To do this, a sequence of segments was first formed by segmenting the AWs, and their corresponding decomposed detail, and approximation parts obtained by wavelet packet decomposition method, and extracting features from each segment. Then, a label was assigned to each segment, i.e. (i) between systolic, and diastolic segments, and (ii) otherwise, and a bidirectional long short term memory recurrent neural network (BiLSTM-RNN) was devised to solve the resulting sequence-to-sequence classification problem. Adopting a 5-fold cross-validation scheme, and using a data base of 350 NIBP recordings gave an average mean absolute error of 1.7±3.7 mmHg for systolic BP (SBP), and 3.4 ±5.0 mmHg for diastolic BP (DBP) relative to reference values. Based on the results achieved, and comparisons made with the existing literature, it is concluded that the proposed automated BP estimation algorithm based on deep learning methods, and auscultatory waveform brings plausible benefits to the field of BP estimation.

Continuous Description of Human 3D Motion Intent Through Switching Mechanism.

Post-stroke motor recovery highly relies on voluntarily participating in active rehabilitation as early as possible for promoting the reorganization of the patient's brain. In this paper, a new method is proposed which manipulates cable-based rehabilitation robots to assist multi-joint body motions. This uses an electromyography (EMG) decoder for continuous estimation of voluntary motion intention to establish a cooperative human-machine interface for promoting the participation in rehabilitation exercises. In particular, for multi-joint complex tasks in three-dimensional space, a switching mechanism has been developed which can carve up tasks into separate simple motions. For each simple motion, a linear six-inputs and three-outputs time-invariant model is established respectively. The inputs are the processed muscle activations of six arm muscles, and the outputs are voluntary forces of participants when executing a multi-directional tracking task with visual feedback. The experiments for examining the decoder model and EMG-based controller include model training, testing and controller application phases with seven healthy participants. Experimental results demonstrate that the decoder model with the switching mechanism could effectively recognize arm movement intention and provide appropriate assistance to the participants. This study finds that the switching mechanism can improve both the model estimation accuracy and the completeness for executing complex tasks.

Blood Pressure Estimation Using Time Domain Features of Auscultatory Waveforms and GMM-HMM Classification Approach.

This paper presents a novel method to estimate systolic blood pressure (SBP) and diastolic blood pressure (DBP) from time domain features extracted from auscultatory waveforms (AWs) and using a Gaussian Mixture Models and Hidden Markov Model (GMM-HMM) classification approach. The three time domain features selected include the cuff pressure (CP), the energy of the Korotkoff pulses (KE), and the slope of the KE (SKE). The proposed GMM-HMM can effectively discover the latent structure in AW sequences and automatically learn such structures. The SBP and DBP points are then detected as the cuff pressures at which AW sequence changes its structure. We conclude that the proposed GMM-HMM estimation method is a very promising method improving the accuracy of automated non-invasive measurement of blood pressure.

The effects of different tracking tasks on muscle synergy through visual feedback.

By recruiting a modular organization of muscle with relative activities, the arm motion can be indicated by the neural system and generated for performing a variety of motor tasks. In this study, a Non-negative Matrix Factorization with initial estimation is applied to identify and extract primary muscle synergies and their activation patterns from the processed EMG recordings during three multidirectional tracking tasks with visual feedback interaction. The effects of task variety and tracking accuracy by visual feedback on muscle synergies and their activation patterns are explored by statistic analysis. The results showed that only the task variety affected what synergies were indicated by the neural system, but both task variety and visual feedback affected the duration and magnitude of the primary synergies. Thus, for active rehabilitation application, it is advised that if the purpose is to enhance the synergy indication from the neural system, the task completion accuracy should be emphasized, but if the purpose is to expand the motion area, the task variety should be diversified.

Blood Pressure Estimation Using Time Domain Features of Auscultatory Waveforms and Deep Learning.

This paper presents a novel method to estimate systolic blood pressure (SBP) and diastolic blood pressure (DBP) from time domain features extracted on auscultatory waveforms (AWs) using a long short term memory (LSTM) recurrent neural network (RNN). The proposed LSTM-RNN can effectively discover the latent structure in AW sequences and automatically learn such structures. The SBP and DBP points are then detected as the cuff pressures at which AW sequence changes its structure. Our LSTM-RNN is a powerful technique for sequence learning and can be used in blood pressure estimation as an alternative way for replacing traditional approaches.

Neural Adaptive Backstepping Control of a Robotic Manipulator With Prescribed Performance Constraint.

This paper presents an adaptive neural network (NN) control of a two-degree-of-freedom manipulator driven by an electrohydraulic actuator. To restrict the system output in a prescribed performance constraint, a weighted performance function is designed to guarantee the dynamic and steady tracking errors of joint angle in a required accuracy. Then, a radial-basis-function NN is constructed to train the unknown model dynamics of a manipulator by traditional backstepping control (TBC) and obtain the preliminary estimated model, which can replace the preknown dynamics in the backstepping iteration. Furthermore, an adaptive estimation law is adopted to self-tune every trained-node weight, and the estimated model is online optimized to enhance the robustness of the NN controller. The effectiveness of the proposed control is verified by comparative simulation and experimental results with Proportional-integral-derivative and TBC methods.

Seasonal Variation In An At-Home Telemonitoring Trial.

This paper aims to present findings on seasonal variation in a recently completed Commonwealth Scientific and Industrial Research Organization (CSIRO) national trial of home telemonitoring of patients with chronic conditions, carried out at five locations along the east coast of Australia. Patients in this trial were selected from a list of eligible patients living with a range of chronic conditions. Each test patient was case matched with at least one control patient. A total of 114 test patients and 173 control patients were available in this trial. However, of the 287 patients, we only considered subjects who had one or more admissions in the years 2010-2012. Three different groups were analyzed because of substantially different climates, i.e., Queensland (QLD), Australian Capital Territory & Victoria (ACT + VIC), and Tasmania (TAS). Time series data were analyzed using linear regression for a period of 3 years before the intervention in order to obtain an average seasonal variation pattern.