Classification of vasovagal syncope from physiological signals on tilt table testing.

BACKGROUND: The diagnostic test for vasovagal syncope (VVS), the most common cause of syncope is head-up tilt test (HUTT) assessment. During the test, subjects experienced clinical symptoms such as nausea, sweating, pallor, the feeling of palpitations, being on the verge of passing out, and fainting. The study's goal is to develop an algorithm to classify VVS patients based on physiological signals blood pressure (BP) and electrocardiography (ECG) obtained from the HUTT. METHODS: After 10 min of supine rest, the subject was tilted at a 70-degree angle on a tilt table for approximately a total of 35 min. 400 µg of glyceryl trinitrate (GTN) was administered sublingually after the first 20 min and monitoring continued for another 15 min. Mean imputation and K-nearest neighbors (KNN) imputation approaches to handle missing values. Next, feature selection techniques were implemented, including genetic algorithm, recursive feature elimination, and feature importance, to determine the crucial features. The Mann-Whitney U test was then performed to determine the statistical difference between two groups. Patients with VVS are categorized via machine learning models including Support Vector Machine (SVM), Gaussian Naïve Bayes (GNB), Multinomial Naïve Bayes (MNB), KNN, Logistic Regression (LR), and Random Forest (RF). The developed model is interpreted using an explainable artificial intelligence (XAI) model known as partial dependence plot. RESULTS: A total of 137 subjects aged between 9 and 93 years were recruited for this study, 54 experienced clinical symptoms were considered positive tests, while the remaining 83 tested negative. Optimal results were obtained by combining the KNN imputation technique and three tilting features with SVM with 90.5% accuracy, 87.0% sensitivity, 92.7% specificity, 88.6% precision, 87.8% F1 score, and 95.4% ROC (receiver operating characteristics) AUC (area under curve). CONCLUSIONS: The proposed algorithm effectively classifies VVS patients with over 90% accuracy. However, the study was confined to a small sample size. More clinical datasets are required to ensure that our approach is generalizable.

Non-invasive parameters of autonomic function using beat-to-beat cardiovascular variations and arterial stiffness in hypertensive individuals: a systematic review.

PURPOSE: Non-invasive, beat-to-beat variations in physiological indices provide an opportunity for more accessible assessment of autonomic dysfunction. The potential association between the changes in these parameters and arterial stiffness in hypertension remains poorly understood. This systematic review aims to investigate the association between non-invasive indicators of autonomic function based on beat-to-beat cardiovascular signals with arterial stiffness in individuals with hypertension. METHODS: Four electronic databases were searched from inception to June 2022. Studies that investigated non-invasive parameters of arterial stiffness and autonomic function using beat-to-beat cardiovascular signals over a period of > 5min were included. Study quality was assessed using the STROBE criteria. Two authors screened the titles, abstracts, and full texts independently. RESULTS: Nineteen studies met the inclusion criteria. A comprehensive overview of experimental design for assessing autonomic function in terms of baroreflex sensitivity and beat-to-beat cardiovascular variabilities, as well as arterial stiffness, was presented. Alterations in non-invasive indicators of autonomic function, which included baroreflex sensitivity, beat-to-beat cardiovascular variabilities and hemodynamic changes in response to autonomic challenges, as well as arterial stiffness, were identified in individuals with hypertension. A mixed result was found in terms of the association between non-invasive quantitative autonomic indices and arterial stiffness in hypertensive individuals. Nine out of 12 studies which quantified baroreflex sensitivity revealed a significant association with arterial stiffness parameters. Three studies estimated beat-to-beat heart rate variability and only one study reported a significant relationship with arterial stiffness indices. Three out of five studies which studied beat-to-beat blood pressure variability showed a significant association with arterial structural changes. One study revealed that hemodynamic changes in response to autonomic challenges were significantly correlated with arterial stiffness parameters. CONCLUSIONS: The current review demonstrated alteration in autonomic function, which encompasses both the sympathetic and parasympathetic modulation of sinus node function and vasomotor tone (derived from beat-to-beat cardiovascular signals) in hypertension, and a significant association between some of these parameters with arterial stiffness. By employing non-invasive measurements to monitor changes in autonomic function and arterial remodeling in individuals with hypertension, we would be able to enhance our ability to identify individuals at high risk of cardiovascular disease. Understanding the intricate relationships among these cardiovascular variability measures and arterial stiffness could contribute toward better individualized treatment for hypertension in the future. SYSTEMATIC REVIEW REGISTRATION: PROSPERO ID: CRD42022336703. Date of registration: 12/06/2022.

A Novel Respiratory Rate Estimation Algorithm from Photoplethysmogram Using Deep Learning Model.

Respiratory rate (RR) is a critical vital sign that can provide valuable insights into various medical conditions, including pneumonia. Unfortunately, manual RR counting is often unreliable and discontinuous. Current RR estimation algorithms either lack the necessary accuracy or demand extensive window sizes. In response to these challenges, this study introduces a novel method for continuously estimating RR from photoplethysmogram (PPG) with a reduced window size and lower processing requirements. To evaluate and compare classical and deep learning algorithms, this study leverages the BIDMC and CapnoBase datasets, employing the Respiratory Rate Estimation (RRest) toolbox. The optimal classical techniques combination on the BIDMC datasets achieves a mean absolute error (MAE) of 1.9 breaths/min. Additionally, the developed neural network model utilises convolutional and long short-term memory layers to estimate RR effectively. The best-performing model, with a 50% train-test split and a window size of 7 s, achieves an MAE of 2 breaths/min. Furthermore, compared to other deep learning algorithms with window sizes of 16, 32, and 64 s, this study's model demonstrates superior performance with a smaller window size. The study suggests that further research into more precise signal processing techniques may enhance RR estimation from PPG signals.

Assessing the efficacy of machine learning algorithms for syncope classification: A systematic review.

Syncope is a transient loss of consciousness with rapid onset. The aims of the study were to systematically evaluate available machine learning (ML) algorithm for supporting syncope diagnosis to determine their performance compared to existing point scoring protocols. We systematically searched IEEE Xplore, Web of Science, and Elsevier for English articles (Jan 2011 - Sep 2021) on individuals aged five and above, employing ML algorithms in syncope detection with Head-up titl table test (HUTT)-monitored hemodynamic parameters and reported metrics. Extracted data encompassed subject count, age range, syncope protocols, ML type, hemodynamic parameters, and performance metrics. Of the 6301 studies initially identified, 10 studies, involving 1205 participants aged 5 to 82 years, met the inclusion criteria, and formed the basis for it. Selected studies must use ML algorithms in syncope detection with hemodynamic parameters recorded throughout HUTT. The overall ML algorithm performance achieved a sensitivity of 88.8% (95% CI: 79.4-96.1%), specificity of 81.5% (95% CI: 69.8-92.8%) and accuracy of 85.8% (95% CI: 78.6-92.8%). Machine learning improves syncope diagnosis compared to traditional scoring, requiring fewer parameters. Future enhancements with larger databases are anticipated. Integrating ML can curb needless admissions, refine diagnostics, and enhance the quality of life for syncope patients.

Differences in Cardiovascular Regulation to Head-up Tilt between Healthy and Hypertensive Subjects.

To date there have only been limited studies exploring abnormal hemodynamic responses to head-up tilt tests (HUTs) in elderly, treated patients with hypertension. Cardiovascular regulation in response to HUT as well as upright hemodynamics may be altered when older hypertensive patients with antihypertensive treatments are studied. Hypertensive patients with and without receiving antihypertensive medication and above the age of 45 were recruited in this study. This study compared the cardiovascular responses to HUT and at rest between healthy and hypertensives using non-invasive hemodynamic measurements. Parameters such as systolic blood pressure (SBP), diastolic blood pressure (DBP), heart rate (HR), stroke index (SI) and total peripheral resistance index (TPRI) were measured in 40 subjects (20 healthy and 20 hypertensives) for 10-min supine baseline, 10-min HUT at 70◦ and 6-min supine recovery. At rest and during HUT, SBP and TPRI were significantly higher in hypertensives together with a significantly smaller baseline SI. In response to HUT, both groups showed changes in hemodynamic parameters at differing degrees. During recovery, all parameters returned to the baseline range. Our findings indicated that hypertensive patients of older age being treated by antihypertensive drugs may have different cardiovascular changes in response to orthostatic stress.Clinical Relevance- This pilot study describes how cardiovascular regulation in response to postural change may behave differently in hypertensive elder patients taking antihypertensive drugs.

Investigation of relayed nuclear Overhauser enhancement effect at -1.6 ppm in an ischemic stroke model.

Background: When an ischemic stroke happens, it triggers a complex signalling cascade that may eventually lead to neuronal cell death if no reperfusion. Recently, the relayed nuclear Overhauser enhancement effect at -1.6 ppm [NOE(-1.6 ppm)] has been postulated may allow for a more in-depth analysis of the ischemic injury. This study assessed the potential utility of NOE(-1.6 ppm) in an ischemic stroke model. Methods: Diffusion-weighted imaging, perfusion-weighted imaging, and chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) data were acquired from five rats that underwent scans at 9.4 T after middle cerebral artery occlusion. Results: The apparent diffusion coefficient (ADC), cerebral blood flow (CBF), and apparent exchange-dependent relaxations (AREX) at 3.5 ppm and NOE(-1.6 ppm) were quantified. AREX(3.5 ppm) and NOE(-1.6 ppm) were found to be hypointense and exhibited different signal patterns within the ischemic tissue. The NOE(-1.6 ppm) deficit areas were equal to or larger than the ADC deficit areas, but smaller than the AREX(3.5 ppm) deficit areas. This suggested that NOE(-1.6 ppm) might further delineate the acidotic tissue estimated using AREX(3.5 ppm). Since NOE(-1.6 ppm) is closely related to membrane phospholipids, NOE(-1.6 ppm) potentially highlighted at-risk tissue affected by lipid peroxidation and membrane damage. Altogether, the ADC/NOE(-1.6 ppm)/AREX(3.5 ppm)/CBF mismatches revealed four zones of increasing sizes within the ischemic tissue, potentially reflecting different pathophysiological information. Conclusions: Using CEST coupled with ADC and CBF, the ischemic tissue may thus potentially be separated into four zones to better understand the pathophysiology after stroke and improve ischemic tissue fate definition. Further verification of the potential utility of NOE(-1.6 ppm) may therefore lead to a more precise diagnosis.

Non-annotated renal histopathological image analysis with deep ensemble learning.

Background: Renal cancer is one of the leading causes of cancer-related deaths worldwide, and early detection of renal cancer can significantly improve the patients' survival rate. However, the manual analysis of renal tissue in the current clinical practices is labor-intensive, prone to inter-pathologist variations and easy to miss the important cancer markers, especially in the early stage. Methods: In this work, we developed deep convolutional neural network (CNN) based heterogeneous ensemble models for automated analysis of renal histopathological images without detailed annotations. The proposed method would first segment the histopathological tissue into patches with different magnification factors, then classify the generated patches into normal and tumor tissues using the pre-trained CNNs and lastly perform the deep ensemble learning to determine the final classification. The heterogeneous ensemble models consisted of CNN models from five deep learning architectures, namely VGG, ResNet, DenseNet, MobileNet, and EfficientNet. These CNN models were fine-tuned and used as base learners, they exhibited different performances and had great diversity in histopathological image analysis. The CNN models with superior classification accuracy (Acc) were then selected to undergo ensemble learning for the final classification. The performance of the investigated ensemble approaches was evaluated against the state-of-the-art literature. Results: The performance evaluation demonstrated the superiority of the proposed best performing ensembled model: five-CNN based weighted averaging model, with an Acc (99%), specificity (Sp) (98%), F1-score (F1) (99%) and area under the receiver operating characteristic (ROC) curve (98%) but slightly inferior recall (Re) (99%) compared to the literature. Conclusions: The outstanding robustness of the developed ensemble model with a superiorly high-performance scores in the evaluated metrics suggested its reliability as a diagnosis system for assisting the pathologists in analyzing the renal histopathological tissues. It is expected that the proposed ensemble deep CNN models can greatly improve the early detection of renal cancer by making the diagnosis process more efficient, and less misdetection and misdiagnosis; subsequently, leading to higher patients' survival rate.

Histopathological Gastric Cancer Detection on GasHisSDB Dataset Using Deep Ensemble Learning.

Gastric cancer is a leading cause of cancer-related deaths worldwide, underscoring the need for early detection to improve patient survival rates. The current clinical gold standard for detection is histopathological image analysis, but this process is manual, laborious, and time-consuming. As a result, there has been growing interest in developing computer-aided diagnosis to assist pathologists. Deep learning has shown promise in this regard, but each model can only extract a limited number of image features for classification. To overcome this limitation and improve classification performance, this study proposes ensemble models that combine the decisions of several deep learning models. To evaluate the effectiveness of the proposed models, we tested their performance on the publicly available gastric cancer dataset, Gastric Histopathology Sub-size Image Database. Our experimental results showed that the top 5 ensemble model achieved state-of-the-art detection accuracy in all sub-databases, with the highest detection accuracy of 99.20% in the 160 × 160 pixels sub-database. These results demonstrated that ensemble models could extract important features from smaller patch sizes and achieve promising performance. Overall, our proposed work could assist pathologists in detecting gastric cancer through histopathological image analysis and contribute to early gastric cancer detection to improve patient survival rates.

One-Step Fabrication of Paper-Based Inkjet-Printed Graphene for Breath Monitor Sensors.

Irregularities in breathing patterns can be detected using breath monitor sensors, and this help clinicians to predict health disorders ranging from sleep disorders to heart failures. Variations in humidity during the inhalation and exhalation of breath have been utilized as a marker to detect breath patterns, and graphene-based devices are the favored sensing media for relative humidity (RH). In general, most graphene-based RH sensors have been used to explore resistance change as a measurement parameter to calibrate against the RH value, and they are prone to noise interference. Here, we fabricated RH sensors using graphene ink as a sensing medium and printed them in the shape of interdigital electrodes on glossy paper using an office inkjet printer. Further, we investigated the capacitance change in the sensor for the RH changes in the range of 10-70%. It exhibited excellent sensitivity with 0.03 pF/% RH, good stability, and high intraday and interday repeatability, with relative standard deviations of 1.2% and 2.2%, respectively. Finally, the sensor was embedded into a face mask and interfaced with a microcontroller, and capacitance change was measured under three different breathing situations: normal breathing, deep breathing, and coughing. The result show that the dominant frequency for normal breath is 0.22 Hz, for deep breath, it is 0.11 Hz, and there was no significant dominant cough frequency due to persistent coughing and inconsistent patterns. Moreover, the sensor exhibited a short response and recovery time (<5 s) during inhalation and exhalation. Thus, the proposed paper-based RH sensor is promising wearable and disposable healthcare technology for clinical and home care health applications.

Development of an effective clustering algorithm for older fallers.

Falls are common and often lead to serious physical and psychological consequences for older persons. The occurrence of falls are usually attributed to the interaction between multiple risk factors. The clinical evaluation of falls risks is time-consuming as a result, hence limiting its availability. The purpose of this study was, therefore, to develop a clustering-based algorithm to determine falls risk. Data from the Malaysian Elders Longitudinal Research (MELoR), comprising 1411 subjects aged ≥55 years, were utilized. The proposed algorithm was developed through the stages of: data pre-processing, feature identification and extraction with either t-Distributed Stochastic Neighbour Embedding (t-SNE) or principal component analysis (PCA)), clustering (K-means clustering, Hierarchical clustering, and Fuzzy C-means clustering) and characteristics interpretation with statistical analysis. A total of 1279 subjects and 9 variables were selected for clustering after the data pre-possessing stage. Using feature extraction with the t-SNE and the K-means clustering algorithm, subjects were clustered into low, intermediate A, intermediate B and high fall risk groups which corresponded with fall occurrence of 13%, 19%, 21% and 31% respectively. Slower gait, poorer balance, weaker muscle strength, presence of cardiovascular disorder, poorer cognitive performance, and advancing age were the key variables identified. The proposed fall risk clustering algorithm grouped the subjects according to features. Such a tool could serve as a case identification or clinical decision support tool for clinical practice to enhance access to falls prevention efforts.

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.

Increased Beat-to-Beat Blood Pressure Variability Is Associated With Impaired Cognitive Function.

BACKGROUND: Emerging evidence has linked visit-to-visit, day-to-day and 24-h ABPM blood pressure variability (BPV) with cognitive impairment. Few studies have, however, considered beat-to-beat BPV. This study, therefore, evaluated the relationship between beat-to-beat BPV and cognitive function among community-dwellers aged 55 years and over. METHODS: Data was obtained from the Malaysian Elders Longitudinal Research (MELoR) study, which employed random stratified sampling from three parliamentary constituencies within the Klang Valley. Beat-to-beat blood pressure (BP) was recorded using non-invasive BP monitoring (TaskforceTM, CNSystems). Low frequency (LF), high frequency (HF) and low-to-high frequency (LF:HF) ratio for BPV were derived using fast Fourier transformation. Cognition was evaluated using the Montreal Cognitive Assessment (MoCA) test, and categorized into normal aging, mild impairment and moderate-to-severe impairment. RESULTS: Data from 1,140 individuals, mean age (SD) 68.48 (7.23) years, were included. Individuals with moderate-to-severe impairment had higher HF-BPV for systolic (SBP) and diastolic (DBP) blood pressure compared to individuals within the normal aging group [OR (95% CI) = 2.29 (1.62-3.24)] and [OR (95% CI) = 1.80 (1.32-2.45)], while HF-SBPV [OR (95% CI) = 1.41 (1.03-1.93)] but not HF-DBPV was significantly higher with mild impairment compared to normal aging after adjustments for potential confounders. Moderate-to-severe impairment was associated with significantly lower LF:HF-SBPV [OR (95% CI) = 0.29 (0.18-0.47)] and LF:HF-DBPV [OR (95% CI) = 0.49 (0.34-0.72)], while mild impairment was associated with significantly lower LF:HF-SBPV [OR (95% CI) = 0.52 (0.34-0.80)] but not LF:HF-DBPV [OR (95% CI) = 0.81 (0.57-1.17)], compared to normal aging with similar adjustments. CONCLUSION: Higher HF-BPV, which indicates parasympathetic activation, and lower LF:HF-BPV, which addresses sympathovagal balance, were observed among individuals with moderate-to-severe cognitive impairment. Future studies should determine whether BPV could be a physiological marker or modifiable risk factor for cognitive decline.

Psychological status and physical performance are independently associated with autonomic function.

BACKGROUND: Falls among older adults have become a global concern. While previous studies have established associations between autonomic function indicator; heart rate variability (HRV) and blood pressure variability (BPV) with fall recurrence, as well as physical inactivity and psychological disorders as risk factors for falls, the influence of physical activity and psychological status on autonomic dysfunction observed among older fallers has not been adequately investigated. The aim of this study was to evaluate the relationship between psychological disorder and physical performance on the autonomic nervous system (ANS) in older fallers. We hypothesised that older fallers have poorer autonomic function, greater dependency on others and were associated with psychological disorders. Furthermore, we hypothesised that both physical performance and psychological status can contribute to the worsening of the autonomic function among the elderly. METHODS: In this cross-sectional survey, adults aged ≥ 60 years were recruited. Continuous non-invasive BP was monitored over 5 min of supine and 3 min of standing. Psychological status was assessed in terms of depression, anxiety, stress, and concern about falling, while functional status was measured using time-up-and-go, functional reach, handgrip and Lawton's Instrumental Activities of Daily Life (IADL) scale. RESULTS: A total of 62 participants were recruited consisting of 37 fallers and 25 non-fallers. Multivariate analysis revealed that Lawton IADL was independently associated with systolic blood pressure variability (SBPV) and diastolic blood pressure variability (DBPV) during both supine (SBPV: r2 = 0.080, p = 0.025; DBPV: r2 = 0.064, p = 0.046) and standing (SBPV: r2 = 0.112, p = 0.008; DBPV: r2 = 0.105, p = 0.011), while anxiety score was independently associated with SBPV and DBPV during standing (SBPV: r2 = 0.112, p = 0.009; DBPV: r2 = 0.105, p = 0.011) as compared to the other parameters. CONCLUSION: Our findings suggest that fallers had poorer ANS, greater dependence in IADLs, and were more anxious. IADL dependency and anxiety were the most predictive of autonomic dysfunction, and can be used in practice to identify poor autonomic function for the prevention of falls and cardiovascular diseases among older adults.

Association between mental illness and blood pressure variability: a systematic review.

BACKGROUND: Mental illness represents a major global burden of disease worldwide. It has been hypothesised that individuals with mental illness have greater blood pressure fluctuations that lead to increased cardiovascular risk and target organ damage. This systematic review aims to (i) investigate the association between mental illness and blood pressure variability (BPV) and (ii) describe methods of BPV measurements and analysis which may affect pattern and degree of variability. METHODS: Four electronic databases were searched from inception until 2020. The quality assessment was performed using STROBE criteria. Studies were included if they investigated BPV (including either frequency or time domain analysis) in individuals with mental illness (particularly anxiety/generalised anxiety disorder, depression/major depressive disorder, panic disorder and hostility) and without hypertension. Two authors independently screened titles, abstracts and full texts. A third author resolved any disagreements. RESULTS: Twelve studies met the inclusion criteria. Three studies measured short-term BPV, two measured long-term BPV and seven measured ultra-short-term BPV. All studies related to short-term BPV using ambulatory and home blood pressure monitoring found a higher BPV in individuals with depression or panic disorder. The two studies measuring long-term BPV were limited to the older population and found mixed results. Mental illness is significantly associated with an increased BPV in younger and middle-aged adults. All studies of ultra-short-term BPV using standard cardiac autonomic assessment; non-invasive continuous finger blood pressure and heart rate signals found significant association between BPV and mental illness. A mixed result related to degree of tilt during tilt assessment and between controlled and spontaneous breathing were observed in patients with psychological state. CONCLUSIONS: Current review found that people with mental illness is significantly associated with an increased BPV regardless of age. Since mental illness can contribute to the deterioration of autonomic function (HRV, BPV), early therapeutic intervention in mental illness may prevent diseases associated with autonomic dysregulation and reduce the likelihood of negative cardiac outcomes. Therefore, these findings may have important implications for patients' future physical health and well-being, highlighting the need for comprehensive cardiovascular risk reduction.

Robust PPG motion artifact detection using a 1-D convolution neural network.

BACKGROUND AND OBJECTIVES: Continuous monitoring of physiological parameters such as photoplethysmography (PPG) has attracted increased interest due to advances in wearable sensors. However, PPG recordings are susceptible to various artifacts, and thus reducing the reliability of PPG-driven parameters, such as oxygen saturation, heart rate, blood pressure and respiration. This paper proposes a one-dimensional convolution neural network (1-D-CNN) to classify five-second PPG segments into clean or artifact-affected segments, avoiding data-dependent pulse segmentation techniques and heavy manual feature engineering. METHODS: Continuous raw PPG waveforms were blindly allocated into segments with an equal length (5s) without leveraging any pulse location information and were normalized with Z-score normalization methods. A 1-D-CNN was designed to automatically learn the intrinsic features of the PPG waveform, and perform the required classification. Several training hyperparameters (initial learning rate and gradient threshold) were varied to investigate the effect of these parameters on the performance of the network. Subsequently, this proposed network was trained and validated with 30 subjects, and then tested with eight subjects, with our local dataset. Moreover, two independent datasets downloaded from the PhysioNet MIMIC II database were used to evaluate the robustness of the proposed network. RESULTS: A 13 layer 1-D-CNN model was designed. Within our local study dataset evaluation, the proposed network achieved a testing accuracy of 94.9%. The classification accuracy of two independent datasets also achieved satisfactory accuracy of 93.8% and 86.7% respectively. Our model achieved a comparable performance with most reported works, with the potential to show good generalization as the proposed network was evaluated with multiple cohorts (overall accuracy of 94.5%). CONCLUSION: This paper demonstrated the feasibility and effectiveness of applying blind signal processing and deep learning techniques to PPG motion artifact detection, whereby manual feature thresholding was avoided and yet a high generalization ability was achieved.

Association between different methods of assessing blood pressure variability and incident cardiovascular disease, cardiovascular mortality and all-cause mortality: a systematic review.

BACKGROUND: Blood pressure variability (BPV) is a possible risk factor for adverse cardiovascular outcomes and mortality. There is uncertainty as to whether BPV is related to differences in populations studied, measurement methods or both. We systematically reviewed the evidence for different methods to assess blood pressure variability (BPV) and their association with future cardiovascular events, cardiovascular mortality and all-cause mortality. METHODS: Literature databases were searched to June 2019. Observational studies were eligible if they measured short-term BPV, defined as variability in blood pressure measurements acquired either over a 24-hour period or several days. Data were extracted on method of BPV and reported association (or not) on future cardiovascular events, cardiovascular mortality and all-cause mortality. Methodological quality was assessed using the CASP observational study tool and data narratively synthesised. RESULTS: Sixty-one studies including 3,333,801 individuals were eligible. BPV has been assessed by various methods including ambulatory and home-based BP monitors assessing 24-hour, "day-by-day" and "week-to-week" variability. There was moderate quality evidence of an association between BPV and cardiovascular events (43 studies analysed) or all-cause mortality (26 studies analysed) irrespective of the measurement method in the short- to longer-term. There was moderate quality evidence reporting inconsistent findings on the potential association between cardiovascular mortality, irrespective of methods of BPV assessment (17 studies analysed). CONCLUSION: An association between BPV, cardiovascular mortality and cardiovascular events and/or all-cause mortality were reported by the majority of studies irrespective of method of measurement. Direct comparisons between studies and reporting of pooled effect sizes were not possible.

Ethnic differences in lifetime cumulative incidence of syncope: the Malaysian elders longitudinal research (MELoR) study.

PURPOSE: To determine the lifetime cumulative incidence of syncope, potential ethnic differences and factors associated with syncope using the Malaysian elders longitudinal research (MELoR) study first wave dataset. METHODS: The MELoR study recruited community-dwelling adults aged 55 years and over, selected through stratified random sampling from three parliamentary constituencies. The baseline data collected during the first wave was obtained through face-to-face interviews in participants' homes using computer-assisted questionnaires. During their baseline assessments, participants were asked whether they had ever experienced a blackout in their lifetime and if they had experienced a blackout in the preceding 12 months. RESULTS: Information on blackouts and ethnicity were available for 1530 participants. The weight-adjusted lifetime cumulative incidence of syncope for the overall population aged 55 years and above was 27.7%. The estimated lifetime cumulative incidence according to ethnic groups was 34.6% for Malays, 27.8% for Indians and 23.7% for Chinese. The estimated 12-month incidence of syncope was 6.1% overall, equating to 11.7% for Malays, 8.7 % for Indians and 2.3% for Chinese. Both Malay [odds ratio (OR) 1.46; 95% confidence interval (CI) 1.10-1.95 and OR 3.62, 95% CI 1.96-6.68] and Indian (OR 1.34; 95% CI 1.01-1.80 and OR 3.31, 1.78-6.15) ethnicities were independently associated with lifetime and 12-month cumulative incidence of syncope, respectively, together with falls, dizziness and myocardial infarction. CONCLUSION: Ethnic differences exist for lifetime cumulative incidence of syncope in community-dwelling individuals aged 55 years and over in an urban area in Southeast Asia. Future studies should now seek to determine potential genetic, cultural and lifestyle differences which may predispose to syncope.

Using SERS-based microfluidic paper-based device (µPAD) for calibration-free quantitative measurement of AMI cardiac biomarkers.

Recently, surface enhanced Raman scattering (SERS) has attracted much attention in medical diagnosis applications owing to better detection sensitivity and lower limit of detection (LOD) than colorimetric detection. In this paper, a novel calibration-free SERS-based µPAD with multi-reaction zones for simultaneous quantitative detection of multiple cardiac biomarkers - GPBB, CK-MB and cTnT for early diagnosis and prognosis of acute myocardial infarction (AMI) are presented. Three distinct Raman probes were synthesised, subsequently conjugated with respective detecting antibodies and used as SERS nanotags for cardiac biomarker detection. Using a conventional calibration curve, quantitative simultaneous measurement of multiple cardiac biomarkers on SERS-based µPAD was performed based on the characteristic Raman spectral features of each reporter used in different nanotags. However, a calibration free point-of-care testing device is required for fast screening to rule-in and rule-out AMI patients. Partial least squares predictive models were developed and incorporated into the immunosensing system, to accurately quantify the three unknown cardiac biomarkers levels in serum based on the previously obtained Raman spectral data. This method allows absolute quantitative measurement when conventional calibration curve fails to provide accurate estimation of cardiac biomarkers, especially at low and high concentration ranges. Under an optimised condition, the LOD of our SERS-based µPAD was identified at 8, 10, and 1 pg mL-1, for GPBB, CK-MB and cTnT, respectively, which is well below the clinical cutoff values. Therefore, this proof-of-concept technique shows significant potential for highly sensitive quantitative detection of multiplex cardiac biomarkers in human serum to expedite medical decisions for enhanced patient care.

Orthostatic blood pressure changes and physical, functional and cognitive performance: the MELoR study.

PURPOSE: Consensus definitions currently define initial orthostatic hypotension (IOH) as ≥ 40 mmHg systolic (SBP) or ≥ 20 mmHg in diastolic blood pressure (DBP) reductions within 15 s of standing, while classical orthostatic hypotension (COH) is defined as a sustained reduction ≥ 20 mmHg SBP or ≥ 10 mmHg SBP within 3 min of standing. The clinical relevance of the aforementioned criteria remains unclear. The present study aimed to determine factors influencing postural blood pressure changes and their relationship with physical, functional and cognitive performance in older adults. METHODS: Individuals aged ≥ 55 years were recruited through the Malaysian Elders Longitudinal Research (MELoR) study and continuous non-invasive BP was monitored over 5 min of supine rest and 3 min of standing. Physical performance was measured using the timed-up-and-go test, functional reach, handgrip and Lawton's functional ability scale. Cognition was measured with the Montreal Cognitive Assessment. Participants were categorized according to BP responses into four categories according to changes in SBP/DBP reductions from supine to standing: < 20/10 mmHg within 3 min (no OH), ≥ 20/10 mmHg from 15 s to 3 min (COH), ≥ 40/20 mmHg within 15 s and ≥ 20/10 mmHg from 15 s to 3 min (COH + IOH) and ≥ 40/20 mmHg within 15 s and < 20/10 mmHg within 3 min (IOH). RESULTS: A total of 1245 participants were recruited, COH + IOH 623 (50%), IOH 165 (13%) and COH 145 (12%). Differences between groups existed in age, gender, hypertension, diabetes, use of alpha-blocker and/or beta-blocker, ACE-inhibitors, diuretics, biguanides, and baseline systolic BP. In univariate analyses, differences between groups were present in physical performance and cognition. Multivariate comparisons revealed better physical performance in IOH compared to no OH, better physical and cognitive performance in COH + IOH compared to no OH, and cognition in COH than no OH. CONCLUSION: Our findings suggest that older adults who fulfil current consensus definitions for IOH had better physical performance and cognitive scores. This indicates that an initial postural BP drop in people aged ≥ 55 years may not necessarily be associated with increased frailty, as suggested by previously published literature.

Standing beat-to-beat blood pressure variability is reduced among fallers in the Malaysian Elders Longitudinal Study.

The aim of this study was to determine the relationship between falls and beat-to-beat blood pressure (BP) variability.Continuous noninvasive BP measurement is as accurate as invasive techniques. We evaluated beat-to-beat supine and standing BP variability (BPV) using time and frequency domain analysis from noninvasive continuous BP recordings.A total of 1218 older adults were selected. Continuous BP recordings obtained were analyzed to determine standard deviation (SD) and root mean square of real variability (RMSRV) for time domain BPV and fast-Fourier transform low frequency (LF), high frequency (HF), total power spectral density (PSD), and LF:HF ratio for frequency domain BPV.Comparisons were performed between 256 (21%) individuals with at least 1 fall in the past 12 months and nonfallers. Fallers were significantly older (P = .007), more likely to be female (P = .006), and required a longer time to complete the Timed-Up and Go test (TUG) and frailty walk test (P ≤ .001). Standing systolic BPV (SBPV) was significantly lower in fallers compared to nonfallers (SBPV-SD, P = .016; SBPV-RMSRV, P = .033; SBPV-LF, P = .003; SBPV-total PSD, P = .012). Nonfallers had significantly higher supine to standing ratio (SSR) for SBPV-SD, SBPV-RMSRV, and SBPV-total PSD (P = .017, P = .013, and P = .009). In multivariate analyses, standing BPV remained significantly lower in fallers compared to nonfallers after adjustment for age, sex, diabetes, frailty walk, and supine systolic BP. The reduction in frequency-domain SSR among fallers was attenuated by supine systolic BP, TUG, and frailty walk.In conclusion, reduced beat-to-beat BPV while standing is independently associated with increased risk of falls. Changes between supine and standing BPV are confounded by supine BP and walking speed.