A review of machine learning techniques in photoplethysmography for the non-invasive cuff-less measurement of blood pressure

Hypertension or high blood pressure is a leading cause of death throughout the world and a critical factor for increasing the risk of serious diseases, including cardiovascular diseases such as stroke and heart failure. Blood pressure is a primary vital sign that must be monitored regularly for the early detection, prevention and treatment of cardiovascular diseases. Traditional blood pressure measurement techniques are either invasive or cuff-based, which are impractical, intermittent, and uncomfortable for patients. Over the past few decades, several indirect approaches using photoplethysmogram (PPG) have been investigated, namely, pulse transit time, pulse wave velocity, pulse arrival time and pulse wave analysis, in an effort to utilise PPG for estimating blood pressure. Recent advancements in signal processing techniques, including machine learning and artificial intelligence, have also opened up exciting new horizons for PPG-based cuff less and continuous monitoring of blood pressure. Such a device will have a significant and transformative impact in monitoring patients’ vital signs, especially those at risk of cardiovascular disease. This paper provides a comprehensive review for non-invasive cuff-less blood pressure estimation using the PPG approach along with their challenges and limitations

Cuffless and Continuous Blood Pressure Estimation from PPG Signals Using Recurrent Neural Networks

This paper proposes cuffless and continuous blood pressure estimation utilising Photoplethysmography (PPG) signals and state of the art recurrent network models, namely, Long Short Term Memory and Gated Recurrent Units. The models were validated on wide range of varying blood pressure and PPG signals acquired from the Multiparameter Intelligent Monitoring in Intensive Care database. Many features were extracted from the PPG waveform and several machine learning techniques were employed in an attempt to eliminate collinearity and reduce the size of input feature vector. Consequently, the most effective features for blood pressure estimation were selected. Experimental results show that the accuracy of the proposed methods outperform traditional models applied in the literature. The results satisfy the American National Standards of the Association for the Advancement of Medical Instrumentation

Machine learning techniques for the prediction of systolic and diastolic blood pressure utilising the photoplethysmogram

Blood pressure (BP) is one of the four primary vital signs that provides important information regarding patients' cardiovascular system conditions. Continuous and regular blood pressure monitoring is essential for the early diagnosis, prevention and management of cardiovascular disease (CVD) and haemodynamic diseases (hypertension and hypotension). Current clinical blood pressure measurement techniques are either invasive or cuff-based, which can be impractical, intermittent, and uncomfortable for patients during frequent measurements. Considering these challenges, several studies have suggested new non-invasive and cuffless blood pressuring measurement techniques using physiological signals, such as, the Electrocardiogram (ECG) and the Photoplethysmogram (PPG). In particular, indirect cuffless BP measurement techniques using pulse transit time and pulse arrival time have been extensively investigated over the last few decades. However, these techniques require two measurement sensors, frequent calibration, and hence, they are also impractical and inconvenient for continuous BP measurements. More recently, with the advancement of computational techniques, including machine learning and artificial intelligence, a new simple and innovative approach using only PPG signals have been proposed in the literature for cuffless and continuous monitoring of blood pressure. However, the majority of these studies have been unable to achieve acceptable accuracies that comply or satisfy the international standards for cuffless BP monitoring. Thus, further investigations are required to realise this approach. In this research, a total of 52 features have been extracted from the PPG and their individual impact on BP have been rigorously evaluated using several statistical and machine learning techniques. As a result, only the most important features for estimation of BP were selected, effectively reducing the input vector by more than half. Two datasets were created to accommodate the two input feature vectors. The PPG and reference BP signals were derived from the publicly available MIMIC II database. In order to estimate BP, a total of nine machine learning and neural network models have been implemented and evaluated on the two datasets. Out of the nine models, four are widely used classical machine learning models, and five neural network models, three of which are conventional models and two advanced models have been proposed for BP estimation using only one PPG signal. The results of all these models have also been compared against well established studies in the literature. The results obtained using the classical machine learning models, namely, multilinear regression, random forest, adaboost and support vector machine, were poor and inferior to all the neural network models. A slight performance improvement was achieved using the non-recurrent multi-layer perceptron, however, the error was still much higher than the internationally acceptable range. On the other hand, a significant improvement was achieved for the first time by using the recurrent neural network models, namely, Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU). The two proposed neural network models further enhanced the BP estimation accuracies and were able to reduce the mean absolute error (MAE) to a range below 5 mmHg. In particular, the best performing model was the one bidirectional GRU layer, followed by two unidirectional GRU layers, and an attention layer. The obtained MAE and standard deviation (SD) were 4.79+/-8.08 mmHg for systolic BP (SBP) and 2.77+/-4.72 mmHg for diastolic BP (DBP). Furthermore, the DBP estimation were well below the internationally acceptable limits (referring to the AAMI standards of mean error (ME), ME+/-SD less than 5+/-8), while the ME for the SBP estimation were acceptable but the SD exceeded the limits by only 1.34 mmHg. This research has successfully demonstrated that advanced neural network models can be used for the non-invasive and cuffless prediction of BP utilising the PPG

Re-Emerging Vaccine-Preventable Diseases in War-Affected Peoples of the Eastern Mediterranean Region\u2014An Update

For the past few decades, the Eastern Mediterranean Region has been one area of the world profoundly shaped by war and political instability. On-going conflict and destruction have left the region struggling with innumerable health concerns that have claimed the lives of many. Wars, and the chaos they leave behind, often provide the optimal conditions for the growth and re-emergence of communicable diseases. In this article, we highlight a few of the major re-emerging vaccine preventable diseases in four countries of the Eastern Mediterranean Region that are currently affected by war leading to a migration crisis: Iraq, South Sudan, Syria, and Yemen. We will also describe the impact these infections have had on patients, societies, and national health care services. This article also describes the efforts, both local and international, which have been made to address these crises, as well as future endeavors that can be done to contain and control further devastation left by these diseases

Recurrent Neural Network Models for Blood Pressure Monitoring Using PPG Morphological Features

Continuous non-invasive Blood Pressure (BP) monitoring is vital for the early detection and control of hypertension. However, this is yet not possible as all current non-invasive BP devices are cuff-based devices and hence precluding continuous monitoring. Several methods have been proposed to overcome this challenge, one of which utilises the Photoplethysmograph (PPG) signal in an effort to predict reliable BP values from this signal using various computational approaches. Although, good performance has been reported in the literature, it was mainly achieved on a small inadequate sample size using conventional models that are unable to account for the temporal variations in the input vector. To address these limitations, this paper proposes cuff-less and continuous blood pressure estimation using Long Short-term Memory (LSTM) and Gated Recurrent Units (GRU). The models were evaluated on 942 patients acquired from the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC II) dataset. The proposed models produced superior results in comparison with conventional artificial neural network. In particular, the best performance was achieved by the GRU, with mean absolute error and standard deviation of 5.77 ± 8.52 mmHg and 3.33±5.02 mmHg for systolic (SBP) and diastolic blood pressure (DBP), respectively. Furthermore, the results comply with the international standards for cuff-less blood pressure estimation

Crystal structure and chemistry of barium-graphite intercalation compounds

Graphite can accommodate various chemical species between graphene layers to form graphite intercalation compounds (GIC) [1]. Alkali metals can easily lead to bulk stage-1 intercalation compounds by vapor transport but for more electronegative elements, such as alkaline-earth metals or lanthanides, only a superficial intercalation is obtained and other synthesis methods have to be envisaged. Molten alloys, formed between an alkali metal and the targeted metal, have demonstrated their efficiency to prepare bulk and homogeneous GIC from these latter elements, for example the superconducting CaC6 phase [2], but some elements remain difficult to intercalate by this method. More recently, our team developed a method based on the work of Hagiwara et al., consisting in using a LiCl-KCl eutectic molten medium [3], which for example allowed to prepare for the first time a bulk SrC6 compound [4]. This work is focused on the intercalation of barium into graphite from the LiCl-KCl molten salts method. A bulk stage-1 BaC6 compound has been prepared and X-ray diffraction measurements confirmed its crystal structure [5]. Moreover, by varying the experimental conditions, two completely novel phases, denoted α and β, have been isolated. From ion beam analyses, Li0,2K0,6Ba0,35C6 and Li0,2K0,75Ba0,6C6 chemical formulae have been determined for α and β phases, respectively, showing that lithium and potassium are intercalated together with barium. X- ray diffraction led to the determination of the stacking sequence of each compound, and their planar unit cells. Lastly, a reaction mechanism is proposed, which explains the formation of the different phases observed in this study

Role of per-oral pancreatoscopy in the evaluation of suspected pancreatic duct neoplasia: a 13-year U.S. singlecenter experience

Background and Aims The role of per-oral pancreatoscopy (POP) in the evaluation of occult pancreatic duct (PD) lesions remains limited to case series. The aim of this study was to evaluate the ability of POP to differentiate malignant from benign diseases of the PD. Methods Patients who underwent POP between 2000 and 2013 for the evaluation of indeterminate PD strictures, dilatations, or with suspected or known main duct intraductal papillary mucinous neoplasm were identified. Main outcome measurements were visual impression accuracy, POP tissue sampling, efficacy, and safety of POP. Results During the study period, 79 patients who underwent POP for the evaluation of pancreatic stricture or dilatation were identified. Technical success was achieved in 78 (97%). In the PD neoplasia group (n = 33), the final diagnosis was based on index confirmatory POP-guided tissue sampling in 29 (88%). For the detection of PD neoplasia, POP visual impression had a sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 87%, 86%, 83%, 91%, and 87%, respectively. When combined with POP-guided tissue sampling, the values were 91%, 95%, 94%, 93%, and 94%, respectively. Of 102 POPs performed, adverse events were noted in 12 (12%) cases. Conclusions This study demonstrates a high technical success rate, visual impression accuracy, and tissue sampling capability of POP. Examinations were performed by endoscopists with expertise in pancreatoscopy interpretation, and the results may not be generalizable

Does cyst growth predict malignancy in branch duct intraductal papillary mucinous neoplasms? Results of a large multicenter experience

Background Cyst growth of BD-IPMNs on follow-up imaging remains a concerning sign. Aims To describe cyst size changes over time in BD-IPMNs, and determine whether cyst growth rate is associated with increased risk of malignancy. Methods This is a retrospective study performed at two high volume tertiary centers. Mean cyst size at baseline (MCSB) and mean growth rate percentage (MGRP) were calculated. Rapid cyst growth was defined as MGRP ≥ 30%/year. Patient and cyst related characteristics were studied. Results 160 patients were followed for a median of 27.4 (12-114.5) months. MCSB was 15.1 ± 8.0 mm. During follow-up, 73 (45.6%) showed any cyst size increase, of which 15 cysts (9.4%) exhibited MGRP ≥ 30%/year. Rapid cyst growth was not associated with patient or cyst characteristics. Cyst fluid molecular analysis from 101 cysts showed KRAS mutation in 26. Compared to KRAS-negative cysts, neither MCSB (16.0 mm vs. 17.7 mm; p = 0.3) nor MGRP (3.9%/year vs. 5.8%/year; p = 0.7) was significantly different. Eighteen patients underwent surgery; 15 (83%) had LGD, and 3 had advanced neoplasia. Two cysts with LGD and one cyst with advanced neoplasia had MGRP ≥ 30%/year. Conclusion Increase in BD-IPMNs size was not associated with the known high risk patient or cyst-related characteristics. Rapid growth of BD-IPMNs was not associated with advanced neoplasia on surgical pathology