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

TREX1 is expressed by microglia in normal human brain and increases in regions affected by ischemia

BACKGROUND: Mutations in the three-prime repair exonuclease 1 (TREX1) gene have been associated with neurological diseases, including Retinal Vasculopathy with Cerebral Leukoencephalopathy (RVCL). However, the endogenous expression of TREX1 in human brain has not been studied. METHODS: We produced a rabbit polyclonal antibody (pAb) to TREX1 to characterize TREX1 by Western blotting (WB) of cell lysates from normal controls and subjects carrying an RVCL frame-shift mutation. Dual staining was performed to determine cell types expressing TREX1 in human brain tissue. TREX1 distribution in human brain was further evaluated by immunohistochemical analyses of formalin-fixed, paraffin-embedded samples from normal controls and patients with RVCL and ischemic stroke. RESULTS: After validating the specificity of our anti-TREX1 rabbit pAb, WB analysis was utilized to detect the endogenous wild-type and frame-shift mutant of TREX1 in cell lysates. Dual staining in human brain tissues from patients with RVCL and normal controls localized TREX1 to a subset of microglia and macrophages. Quantification of immunohistochemical staining of the cerebral cortex revealed that TREX1 CONCLUSIONS: TREX1 is expressed by a subset of microglia in normal human brain, often in close proximity to the microvasculature, and increases in the setting of ischemic lesions. These findings suggest a role for TREX

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

Assessment of Damage to Nucleic Acids and Repair Machinery in Salmonella typhimurium Exposed to Chlorine

Water disinfection is usually evaluated using mandatory methods based on cell culturability. However, such methods do not consider the potential of cells to recover, which should also be kept as low as possible. In this paper, we hypothesized that a successful disinfection is achieved only when the applied chlorine leads to both intracellular nucleic acid damage and strong alterations of the DNA repair machinery. Monitoring the SOS system responsiveness with a umuC’-‘lacZ reporter fusion, we found that the expression of this important cellular machinery was altered after the beginning of membrane permeabilization but prior to the total decline of both the cell culturability and the nucleic acid integrity as revealed by Sybr-II staining. Rapid measurement of such nucleic acid alterations by fluorochrome-based staining could be used as an alternative method for assessing the effectiveness of disinfection with chlorine

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