A Novel Deep Ensemble Method for Selective Classification of Electrocardiograms.

OBJECTIVE: Telehealth paradigms are essential for remotely managing patients with chronic conditions. To assist clinicians in handling the large volumes of data collected through these systems, clinical decision support systems (CDSSs) have been developed. However, the effectiveness of CDSSs depends on the quality of remotely recorded physiological data and the reliability of the algorithms used for processing this data. This study aims to reliably detect atrial fibrillation (AF) from short-term single-lead (STSL) electrocardiogram (ECG) recordings obtained in unsupervised telehealth environments. METHODS: A novel deep ensemble-based method was developed for detecting AF from STSL ECG recordings. Following this, a postprocessing algorithm was created to assess uncertainty in classified STSL ECGs and to refrain from interpretation when confidence is low. The proposed method was validated through a 5-fold cross-validation on the Cardiology Challenge 2017 (CinC2017) dataset. RESULTS: The deep ensemble method achieved 83.5 ± 1.5% sensitivity, 98.4 ± 0.2% specificity, and an F 1-score of 0.847 ± 0.016in AF detection. Implementing the selective classification algorithm resulted in significant improvements, with sensitivity increasing to 92.8 ± 2.2%, specificity to 99.7 ± 0.0%, and an F 1-score of 0.919 ± 0.016. CONCLUSION: The proposed method demonstrates the feasibility of accurately detecting AF from STSL ECG recordings. The selective classification approach offers a substantial enhancement to automated ECG interpretation algorithms in telehealth solutions. SIGNIFICANCE: These findings highlight the potential for improving the utility of telehealth systems by integrating advanced CDSSs capable of managing uncertainty and ensuring higher accuracy, thereby improving patient outcomes in remote healthcare settings.

Post-Cardiac arrest outcome prediction using machine learning: A systematic review and meta-analysis.

BACKGROUND: Early and reliable prognostication in post-cardiac arrest patients remains challenging, with various factors linked to return of spontaneous circulation (ROSC), survival, and neurological results. Machine learning and deep learning models show promise in improving these predictions. This systematic review and meta-analysis evaluates how effective these approaches are in predicting clinical outcomes at different time points using structured data. METHODS: This study followed PRISMA guidelines, involving a comprehensive search across PubMed, Scopus, and Web of Science databases until March 2024. Studies aimed at predicting ROSC, survival (or mortality), and neurological outcomes after cardiac arrest through the application of machine learning or deep learning techniques with structured data were included. Data extraction followed the guidelines of the CHARMS checklist, and the bias risk was evaluated using PROBAST tool. Models reporting the AUC metric with 95 % confidence intervals were incorporated into the quantitative synthesis and meta-analysis. RESULTS: After extracting 2,753 initial records, 41 studies met the inclusion criteria, yielding 97 machine learning and 16 deep learning models. The pooled AUC for predicting favorable neurological outcomes (CPC 1 or 2) at hospital discharge was 0.871 (95 % CI: 0.813 - 0.928) for machine learning models and 0.877 (95 % CI: 0.831-0.924) across deep learning algorithms. For survival prediction, this value was found to be 0.837 (95 % CI: 0.757-0.916). Considerable heterogeneity and high risk of bias were observed, mainly attributable to inadequate management of missing data and the absence of calibration plots. Most studies focused on pre-hospital factors, with age, sex, and initial arrest rhythm being the most frequent features. CONCLUSION: Predictive models utilizing AI-based approaches, including machine and deep learning models exhibit enhanced effectiveness compared to previous regression algorithms, but significant heterogeneity and high risk of bias limit their dependability. Evaluating state-of-the-art deep learning models tailored for tabular data and their clinical generalizability can enhance outcome prediction after cardiac arrest.

3DECG-Net: ECG fusion network for multi-label cardiac arrhythmia detection.

Cardiovascular diseases represent the leading global cause of death, typically diagnosed and addressed through electrocardiograms (ECG), which record the heart's electrical activity. In recent years, there has been a notable surge in ECG recordings, driven by the widespread use of wearable devices. However, the limited availability of medical experts to analyze these recordings underscores the necessity for automated ECG analysis using computer-aided methods. In this study, we introduced 3DECG-Net, a deep learning model designed to detect and classify seven distinct heart states through the analysis of data fusion from 12-lead ECG in a multi-label framework. Our model leverages a residual architecture with a multi-head attention mechanism, undergoing training within a five-fold cross-validation scheme. By transforming 12-lead ECG signals into 3D data with the help of Recurrent Plot technique, 3DECG-Net achieves a noteworthy micro F1-score of 80.3 %, surpassing the performance of other state-of-the-art deep learning models developed for this specific task. Also, we present an ECG preprocessing framework to generate compact, high-quality ECG signals for potential application in future studies within this domain. We conduct an explainable AI experiment using Local Interpretable Model-agnostic Explanations (LIME) to elucidate the significance of each lead in accurately diagnosing specific arrhythmias, ensuring the logical processing of ECG data by 3DECG-Net. The findings of this study suggest that the proposed model is trustworthy and has the potential to be used as an effective diagnostic toolset for identifying heart arrhythmias. Its effectiveness can improve the diagnostic process, facilitate early treatment, and enhance overall efficiency in medical settings.

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.

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.

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.

Deep learning in spatially resolved transcriptfomics: a comprehensive technical view

Spatially resolved transcriptomics (SRT) is a pioneering method for simultaneously studying morphological contexts and gene expression at single-cell precision. Data emerging from SRT are multifaceted, presenting researchers with intricate gene expression matrices, precise spatial details and comprehensive histology visuals. Such rich and intricate datasets, unfortunately, render many conventional methods like traditional machine learning and statistical models ineffective. The unique challenges posed by the specialized nature of SRT data have led the scientific community to explore more sophisticated analytical avenues. Recent trends indicate an increasing reliance on deep learning algorithms, especially in areas such as spatial clustering, identification of spatially variable genes and data alignment tasks. In this manuscript, we provide a rigorous critique of these advanced deep learning methodologies, probing into their merits, limitations and avenues for further refinement. Our in-depth analysis underscores that while the recent innovations in deep learning tailored for SRT have been promising, there remains a substantial potential for enhancement. A crucial area that demands attention is the development of models that can incorporate intricate biological nuances, such as phylogeny-aware processing or in-depth analysis of minuscule histology image segments. Furthermore, addressing challenges like the elimination of batch effects, perfecting data normalization techniques and countering the overdispersion and zero inflation patterns seen in gene expression is pivotal. To support the broader scientific community in their SRT endeavors, we have meticulously assembled a comprehensive directory of readily accessible SRT databases, hoping to serve as a foundation for future research initiatives.

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.

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 Comprehensive Investigation of Genomic Variants in Prostate Cancer Reveals 30 Putative Regulatory Variants.

Prostate cancer (PC) is the most frequently diagnosed non-skin cancer in the world. Previous studies have shown that genomic alterations represent the most common mechanism for molecular alterations responsible for the development and progression of PC. This highlights the importance of identifying functional genomic variants for early detection in high-risk PC individuals. Great efforts have been made to identify common protein-coding genetic variations; however, the impact of non-coding variations, including regulatory genetic variants, is not well understood. Identification of these variants and the underlying target genes will be a key step in improving the detection and treatment of PC. To gain an understanding of the functional impact of genetic variants, and in particular, regulatory variants in PC, we developed an integrative pipeline (AGV) that uses whole genome/exome sequences, GWAS SNPs, chromosome conformation capture data, and ChIP-Seq signals to investigate the potential impact of genomic variants on the underlying target genes in PC. We identified 646 putative regulatory variants, of which 30 significantly altered the expression of at least one protein-coding gene. Our analysis of chromatin interactions data (Hi-C) revealed that the 30 putative regulatory variants could affect 131 coding and non-coding genes. Interestingly, our study identified the 131 protein-coding genes that are involved in disease-related pathways, including Reactome and MSigDB, for most of which targeted treatment options are currently available. Notably, our analysis revealed several non-coding RNAs, including RP11-136K7.2 and RAMP2-AS1, as potential enhancer elements of the protein-coding genes CDH12 and EZH1, respectively. Our results provide a comprehensive map of genomic variants in PC and reveal their potential contribution to prostate cancer progression and development.

An Unobtrusive Human Activity Recognition System Using Low Resolution Thermal Sensors, Machine and Deep Learning.

Given the aging population, healthcare systems need to be established to deal with health issues such as injurious falls. Wearable devices can be used to detect falls. However, most wearable devices are obtrusive, and patients generally do not like or may forget to wear them. In this study, we developed an unobtrusive monitoring system using infrared technology to unobtrusively detect locations and recognize human activities such as sitting, standing, walking, lying, and falling. We prototyped a system consisting of two 24×32 thermal array sensors and collected data from healthy young volunteers performing ten different scenarios. A supervised deep learning (DL)-based approach classified activities and detected locations from images. The performance of the DL approach was also compared with the machine learning (ML)-based methods. In addition, we fused the data of two sensors and formed a stereo system, which resulted in better performance compared to a single sensor. Furthermore, to detect critical activities such as falling and lying on floor, we performed a binary classification in which one class was falling plus lying on floor and another class was all the remaining activities. Using the DL-based algorithm on the stereo dataset to recognize activities, overall average accuracy and F1-score were achieved as 97.6%, and 0.935, respectively. These scores for location detection were 97.3%, and 0.927, respectively. These scores for binary classification were 97.9%, and 0.945, respectively. Our results suggest the proposed system recognized human activities, detected locations, and detected critical activities namely falling and lying on floor accurately.

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.

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.

An Unobtrusive Fall Detection System Using Low Resolution Thermal Sensors and Convolutional Neural Networks.

Human activity recognition has many potential applications. In an aged care facility, it is crucial to monitor elderly patients and assist them in the case of falls or other needs. Wearable devices can be used for such a purpose. However, most of them have been proven to be obtrusive, and patients reluctate or forget to wear them. In this study, we used infrared technology to recognize certain human activities including sitting, standing, walking, laying in bed, laying down, and falling. We evaluated a system consisting of two 24×32 thermal array sensors. One infrared sensor was installed on side and another one was installed on the ceiling of an experimental room capturing the same scene. We chose side and overhead mounts to compare the performance of classifiers. We used our prototypes to collect data from healthy young volunteers while performing eight different scenarios. After that, we converted data coming from the sensors into images and applied a supervised deep learning approach. The scene was captured by a visible camera and the video from the visible camera was used as the ground truth. The deep learning network consisted of a convolutional neural network which automatically extracted features from infrared images. Overall average F1-score of all classes for the side mount was 0.9044 and for the overhead mount was 0.8893. Overall average accuracy of all classes for the side mount was 96.65% and for the overhead mount was 95.77%. Our results suggested that our infrared-based method not only could unobtrusively recognize human activities but also was reasonably accurate.

Are Korotkoff Sounds Reliable Markers for Accurate Estimation of Systolic and Diastolic Pressure Using Brachial Cuff Sphygmomanometry?

It is well known that non-invasive blood pressure measurements significantly underestimate true systolic blood pressure (SBP), and overestimate diastolic blood pressure (DBP). The aetiology for these errors has not yet been fully established. This study aimed to investigate the accuracy of Korotkoff sounds for detection of SBP and DBP points as used in brachial cuff sphygmomanometry. Brachial cuff pressure and Korotkoff sounds were obtained in 11 patients (6 males: 69.0 ± 6.2 years, 5 females: 71.8 ± 5.5 years) undergoing diagnostic coronary angiography. K2 Korotkoff sounds were obtained by high-pass filtering (>20 Hz) the microphone-recorded signal to eliminate low frequency components. Analysis of the timing of K2 Korotkoff sounds relative to cuff pressure and intra-arterial pressure shows that the onset of K2 Korotkoff sounds reliably detect the start of blood flow under the brachial cuff and their termination, marks the cuff pressure closely coincident with DBP. We have made the critical observation that blood flow under the cuff does not begin when cuff pressure falls just below SBP as is conventionally assumed, and that the delay in the opening of the artery following occlusion, and the consequent delay in the generation of K2 Korotkoff sounds, may lead to significant errors in the determination of SBP of up to 24 mmHg. Our data suggest a potential role of arterial stiffness as a major component of the errors recorded, with underestimation of SBP much more significant for subjects with stiff arteries than for subjects with more compliant arteries.