Manual Auscultation Research Articles

Relationship between triradii angles and blood pressure among adolescents of Ebira ethnic group of Kogi State, Nigeria

Dermatoglyphics have been wildly studies to identify and classify individual and population. Several health and disease conditions have been reported to present unique dermatoglyphic patterns. This study was aimed at investigating the relationship between triradii angles on the palm and the blood pressure of selected dialects of Ebira ethnic group. A total of 591 subjects (Ebira-Tao, n=199; Ebira-Etuno, n=196; Ebira-Koto, n=196), both males and females randomly selected participated in this study. The palm prints of participants were collected using the ink and roller method and analyzed for the triradii points and measurements of triradii angles using a hand magnifying lens. The participants’ resting blood pressure; systolic (SBP) and diastolic blood pressure (DBP) were measured by manual auscultation using a sphygmomanometer. Data obtained were analyzed using descriptive statistics, one-way ANOVA, and Chi-Square. The three dialects had mean SBP and DBP within the normal range. The triradii angles revealed mean acute angles across the three dialects in the order atd < dat < adt angles. There were differences in means triradii angles across the blood pressure categories (normal, low and high) for the three dialects in males and females as higher atd and dat angles were significantly related to higher blood pressure. This was also corroborated by the test for association. In conclusion, there was a significant relationship between blood pressure and some triradii angles in two out of the three dialects. Hence, triradii angles may be an indicator for individuals with the tendency to develop high blood pressure.

Contactless and Calibration-Free Blood Pressure and Pulse Rate Monitor for Screening and Monitoring of Hypertension: Cross-Sectional Validation Study.

The key to reducing the immense morbidity and mortality burdens of cardiovascular diseases is to help people keep their blood pressure (BP) at safe levels. This requires that more people with hypertension be identified, diagnosed, and given tools to lower their BP. BP monitors are critical to hypertension diagnosis and management. However, there are characteristics of conventional BP monitors (oscillometric cuff sphygmomanometers) that hinder rapid and effective hypertension diagnosis and management. Calibration-free, software-only BP monitors that operate on ubiquitous mobile devices can enable on-demand BP monitoring, overcoming the hardware barriers of conventional BP monitors. This study aims to investigate the accuracy of a contactless BP monitor software app for classifying the full range of clinically relevant BPs as hypertensive or nonhypertensive and to evaluate its accuracy for measuring the pulse rate (PR) and BP of people with BPs relevant to stage-1 hypertension. The software app, known commercially as Lifelight, was investigated following the data collection and data analysis methodology outlined in International Organization for Standardization (ISO) 81060-2:2018/AMD 1:2020 "Non-invasive Sphygmomanometers-Part 2: Clinical investigation of automated measurement type." This validation study was conducted by the independent laboratory Element Materials Technology Boulder (formerly Clinimark). The study generated data from 85 people aged 18-85 years with a wide-ranging distribution of BPs specified in ISO 81060-2:2018/AMD 1:2020. At least 20% were required to have Fitzpatrick scale skin tones of 5 or 6 (ie, dark skin tones). The accuracy of the app's BP measurements was assessed by comparing its BP measurements with measurements made by dual-observer manual auscultation using the same-arm sequential method specified in ISO 81060-2:2018/AMD 1:2020. The accuracy of the app's PR measurements was assessed by comparing its measurements with concurrent electroencephalography-derived heart rate values. The app measured PR with an accuracy root-mean-square of 1.3 beats per minute and mean absolute error of 1.1 (SD 0.8) beats per minute. The sensitivity and specificity with which it determined that BPs exceeded the in-clinic systolic threshold for hypertension diagnosis were 70.1% and 71.7%, respectively. These rates are consistent with those reported for conventional BP monitors in a literature review by The National Institute for Health and Care Excellence. The app's mean error for measuring BP in the range of normotension and stage-1 hypertension (ie, 65/85, 76% of participants) was 6.5 (SD 12.9) mm Hg for systolic BP and 0.4 (SD 10.6) mm Hg for diastolic BP. Mean absolute error was 11.3 (SD 10.0) mm Hg and 8.6 (SD 6.8) mm Hg, respectively. A calibration-free, software-only medical device was independently tested against ISO 81060-2:2018/AMD 1:2020. The safety and performance demonstrated in this study suggest that this technique could be a potential solution for rapid and scalable screening and management of hypertension.

Abnormal noise identification of engines based on wavelet packet transform and bispectrum analysis

Abnormal noise is the most prominent problem for motorcycles and affects the consumers’ purchasing desire and driving experience and the enterprise’s competitiveness. Usually, the noise from a newly assembled engine is detected by manual auscultation (MA) to determine if the engine is operating normally. However, MA is also affected by subjective and objective factors with severe labor intensity, and its accuracy greatly fluctuates. Importantly, MA cannot be applied in a corporation with mass production and high-quality requirements. To improve the efficiency and accuracy of motorcycle engine quality inspection and achieve intelligent production, an online engine abnormal noise detection method was proposed based on wavelet packet transform (WPT) and bispectrum analysis (BA); this method improved the accuracy and stability of the identification of the abnormal noise engine and reduced the cost of the check. First, the acoustic signal of the engine of the motorcycle was acquired by using a free-field microphone. Second, the background noise of signals was eliminated by using the wavelet correlation coefficient (WCC) theory, and the signal features were extracted by applying WPT and BA. Third, the feature vectors were normalized before being used as support vector machine (SVM) samples. Fourth, an appropriate kernel function and parameters were selected to train the vector machine using the training sets. Finally, the testing sets were used to inspect the accuracy of the vector machines. The result showed that the training accuracy is 95% and the testing accuracy is 97.5 of the samples were suitable by using the method of wavelet packet transform-bispectrum analysis-support vector machines (WPT-BA-SVM). WPT-BA-SVM effectively identified engine fault types and provided the theoretical foundation for the establishment of an engine abnormal noise online detection system.

Enabling Continuous Breathing-Phase Contextualization via Wearable-Based Impedance Pneumography and Lung Sounds: A Feasibility Study.

Chronic respiratory diseases affect millions and are leading causes of death in the US and worldwide. Pulmonary auscultation provides clinicians with critical respiratory health information through the study of Lung Sounds (LS) and the context of the breathing-phase and chest location in which they are measured. Existing auscultation technologies, however, do not enable the simultaneous measurement of this context, thereby potentially limiting computerized LS analysis. In this work, LS and Impedance Pneumography (IP) measurements were obtained from 10 healthy volunteers while performing normal and forced-expiratory (FE) breathing maneuvers using our wearable IP and respiratory sounds (WIRS) system. Simultaneous auscultation was performed with the Eko CORE stethoscope (EKO). The breathing-phase context was extracted from the IP signals and used to compute phase-by-phase (Inspiratory (I), expiratory (E), and their ratio (I:E)) and breath-by-breath acoustic features. Their individual and added value was then elucidated through machine learning analysis. We found that the phase-contextualized features effectively captured the underlying acoustic differences between deep and FE breaths, yielding a maximum F1 Score of 84.1 ±11.4% with the phase-by-phase features as the strongest contributors to this performance. Further, the individual phase-contextualized models outperformed the traditional breath-by-breath models in all cases. The validity of the results was demonstrated for the LS obtained with WIRS, EKO, and their combination. These results suggest that incorporating breathing-phase context may enhance computerized LS analysis. Hence, multimodal sensing systems that enable this, such as WIRS, have the potential to advance LS clinical utility beyond traditional manual auscultation and improve patient care.

Development and clinical validation of a miniaturized finger probe for bedside hemodynamic monitoring

Our aim is to develop a blood pressure (BP) measurement technology that could be integrated into a finger-worn pulse oximeter, eliminating the need for a brachial cuff. We present a miniature cuffless tonometric finger probe system that uses the oscillometric method to measure BP. Our approach uses a motorized press that is used to apply pressure to the fingertip to measure BP. We verified the functionality of the device in a clinical trial (n= 43) resulting in systolic and diastolic pressures ((mean± SD) mmHg) of (-3.5± 8.4) mmHg and (-4.0± 4.4) mmHg, respectively. Comparison was made with manual auscultation (n= 26) and automated cuff oscillometry (n= 18). In addition to BP, we demonstrated the ability of the device to assess arterial stiffness (n= 18) and detect atrial fibrillation (n= 6). We were able to introduce a sufficiently small device that could be used for convenient ambulatory measurements with minimal discomfort.

CNN-based classification of phonocardiograms using fractal techniques

Deep Learning based heart sound classification is of significant interest in reducing the burden of manual auscultation through the automated detection of signals, including abnormal heartbeats. This work presents a method for classifying phonocardiogram (PCG) signals as normal or abnormal by applying a deep Convolutional Neural Network (CNN) after transforming the signals into 2D color images. In particular, a new methodology based on fractal theory, which exploits Partitioned Iterated Function Systems (PIFS) to generate 2D color images from 1D signals is presented. PIFS have been extensively investigated in the context of image coding and indexing on account of their ability to interpolate and identify self-similar features in an image. Our classification approach has shown a high potential in terms of noise robustness and does not require any pre-processing steps or an initial segmentation of the signal, as instead happens in most of the approaches proposed in the literature. In this preliminary work, we have carried out several experiments on the database released for the 2016 Physionet Challenge, both in terms of different classification networks and different inputs to the networks, thus also evaluating the data quality. Among all experiments, we have obtained the best result of 0.85 in terms of modified Accuracy (MAcc).

A Machine Learning Approach to the Non-Invasive Estimation of Continuous Blood Pressure Using Photoplethysmography

Blood pressure is an important vital sign that sometimes requires continuous measurement. The current methods include cuff measurements (manual auscultation and oscillometric techniques) for non-continuous measurement and invasive arterial cannulation for continuous measurement. The use of photoplethysmography as a cuffless, non-invasive, and continuous blood pressure measurement system is investigated through the use of four neural networks. These predict the systolic blood pressure, diastolic blood pressure, mean arterial blood pressure, and waveform shape. The models are trained on 890 h of data from 1669 patients in the MIMIC-III database. Feature-trained artificial neural networks predict the systolic blood pressure to 5.26 ± 6.53 mmHg (mean error ± standard deviation), the diastolic blood pressure to 2.96 ± 3.31 mmHg, and the mean arterial pressure to 3.27 ± 3.55 mmHg. These are used to shift and scale the predicted waveform, allowing the waveform prediction neural network to optimise for the wave shape rather than the amplitude. The waveform prediction has 86.4% correlation with the actual arterial blood pressure waveform. All results meet international clinical blood pressure measurement standards and could potentially change how blood pressure is measured in both clinical and research settings. However, more data from healthy individuals and analysis of the models’ biases based on clinical features is required.

Masking of Korotkoff sounds used in blood pressure measurement through auscultation.

Measurement of blood pressure (BP) through manual auscultation and the observation of Korotkoff sounds (KSs) remains the gold standard in BP methodology. Critical to determining BP levels via auscultation is the determination of KS audibility. While absolute sound level audibility is well researched, the problem has not been approached from the point of view of psychoacoustic masking of the sounds. Here, during manual auscultation of BP, a direct comparison is made between what an observer perceives as audible and the electronic analysis of audibility level determined from masking of sound signal levels. KSs are collected during auscultation with an electronic stethoscope, which allows simultaneously observing sound audibility and recording the sound electronically. By time-segmenting the recorded sound around Korotkoff peaks into a test segment and a masking segment, performing Fourier transforms on the segments, and comparing frequency-band sound energy levels, signal-to-noise ratios of a sound to its masking counterpart can be defined. Comparing these ratios to difference limen in the psychoacoustic masking literature, an approximate threshold for sound audibility is obtained. It is anticipated that this approach could have profound effects on future development of automated auscultation BP measurements.

Technical characterisation of digital stethoscopes: towards scalable artificial intelligence-based auscultation

Digital stethoscopes can enable the development of integrated artificial intelligence (AI) systems that can remove the subjectivity of manual auscultation, improve diagnostic accuracy, and compensate for diminishing auscultatory skills. Developing scalable AI systems can be challenging, especially when acquisition devices differ and thus introduce sensor bias. To address this issue, a precise knowledge of these differences, i.e., frequency responses of these devices, is needed, but the manufacturers often do not provide complete device specifications. In this study, we reported an effective methodology for determining the frequency response of a digital stethoscope and used it to characterise three common digital stethoscopes: Littmann 3200, Eko Core, and Thinklabs One. Our results show significant inter-device variability in that the frequency responses of the three studied stethoscopes were distinctly different. A moderate intra-device variability was seen when comparing two separate units of Littmann 3200. The study highlights the need for normalisation across devices for developing successful AI-assisted auscultation and provides a technical characterisation approach as a first step to accomplish it.

Detection of Phonocardiogram Event Patterns in Mitral Valve Prolapse: An Automated Clinically Relevant Explainable Diagnostic Framework

The objective of this study is to develop an automated clinically relevant diagnostic framework for a group of cardiovascular diseases, namely, valvular heart diseases (VHDs). Though for clinical screening, manual auscultation using a stethoscope is the current standard, it is losing its significance due to the demand for years of skill and experience from the clinician. While the physiological location for capturing heart sound signal, called phonocardiogram (PCG), is standardized, the measurement and classification of PCG into different VHDs need automation to redeem its importance. Recent research on the applicability of machine learning (ML) and deep learning (DL) in PCG-based classification of VHDs produced high accuracy, but often loses the clinical explanation behind them, and hence, often not endorsed by the healthcare fraternity. The proposed study attempts to bridge this gap with two types of clinically explainable automated frameworks: 1) one utilizes clinically relevant PCG features in statistical rule-based models (RBM) as well as ML-based decision-tree (DT) frameworks; 2) the other uses time-frequency representation (TFR) with 2-D convolution neural network (CNN) and signal of PCG cycle with 1-D CNN, reveals similar clinically explainable intelligence. As a case study, mitral valve prolapse (MVP) − a specific VHD categorized into three severity subclasses of early systolic click (ESC), ESC with murmur (ESCM), and mid-SC with murmur (MSCM). The signature events (S1, S2, SC, and murmur) in PCG are segmented using a novel Teager–Kaiser energy operator and continuous wavelet transform (TKEO-CWT) algorithm. Clinically relevant intra- and inter-event features like duration, intensity, and pitch (dominant frequency) in RBM and DT produced 100% and 98.6% accuracies, respectively. Interestingly, gradient class activation (Grad-CAM) of 1-D and 2-D CNN with PCG signal and scalogram as input also converges to the same clinical explanations. Thus, the proposed clinically explainable frameworks demonstrate excellent potential in measurements and classifications in future clinical diagnostic products.

Bowel Sounds Detection Method and Experiment Based on Multi-feature Combination

Bowel sounds is an important indicator to monitor and reflect intestinal motor function, and traditional manual auscultation requires high professional knowledge and rich clinical experience of doctors. In addition, long-time auscultation is time-consuming and laborious， which may lead to misjudgment caused by subjective error. To solve the problem, firstly, the wavelet transform is used to preprocess the bowel sounds signal for noise reduction and enhancement. Secondly, three typical features of intestinal sound were extracted. According to the combination of these features, a three-stage decision was designed to carry out multi-parameter and multi-feature joint threshold detection. This algorithm realized the detection of bowel sound signal and the location of its start and end points, making it possible that the complete bowel sound signal was extracted effectively. In this study, a large number of clinical data and label of bowel sounds were collected, and a new effective evaluation method was proposed to verify the proposed method. The accuracy rate is 83.51%. Results of this study will provide systematic support and theoretical guarantee for the diagnosis of intestinal diseases and the monitoring of postoperative intestinal function recovery of patients.

Rheumatic Heart Disease Screening Based on Phonocardiogram.

Rheumatic heart disease (RHD) is one of the most common causes of cardiovascular complications in developing countries. It is a heart valve disease that typically affects children. Impaired heart valves stop functioning properly, resulting in a turbulent blood flow within the heart known as a murmur. This murmur can be detected by cardiac auscultation. However, the specificity and sensitivity of manual auscultation were reported to be low. The other alternative is echocardiography, which is costly and requires a highly qualified physician. Given the disease’s current high prevalence rate (the latest reported rate in the study area (Ethiopia) was 5.65%), there is a pressing need for early detection of the disease through mass screening programs. This paper proposes an automated RHD screening approach using machine learning that can be used by non-medically trained persons outside of a clinical setting. Heart sound data was collected from 124 persons with RHD (PwRHD) and 46 healthy controls (HC) in Ethiopia with an additional 81 HC records from an open-access dataset. Thirty-one distinct features were extracted to correctly represent RHD. A support vector machine (SVM) classifier was evaluated using two nested cross-validation approaches to quantitatively assess the generalization of the system to previously unseen subjects. For regular nested 10-fold cross-validation, an f1-score of 96.0 ± 0.9%, recall 95.8 ± 1.5%, precision 96.2 ± 0.6% and a specificity of 96.0 ± 0.6% were achieved. In the imbalanced nested cross-validation at a prevalence rate of 5%, it achieved an f1-score of 72.2 ± 0.8%, recall 92.3 ± 0.4%, precision 59.2 ± 3.6%, and a specificity of 94.8 ± 0.6%. In screening tasks where the prevalence of the disease is small, recall is more important than precision. The findings are encouraging, and the proposed screening tool can be inexpensive, easy to deploy, and has an excellent detection rate. As a result, it has the potential for mass screening and early detection of RHD in developing countries.

Screening of COVID-19 using Cough Audio Frequencies

Clinicians routinely use biomedical and audio signals (e.g. sighs, breathing, pulse, digestion, sounds of vibration) as markers to diagnose diseases or to evaluate the progression of diseases. Until recently, these signals were normally obtained during scheduled visits by manual auscultation. With the advancement of technologies, digital methods are used to collect the body sounds for cardiovascular or respiratory testing (e.g. digital stethoscopes to predict the progression of diseases. A few early studies showed promising results for the detection of COVID-19 using voice and diagnostic signals. In the proposed model, an effective analysis is performed through the collection of large, multi-group, airborne acoustic sound data to perform the screening of COVID-19. The technique uses cough and breathing patterns to show the distinctive features of COVID-19 and it is reported that the cough patterns of COVID-19 are identifiable from asthma cough patterns. Using machine learning algorithms, an efficient classification model is developed for the screening of COVID-19.The area below the curve (AUC) of our proposed model exceeds 80%. The present study also explores the analysis of air patterns that can be recorded using the breathing styles of the infected persons to enhance the efficiency of the proposed screening techniques.

Validation of the STEP deflation algorithm of the Midmark IQvitals Zone Vital Signs Monitor: part of a novel clinical ecosystem.

ObjectivesAssess the accuracy of the Midmark IQvitals Zone Vital Signs Monitor STEP deflation algorithm according to the ANSI/AAMI/ISO 81060-2 Standard.MethodsA total of 85 subjects completed the testing protocol. All standard requirements for gender, blood pressure (BP) values, and arm circumferences were met. Manual auscultation was performed by testers blinded to the device; the manual BP values were compared to the device readings.Results:The Standard Criterion 1 data analyses showed mean ± SD device minus manual BP values of 1.22 ± 6.3 mmHg for SBP and −1.67 ± 6.09 mmHg for DBP. The SD values for criterion 2 were 5.06 mmHg (SBP) and 4.98 mmHg (DBP).ConclusionsThe device passed all Standard requirements. The Midmark IQvitals Zone device has features to improve accuracy and reduce or eliminate transcription errors and inaccuracy from improper patient positioning.

Transfer Learning Model to Indicate Heart Health Status Using Phonocardiogram

The early diagnosis of pre-existing coronary disorders helps to control complications such as pulmonary hypertension, irregular cardiac functioning, and heart failure. Machine-based learning of heart sound is an {efficient} technology which can help minimize the workload of manual auscultation by automatically identifying irregular cardiac sounds. Phonocardiogram (PCG) and electrocardiogram (ECG) waveforms provide the much-needed information for the diagnosis of these diseases. In this work, the researchers have converted the heart sound signal into its corresponding repeating pattern-based spectrogram. PhysioNet 2016 and PASCAL 2011 have been taken as the benchmark datasets to perform experimentation. The existing models, viz. MobileNet, Xception, Visual Geometry Group (VGG16), ResNet, DenseNet, and InceptionV3 of Transfer Learning have been used for classifying the heart sound signals as normal and abnormal. For PhysioNet 2016, DenseNet has outperformed its peer models with an accuracy of 89.04 percent, whereas for PASCAL 2011, VGG has outperformed its peer approaches with an accuracy of 92.96 percent.

Peak Responses To Graded Exercise Protocols In Young Children

The Bruce protocol (Bruce) is a progressive treadmill test with an aggressive initial grade that was developed for use in adults, and is often used to assess cardiac patients. Due to the steep grade, young children may terminate the Bruce prematurely. The University of Kentucky Pediatric Exercise Physiology Lab has developed a protocol (PEP Lab) with lower grades which we hypothesized would be better tolerated and potentially elicit greater peak VO2 and cardiovascular responses. PURPOSE: To compare peak oxygen uptake (pVO2; ml·kg-1·min-1), heart rate (pHR; bpm), systolic blood pressure (pSBP; mmHg), and respiratory exchange ratio (pRER) responses to the Bruce versus the PEP Lab in 43 (22 boys) young (7-11 yr old) children of varying adiposities. We also evaluated each subject’s perception of difficulty between the two protocols. METHODS: Subjects completed the Bruce and the PEP Lab protocols in a random order 1 week apart. pVO2 and pHR were determined with an integrated metabolic system, and pSBP was determined by manual auscultation. Verbal encouragement was provided during both testing sessions and test completion based on volitional fatigue. Results are expressed as mean ± SE and significance p < 0.05. RESULTS: The Bruce versus PEP Lab pVO2 (43.6 ± 1.5 vs 43.9 ± 1.5) and pHR (186.6 ± 2.0 vs 188.2 ± 2.2) did not significantly differ. However, pSBP during the Bruce was significantly lower (136.4 ± 1.4 vs 141.3 ± 1.1) and the Bruce pRER was significantly higher (1.065 ± 0.018 vs 1.013 ± 0.014) than the PEP Lab. Bruce and PEP Lab protocol pVO2, pHR, pSBP, pRER were significantly correlated (r = 0.61, 0.47, 0.53 and 0.42, respectively). The majority (88%) of the children perceived the Bruce to be more difficult. CONCLUSIONS: : A less steep protocol can elicit similar cardiopulmonary results as the Bruce. Protocols that are perceived as less difficult may help guarantee that children do not terminate their exercise tests prematurely to reaching their true pVO2. Supported by the University of Kentucky Pediatric Exercise Physiology Lab Endowment; NIH National Center for Advancing Translational Sciences through grant number UL1TR001998. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Design and evaluation of a low-cost sphygmomanometer to monitor women with pre-eclampsia in low-resource settings

We developed and pilot tested a sphygmomanometer designed to monitor pregnant women in low-resource settings. Blood pressure was assessed in 138 subjects, including healthy adults (n=85), pregnant women (n=42), and women at-risk for pre-eclampsia (n=11) using the novel sphygmomanometer, manual auscultation, and the GE Dinamap Procare 400. Accuracy of the device was evaluated by comparing measurements of the test device and the Dinamap in healthy volunteers and pregnant women in Texas and in women at risk for pre-eclampsia in Malawi. Measurements from the test device in pregnant and healthy volunteer populations differed from those collected using the auscultatory method by 0.2 mmHg (95% CI: -18.8 to 19.2, systolic) and -2.8 mmHg (95% CI: -21.0 to 15.4, diastolic). In women at risk for pre-eclampsia, measurements with the test device differed from those of the Dinamap on average by 2.9 mmHg (95% CI: -29.3 to 35.1, systolic) and -5.4 mmHg (95% CI: -45.8 to 34.9, diastolic). Compared against the auscultatory method, measurements with the Dinamap differed on average by 0.0 mmHg (95% CI: -31.8 to 31.9, systolic) and -3.7 mmHg (95% CI: -28.6 to 21.3, diastolic). Accuracy was reduced when patients were moving or not seated during measurement. When testing the device against British Hypertension Society standards, the device achieved a grade of A/A in pregnant persons. This sphygmomanometer has the potential to provide low-resource hospitals with an affordable, accurate option for regular blood pressure monitoring. However, algorithm improvements are needed to reduce sensitivity to subject motion and posture.

Abstract P091: Short-term Increased Dietary Potassium From Potato and Potassium Gluconate Has No Effect on Blood Pressure and Microcirculation in Pre-hypertensive-to-hypertensive Adults

Potassium is an essential nutrient of concern according to recent Dietary Guidelines for Americans Advisory Committees, with less than 3% of the population meeting the current adequate intake (AI) level of 4700mg/d. Increases in potassium intake have been linked to improvements in cardiovascular and other metabolic health outcomes. Blood pressure (BP) is currently the primary criterion for determining potassium requirements. In this clinical trial, we assessed the effects of increasing potassium intake on BP and microcirculation (endothelial function). Thirty pre-hypertensive-to-hypertensive (Systolic BP ≥ 120mmHg) men (N=15) and Women (N=15) with a mean ± SD age, BMI, and BP (systolic(SBP)/ diastolic(DBP)) of 48.2 ± 15 y, 31.4 ± 6.1, and 136.3 ± 11.9/ 86.1 ± 7.1 mmHg, respectively, were enrolled in a cross-over randomized control diet trial. Participants were assigned to a random order of four 16-day dietary potassium interventions including a basal diet (control) of 2300mg/d(~60mmol/d), and three periods of an additional 1000mg/d(3300mg/d(~85mmol/d) total) of potassium in the form of potatoes (baked, boiled, or pan-heated with no additional fat), French fries, or a potassium gluconate supplement. Each intervention period was separated by two or more weeks of wash out. Blood pressure was measured in triplicate using manual auscultation on days 1, 4, 6, 8, 11, 13, 15, 16, and 17 of each intervention. Microvascular and endothelial function were assessed via thermal hyperemia, utilizing laser Doppler flowmetry (LDF), at baseline and at the end of each intervention. Results for differences in both SBP and DBP ± SE were assessed at the end treatment (average of day 15, 16, and 17 measurements) using a mixed model with repeated measures and Dunnett adjustment (P &lt; 0.05), with no significant differences among groups (control: 128.7 ± 2.1/ 83.6 ± 1.6, French fries: 127.7 ± 2.2/ 83.7 ± 1.6, potatoes: 127.6 ± 2.3/ 85.1 ± 1.6, supplement: 128.0 ± 2.2/ 83.7 ± 1.7). Utilizing the same statistical analysis, there were also no significant differences in endothelial function (measured as percent of cutaneous vascular conductance max (%CVCmax) ± SE) among groups (control: 87.4 ± 1.5%, French Fries: 87.7 ± 1.4%, potatoes: 88.3 ± 1.1%, supplement: 88.9 ± 1.0%). In this small, controlled feeding study of short duration, increased potassium from potatoes or supplement achieved 70% of recommended intakes, but had no significant benefit to blood pressure or microvascular function in men and women with higher cardiometabolic risk.

Bowel Sounds Identification and Migrating Motor Complex Detection with Low-Cost Piezoelectric Acoustic Sensing Device.

Interpretation of bowel sounds (BS) provides a convenient and non-invasive technique to aid in the diagnosis of gastrointestinal (GI) conditions. However, the approach’s potential is limited by variation between BS and their irregular occurrence. A short, manual auscultation is sufficient to aid in diagnosis of only a few conditions. A longer recording has the potential to unlock additional understanding of GI physiology and clinical utility. In this paper, a low-cost and straightforward piezoelectric acoustic sensing device was designed and used for long BS recordings. The migrating motor complex (MMC) cycle was detected using this device and the sound index as the biomarker for MMC phases. This cycle of recurring motility is typically measured using expensive and invasive equipment. We also used our recordings to develop an improved categorization system for BS. Five different types of BS were extracted: the single burst, multiple bursts, continuous random sound, harmonic sound, and their combination. Their acoustic characteristics and distribution are described. The quantities of different BS during two-hour recordings varied considerably from person to person, while the proportions of different types were consistent. The sensing devices provide a useful tool for MMC detection and study of GI physiology and function.

Phonocardiographic Sensing Using Deep Learning for Abnormal Heartbeat Detection

Deep learning-based cardiac auscultation is of significant interest to the healthcare community as it can help reducing the burden of manual auscultation with automated detection of abnormal heartbeats. However, the problem of automatic cardiac auscultation is complicated due to the requirement of reliable and highly accurate systems, which are robust to the background noise in the heartbeat sound. In this paper, we propose a Recurrent Neural Networks (RNNs)-based automated cardiac auscultation solution. Our choice of RNNs is motivated by their great success of modeling sequential or temporal data even in the presence of noise. We explore the use of various RNN models, and demonstrate that these models significantly outperform the best reported results in the literature. We also present the run-time complexity of various RNNs, which provides insight about their complexity versus performance trade-offs.