Segmental Spectral Decomposition as a Time Persistent Method of BioImpedance Spectroscopy Feature Extraction

BioImpedance Spectroscopy (BIS) have been investigated in many research areas as a method to detect changes in living tissues. However, BIS measurements are known to be hardly reproducible in clinical applications. This article proposes segmental spectral decomposition as a method of extracting reproducible parameters from raw BIS. The efficiency of this method is then compared to conventional Cole-Cole parameter extraction in a classification task

Human Breathing Rate Estimation from Radar Returns Using Harmonically Related Filters

Radar-based noncontact sensing of life sign signals is often used in safety and rescue missions during disasters such as earthquakes and avalanches and for home care applications. The radar returns obtained from a human target contain the breathing frequency along with its strong higher harmonics depending on the target’s posture. As a consequence, well understood, computationally efficient, and the most popular traditional FFT-based estimators that rely only on the strongest peak for estimates of breathing rates may be inaccurate. The paper proposes a solution for correcting the estimation errors of such single peak-based algorithms. The proposed method is based on using harmonically related comb filters over a set of all possible breathing frequencies. The method is tested on three subjects for different postures, for different distances between the radar and the subject, and for two different radar platforms: PN-UWB and phase modulated-CW (PM-CW) radars. Simplified algorithms more suitable for real-time implementation have also been proposed and compared using accuracy and computational complexity. The proposed breathing rate estimation algorithms provide a reduction of about 81% and 80% in the mean absolute error of breathing rates in comparison to the traditional FFT-based methods using strongest peak detection, for PN-UWB and PM-CW radars, respectively

Continuous Multi-Parameter Heart Rate Variability Analysis Heralds Onset of Sepsis in Adults

BACKGROUND: Early diagnosis of sepsis enables timely resuscitation and antibiotics and prevents subsequent morbidity and mortality. Clinical approaches relying on point-in-time analysis of vital signs or lab values are often insensitive, non-specific and late diagnostic markers of sepsis. Exploring otherwise hidden information within intervals-in-time, heart rate variability (HRV) has been documented to be both altered in the presence of sepsis, and correlated with its severity. We hypothesized that by continuously tracking individual patient HRV over time in patients as they develop sepsis, we would demonstrate reduced HRV in association with the onset of sepsis. METHODOLOGY/PRINCIPAL FINDINGS: We monitored heart rate continuously in adult bone marrow transplant (BMT) patients (n = 21) beginning a day before their BMT and continuing until recovery or withdrawal (12+/-4 days). We characterized HRV continuously over time with a panel of time, frequency, complexity, and scale-invariant domain techniques. We defined baseline HRV as mean variability for the first 24 h of monitoring and studied individual and population average percentage change (from baseline) over time in diverse HRV metrics, in comparison with the time of clinical diagnosis and treatment of sepsis (defined as systemic inflammatory response syndrome along with clinically suspected infection requiring treatment). Of the 21 patients enrolled, 4 patients withdrew, leaving 17 patients who completed the study. Fourteen patients developed sepsis requiring antibiotic therapy, whereas 3 did not. On average, for 12 out of 14 infected patients, a significant (25%) reduction prior to the clinical diagnosis and treatment of sepsis was observed in standard deviation, root mean square successive difference, sample and multiscale entropy, fast Fourier transform, detrended fluctuation analysis, and wavelet variability metrics. For infected patients (n = 14), wavelet HRV demonstrated a 25% drop from baseline 35 h prior to sepsis on average. For 3 out of 3 non-infected patients, all measures, except root mean square successive difference and entropy, showed no significant reduction. Significant correlation was present amongst these HRV metrics for the entire population. CONCLUSIONS/SIGNIFICANCE: Continuous HRV monitoring is feasible in ambulatory patients, demonstrates significant HRV alteration in individual patients in association with, and prior to clinical diagnosis and treatment of sepsis, and merits further investigation as a means of providing early warning of sepsis

Cherenkov radiation from electrons passing through human tissue

A method for investigating the optical properties of human tissues is suggested. The method is based on the measurement of Cherenkov radiation produced by relativistic electrons passing through the tissue. Monte-Carlo simulation of visible photon emission and propagation is carried out taking into account multiple electron and photon scattering processes. Sensitivity of the Cherenkov radiation to the optical characteristics of human tissues is demonstrated

Extraction of breathing signal and suppression of its effects in oscillometric blood pressure measurement

Breathing causes fluctuations in blood pressure and contributes to variations in blood pressure estimates. In order to reduce the variability in the blood pressure estimates induced by breathing, either the breathing signal should be removed from the oscillometric blood pressure signal, or its effects should be suppressed. This paper presents a hybrid method that combines homomorphic and adaptive signal processing techniques to extract the breathing signal from the oscillometric signal with or without a simultaneously recorded electrocardiogram (ECG). The quality of the extracted breathing signal and the depth of breathing are assessed using the reference breathing signals. The breathing signals extracted using the accompanying ECG signal were found to be superior in quality compared to the ones extracted from the oscillometric waveform. The blood pressure estimates were evaluated before and after the breathing suppression techniques were implemented. As a result of the breathing suppression, the fluctuation of the systolic and diastolic blood pressure estimates obtained from a database of 85 healthy subjects is reduced

Model of human breathing reflected signal received by PN-UWB radar

Human detection is an integral component of civilian and military rescue operations, military surveillance and combat operations. Human detection can be achieved through monitoring of vit

Measurement of heart rate variability using an oscillometric blood pressure monitor

We apply the maximal overlap discrete wavelet transform (MODWT)-based spectral density estimation method to measure heart rate variability (HRV) from short-duration pulse wave signals produced by an automated oscillometric blood pressure (BP) monitor d

Bayesian fusion algorithm for improved oscillometric blood pressure estimation

A variety of oscillometric algorithms have been recently proposed in the literature for estimation of blood pressure (BP). However, these algorithms possess specific strengths and weaknesses that should be taken into account before selecting the most appropriate one. In this paper, we propose a fusion method to exploit the advantages of the oscillometric algorithms and circumvent their limitations. The proposed fusion method is based on the computation of the weighted arithmetic mean of the oscillometric algorithms estimates, and the weights are obtained using a Bayesian approach by minimizing the mean square error. The proposed approach is used to fuse four different oscillometric blood pressure estimation algorithms. The performance of the proposed method is evaluated on a pilot dataset of 150 oscillometric recordings from 10 subjects. It is found that the mean error and standard deviation of error are reduced relative to the individual estimation algorithms by up to 7 mmHg and 3 mmHg in estimation of systolic pressure, respectively, and by up to 2 mmHg and 3 mmHg in estimation of diastolic pressure, respectively

Oscillometric blood pressure estimation: Past, present, and future

The use of automated blood pressure (BP) monitoring is growing as it does not require much expertise and can be performed by patients several times a day at home. Oscillometry is one of the most common measurement methods used in automated BP monitors. A review of the literature shows that a large variety of oscillometric algorithms have been developed for accurate estimation of BP but these algorithms are scattered in many different publications or patents. Moreover, considering that oscillometric devices dominate the home BP monitoring market, little effort has been made to survey the underlying algorithms that are used to estimate BP. In this review, a comprehensive survey of the existing oscillometric BP estimation algorithms is presented. The survey covers a broad spectrum of algorithms including the conventional maximum amplitude and derivative oscillometry as well as the recently proposed learning algorithms, model-based algorithms, and algorithms that are based on analysis of pulse morphology and pulse transit time. The aim is to classify the diverse underlying algorithms, describe each algorithm briefly, and discuss their advantages and disadvantages. This paper will also review the artifact removal techniques in oscillometry and the current standards for the automated BP monitors