Systems and methods for physiological signal enhancement and biometric extraction using non-invasive optical sensors

A system and method for signal processing to remove unwanted noise components including: (i) wavelength-independent motion artifacts such as tissue, bone and skin effects, and (ii) wavelength-dependent motion artifact/noise components such as venous blood pulsation and movement due to various sources including muscle pump, respiratory pump and physical perturbation. Disclosed are methods, analytics, and their uses for reliable perfusion monitoring, arterial oxygen saturation monitoring, heart rate monitoring during daily activities and in hospital settings and for extraction of physiological parameters such as respiration information, hemodynamic parameters, venous capacity, and fluid responsiveness. The system and methods disclosed are extendable to include monitoring platforms for perfusion, hypoxia, arrhythmia detection, airway obstruction detection and sleep disorders including apnea.Board of Regents, University of Texas Syste

Investigations on efficient adaptation algorithms

Ankara : Department of Electrical and Electronics Engineering and Institute of Engineering and Sciences, Bilkent Univ., 1995.Thesis (Master's) -- Bilkent University, 1995.Includes bibliographical references leaves 71-75.Efficient adaptation algorithms, which are intended to improve the performances of the LMS and the RLS algorithms are introduced. It is shown that nonlinear transformations of the input and the desired signals by a softlimiter improve the convergence speed of the LMS algorithm at no cost, with a small bias in the optimal filter coefficients. Also, the new algorithm can be used to filter a-stable non-Gaussian processes for which the conventional adaptive algorithms are useless. In a second approach, a prewhitening filter is used to increase the convergence speed of the LMS algorithm. It is shown that prewhitening does not change the relation between the input and the desired signals provided that the relation is a linear one. A low order adaptive prewhitening filter can provide significant speed up in the convergence. Finally, adaptive filtering algorithms running on roughly quantized signals are proposed to decrease the number of multiplications in the LMS and the RLS algorithms. Although, they require significantly less computations their preformances are comparable to those of the conventional LMS and RLS algorithms.Belge, MuratM.S

Disease-related p63 DBD mutations impair DNA binding by distinct mechanisms and varying degree

: The transcription factor p63 shares a high sequence identity with the tumour suppressor p53 which manifests itself in high structural similarity and preference for DNA sequences. Mutations in the DNA binding domain (DBD) of p53 have been studied in great detail, enabling a general mechanism-based classification. In this study we provide a detailed investigation of all currently known mutations in the p63 DBD, which are associated with developmental syndromes, by measuring their impact on transcriptional activity, DNA binding affinity, zinc binding capacity and thermodynamic stability. Some of the mutations we have further characterized with respect to their ability to convert human dermal fibroblasts into induced keratinocytes. Here we propose a classification of the p63 DBD mutations based on the four different mechanisms of DNA binding impairment which we identified: direct DNA contact, zinc finger region, H2 region, and dimer interface mutations. The data also demonstrate that, in contrast to p53 cancer mutations, no p63 mutation induces global unfolding and subsequent aggregation of the domain. The dimer interface mutations that affect the DNA binding affinity by disturbing the interaction between the individual DBDs retain partial DNA binding capacity which correlates with a milder patient phenotype

REDUCTION OF SKIN STRETCH INDUCED MOTION ARTIFACTS IN ELECTROCARDIOGRAM MONITORING USING ADAPTIVE FILTERING

Cardiovascular disease (CVD) is the leading cause of death in many regions worldwide, accounting for nearly one third of global deaths in 2001. Wearable electrocardiographic cardiovascular monitoring devices have contributed to reduce CVD mortality and cost by enabling the diagnosis of conditions with infrequent symptoms, the timely detection of critical signs that can be precursor to sudden cardiac death, and the long-term assessment/monitoring of symptoms, risk factors, and the effects of therapy. However, the effectiveness of ambulatory electrocardiography to improve the treatment of CVD can be significantly impaired by motion artifacts which can cause misdiagnoses, inappropriate treatment decisions, and trigger false alarms. Skin stretch associated with patient motion is a main source of motion artifact in current ECG monitors. A promising approach to reduce motion artifact is the use of adaptive filtering that utilizes a measured reference input correlated with the motion artifact to extract noise from the ECG signal. Previous attempts to apply adaptive filtering to electrocardiography have employed either electrode deformation or acceleration, body acceleration, or skin/electrode impedance as a reference input, and were not successful at reducing motion artifacts in a consistent and reproducible manner. This has been essentially attributed to the lack of correlation between the reference input selected and the induced noise. In this study, motion artifacts are adaptively filtered by using skin strain as the reference signal. Skin strain is measured non-invasively using a light emitting diode (LED) and an optical sensor incorporated in an ECG electrode. The optical strain sensor is calibrated on animal skin samples and finally in-vivo, in terms of sensitivity and measurement range. Skin stretch induced artifacts are extracted in-vivo using adaptive filters. The system and method are tested for different individuals and under various types of ambulatory conditions with the noise reduction performance quantified

Adaptive Interference Mitigation in GPS Receivers

Satellite navigation systems (GNSS) are among the most complex radio-navigation systems, providing positioning, navigation, and timing (PNT) information. A growing number of public sector and commercial applications rely on the GNSS PNT service to support business growth, technical development, and the day-to-day operation of technology and socioeconomic systems. As GNSS signals have inherent limitations, they are highly vulnerable to intentional and unintentional interference. GNSS signals have spectral power densities far below ambient thermal noise. Consequently, GNSS receivers must meet high standards of reliability and integrity to be used within a broad spectrum of applications. GNSS receivers must employ effective interference mitigation techniques to ensure robust, accurate, and reliable PNT service. This research aims to evaluate the effectiveness of the Adaptive Notch Filter (ANF), a precorrelation mitigation technique that can be used to excise Continuous Wave Interference (CWI), hop-frequency and chirp-type interferences from GPS L1 signals. To mitigate unwanted interference, state-of-the-art ANFs typically adjust a single parameter, the notch centre frequency, and zeros are constrained extremely close to unity. Because of this, the notch centre frequency converges slowly to the target frequency. During this slow converge period, interference leaks into the acquisition block, thus sabotaging the operation of the acquisition block. Furthermore, if the CWI continuously hops within the GPS L1 in-band region, the subsequent interference frequency is locked onto after a delay, which means constant interference occurs in the receiver throughout the delay period. This research contributes to the field of interference mitigation at GNSS's receiver end using adaptive signal processing, predominately for GPS. This research can be divided into three stages. I first designed, modelled and developed a Simulink-based GPS L1 signal simulator, providing a homogenous test signal for existing and proposed interference mitigation algorithms. Simulink-based GPS L1 signal simulator provided great flexibility to change various parameters to generate GPS L1 signal under different conditions, e.g. Doppler Shift, code phase delay and amount of propagation degradation. Furthermore, I modelled three acquisition schemes for GPS signals and tested GPS L1 signals acquisition via coherent and non-coherent integration methods. As a next step, I modelled different types of interference signals precisely and implemented and evaluated existing adaptive notch filters in MATLAB in terms of Carrier to Noise Density (\u1d436/\u1d4410), Signal to Noise Ratio (SNR), Peak Degradation Metric, and Mean Square Error (MSE) at the output of the acquisition module in order to create benchmarks. Finally, I designed, developed and implemented a novel algorithm that simultaneously adapts both coefficients in lattice-based ANF. Mathematically, I derived the full-gradient term for the notch's bandwidth parameter adaptation and developed a framework for simultaneously adapting both coefficients of a lattice-based adaptive notch filter. I evaluated the performance of existing and proposed interference mitigation techniques under different types of interference signals. Moreover, I critically analysed different internal signals within the ANF structure in order to develop a new threshold parameter that resets the notch bandwidth at the start of each subsequent interference frequency. As a result, I further reduce the complexity of the structural implementation of lattice-based ANF, allowing for efficient hardware realisation and lower computational costs. It is concluded from extensive simulation results that the proposed fully adaptive lattice-based provides better interference mitigation performance and superior convergence properties to target frequency compared to traditional ANF algorithms. It is demonstrated that by employing the proposed algorithm, a receiver is able to operate with a higher dynamic range of JNR than is possible with existing methods. This research also presents the design and MATLAB implementation of a parameterisable Complex Adaptive Notch Filer (CANF). Present analysis on higher order CANF for detecting and mitigating various types of interference for complex baseband GPS L1 signals. In the end, further research was conducted to suppress interference in the GPS L1 signal by exploiting autocorrelation properties and discarding some portion of the main lobe of the GPS L1 signal. It is shown that by removing 30% spectrum of the main lobe, either from left, right, or centre, the GPS L1 signal is still acquirable

NASA Space Engineering Research Center Symposium on VLSI Design

The NASA Space Engineering Research Center (SERC) is proud to offer, at its second symposium on VLSI design, presentations by an outstanding set of individuals from national laboratories and the electronics industry. These featured speakers share insights into next generation advances that will serve as a basis for future VLSI design. Questions of reliability in the space environment along with new directions in CAD and design are addressed by the featured speakers

AP-1 imprints a reversible transcriptional program of senescent cells

Senescent cells affect many physiological and pathophysiological processes. While select genetic and epigenetic elements for senescence induction have been identified, the dynamics, epigenetic mechanisms and regulatory networks defining senescence competence, induction and maintenance remain poorly understood, precluding the deliberate therapeutic targeting of senescence for health benefits. Here, we examined the possibility that the epigenetic state of enhancers determines senescent cell fate. We explored this by generating time-resolved transcriptomes and epigenome profiles during oncogenic RAS-induced senescence and validating central findings in different cell biology and disease models of senescence. Through integrative analysis and functional validation, we reveal links between enhancer chromatin, transcription factor recruitment and senescence competence. We demonstrate that activator protein 1 (AP-1) ‘pioneers’ the senescence enhancer landscape and defines the organizational principles of the transcription factor network that drives the transcriptional programme of senescent cells. Together, our findings enabled us to manipulate the senescence phenotype with potential therapeutic implications

Application of Higher-Order Statistics and Subspace-Based Techniques to the Analysis and Diagnosis of Electrocardiogram Signals

The first and main contribution of this research work is the higher-order statistics (HOS)-based non-linear analysis and subsequent diagnosis of abnormal electrocardiogram (ECG) signals, particularly myocardial ischaemia. In the time domain; the second-, third-, and the fourth-order cumulants have been used in the analysis. In the frequency domain; up to the tenth-order polyspectra have been exploited. This HOS-based analysis of normal and ischaemic electrocardiogram signals has led to the identification of certain key discriminant features for the two physiological states of the heart. These features are then fed to different backpropagation-based multiple layer perceptrons for classification. The second contribution is a proposed new methodology to discriminate patients with angina pectoris or with old myocardial infarction (MI) during the first 60 seconds of stress test (or in some cases using rest ECG). It is based on the pseudo-spectral Multiple Signal Classification (MUSIC) and has the potential of being highly sensitive diagnostic signal processing tool. The third contribution is the development of a novel higher-order statistics, high-resolution estimator for quadratically coupled frequencies based on subspace spectral estimation. Extensive studies of cumulants, bispectra and bicoherence-squared of normal and ischaemic ECG signals collected from MIT and ST-T European databases has enabled us to see key discriminant features in both the third- and fourth-order cumulant domains. In the frequency domain, the polyspectral study has been extended to the lOth-order poly spectra. By calculating one-dimensional polyspectrum slices using an algorithm developed by Zhou and Giannakis (1995) a considerable reduction in the CPU time has been achieved. Furthermore, Zhou’s algorithm has been further extended to estimate the polycoherency slices which are used to characterise non-linearities in normal and ischaemic ECG signals. An important finding in this thesis is the decrease of the order of non-linearity representing the electrocardiogram signals of ischaemic patients. This thesis also includes the results of a pilot study involving eighteen healthy subjects (MIT database) and confirmed that the ECG signal is non-Gaussian, cyclostationary and quasi periodic. Combined spectral and bispectral analysis of the signal revealed that there are unique harmonic characteristics for the P-wave, QRS complex and T-wave and other frequencies due to harmonic interactions. In this work three linear and one non-linear adaptive filtering/predictions techniques have been applied to noisy ECG signals and their respective performances appraised. It is shown that the Kalman filter gives the best mean-square error MSE error but its comparatively long execution time and problems arising from ill-conditioning of the state-error covariance matrix render it of limited use in ECG applications. It is also shown that the LMS-based quadratic and cubic Volterra filters are the most superior for the ECG signal prediction. For ECG classifications; three multi-layer perceptrons employing back-propagation and modified back-propagation algorithms, and using two sets from the higher-order most discriminant features as their inputs, have yielded fairly high classification rates