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

An algorithm for extracting the PPG Baseline Drift in real-time

Photoplethysmography is an optical technique for measuring the perfusion of blood in skin and tissue arterial vessels. Due to its simplicity, accessibility and abundance of information on an individual’s cardiovascular system, it has been a pervasive topic of research within recent years. With these benefits however there are many challenges concerning the processing and conditioning of the signal in order to allow information to be extracted. One such challenge is removing the baseline drift of the signal, which is caused by respiratory rate, muscle tremor and physiological changes within the body as a response to various stimuli. Over the years there have been many methods developed in order to condition the signal such as Wavelet Transform, Cubic Spline Interpolation, Morphological Operators and Fourier-Based filtering techniques. All have their own individual benefits and drawbacks. These drawbacks are that they are unsuitable for real-time usage due to the computation power needed, or have the trade-off of being real-time at the cost of deforming the signal which is unideal for accurate analysis. This thesis aims to explore these techniques in order to develop an algorithm that can be used to condition the signal against the baseline drift in real-time, while being able to achieve good computational efficiency and the preservation of the signal form

Eulerian Video Processing and medical applications

Thesis (M. Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 68-69).Our goal is to reveal subtle yet informative signals in videos that are difficult or impossible to see with the naked eye. We can either display them in an indicative manner, or analyse them to extract important measurements, such as vital signs. Our method, which we call Eulerian Video Processing, takes a standard video sequence as input, and applies spatial decomposition, followed by temporal filtering to the frames. The resulting signals can be visually amplified to reveal hidden information, the process we called Eulerian Video Magnification. Using Eulerian Video Magnification, we are able to visualize the flow of blood as it fills the face and to amplify and reveal small motions. Our technique can be run in real time to instantly show phenomena occurring at the temporal frequencies selected by the user. Those signals can also be used to extract vital signs contactlessly. We presented a heart rate extraction system that is able to estimate heart rate of newborns from videos recorded in the real nursery environment. Our system can produce heart rate measurement that has clinical accuracy when newborns only have mild motions, and when the videos are acquired in brightly lit environments.by Hao-Yu Wu.M.Eng.and S.B

An automated approach: from physiological signals classification to signal processing and analysis

By increased and widespread usage of wearable monitoring devices a huge volume of data is generated which requires various automated methods for analyzing and processing them and also extracting useful information from them. Since it is almost impossible for physicians and nurses to monitor physical activities of their patients for a long time, there is a need for automated data analysis techniques that abstract the information and highlight the significant events for clinicians and healthcare experts. The main objective of this thesis work was towards an automatic digital signal processing approach from physiological signal classification to processing and analyzing the two most vital physiological signals in long-term healthcare monitoring (ECG and IP). At the first stage, an automated generic physiological signal classifier for detecting an unknown recorded signal was introduced and then different algorithms for processing and analyzing the ECG and IP signals were developed and evaluated. This master thesis was a part of DISSE project which its aim was to design a new health-care system with the aim of providing medical expertise more accessible, affordable, and convenient. In this work, different publicly available databases such as MIT-BIH arrhythmia and CEBS were used in the development and evaluation phases. The proposed novel generic physiological signal classifier has the ability to distinguish five types of physiological signals (ECG, Resp, SCG, EMG and PPG) from each other with 100 % accuracy. Although the proposed classifier was not very successful in distinguishing lead I and II of ECG signal from each other (error of 27% was reported) which means that the general purpose features were enough discriminating to recognize different physiological signals from each other but not enough for classifying different ECG leads. For ECG processing and analysis section, three QRS detection methods were implemented which modified Pan-Tompkins gave the best performance with 97% sensitivity and 96,45% precision. The morphological based ectopic detection method resulted in sensitivity of 85,74% and specificity of 84,34%. Furthermore, for the first PVC detection algorithm (sum of trough) the optimal threshold and range were studied according to the AUC of ROC plot which the highest sensitivity and specificity were obtained with threshold of −5 and range of 11 : 25 that were equal to 87% and 82%, respectively. For the second PVC detection method (R-peak with minimum) the best performance was achieved with threshold of −0.7 that resulted in sensitivity of 68% and specificity of 72%. In the IP analysis section, an ACF approach was implemented for respiratory rate estimation. The estimated respira- tion rate obtained from IP signal and oronasal mask were compared and the total MAE and RMSE errors were computed that were equal to 0.40 cpm and 1.20 cpm, respectively. The implemented signal processing techniques and algorithms can be tested and improved with measured data from wearable devices for ambulatory applications

Non-invasive blood pressure estimation based on electro/phonocardiogram

Ingeniero (a) ElectrónicoPregrad

Motion artifact reduction in PPG signals

The aim of this thesis was to investigate methods for artifact removal in PPG signals and to implement and evaluate a few existing algorithms claiming that the amplitude information is recovered when removing motion artifacts from photoplethysmographic signals (PPG) captured from pulse oximeters. We developed a new proposed method that uses a two-stage based approach with singular value decomposition and fixed fast ICA algorithm in order to generate a PPG-correlated reference signal that is used in adaptive noise cancellation. The results were promising and our proposed method is easy to implement and converges quickly with good extraction performance. It has a few design parameters and only needs the estimated period of the PPG signal. Our method could be used in a clinical routine for prediction of intradialytic hypotension. However it should be mentioned that although our method has great potential the simulations were only conducted on two healthy males. Further studies on a larger dataset might be needed in order to establish a full value of the efficacy of our method.Felaktiga mätresultat vid användning av pulsoximeter under patientövervakning En felaktig diagnos är ju inget kul att få av sin läkare. I sjukhusmiljö samt kliniska omgivningar eller under akuttransport kan pulsoximetern, som bland annat mäter syremättnaden i blodet via fingret, ge felaktiga mätresultat på grund av frivilliga eller ofrivilliga rörelser hos patienten. Under de senaste åren har biomedicinsk teknologi ökat drastiskt för mer effektiva behandlingar samt tillförlitliga diagnoser. För att få kliniskt korrekta mätningar från medicinsk utrustning måste dessa apparater vara optimerade på bästa sätt. Detta kommer att underlätta för sjukvårdspersonalen att dra korrekta slutsatser vid beslut under patient övervakning. En patient med t.ex. njursvikt får problem med rening av restprodukter och avlägsnandet av vatten från blodet, vilket är njurarnas uppgift i huvudsak. Vid hemodialys behandling pumpas blodet ut ur kroppen via nålar för att därefter renas i en dialysator som ska ersätta njurarnas funktion. En vanlig biverkning till följd av behandlingen är blodtrycksfall (intradialytisk hypotoni) vilket sker i 25% av alla behandlingar. Resultat från tidigare forskning visar att man kan prediktera blodtrycksfall i samband med hemodialys behandling med hjälp av amplituden hos fotopletysmografi (PPG) signal. PPG signalen fås av pulseoximetern som har en klämma man kan fästa på fingertoppen. Genom att ljus av två våglängder passerar huden kan man med hjälp av absorptionen i blodet avläsa syremättnad och hjärtpuls. Problemet med PPG signalen är att om patienten rör sig påverkar detta amplituden. Därför är det viktigt att ta bort effekten av rörelser på ett sådant sätt att amplitudinformationen är bevarad. Vi undersökte metoder för borttagning av dessa effekter från rörelser hos patienten och föreslog en ny metod som på ett effektivt sätt estimerar en ren PPG signal med amplitudinformationen bevarad. Metoden har ett fåtal designparameterar och konvergerar snabbt mot lösningen. Vår metod skulle kunna användas i en klinisk rutin för prediktering av intradialytisk hypotoni i samband med hemodialys behandling. Det bör dock nämnas att vår studie utfördes på två friska testpersoner och att mer data hade krävts för en fullskalig utvärdering av metoden

Improving Maternal and Fetal Cardiac Monitoring Using Artificial Intelligence

Early diagnosis of possible risks in the physiological status of fetus and mother during pregnancy and delivery is critical and can reduce mortality and morbidity. For example, early detection of life-threatening congenital heart disease may increase survival rate and reduce morbidity while allowing parents to make informed decisions. To study cardiac function, a variety of signals are required to be collected. In practice, several heart monitoring methods, such as electrocardiogram (ECG) and photoplethysmography (PPG), are commonly performed. Although there are several methods for monitoring fetal and maternal health, research is currently underway to enhance the mobility, accuracy, automation, and noise resistance of these methods to be used extensively, even at home. Artificial Intelligence (AI) can help to design a precise and convenient monitoring system. To achieve the goals, the following objectives are defined in this research: The first step for a signal acquisition system is to obtain high-quality signals. As the first objective, a signal processing scheme is explored to improve the signal-to-noise ratio (SNR) of signals and extract the desired signal from a noisy one with negative SNR (i.e., power of noise is greater than signal). It is worth mentioning that ECG and PPG signals are sensitive to noise from a variety of sources, increasing the risk of misunderstanding and interfering with the diagnostic process. The noises typically arise from power line interference, white noise, electrode contact noise, muscle contraction, baseline wandering, instrument noise, motion artifacts, electrosurgical noise. Even a slight variation in the obtained ECG waveform can impair the understanding of the patient's heart condition and affect the treatment procedure. Recent solutions, such as adaptive and blind source separation (BSS) algorithms, still have drawbacks, such as the need for noise or desired signal model, tuning and calibration, and inefficiency when dealing with excessively noisy signals. Therefore, the final goal of this step is to develop a robust algorithm that can estimate noise, even when SNR is negative, using the BSS method and remove it based on an adaptive filter. The second objective is defined for monitoring maternal and fetal ECG. Previous methods that were non-invasive used maternal abdominal ECG (MECG) for extracting fetal ECG (FECG). These methods need to be calibrated to generalize well. In other words, for each new subject, a calibration with a trustable device is required, which makes it difficult and time-consuming. The calibration is also susceptible to errors. We explore deep learning (DL) models for domain mapping, such as Cycle-Consistent Adversarial Networks, to map MECG to fetal ECG (FECG) and vice versa. The advantages of the proposed DL method over state-of-the-art approaches, such as adaptive filters or blind source separation, are that the proposed method is generalized well on unseen subjects. Moreover, it does not need calibration and is not sensitive to the heart rate variability of mother and fetal; it can also handle low signal-to-noise ratio (SNR) conditions. Thirdly, AI-based system that can measure continuous systolic blood pressure (SBP) and diastolic blood pressure (DBP) with minimum electrode requirements is explored. The most common method of measuring blood pressure is using cuff-based equipment, which cannot monitor blood pressure continuously, requires calibration, and is difficult to use. Other solutions use a synchronized ECG and PPG combination, which is still inconvenient and challenging to synchronize. The proposed method overcomes those issues and only uses PPG signal, comparing to other solutions. Using only PPG for blood pressure is more convenient since it is only one electrode on the finger where its acquisition is more resilient against error due to movement. The fourth objective is to detect anomalies on FECG data. The requirement of thousands of manually annotated samples is a concern for state-of-the-art detection systems, especially for fetal ECG (FECG), where there are few publicly available FECG datasets annotated for each FECG beat. Therefore, we will utilize active learning and transfer-learning concept to train a FECG anomaly detection system with the least training samples and high accuracy. In this part, a model is trained for detecting ECG anomalies in adults. Later this model is trained to detect anomalies on FECG. We only select more influential samples from the training set for training, which leads to training with the least effort. Because of physician shortages and rural geography, pregnant women's ability to get prenatal care might be improved through remote monitoring, especially when access to prenatal care is limited. Increased compliance with prenatal treatment and linked care amongst various providers are two possible benefits of remote monitoring. If recorded signals are transmitted correctly, maternal and fetal remote monitoring can be effective. Therefore, the last objective is to design a compression algorithm that can compress signals (like ECG) with a higher ratio than state-of-the-art and perform decompression fast without distortion. The proposed compression is fast thanks to the time domain B-Spline approach, and compressed data can be used for visualization and monitoring without decompression owing to the B-spline properties. Moreover, the stochastic optimization is designed to retain the signal quality and does not distort signal for diagnosis purposes while having a high compression ratio. In summary, components for creating an end-to-end system for day-to-day maternal and fetal cardiac monitoring can be envisioned as a mix of all tasks listed above. PPG and ECG recorded from the mother can be denoised using deconvolution strategy. Then, compression can be employed for transmitting signal. The trained CycleGAN model can be used for extracting FECG from MECG. Then, trained model using active transfer learning can detect anomaly on both MECG and FECG. Simultaneously, maternal BP is retrieved from the PPG signal. This information can be used for monitoring the cardiac status of mother and fetus, and also can be used for filling reports such as partogram

Estimation of Blood Pressure and Pulse Transit Time Using Your Smartphone

It is widely recognized today that there is an alarming rise of lifestyle-induced chronic diseases (e.g., type II diabetes) in our society. Therefore, a strong need exists for cost-effective and non-invasive devices that can measure blood pressure (BP) to monitor, diagnose and follow-up patients at risk, but also healthy population in general. One promising method for arterial BP estimation is to measure a surrogate marker of it, such as, Pulse Transit Time (PTT) and derive pressure values from it. However, current methods for measuring PTT require complex sensing and analysis circuitry and the related medical devices are expensive and inconvenient for the user to wear. In this paper, we present a new smartphone-based method to estimate PTT reliably and subsequently BP from the baseline sensors on smartphones. This new approach involves determining PTT by simultaneously measuring the time the blood leaves the heart, by recording the heart sound using the standard microphone of the phone and the time it reaches the finger, by measuring the pulse wave using the phone’s camera. Moreover, we also describe algorithms that can be executed directly on current smartphones to obtain clean and robust heart sound signals and to extract the pulse wave characteristics using smartphones. We also present methods to ensure a synchronous capture of the waveforms, which is essential to obtain reliable PTT values with inexpensive sensors. Our experiments show that the computational overhead of the proposed two-phase processing method is minimum, with the ability to reliably measure the PTT values in a fully accurate (beat-to-beat) fashion using directly state-of-the-art smartphones as medical devices

Novel Low Complexity Biomedical Signal Processing Techniques for Online Applications

Biomedical signal processing has become a very active domain of research nowadays. With the advent of portable monitoring devices, from accelerometer-enabled bracelets and smart-phones to more advanced vital sign tracking body area networks, this field has been receiving unprecedented attention. Indeed, portable health monitoring can help uncover the underlying dynamics of human health in a way that has not been possible before. Several challenges have emerged however, as these devices present key differences in terms of signal acquisition and processing in comparison with conventional methods. Hardware constraints such as processing power and limited battery capacity make most established techniques unsuitable and therefore, the need for low-complexity yet robust signal processing methods has appeared. Another issue that needs to be addressed is the quality of the signals captured by these devices. Unlike in clinical scenarios, in portable health monitoring subjects are constantly performing their daily activities. Moreover, signals maybe captured from unconventional locations and subsequently, be prone to perturbations. In order to obtain reliable measures from these monitoring devices, one needs to acquire dependable signal quality measures, to avoid false alarms. Indeed, hardware limitations and low-quality signals can greatly influence the performance of portable monitoring devices. Nevertheless, most devices offer simultaneous acquisition of multiple physiological parameters, such as electrocardiogram (ECG) and photoplethysmogram (PPG). Through multi-modal signal processing the overall performance can be improved, for instance by deriving parameters such as heart rate estimation from the most reliable and uncontaminated source. This thesis is therefore, dedicated to propose novel low-complexity biomedical processing techniques for real-time/online applications. Throughout this dissertation, several bio-signals such as the ECG, PPG, and electroencephalogram (EEG) are investigated. %There is an emphasis on ECG processing techniques, as most of the bio-signals recorded today reflect information about the heart. The main contribution of this dissertation consists in two signal processing techniques: 1) a novel ECG QRS-complex detection and delineation technique, and 2) a short-term event extraction technique for biomedical signals. The former is based on a processing technique called mathematical morphology (MM), and adaptively uses subject QRS-complex amplitude- and morphological attributes for a robust detection and delineation. This method is generalized to intra-cardiac electrograms for atrial activation detection during atrial fibrillation. The second method, called the Relative-Energy algorithm, uses short- and long-term signal energies to highlight events of interest and discard unwanted activities. Collectively, the results obtained by these methods suggest that while presenting low-computational costs, they can efficiently and robustly extract biomedical events of interest. Using the relative energy algorithm, a continuous non-binary ECG signal quality index is presented. The ECG quality is determined by creating a cleaned-up version of the input ECG and calculating the correlation coefficient between the cleaned-up and the original ECG. The proposed quality index is fast and can be implemented online, making it suitable for portable monitoring scenarios

Development of non-invasive, optical methods for central cardiovascular and blood chemistry monitoring.

Cardiovascular disease and sepsis are leading causes of mortality, morbidity and high cost in hospitals around the world. Failure of the circulatory system during cardiogenic shock and sepsis both can significantly impair the perfusion of oxygen through organs, resulting in poor patient outcome if not detected and corrected early. Another common disorder which goes hand-in-hand with cardiovascular disease is Diabetes Mellitus. Diabetes is a metabolic disorder resulting from the inability of the body to regulate the level of glucose in the blood. The prevalence of diabetes worldwide is increasing faster than society’s ability to manage cost effectively, with an estimated 9% of the world population diagnosed with metabolic disease. The current gold standard measurements for venous oxygen saturation, arterial pulse wave velocity (PWV), and diabetes management through blood glucose concentration monitoring are all invasive. Invasive measurements increase risk of infection and com- plications, are often high cost and disposable, and have a low patient compliance to regular measurements. The aim of this thesis is to develop non-invasive methods of monitoring these important dynamic physiological variables, including, venous oxygen saturation, pulse wave velocity, and blood glucose concentration. A novel photoplethysmography-based NIR discrete wavelength spectrometer was developed using LEDs to both emit light, and detect the light reflected back through the tissue. Using LEDs to detect light simplifies sensing circuit design, lowering hardware costs, allowing adaptable sensing specific to the needs of the user. A reflectance pulse oximeter was developed to measure the oxygen saturation at both the external jugular vein, and carotid artery. Measuring the jugular venous pulse (JVP) allows estimation of the venous oxygen saturation through either the JVP, or through breathing induced variation of the JVP. In addition to oxygenation, the de- vice developed is capable of adapting the sensing layout to measure the arterial pulse waveform at multiple sites along a peripheral artery, such as the carotid or radial. The PWV local to the carotid artery, and radial artery can then be measured, providing key information of cardiovascular risk. A novel algorithm for PWV measurement over multiple pulse waveforms was also developed. Expanding the sensor to use multiple different wavelength LEDs allow discrete spectroscopy in pulsatile blood. An absorption model of components in blood at specific wavelengths was created to isolate the spectral fingerprint of glucose. The sensor successfully measured the oxygen saturation at the carotid artery, and external jugular vein across 15 subjects, giving mean oxygen saturations of 92% and 85% respectively, within the expected physiological ranges. Venous oxygen saturation calculated using breathing induced changes to JVP was 3.3% less than when calculated on the JVP alone, with a standard deviation of 5.3%, compared to 6.9%. Thus, future work on the sensor will focus on extraction of the breathing induced venous pulse, rather than measuring from the JVP itself. The PWV on the carotid and radial artery was successfully measured within the ex- pected physiological ranges, with the novel phase difference algorithm proving more robust to noise than the gold standard foot-foot method. The phase difference method returned a mean PWV at the radial artery of 4.7 ±0.6 m s−1, and a mean CoV of 20%, compared to 4.0 ±1.4 m s−1, and a moan CoV of 58% for the foot-foot method. The proof of concept PWV sensor gives promising results, but needs to be calibrated against invasive gold standards, such as aorta and femoral pressure catheters. A glucose trial involving adult and neonatal subjects provided validation of the NIR non-invasive pulse glucometer. The sensor has an R2 of 0.47, and a mean absolute relative difference (MARD) of 19% compared to gold standard reference measurements. Clarke error grid analysis returns 85% of measurements in Zone A, 11% in Zone B, and 4% in Zone C. While the sensor is not as accurate as the gold standard invasive measurements, the ability to constantly measure without any pain or discomfort will help increase measurement compliance, improving user quality of life, plus further development may improve this. Overall, this thesis provided novel contributions in non-invasive venous oxygen saturation, PWV, and glucose concentration monitoring. The adaptability of the sensor shows promise in helping reduce the pain and inconvenience of the current invasive measurements, especially in diabetes management, where the sensor has the most potential for impact