Noise reduction method for the heart sound records from digital stethoscope

In recent years, digital instruments have been widely used in the medical area with the rapid development of digital technology. The digital stethoscope, which converts the acoustic sound waves in to electrical signals and then amplifies them, is gradually replacing the conventional acoustic stethoscope with the advantage of additional usage such as restoring, replaying and processing the signals for optimal listening. As the sounds are transmitted in to electrical form, they can be recorded for further signal processing. One of the major problems with recording heart sounds is noise corruption. Although there are many solutions available to noise reduction problems, it was found that most of them are based on the assumption that the noise is an additive white noise [1]. More research is required to find different de-noising techniques based on the specific noise present. Therefore, this study is motivated to answer the research question: ‘How might the noise be reduced from the heart sound records collected from digital stethoscope with suitable noise reduction method’. This research question is divided into three sub-questions, including the identification of the noise spectrum, the design of noise reduction method and the assessment of the method. In the identification stage, five main kinds of noise were chosen and their characteristics and spectrums were discussed. Compared with different kinds of adaptive filters, the suitable noise reduction filter for this study was confirmed. To assess the effect of the method, 68 pieces of sound resources were collected for the experiment. These sounds were selected based on the noise they contain. A special noise reduction method was developed for the noise. This method was tested and assessed with those sound samples by two factors: the noise level and the noise kind. The results of the experiment showed the effect of the noise reduction method for each kind of noise. The outcomes indicated that this method was suitable for heart sound noise reduction. The findings of this study, including the analysis of noise level and noise kind, indicated and concluded that the chosen method for heart sound noise reduction performed well. This is perhaps the first attempt to understand and assess the noise reduction method with classified heart sound signals which are collected from the real healthcare environment. This noise reduction method may provide a de-noising solution for the specific noise present in heart sound

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

Eye Detection and Face Recognition Across the Electromagnetic Spectrum

Biometrics, or the science of identifying individuals based on their physiological or behavioral traits, has increasingly been used to replace typical identifying markers such as passwords, PIN numbers, passports, etc. Different modalities, such as face, fingerprint, iris, gait, etc. can be used for this purpose. One of the most studied forms of biometrics is face recognition (FR). Due to a number of advantages over typical visible to visible FR, recent trends have been pushing the FR community to perform cross-spectral matching of visible images to face images from higher spectra in the electromagnetic spectrum.;In this work, the SWIR band of the EM spectrum is the primary focus. Four main contributions relating to automatic eye detection and cross-spectral FR are discussed. First, a novel eye localization algorithm for the purpose of geometrically normalizing a face across multiple SWIR bands for FR algorithms is introduced. Using a template based scheme and a novel summation range filter, an extensive experimental analysis show that this algorithm is fast, robust, and highly accurate when compared to other available eye detection methods. Also, the eye locations produced by this algorithm provides higher FR results than all other tested approaches. This algorithm is then augmented and updated to quickly and accurately detect eyes in more challenging unconstrained datasets, spanning the EM spectrum. Additionally, a novel cross-spectral matching algorithm is introduced that attempts to bridge the gap between the visible and SWIR spectra. By fusing multiple photometric normalization combinations, the proposed algorithm is not only more efficient than other visible-SWIR matching algorithms, but more accurate in multiple challenging datasets. Finally, a novel pre-processing algorithm is discussed that bridges the gap between document (passport) and live face images. It is shown that the pre-processing scheme proposed, using inpainting and denoising techniques, significantly increases the cross-document face recognition performance

Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)

The implicit objective of the biennial "international - Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST) is to foster collaboration between international scientific teams by disseminating ideas through both specific oral/poster presentations and free discussions. For its second edition, the iTWIST workshop took place in the medieval and picturesque town of Namur in Belgium, from Wednesday August 27th till Friday August 29th, 2014. The workshop was conveniently located in "The Arsenal" building within walking distance of both hotels and town center. iTWIST'14 has gathered about 70 international participants and has featured 9 invited talks, 10 oral presentations, and 14 posters on the following themes, all related to the theory, application and generalization of the "sparsity paradigm": Sparsity-driven data sensing and processing; Union of low dimensional subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph sensing/processing; Blind inverse problems and dictionary learning; Sparsity and computational neuroscience; Information theory, geometry and randomness; Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?; Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website: http://sites.google.com/site/itwist1

Underground distribution cable incipient fault diagnosis system

This dissertation presents a methodology for an efficient, non-destructive, and online incipient fault diagnosis system (IFDS) to detect underground cable incipient faults before they become catastrophic. The system provides vital information to help the operator with the decision-making process regarding the condition assessment of the underground cable. It incorporates advanced digital signal processing and pattern recognition methods to classify recorded data into designated classes. Additionally, the IFDS utilizes novel detection methodologies to detect when the cable is near failure. The classification functionality is achieved through employing an ensemble of rule-based and supervised classifiers. The Support Vector Machines, designed and used as a supervised classifier, was found to perform superior. In addition to the normalized energy features computed from wavelet packet analysis, two new features, namely Horizontal Severity Index, and Vertical Severity Index are defined and used in the classification problem. The detection functionality of the IFDS is achieved through incorporating a temporal severity measure and a detection method. The novel severity measure is based on the temporal analysis of arrival times of incipient abnormalities, which gives rise to a numeric index called the Global Severity Index (GSI). This index portrays the progressive degradation path of underground cable as catastrophic failure time approaches. The detection approach utilizes the numerical modeling capabilities of SOM as well as statistical change detection techniques. The natural logarithm of the chronologically ordered minimum modeling errors, computed from exposing feature vectors to a trained SOM, is used as the detection index. Three modified change detection algorithms, namely Cumulative Sum, Exponentially Weighted Moving Averages, and Generalized Likelihood Ratio, are introduced and applied to this application. These algorithms determine the change point or near failure time of cable from the instantaneous values of the detection index. Performance studies using field recorded data were conducted at three warning levels to assess the capability of the IFDS in predicting the faults that actually occurred in the monitored underground cable. The IFDS presents a high classification rate and satisfactory detection capability at each warning level. Specifically, it demonstrates that at least one detection technique successfully provides an early warning that a fault is imminent

Learning to compress and search visual data in large-scale systems

The problem of high-dimensional and large-scale representation of visual data is addressed from an unsupervised learning perspective. The emphasis is put on discrete representations, where the description length can be measured in bits and hence the model capacity can be controlled. The algorithmic infrastructure is developed based on the synthesis and analysis prior models whose rate-distortion properties, as well as capacity vs. sample complexity trade-offs are carefully optimized. These models are then extended to multi-layers, namely the RRQ and the ML-STC frameworks, where the latter is further evolved as a powerful deep neural network architecture with fast and sample-efficient training and discrete representations. For the developed algorithms, three important applications are developed. First, the problem of large-scale similarity search in retrieval systems is addressed, where a double-stage solution is proposed leading to faster query times and shorter database storage. Second, the problem of learned image compression is targeted, where the proposed models can capture more redundancies from the training images than the conventional compression codecs. Finally, the proposed algorithms are used to solve ill-posed inverse problems. In particular, the problems of image denoising and compressive sensing are addressed with promising results.Comment: PhD thesis dissertatio

ANALYSIS OF VOCAL FOLD KINEMATICS USING HIGH SPEED VIDEO

Vocal folds are the twin in-folding of the mucous membrane stretched horizontally across the larynx. They vibrate modulating the constant air flow initiated from the lungs. The pulsating pressure wave blowing through the glottis is thus the source for voiced speech production. Study of vocal fold dynamics during voicing are critical for the treatment of voice pathologies. Since the vocal folds move at 100 - 350 cycles per second, their visual inspection is currently done by strobosocopy which merges information from multiple cycles to present an apparent motion. High Speed Digital Laryngeal Imaging(HSDLI) with a temporal resolution of up to 10,000 frames per second has been established as better suited for assessing the vocal fold vibratory function through direct recording. But the widespread use of HSDLI is limited due to lack of consensus on the modalities like features to be examined. Development of the image processing techniques which circumvents the need for the tedious and time consuming effort of examining large volumes of recording has room for improvement. Fundamental questions like the required frame rate or resolution for the recordings is still not adequately answered. HSDLI cannot get the absolute physical measurement of the anatomical features and vocal fold displacement. This work addresses these challenges through improved signal processing. A vocal fold edge extraction technique with subpixel accuracy, suited even for hard to record pediatric population is developed first. The algorithm which is equally applicable for pediatric and adult subjects, is implemented to facilitate user inspection and intervention. Objective features describing the fold dynamics, which are extracted from the edge displacement waveform are proposed and analyzed on a diverse dataset of healthy males, females and children. The sampling and quantization noise present in the recordings are analyzed and methods to mitigate them are investigated. A customized Kalman smoothing and spline interpolation on the displacement waveform is found to improve the feature estimation stability. The relationship between frame rate, spatial resolution and vibration for efficient capturing of information is derived. Finally, to address the inability to measure physical measurement, a structured light projection calibrated with respect to the endoscope is prototyped

Visibility recovery on images acquired in attenuating media. Application to underwater, fog, and mammographic imaging

136 p.When acquired in attenuating media, digital images of ten suffer from a particularly complex degradation that reduces their visual quality, hindering their suitability for further computational applications, or simply decreasing the visual pleasan tness for the user. In these cases, mathematical image processing reveals it self as an ideal tool to recover some of the information lost during the degradation process. In this dissertation,we deal with three of such practical scenarios in which this problematic is specially relevant, namely, underwater image enhancement, fogremoval and mammographic image processing. In the case of digital mammograms,X-ray beams traverse human tissue, and electronic detectorscapture them as they reach the other side. However, the superposition on a bidimensional image of three-dimensional structures produces low contraste dimages in which structures of interest suffer from a diminished visibility, obstructing diagnosis tasks. Regarding fog removal, the loss of contrast is produced by the atmospheric conditions, and white colour takes over the scene uniformly as distance increases, also reducing visibility.For underwater images, there is an added difficulty, since colour is not lost uniformly; instead, red colours decay the fastest, and green and blue colours typically dominate the acquired images. To address all these challenges,in this dissertation we develop new methodologies that rely on: a)physical models of the observed degradation, and b) the calculus of variations.Equipped with this powerful machinery, we design novel theoreticaland computational tools, including image-dependent functional energies that capture the particularities of each degradation model. These energie sare composed of different integral terms that are simultaneous lyminimized by means of efficient numerical schemes, producing a clean,visually-pleasant and use ful output image, with better contrast and increased visibility. In every considered application, we provide comprehensive qualitative (visual) and quantitative experimental results to validateour methods, confirming that the developed techniques out perform other existing approaches in the literature