Unsupervised Heart-rate Estimation in Wearables With Liquid States and A Probabilistic Readout

Heart-rate estimation is a fundamental feature of modern wearable devices. In this paper we propose a machine intelligent approach for heart-rate estimation from electrocardiogram (ECG) data collected using wearable devices. The novelty of our approach lies in (1) encoding spatio-temporal properties of ECG signals directly into spike train and using this to excite recurrently connected spiking neurons in a Liquid State Machine computation model; (2) a novel learning algorithm; and (3) an intelligently designed unsupervised readout based on Fuzzy c-Means clustering of spike responses from a subset of neurons (Liquid states), selected using particle swarm optimization. Our approach differs from existing works by learning directly from ECG signals (allowing personalization), without requiring costly data annotations. Additionally, our approach can be easily implemented on state-of-the-art spiking-based neuromorphic systems, offering high accuracy, yet significantly low energy footprint, leading to an extended battery life of wearable devices. We validated our approach with CARLsim, a GPU accelerated spiking neural network simulator modeling Izhikevich spiking neurons with Spike Timing Dependent Plasticity (STDP) and homeostatic scaling. A range of subjects are considered from in-house clinical trials and public ECG databases. Results show high accuracy and low energy footprint in heart-rate estimation across subjects with and without cardiac irregularities, signifying the strong potential of this approach to be integrated in future wearable devices.Comment: 51 pages, 12 figures, 6 tables, 95 references. Under submission at Elsevier Neural Network

ART Neural Networks: Distributed Coding and ARTMAP Applications

ART (Adaptive Resonance Theory) neural networks for fast, stable learning and prediction have been applied in a variety of areas. Applications include airplane design and manufacturing, automatic target recognition, financial forecasting, machine tool monitoring, digital circuit design, chemical analysis, and robot vision. Supervised ART architectures, called ARTMAP systems, feature internal control mechanisms that create stable recognition categories of optimal size by maximizing code compression while minimizing predictive error in an on-line setting. Special-purpose requirements of various application domains have led to a number of ARTMAP variants, including fuzzy ARTMAP, ART-EMAP, Gaussian ARTMAP, and distributed ARTMAP. ARTMAP has been used for a variety of applications, including computer-assisted medical diagnosis. Medical databases present many of the challenges found in general information management settings where speed, efficiency, ease of use, and accuracy are at a premium. A direct goal of improved computer-assisted medicine is to help deliver quality emergency care in situations that may be less than ideal. Working with these problems has stimulated a number of ART architecture developments, including ARTMAP-IC [1]. This paper describes a recent collaborative effort, using a new cardiac care database for system development, has brought together medical statisticians and clinicians at the New England Medical Center with researchers developing expert systems and neural networks, in order to create a hybrid method for medical diagnosis. The paper also considers new neural network architectures, including distributed ART {dART), a real-time model of parallel distributed pattern learning that permits fast as well as slow adaptation, without catastrophic forgetting. Local synaptic computations in the dART model quantitatively match the paradoxical phenomenon of Markram-Tsodyks [2] redistribution of synaptic efficacy, as a consequence of global system hypotheses.Office of Naval Research (N00014-95-1-0409, N00014-95-1-0657

Extraction and Detection of Fetal Electrocardiograms from Abdominal Recordings

The non-invasive fetal ECG (NIFECG), derived from abdominal surface electrodes, offers novel diagnostic possibilities for prenatal medicine. Despite its straightforward applicability, NIFECG signals are usually corrupted by many interfering sources. Most significantly, by the maternal ECG (MECG), whose amplitude usually exceeds that of the fetal ECG (FECG) by multiple times. The presence of additional noise sources (e.g. muscular/uterine noise, electrode motion, etc.) further affects the signal-to-noise ratio (SNR) of the FECG. These interfering sources, which typically show a strong non-stationary behavior, render the FECG extraction and fetal QRS (FQRS) detection demanding signal processing tasks. In this thesis, several of the challenges regarding NIFECG signal analysis were addressed. In order to improve NIFECG extraction, the dynamic model of a Kalman filter approach was extended, thus, providing a more adequate representation of the mixture of FECG, MECG, and noise. In addition, aiming at the FECG signal quality assessment, novel metrics were proposed and evaluated. Further, these quality metrics were applied in improving FQRS detection and fetal heart rate estimation based on an innovative evolutionary algorithm and Kalman filtering signal fusion, respectively. The elaborated methods were characterized in depth using both simulated and clinical data, produced throughout this thesis. To stress-test extraction algorithms under ideal circumstances, a comprehensive benchmark protocol was created and contributed to an extensively improved NIFECG simulation toolbox. The developed toolbox and a large simulated dataset were released under an open-source license, allowing researchers to compare results in a reproducible manner. Furthermore, to validate the developed approaches under more realistic and challenging situations, a clinical trial was performed in collaboration with the University Hospital of Leipzig. Aside from serving as a test set for the developed algorithms, the clinical trial enabled an exploratory research. This enables a better understanding about the pathophysiological variables and measurement setup configurations that lead to changes in the abdominal signal's SNR. With such broad scope, this dissertation addresses many of the current aspects of NIFECG analysis and provides future suggestions to establish NIFECG in clinical settings.:Abstract Acknowledgment Contents List of Figures List of Tables List of Abbreviations List of Symbols (1)Introduction 1.1)Background and Motivation 1.2)Aim of this Work 1.3)Dissertation Outline 1.4)Collaborators and Conflicts of Interest (2)Clinical Background 2.1)Physiology 2.1.1)Changes in the maternal circulatory system 2.1.2)Intrauterine structures and feto-maternal connection 2.1.3)Fetal growth and presentation 2.1.4)Fetal circulatory system 2.1.5)Fetal autonomic nervous system 2.1.6)Fetal heart activity and underlying factors 2.2)Pathology 2.2.1)Premature rupture of membrane 2.2.2)Intrauterine growth restriction 2.2.3)Fetal anemia 2.3)Interpretation of Fetal Heart Activity 2.3.1)Summary of clinical studies on FHR/FHRV 2.3.2)Summary of studies on heart conduction 2.4)Chapter Summary (3)Technical State of the Art 3.1)Prenatal Diagnostic and Measuring Technique 3.1.1)Fetal heart monitoring 3.1.2)Related metrics 3.2)Non-Invasive Fetal ECG Acquisition 3.2.1)Overview 3.2.2)Commercial equipment 3.2.3)Electrode configurations 3.2.4)Available NIFECG databases 3.2.5)Validity and usability of the non-invasive fetal ECG 3.3)Non-Invasive Fetal ECG Extraction Methods 3.3.1)Overview on the non-invasive fetal ECG extraction methods 3.3.2)Kalman filtering basics 3.3.3)Nonlinear Kalman filtering 3.3.4)Extended Kalman filter for FECG estimation 3.4)Fetal QRS Detection 3.4.1)Merging multichannel fetal QRS detections 3.4.2)Detection performance 3.5)Fetal Heart Rate Estimation 3.5.1)Preprocessing the fetal heart rate 3.5.2)Fetal heart rate statistics 3.6)Fetal ECG Morphological Analysis 3.7)Problem Description 3.8)Chapter Summary (4)Novel Approaches for Fetal ECG Analysis 4.1)Preliminary Considerations 4.2)Fetal ECG Extraction by means of Kalman Filtering 4.2.1)Optimized Gaussian approximation 4.2.2)Time-varying covariance matrices 4.2.3)Extended Kalman filter with unknown inputs 4.2.4)Filter calibration 4.3)Accurate Fetal QRS and Heart Rate Detection 4.3.1)Multichannel evolutionary QRS correction 4.3.2)Multichannel fetal heart rate estimation using Kalman filters 4.4)Chapter Summary (5)Data Material 5.1)Simulated Data 5.1.1)The FECG Synthetic Generator (FECGSYN) 5.1.2)The FECG Synthetic Database (FECGSYNDB) 5.2)Clinical Data 5.2.1)Clinical NIFECG recording 5.2.2)Scope and limitations of this study 5.2.3)Data annotation: signal quality and fetal amplitude 5.2.4)Data annotation: fetal QRS annotation 5.3)Chapter Summary (6)Results for Data Analysis 6.1)Simulated Data 6.1.1)Fetal QRS detection 6.1.2)Morphological analysis 6.2)Own Clinical Data 6.2.1)FQRS correction using the evolutionary algorithm 6.2.2)FHR correction by means of Kalman filtering (7)Discussion and Prospective 7.1)Data Availability 7.1.1)New measurement protocol 7.2)Signal Quality 7.3)Extraction Methods 7.4)FQRS and FHR Correction Algorithms (8)Conclusion References (A)Appendix A - Signal Quality Annotation (B)Appendix B - Fetal QRS Annotation (C)Appendix C - Data Recording GU

A Survey Study of the Current Challenges and Opportunities of Deploying the ECG Biometric Authentication Method in IoT and 5G Environments

The environment prototype of the Internet of Things (IoT) has opened the horizon for researchers to utilize such environments in deploying useful new techniques and methods in different fields and areas. The deployment process takes place when numerous IoT devices are utilized in the implementation phase for new techniques and methods. With the wide use of IoT devices in our daily lives in many fields, personal identification is becoming increasingly important for our society. This survey aims to demonstrate various aspects related to the implementation of biometric authentication in healthcare monitoring systems based on acquiring vital ECG signals via designated wearable devices that are compatible with 5G technology. The nature of ECG signals and current ongoing research related to ECG authentication are investigated in this survey along with the factors that may affect the signal acquisition process. In addition, the survey addresses the psycho-physiological factors that pose a challenge to the usage of ECG signals as a biometric trait in biometric authentication systems along with other challenges that must be addressed and resolved in any future related research.

An Error-Based Approximation Sensing Circuit for Event-Triggered, Low Power Wearable Sensors

Event-based sensors have the potential to optimize energy consumption at every stage in the signal processing pipeline, including data acquisition, transmission, processing and storage. However, almost all state-of-the-art systems are still built upon the classical Nyquist-based periodic signal acquisition. In this work, we design and validate the Polygonal Approximation Sampler (PAS), a novel circuit to implement a general-purpose event-based sampler using a polygonal approximation algorithm as the underlying sampling trigger. The circuit can be dynamically reconfigured to produce a coarse or a detailed reconstruction of the analog input, by adjusting the error threshold of the approximation. The proposed circuit is designed at the Register Transfer Level and processes each input sample received from the ADC in a single clock cycle. The PAS has been tested with three different types of archetypal signals captured by wearable devices (electrocardiogram, accelerometer and respiration data) and compared with a standard periodic ADC. These tests show that single-channel signals, with slow variations and constant segments (like the used single-lead ECG and the respiration signals) take great advantage from the used sampling technique, reducing the amount of data used up to 99% without significant performance degradation. At the same time, multi-channel signals (like the six-dimensional accelerometer signal) can still benefit from the designed circuit, achieving a reduction factor up to 80% with minor performance degradation. These results open the door to new types of wearable sensors with reduced size and higher battery lifetime

Counterpulsation cardiac assist device controller defection filter simulation and canine experiments

Electronic control systems for counterpulsation Cardiac Assist Devices (CADs) are an essential part of cardiac assistance. Synchronization of the counterpulsation CAD controller with the cardiac cycle is critical to the efficacy of the CAD. The robustness of counterpulsation CAD controllers varies with the ability of the CAD controller to properly trigger on aortic pressure (Pa) and electrocardiogram (ECG) signals for sinusoid rhythms, non-sinusoid rhythms and non-ideal signals resulting from surgical intervention. An analog-to-digital converter and digital-to-analog converter based CAD controller development platform was devised on a 33Mhz PC-AT. Counterpulsation Pa systolic rise and dicrotic notch detectors were demonstrated with a 15cc pediatric Intraaortic Balloon (IAB) and 50cc Extraaortic Counterpulsation Device (EACD) CADs using mongrel canine experimental models in which biological variation due to changing heart rate and arrhythmia as well as surgical interference due to mechanical ventilation, electrocautery, signal attenuation and random noise was present. The robust Pa triggering algorithm was based on a derivative comparator riding clipper algorithm for the Pa-based controller. In order to empirically determine the robustness of the Pa triggering algorithms, a simulation platform, Pa trace model, and Pa trace artifact and physiological variation models were devised. Each set of simulation experiments utilized a different Pa trace artifact or physiological variation model to determine the capability of the Pa trigger algorithm to withstand the effects of the Pa detection impediments while maintaining 100% accuracy of the dicrotic notch detection. Multiple simulation experiments were conducted in which the same nominally adjusted interference was increased to benchmark the immunity threshold of the dicrotic notch detector. Biological variation and deviations in Pa artifacts due to clinical conditions experienced in cardiothoracic surgery were investigated. Pa triggering was unhindered by biological variation of a Pa trace with a 3 mmHg dicrotic notch deflection along with a Pa trace with no dicrotic notch deflection present. Pa triggering was unhindered by heart rate variability ranging from 60 to 80 bpm due to respiration. Pa triggering was unhindered by clinical conditions including 40 mmHg changes in the Pa baseline modeling mechanical ventilation, aortic trace attenuation modeling variations in pressure transducer positioning and blood coagulation on the pressure catheter tip ranging from 100% to 200% of the Pa trace amplitude every four seconds, uniformly distributed noise with a mean of 0.5mmHg and standard deviation of 0.289mmHg and Gaussian distributed noise with a zero mean and standard deviation of 0.6nunHg. The results of the simulation experiments performed quantified the robustness of the Pa detection algorithm. Development of a fault tolerant counterpulsation CAD control system required the development of a robust ECG triggering algorithm to operate in tandem with the Pa triggering algorithm. An ECG detector was developed to provide robust control for a range of ECG traces due to biological variation and signal interference. The ECG R-wave detection algorithm is based on a modified version of the Washington University QRS-complex DD/1 algorithm (Detection and Delineation 1) which uses the associated AZTEC (Amplitude Zero Threshold Epic Coding) preprocessing algorithm and provides accurate ECG-based CAD control R-wave detection for 96.56% of the R-waves stored within the MIT/BIH ECG Arrhythmia database with a maximum detection delay of 8 milliseconds. Further IAB experiments performed with mongrel canine experimental models demonstrated that the systolic time interval to heart rate relationship existing in humans (essential to human patient CAD control inflation prediction) is not prevalent in canine mongrels particularly when treated with beta-blockers. In order to execute both Pa and ECG C software detection algorithms for a fault tolerant counterpulsation CAD controller, investigation into the communications throughput of a quad-transputer board was performed. Development of streamlined communication primitives led to a communication processor utilization of 8.3%, deemed efficient enough for fault tolerant multiprocessor CAD control implementation

Connectionist Modelling of Category Learning

A shortcoming is identified with respect to the ability of exemplar-based connectionist models of category learning to offer accounts of learning about stimuli with variable dimensionality. Models which may simulate these tasks, such as the configural-cue network (Gluck & Bower, 1988b), appear to be unable to accurately simulate certain data well simulated by exemplar-based models such as ALCOVE (Kruschke, 1992). A task in which the advantage of ALCOVE is exemplified is the prediction of human learning rates on the six category structures tested by Shepard, Hovland, and Jenkins (1961). The ability of ALCOVE to simulate the observed order of difficulty depends on its incorporation of selective attention processes (Nosofsky, Gluck, Palmeri, McKinley, & Glauthier, 1994). This thesis focuses on developing configural-cue network models which incorporate these processes. Informed by an information-theoretic approach to modelling the implementation of selective attention using a configural-cue representation, five connectionist models are developed. Each is capable of predicting the order of difficulty reported by Shepard et al. (1961). Two models employ a modular structure, but analysis suggests that these may lack much of the functionality of the basic configural-cue network. The remaining three incorporate dimensional attention schemes. These models appear to offer superior generalisability in relation to the simulation of learning about variable dimensionality stimuli. This generalisability is examined by applying a variant of one of these dimensional attention models, to data collected by Kruschke (1996a) on the inverse base-rate effect and base-rate neglect. The model provides a qualitative fit to this data. The success of these configural-cue models on these two tasks, only successfully modelled previously using two distinct types of representation, indicates that the approach has some potential for further applications. Differences between the models applied, however, indicates that more sophisticated conceptions of the attention process may be required to allow further generalisability.

Individual identification via electrocardiogram analysis

Background: During last decade the use of ECG recordings in biometric recognition studies has increased. ECG characteristics made it suitable for subject identification: it is unique, present in all living individuals, and hard to forge. However, in spite of the great number of approaches found in literature, no agreement exists on the most appropriate methodology. This study aimed at providing a survey of the techniques used so far in ECG-based human identification. Specifically, a pattern recognition perspective is here proposed providing a unifying framework to appreciate previous studies and, hopefully, guide future research. Methods: We searched for papers on the subject from the earliest available date using relevant electronic databases (Medline, IEEEXplore, Scopus, and Web of Knowledge). The following terms were used in different combinations: electrocardiogram, ECG, human identification, biometric, authentication and individual variability. The electronic sources were last searched on 1st March 2015. In our selection we included published research on peer-reviewed journals, books chapters and conferences proceedings. The search was performed for English language documents. Results: 100 pertinent papers were found. Number of subjects involved in the journal studies ranges from 10 to 502, age from 16 to 86, male and female subjects are generally present. Number of analysed leads varies as well as the recording conditions. Identification performance differs widely as well as verification rate. Many studies refer to publicly available databases (Physionet ECG databases repository) while others rely on proprietary recordings making difficult them to compare. As a measure of overall accuracy we computed a weighted average of the identification rate and equal error rate in authentication scenarios. Identification rate resulted equal to 94.95 % while the equal error rate equal to 0.92 %. Conclusions: Biometric recognition is a mature field of research. Nevertheless, the use of physiological signals features, such as the ECG traits, needs further improvements. ECG features have the potential to be used in daily activities such as access control and patient handling as well as in wearable electronics applications. However, some barriers still limit its growth. Further analysis should be addressed on the use of single lead recordings and the study of features which are not dependent on the recording sites (e.g. fingers, hand palms). Moreover, it is expected that new techniques will be developed using fiducials and non-fiducial based features in order to catch the best of both approaches. ECG recognition in pathological subjects is also worth of additional investigations