Optical and other methods for the assessment of arterial and venous insufficiency

There are a number of different techniques used to diagnose vascular insufficiency ranging from expensive hospital based equipment to less expensive devices used in primary care centres. Currently, some of these devices are unsuitable for use on patients with diabetes or DVI and have poor sensitivity for detecting moderate PAD patients. Additionally, some of the tests, particularly for DVI, require tourniquets or the patient to perform postural changes which some may find difficult. This may extend testing time. The study investigated 2 groups of patients, one with PAD and the other with DVI. The arterial group consisted of 46 controls and 57 patients. PPG probes were placed on the index finger and great toe. The venous group consisted of 24 controls and 25 patients and PPG probes were placed behind the knee and 10 cm above the medial malleolus. Duplex ultrasound was used as the gold standard to assess the arteries and veins in the lower limbs. The aim was to investigate whether signals acquired from patients at rest using Photoplethysmography (PPG) could be used as a screening tool. Pulse wave transit time (PWTT) and shape analysis techniques were used on the pulses from the patients with PAD, while time base and spectral analysis techniques were used on the waveforms of patients with DVI. PWTT and shape analysis techniques achieved sensitivities and specificities of 82% and 84% respectively. Accuracy dropped to 70% for detecting patients with moderate PAD. Spectral analysis techniques gave the best results for detecting patients with DVI achieving sensitivities and specificities of 69% and 80% respectively. In conclusion, reducing the signal acquisition time on patients with PAD did not significantly reduce the sensitivity and specificity. Without any patient movement it was difficult to separate patients with DVI from healthy normals.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Optical and other methods for the assessment of arterial and venous insufficiency

There are a number of different techniques used to diagnose vascular insufficiency ranging from expensive hospital based equipment to less expensive devices used in primary care centres. Currently, some of these devices are unsuitable for use on patients with diabetes or DVI and have poor sensitivity for detecting moderate PAD patients. Additionally, some of the tests, particularly for DVI, require tourniquets or the patient to perform postural changes which some may find difficult. This may extend testing time. The study investigated 2 groups of patients, one with PAD and the other with DVI. The arterial group consisted of 46 controls and 57 patients. PPG probes were placed on the index finger and great toe. The venous group consisted of 24 controls and 25 patients and PPG probes were placed behind the knee and 10 cm above the medial malleolus. Duplex ultrasound was used as the gold standard to assess the arteries and veins in the lower limbs. The aim was to investigate whether signals acquired from patients at rest using Photoplethysmography (PPG) could be used as a screening tool. Pulse wave transit time (PWTT) and shape analysis techniques were used on the pulses from the patients with PAD, while time base and spectral analysis techniques were used on the waveforms of patients with DVI. PWTT and shape analysis techniques achieved sensitivities and specificities of 82% and 84% respectively. Accuracy dropped to 70% for detecting patients with moderate PAD. Spectral analysis techniques gave the best results for detecting patients with DVI achieving sensitivities and specificities of 69% and 80% respectively. In conclusion, reducing the signal acquisition time on patients with PAD did not significantly reduce the sensitivity and specificity. Without any patient movement it was difficult to separate patients with DVI from healthy normals

Simultaneous ambulatory cardial and oesophageal monitoring

A method for recording and analyzing patient signals is described. The electrocardiogram and the pH in the lower oesophagus are simultaneously monitored over a 24 hour period. Signals are recorded onto cassette tape for later analysis. The pH record is analyzed for reflux episodes which are correlated with patient symptoms. The corresponding ECG episodes are checked for ischemia which appears as depression of the ST segment

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

Analysis and interpretation of electrocardiogram signals for the detection of hypoglycaemia.

Diabetes is a complication of metabolism where the glucose control system of the human body is impaired and cannot preserve the blood glucose levels in the normal range. This research investigated the relationship between abnormally low glucose levels (hypoglycaemia) and cardiac function in human subjects with Type 1 diabetes. The aim of the research was to detect the onset of spontaneous nocturnal hypoglycaemia indirectly through analysis of the subject's Electrocardiogram (ECG). The research hypothesis follows from previous studies, that suggested changes in ECG morphology, in particular prolongation of the QT interval and flattening of the T-wave, during hypoglycaemia.The research methodology involved ECG feature extraction and classification of extracted features into euglycaemic (normal glucose levels) and hypoglycaemic categories. A number of time-domain ECG features were evaluated and a few ECG annotation algorithms were investigated for detection of onsets, peaks and offsets of the ECG components. Autoregressive (AR) modelling was also employed as a means of describing and characterising post-QRS ECG segments. ECG segment classification was carried out using Multi-layer Perceptron (MLP) neural networks. Statistical classifiers were also employed namely, Linear Discriminant Analysis (LDA) and the k-Nearest Neighbour (kNN).This research proposed a new methodology for detection of spontaneous nocturnal hypoglycaemia by combining time-domain characterisation and classification of the post-QRS ECG segment. Two novel ECG features were introduced to characterise T-wave morphology. MLPs achieved better classification of ECG feature vectors compared to LDA. Also ECG representation by AR coefficients was marginally superior to individual ECG features, according to classification performance by LDA. Finally a Knowledge-Based System (KBS) was designed for ECG monitoring during the night. It was developed and tested onoffline data in a m anner that simulated an online monitoring scenario. The system was able to detect ECG abnormalities related to spontaneous nocturnal hypoglycaemia and to raise an alarm if necessary. In its optimal configuration, the system correctly monitored 30 out of the 32 recorded nights (originating from 19 patients) while there were 2 false alarms. This performance corresponds to accuracy, sensitivity and specificity of 93.75%, 100% and 91.30% respectively.The main contribution to knowledge from this research was successful detection of the onset of spontaneous nocturnal hypoglycaemia indirectly, using solely ECG information. This result supports the hypothesis stating that spontaneous hypoglycaemia affects the cardiac function and is manifested on the ECG. A. detailed analysis of the ECG signal for the detection of hypoglycaemia was carried out in the thesis. ECG features were extracted and assessed as predictors of the clinical condition. A number of approaches for ECG representation and classification (MLP, kNN, LDA) were examined and compared. Moreover, a KBS capable of achieving satisfactory monitoring performance on offline data from diabetic patients was designed. It was found that ECG changes in response to hypoglycaemia were short-time transients and incorporation of temporal information in the classification system caused significant improvement in performance. Successful continuation of this work may lead to a hypoglycaemia-detection system for the bedside

High Resolution Multi-parametric Diagnostics and Therapy of Atrial Fibrillation: Chasing Arrhythmia Vulnerabilities in the Spatial Domain

After a century of research, atrial fibrillation (AF) remains a challenging disease to study and exceptionally resilient to treatment. Unfortunately, AF is becoming a massive burden on the health care system with an increasing population of susceptible elderly patients and expensive unreliable treatment options. Pharmacological therapies continue to be disappointingly ineffective or are hampered by side effects due to the ubiquitous nature of ion channel targets throughout the body. Ablative therapy for atrial tachyarrhythmias is growing in acceptance. However, ablation procedures can be complex, leading to varying levels of recurrence, and have a number of serious risks. The high recurrence rate could be due to the difficulty of accurately predicting where to draw the ablation lines in order to target the pathophysiology that initiates and maintains the arrhythmia or an inability to distinguish sub-populations of patients who would respond well to such treatments. There are electrical cardioversion options but there is not a practical implanted deployment of this strategy. Under the current bioelectric therapy paradigm there is a trade-off between efficacy and the pain and risk of myocardial damage, all of which are positively correlated with shock strength. Contrary to ventricular fibrillation, pain becomes a significant concern for electrical defibrillation of AF due to the fact that a patient is conscious when experiencing the arrhythmia. Limiting the risk of myocardial injury is key for both forms of fibrillation. In this project we aim to address the limitations of current electrotherapy by diverging from traditional single shock protocols. We seek to further clarify the dynamics of arrhythmia drivers in space and to target therapy in both the temporal and spatial domain; ultimately culminating in the design of physiologically guided applied energy protocols. In an effort to provide further characterization of the organization of AF, we used transillumination optical mapping to evaluate the presence of three-dimensional electrical substrate variations within the transmural wall during acutely induced episodes of AF. The results of this study suggest that transmural propagation may play a role in AF maintenance mechanisms, with a demonstrated range of discordance between the epicardial and endocardial dynamic propagation patterns. After confirming the presence of epi-endo dyssynchrony in multiple animal models, we further investigated the anatomical structure to look for regional trends in transmural fiber orientation that could help explain the spectrum of observed patterns. Simultaneously, we designed and optimized a multi-stage, multi-path defibrillation paradigm that can be tailored to individual AF frequency content in the spatial and temporal domain. These studies continue to drive down the defibrillation threshold of electrotherapies in an attempt to achieve a pain-free AF defibrillation solution. Finally, we designed and characterized a novel platform of stretchable electronics that provide instrumented membranes across the epicardial surface or implanted within the transmural wall to provide physiological feedback during electrotherapy beyond just the electrical state of the tissue. By combining a spatial analysis of the arrhythmia drivers, the energy delivered and the resulting damage, we hope to enhance the biophysical understanding of AF electrical cardioversion and xiii design an ideal targeted energy delivery protocol to improve upon all limitations of current electrotherapy

On the Utility of Representation Learning Algorithms for Myoelectric Interfacing

Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steer—a gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies a Recurrent Neural Network (RNN) model to decode finger forces from intramuscular EMG. Paper vI introduces a Transformer model for myoelectric interfacing that do not need additional training data to function with previously unseen users. Paper vII compares the performance of a Long Short-Term Memory (LSTM) network to that of classical pattern recognition algorithms. Lastly, paper vIII describes a framework for synthesizing EMG from multi-articulate gestures intended to reduce training burden

Abstracts on Radio Direction Finding (1899 - 1995)

The files on this record represent the various databases that originally composed the CD-ROM issue of "Abstracts on Radio Direction Finding" database, which is now part of the Dudley Knox Library's Abstracts and Selected Full Text Documents on Radio Direction Finding (1899 - 1995) Collection. (See Calhoun record https://calhoun.nps.edu/handle/10945/57364 for further information on this collection and the bibliography). Due to issues of technological obsolescence preventing current and future audiences from accessing the bibliography, DKL exported and converted into the three files on this record the various databases contained in the CD-ROM. The contents of these files are: 1) RDFA_CompleteBibliography_xls.zip [RDFA_CompleteBibliography.xls: Metadata for the complete bibliography, in Excel 97-2003 Workbook format; RDFA_Glossary.xls: Glossary of terms, in Excel 97-2003 Workbookformat; RDFA_Biographies.xls: Biographies of leading figures, in Excel 97-2003 Workbook format]; 2) RDFA_CompleteBibliography_csv.zip [RDFA_CompleteBibliography.TXT: Metadata for the complete bibliography, in CSV format; RDFA_Glossary.TXT: Glossary of terms, in CSV format; RDFA_Biographies.TXT: Biographies of leading figures, in CSV format]; 3) RDFA_CompleteBibliography.pdf: A human readable display of the bibliographic data, as a means of double-checking any possible deviations due to conversion

XXIV congreso anual de la sociedad española de ingeniería biomédica (CASEIB2016)

En la presente edición, más de 150 trabajos de alto nivel científico van a ser presentados en 18 sesiones paralelas y 3 sesiones de póster, que se centrarán en áreas relevantes de la Ingeniería Biomédica. Entre las sesiones paralelas se pueden destacar la sesión plenaria Premio José María Ferrero Corral y la sesión de Competición de alumnos de Grado en Ingeniería Biomédica, con la participación de 16 alumnos de los Grados en Ingeniería Biomédica a nivel nacional. El programa científico se complementa con dos ponencias invitadas de científicos reconocidos internacionalmente, dos mesas redondas con una importante participación de sociedades científicas médicas y de profesionales de la industria de tecnología médica, y dos actos sociales que permitirán a los participantes acercarse a la historia y cultura valenciana. Por primera vez, en colaboración con FENIN, seJane Campos, R. (2017). XXIV congreso anual de la sociedad española de ingeniería biomédica (CASEIB2016). Editorial Universitat Politècnica de València. http://hdl.handle.net/10251/79277EDITORIA