Extracting fetal heart beats from maternal abdominal recordings: Selection of the optimal principal components

This study presents a systematic comparison of different approaches to the automated selection of the principal components (PC) which optimise the detection of maternal and fetal heart beats from non-invasive maternal abdominal recordings. A public database of 75 4-channel non-invasive maternal abdominal recordings was used for training the algorithm. Four methods were developed and assessed to determine the optimal PC: (1) power spectral distribution, (2) root mean square, (3) sample entropy, and (4) QRS template. The sensitivity of the performance of the algorithm to large-amplitude noise removal (by wavelet de-noising) and maternal beat cancellation methods were also assessed. The accuracy of maternal and fetal beat detection was assessed against reference annotations and quantified using the detection accuracy score F1 [2*PPV*Se / (PPV + Se)], sensitivity (Se), and positive predictive value (PPV). The best performing implementation was assessed on a test dataset of 100 recordings and the agreement between the computed and the reference fetal heart rate (fHR) and fetal RR (fRR) time series quantified. The best performance for detecting maternal beats (F1 99.3%, Se 99.0%, PPV 99.7%) was obtained when using the QRS template method to select the optimal maternal PC and applying wavelet de-noising. The best performance for detecting fetal beats (F1 89.8%, Se 89.3%, PPV 90.5%) was obtained when the optimal fetal PC was selected using the sample entropy method and utilising a fixed-length time window for the cancellation of the maternal beats. The performance on the test dataset was 142.7 beats2/min2 for fHR and 19.9 ms for fRR, ranking respectively 14 and 17 (out of 29) when compared to the other algorithms presented at the Physionet Challenge 2013

Algorithms and systems for home telemonitoring in biomedical applications

During the past decades, the interest of the healthcare community shifted from the simple treatment of the diseases towards the prevention and maintenance of a healthy lifestyle. This approach is associated to a reduced cost for the Health Systems, having to face the constantly increased expenditures due to the reduced mortality for chronical diseases and to the progressive population ageing. Nevertheless, the high costs related to hospitalization of patients for monitoring procedures that could be better performed at home hamper the full implementation of this approach in a traditional way. Information and Communication Technology can provide a solution to implement a care model closer to the patient, crossing the physical boundaries of the hospitals and thus allowing to reach also those patients that, for a geographical or social condition, could not access the health services as other luckier subjects. This is the case of telemonitoring systems, whose aim is that of providing monitoring services for some health-related parameters at a distance, by means of custom-designed electronic devices. In this thesis, the specific issues associated to two telemonitoring applications are presented, along with the proposed solutions and the achieved results. The first telemonitoring application considered is the fetal electrocardiography. Non-invasive fetal electrocardiography is the recording of the fetal heart electrical activity using electrodes placed on the maternal abdomen. It can provide important diagnostic parameters, such as the beat-to-beat heart rate variability, whose recurring analysis would be useful in assessing and monitoring fetal health during pregnancy. Long term electrocardiographic monitoring is sustained by the absence of any collateral effects for both the mother and the fetus. This application has been tackled from several perspectives, mainly acquisition and processing. From the acquisition viewpoint a study on different skin treatments, disposable commercial electrodes and textile electrodes has been performed with the aim of improving the signal acquisition quality, while simplifying the measurement setup. From the processing viewpoint, different algorithms have been developed to allow extracting the fetal ECG heart rate, starting from an on-line ICA algorithm or exploiting a subtractive approach to work on recordings acquired with a reduced number of electrodes. The latter, took part to the international "Physionet/Computing in Cardiology Challenge" in 2013 entering into the top ten best-performing open-source algorithms. The improved version of this algorithm is also presented, which would mark the 5th and 4th position in the final ranking related to the fetal heart rate and fetal RR interval measurements performance, reserved to the open-source challenge entries, taking into account both official and unofficial entrants. The research in this field has been carried out in collaboration with the Pediatric Cardiology Unit of the Hospital G. Brotzu in Cagliari, for the acquisition of non-invasive fetal ECG signals from pregnant voluntary patients. The second telemonitoring application considered is the telerehabilitation of the hand. The execution of rehabilitation exercises has been proven to be effective in recovering hand functionality in a wide variety of invalidating diseases, but the lack of standardization and continuous medical control cause the patients neglecting this therapeutic procedures. Telemonitoring the rehabilitation sessions would allow the physician to closely follow the patients' progresses and compliance to the prescribed adapted exercises. This application leads to the development of a sensorized telerehabilitation system for the execution and objective monitoring of therapeutic exercises at the patients' home and of the telemedicine infrastructure that give the physician the opportunity to monitor patients' progresses through parameters summarizing the patients' performance. The proposed non-CE marked medical device, patent pending, underwent a clinical trial, reviewed and approved by the Italian Public Health Department, involving 20 patients with Rheumatoid Arthritis and 20 with Systemic Sclerosis randomly assigned to the experimental or the control arm, enrolled for 12 weeks in a home rehabilitation program. The trial, carried out with the collaboration of the Rheumatology Department of the Policlinico Universitario of Cagliari, revealed promising results in terms of hand functionality recovering, highlighting greater improvements for the patients enrolled in the experimental arm, that use the proposed telerehabilitation system, with respect to those of the control arm, which perform similar rehabilitation exercises using common objects

Quality Control in ECG-based Atrial Fibrillation Screening

This thesis comprises an introductory chapter and four papers related to quality control in ECG-based atrial fibrillation (AF) screening. Atrial fibrillation is a cardiac arrhythmia characterized by an irregular rhythm and constitutes a major risk factor for stroke. Anticoagulation therapy significantly reduces this risk, and therefore, AF screening is motivated. Atrial fibrillation screening is often done using ECGs recorded outside the clinical environment. However, the higher susceptibility of such ECGs to noise and artifacts makes the identification of patients with AF challenging. The present thesis addresses these challenges at different levels in the data analysis chain. Paper I presents a convolutional neural network (CNN)-based approach to identify transient noise and artifacts in the detected beat sequence before AF detection. The results show that by inserting a CNN, prior to the AF detector, the number of false AF detections is reduced by 22.5% without any loss in the sensitivity, suggesting that the number of recordings requiring expert review can be significantly reduced. Paper II investigates the signal quality of a novel wet electrode technology, and how the improved signal quality translates to improved beat detection and AF detection performance. The novel electrode technology is designed for reduction of motion artifacts typically present in Holter ECG recordings. The novel electrode technology shows a better signal quality and detection performance when compared to a commercially available counterpart, especially when the subject becomes more active. Thus, it has the potential to reduce the review burden and costs associated with ambulatory monitoring.Paper III introduces a detector for short-episode supraventricular tachycardia (sSVT) in AF screening recordings, which has been shown to be associated with an increased risk for future AF. Therefore, the identification of subjects with suchepisodes may increase the usefulness of AF screening. The proposed detector is based on the assumption that the beats in an sSVT episode display similar morphology, and that episodes including detections of deviating morphology should be excluded. The results show that the number of false sSVT detections can be significantly reduced (by a factor of 6) using the proposed detector.Paper IV introduces a novel ECG simulation tool, which is capable of producing ECGs with various arrhythmia patterns and with several different types of noise and artifacts. Specifically, the ECG simulator includes models to generate noise observed in ambulatory recordings, and when recording using handheld recording devices. The usefulness of the simulator is illustrated in terms of AF detection performance when the CNN training in Paper I is performed using simulated data. The results show a very similar performance when training with simulated data compared to when training with real data. Thus, the proposed simulator is a valuable tool in the development and training of automated ECG processing algorithms. Together, the four parts, in different ways, contribute to improved algorithmic efficiency in AF screening

Blind Source Separation for the Processing of Contact-Less Biosignals

(Spatio-temporale) Blind Source Separation (BSS) eignet sich für die Verarbeitung von Multikanal-Messungen im Bereich der kontaktlosen Biosignalerfassung. Ziel der BSS ist dabei die Trennung von (z.B. kardialen) Nutzsignalen und Störsignalen typisch für die kontaktlosen Messtechniken. Das Potential der BSS kann praktisch nur ausgeschöpft werden, wenn (1) ein geeignetes BSS-Modell verwendet wird, welches der Komplexität der Multikanal-Messung gerecht wird und (2) die unbestimmte Permutation unter den BSS-Ausgangssignalen gelöst wird, d.h. das Nutzsignal praktisch automatisiert identifiziert werden kann. Die vorliegende Arbeit entwirft ein Framework, mit dessen Hilfe die Effizienz von BSS-Algorithmen im Kontext des kamera-basierten Photoplethysmogramms bewertet werden kann. Empfehlungen zur Auswahl bestimmter Algorithmen im Zusammenhang mit spezifischen Signal-Charakteristiken werden abgeleitet. Außerdem werden im Rahmen der Arbeit Konzepte für die automatisierte Kanalauswahl nach BSS im Bereich der kontaktlosen Messung des Elektrokardiogramms entwickelt und bewertet. Neuartige Algorithmen basierend auf Sparse Coding erwiesen sich dabei als besonders effizient im Vergleich zu Standard-Methoden.(Spatio-temporal) Blind Source Separation (BSS) provides a large potential to process distorted multichannel biosignal measurements in the context of novel contact-less recording techniques for separating distortions from the cardiac signal of interest. This potential can only be practically utilized (1) if a BSS model is applied that matches the complexity of the measurement, i.e. the signal mixture and (2) if permutation indeterminacy is solved among the BSS output components, i.e the component of interest can be practically selected. The present work, first, designs a framework to assess the efficacy of BSS algorithms in the context of the camera-based photoplethysmogram (cbPPG) and characterizes multiple BSS algorithms, accordingly. Algorithm selection recommendations for certain mixture characteristics are derived. Second, the present work develops and evaluates concepts to solve permutation indeterminacy for BSS outputs of contact-less electrocardiogram (ECG) recordings. The novel approach based on sparse coding is shown to outperform the existing concepts of higher order moments and frequency-domain features

Development of a Novel Dataset and Tools for Non-Invasive Fetal Electrocardiography Research

This PhD thesis presents the development of a novel open multi-modal dataset for advanced studies on fetal cardiological assessment, along with a set of signal processing tools for its exploitation. The Non-Invasive Fetal Electrocardiography (ECG) Analysis (NInFEA) dataset features multi-channel electrophysiological recordings characterized by high sampling frequency and digital resolution, maternal respiration signal, synchronized fetal trans-abdominal pulsed-wave Doppler (PWD) recordings and clinical annotations provided by expert clinicians at the time of the signal collection. To the best of our knowledge, there are no similar dataset available. The signal processing tools targeted both the PWD and the non-invasive fetal ECG, exploiting the recorded dataset. About the former, the study focuses on the processing aimed at the preparation of the signal for the automatic measurement of relevant morphological features, already adopted in the clinical practice for cardiac assessment. To this aim, a relevant step is the automatic identification of the complete and measurable cardiac cycles in the PWD videos: a rigorous methodology was deployed for the analysis of the different processing steps involved in the automatic delineation of the PWD envelope, then implementing different approaches for the supervised classification of the cardiac cycles, discriminating between complete and measurable vs. malformed or incomplete ones. Finally, preliminary measurement algorithms were also developed in order to extract clinically relevant parameters from the PWD. About the fetal ECG, this thesis concentrated on the systematic analysis of the adaptive filters performance for non-invasive fetal ECG extraction processing, identified as the reference tool throughout the thesis. Then, two studies are reported: one on the wavelet-based denoising of the extracted fetal ECG and another one on the fetal ECG quality assessment from the analysis of the raw abdominal recordings. Overall, the thesis represents an important milestone in the field, by promoting the open-data approach and introducing automated analysis tools that could be easily integrated in future medical devices

ASIC based recorders of electrophysiological signals

The ability of application specific integrated circuits (ASICs) to minimise the size and power consumption of electronic circuitry, makes their application to the design of ambulatory monitoring equipment, an attractive option. To this end, a multi-purpose mixed analogue and digital ASIC has been fabricated and incorporated into both a long-term recorder of adult heart rate (HR) and a recorder of electrophysiological signals. The adult HR recorder has been employed in a study of long-term daily HR patterns, which verified the ambulatory nature of this instrument, as well as its suitability for investigating HR variability. The electrophysiological signal recorder uses the ASIC to amplify, filter and digitise signals, which are then stored directly into static RAM. The analogue front-end of this instrument is flexible in terms of gain, bandwidth and sampling frequency allowing it be applied to a whole range of signals. This instrument has been used to record the antepartum fetal HR, as part of the development of an ambulatory, ASIC based recorder of fetal HR (FHR). These recordings have shown that a usable signal can be obtained from a mother in her home environment, whilst in various postures. The electrophysiological signal recorder has also been used to record the electrohysterogram (EHG), which is the name given to the electrical activity of the uterus, from abdominal electrodes during labour. A strong correlation was found to exist between tocographs derived from the EHG and tocographs produced by conventional means

Neonatal seizure detection based on single-channel EEG: instrumentation and algorithms

Seizure activity in the perinatal period, which constitutes the most common neurological emergency in the neonate, can cause brain disorders later in life or even death depending on their severity. This issue remains unsolved to date, despite the several attempts in tackling it using numerous methods. Therefore, a method is still needed that can enable neonatal cerebral activity monitoring to identify those at risk. Currently, electroencephalography (EEG) and amplitude-integrated EEG (aEEG) have been exploited for the identification of seizures in neonates, however both lack automation. EEG and aEEG are mainly visually analysed, requiring a specific skill set and as a result the presence of an expert on a 24/7 basis, which is not feasible. Additionally, EEG devices employed in neonatal intensive care units (NICU) are mainly designed around adults, meaning that their design specifications are not neonate specific, including their size due to multi-channel requirement in adults - adults minimum requirement is ≥ 32 channels, while gold standard in neonatal is equal to 10; they are bulky and occupy significant space in NICU. This thesis addresses the challenge of reliably, efficiently and effectively detecting seizures in the neonatal brain in a fully automated manner. Two novel instruments and two novel neonatal seizure detection algorithms (SDAs) are presented. The first instrument, named PANACEA, is a high-performance, wireless, wearable and portable multi-instrument, able to record neonatal EEG, as well as a plethora of (bio)signals. This device despite its high-performance characteristics and ability to record EEG, is mostly suggested to be used for the concurrent monitoring of other vital biosignals, such as electrocardiogram (ECG) and respiration, which provide vital information about a neonate's medical condition. The two aforementioned biosignals constitute two of the most important artefacts in the EEG and their concurrent acquisition benefit the SDA by providing information to an artefact removal algorithm. The second instrument, called neoEEG Board, is an ultra-low noise, wireless, portable and high precision neonatal EEG recording instrument. It is able to detect and record minute signals (< 10 nVp) enabling cerebral activity monitoring even from lower layers in the cortex. The neoEEG Board accommodates 8 inputs each one equipped with a patent-pending tunable filter topology, which allows passband formation based on the application. Both the PANACEA and the neoEEG Board are able to host low- to middle-complexity SDAs and they can operate continuously for at least 8 hours on 3-AA batteries. Along with PANACEA and the neoEEG Board, two novel neonatal SDAs have been developed. The first one, termed G prime-smoothed (G ́_s), is an on-line, automated, patient-specific, single-feature and single-channel EEG based SDA. The G ́_s SDA, is enabled by the invention of a novel feature, termed G prime (G ́) and can be characterised as an energy operator. The trace that the G ́_s creates, can also be used as a visualisation tool because of its distinct change at a presence of a seizure. Finally, the second SDA is machine learning (ML)-based and uses numerous features and a support vector machine (SVM) classifier. It can be characterised as automated, on-line and patient-independent, and similarly to G ́_s it makes use of a single-channel EEG. The proposed neonatal SDA introduces the use of the Hilbert-Huang transforms (HHT) in the field of neonatal seizure detection. The HHT analyses the non-linear and non-stationary EEG signal providing information for the signal as it evolves. Through the use of HHT novel features, such as the per intrinsic mode function (IMF) (0-3 Hz) sub-band power, were also employed. Detection rates of this novel neonatal SDA is comparable to multi-channel SDAs.Open Acces

Multimodal Wearable Sensors for Human-Machine Interfaces

Certain areas of the body, such as the hands, eyes and organs of speech production, provide high-bandwidth information channels from the conscious mind to the outside world. The objective of this research was to develop an innovative wearable sensor device that records signals from these areas more conveniently than has previously been possible, so that they can be harnessed for communication. A novel bioelectrical and biomechanical sensing device, the wearable endogenous biosignal sensor (WEBS), was developed and tested in various communication and clinical measurement applications. One ground-breaking feature of the WEBS system is that it digitises biopotentials almost at the point of measurement. Its electrode connects directly to a high-resolution analog-to-digital converter. A second major advance is that, unlike previous active biopotential electrodes, the WEBS electrode connects to a shared data bus, allowing a large or small number of them to work together with relatively few physical interconnections. Another unique feature is its ability to switch dynamically between recording and signal source modes. An accelerometer within the device captures real-time information about its physical movement, not only facilitating the measurement of biomechanical signals of interest, but also allowing motion artefacts in the bioelectrical signal to be detected. Each of these innovative features has potentially far-reaching implications in biopotential measurement, both in clinical recording and in other applications. Weighing under 0.45 g and being remarkably low-cost, the WEBS is ideally suited for integration into disposable electrodes. Several such devices can be combined to form an inexpensive digital body sensor network, with shorter set-up time than conventional equipment, more flexible topology, and fewer physical interconnections. One phase of this study evaluated areas of the body as communication channels. The throat was selected for detailed study since it yields a range of voluntarily controllable signals, including laryngeal vibrations and gross movements associated with vocal tract articulation. A WEBS device recorded these signals and several novel methods of human-to-machine communication were demonstrated. To evaluate the performance of the WEBS system, recordings were validated against a high-end biopotential recording system for a number of biopotential signal types. To demonstrate an application for use by a clinician, the WEBS system was used to record 12‑lead electrocardiogram with augmented mechanical movement information

Programming and testing of a small-size ECG monitor for small animal gated imaging

Pre-clinical cardiac imaging has become one of the most important non-invasive techniques widely used nowadays for studying the electrophysiology and hemodynamic behaviour of the heart using small animals’ models. Despite the great potential it offers for clinical applications in humans, the introduction of movement artefacts and blurring in the images due to the inherent blood flow and respiration among others, reduces significantly the quality of imaging leading to mistakes in results interpretation. A solution to this problem is the synchronization of the electrocardiogram with the imaging machine in a process called gated imaging to trigger image acquisition at specific moments of the cardiac cycle. In this manner, the principal objective of the presented Bachelor Thesis was to design, develop, program and test a small size ECG monitor for small animal gated imaging. The main functions of this device are monitoring the cardiac activity at the same time a TTL pulse is activated in real-time for gating synchronization with the imaging machine. A microcontroller, an analog-to-digital converter and a touch screen were connected together to form a small ECG device that is able to record bioelectrical signals of the heart, to process them and display the electrocardiogram on the screen and generate a TTL pulse for gated imaging. Communication in between the different modules was enabled by SPI protocol implementation. The evaluation of the ECG prototype was performed first in the laboratory with an ECG simulator showing good performance to be later tried in small animals. On the whole the results were satisfactory except from some issues in TTL pulse activation that might need further improvements to enhance gated imaging implementation. This project and the future improved versions of this ECG device, will have a big scientific and social impact. This small size ECG monitor will not only offer an easy to use portable monitoring device for pre-clinical research but also a powerful tool for gated imaging that will be essential for the development of revolutionary drugs to treat heart diseases.Ingeniería Biomédic

The development of a neonatal vital signs database

Modern intelligent monitoring systems use digital computer technology to analyze and evaluate physiological vital signs. This analytical and evaluative process is performed by algorithms developed for this purpose. The degree of 'intelligence' of the monitoring system is dependent on the 'sensitivity' and 'specificity' of these algorithms. In order to develop robust and clinically valid algorithms, a database of representative waveforms is required. The aim of this thesis was to create a neonatal vital signs database to be used for this purpose, by means of a computer-based central station. The computer was interfaced to a number of neonatal monitors (Neonatal ICU, Groote Schuur Hospital). The monitors were interrogated to obtain patient condition, ECG waveforms and respiration waveforms using the impedance technique. When possible, percentage oxygen saturation was also captured. The database contains 509 documented clinical records obtained from 35 patients and 20 records containing examples of technical alarm conditions and high frequency noise. Additional patient record data is included. Clinical events recorded include apnoea, bradycardia, periodic breathing tachycardia, tachypnoea and normal traces. These events were recorded against a variety of signal quality conditions that have been characterized in Appendix C. A prototype rate detection algorithm was checked using samples from the database