692 research outputs found
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
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
Advances in Digital Processing of Low-Amplitude Components of Electrocardiosignals
This manual has been published within the framework of the BME-ENA project under the responsibility of National Technical University of Ukraine. The BME-ENA “Biomedical Engineering Education Tempus Initiative in Eastern Neighbouring Area”, Project Number: 543904-TEMPUS-1-2013-1-GR-TEMPUS-JPCR is a Joint Project within the TEMPUS IV program. This project has been funded with support from the European Commission.Навчальний посібник присвячено розробці методів та засобів для неінвазивного виявлення та дослідження тонких проявів електричної активності серця. Особлива увага приділяється вдосконаленню інформаційного та алгоритмічного забезпечення систем електрокардіографії високого розрізнення для ранньої діагностики електричної нестабільності міокарда, а також для оцінки функціонального стану плоду під час вагітності.
Теоретичні основи супроводжуються прикладами реалізації алгоритмів за допомогою системи MATLAB. Навчальний посібник призначений для студентів, аспірантів, а також фахівців у галузі біомедичної електроніки та медичних працівників.The teaching book is devoted to development and research of methods and tools for non-invasive detection of subtle manifistations of heart electrical activity. Particular attention is paid to the improvement of information and algorithmic support of high resolution electrocardiography for early diagnosis of myocardial electrical instability, as well as for the evaluation of the functional state of the fetus during pregnancy examination.
The theoretical basis accompanied by the examples of implementation of the discussed algorithms with the help of MATLAB. The teaching book is intended for students, graduate students, as well as specialists in the field of biomedical electronics and medical professionals
Optimization of adaptive filter control parameters for non-invasive fetal electrocardiogram extraction
This paper is focused on the design, implementation and verification of a novel method for the optimization of the control parameters of different hybrid systems used for non-invasive fetal electrocardiogram (fECG) extraction. The tested hybrid systems consist of two different blocks, first for maternal component estimation and second, so-called adaptive block, for maternal component suppression by means of an adaptive algorithm (AA). Herein, we tested and optimized four different AAs: Adaptive Linear Neuron (ADALINE), Standard Least Mean Squares (LMS), Sign-Error LMS, Standard Recursive Least Squares (RLS), and Fast Transversal Filter (FTF). The main criterion for optimal parameter selection was the F1 parameter. We conducted experiments using real signals from publicly available databases and those acquired by our own measurements. Our optimization method enabled us to find the corresponding optimal settings for individual adaptive block of all tested hybrid systems which improves achieved results. These improvements in turn could lead to a more accurate fetal heart rate monitoring and detection of fetal hypoxia. Consequently, our approach could offer the potential to be used in clinical practice to find optimal adaptive filter settings for extracting high quality fetal ECG signals for further processing and analysis, opening new diagnostic possibilities of non-invasive fetal electrocardiography.Web of Science174art. no. e026680
False alarm reduction in critical care
High false alarm rates in the ICU decrease quality of care by slowing staff response times while increasing patient delirium through noise pollution. The 2015 PhysioNet/Computing in Cardiology Challenge provides a set of 1250 multi-parameter ICU data segments associated with critical arrhythmia alarms, and challenges the general research community to address the issue of false alarm suppression using all available signals. Each data segment was 5 minutes long (for real time analysis), ending at the time of the alarm. For retrospective analysis, we provided a further 30 seconds of data after the alarm was triggered. A total of 750 data segments were made available for training and 500 were held back for testing. Each alarm was reviewed by expert annotators, at least two of whom agreed that the alarm was either true or false. Challenge participants were invited to submit a complete, working algorithm to distinguish true from false alarms, and received a score based on their program's performance on the hidden test set. This score was based on the percentage of alarms correct, but with a penalty that weights the suppression of true alarms five times more heavily than acceptance of false alarms. We provided three example entries based on well-known, open source signal processing algorithms, to serve as a basis for comparison and as a starting point for participants to develop their own code. A total of 38 teams submitted a total of 215 entries in this year's Challenge. This editorial reviews the background issues for this challenge, the design of the challenge itself, the key achievements, and the follow-up research generated as a result of the Challenge, published in the concurrent special issue of Physiological Measurement. Additionally we make some recommendations for future changes in the field of patient monitoring as a result of the Challenge.National Institutes of Health (U.S.) (Grant R01-GM104987)National Institute of General Medical Sciences (U.S.) (Grant U01-EB-008577)National Institutes of Health (U.S.) (Grant R01-EB-001659
An evaluation of electronic fetal monitoring with clinical validation of ST waveform analysis during labour
Dissatisfaction with the electronic recording of fetal heart rate and uterine contractions (the cardiotocogram
or CTG) has resulted in a search for new techniques of monitoring the fetus during labour. It is important
that each method has a sound physiological and pathophysiological basis, that a model for the interpretation
of changes is elucidated and that each method is thoroughly evaluated before introduction into clinical
practice. Analysis of the ST waveform of the fetal electrocardiogram (FECG) is the most advanced of the
new techniques under investigation. Experimental studies have shown that elevation of the ST waveform
occurs with a switch to myocardial anaerobic metabolism and a negative waveform occurs during direct
myocardial ischaemia. Human observational studies have suggested that a combination of ST waveform and
CTG analysis may improve the specificity of intrapartum monitoring and reduce unnecessary intervention.
A high quality FECG signal is necessary for waveform analysis. The FECG can be recorded from a scalp
electrode (FSE) during labour. The suitability of 5 commonly available FSEs for ECG waveform analysis
was compared. Single spiral FSEs had the most favourable physical and electrical properties and produced
the best quality signals in a randomised clinical trial of 50 fetuses in labour.
Intervention rates and neonatal outcome in labours monitored with CTG alone were compared with those
monitored with the combination of ST waveform analysis plus CTG (ST+CTG) in a randomised clinical
trial of 2434 high risk labours in a large district general hospital over an 18 month period. There was a 46%
reduction in operative intervention for fetal distress in the ST+CTG group (p<0.001, OR 1.96 [1.42-2.71]).
There was a trend to less neonatal metabolic acidemia (p = 0.09, OR 2.63 [0.93-7.39]) and fewer low five
minute Apgar scores (p = 0.12, OR 1.62 [0.92-2.85]) in the ST+CTG arm.
All recordings were reviewed retrospectively, blind to outcome and the CTG classified as normal,
intermediate or abnormal according to the trial protocol. There was no significant difference in the
proportion of recordings in each category between the trial arms. Operative intervention in the ST+CTG
arm was significantly reduced in recordings classified as normal and intermediate by the review (12/1043
ST+CTG arm versus 48/1066 CTG arm, p <0.001). Three patterns of ST+CTG change were identified. 1.
Normal CTG, persistent stable ST waveform elevation. These fetuses had good outcome and a
significantly higher mean pH (7.29) and lower base deficit (1.1 rnmol/1) at delivery. The raised ST
waveform may reflect sympathoadrenal stimulation from the general arousal of labour or a response to mild
but compensated hypoxaemia and is in keeping with experimental data. 2. CTG abnormal, progressive
elevation in ST waveform. All cases occurred towards the end of second stage. These fetuses had a
significantly lower mean pH (7.05) and higher base deficit (7.6 mmol/1) than all other groups. This
combination identified fetuses who were developing a metabolic acidosis as a result of significant hypoxia.
3. Abnormal CTG and a negative ST waveform. All cases with persistently negative waveforms were
depressed at birth, required resuscitation and had low arterial pHs (where available). This high risk group
probably had depleted myocardial glycogen reserves and suffered direct myocardial hypoxia, as seen in
animal studies. These findings indicate that ST waveform analysis can discriminate CTG change during
labour, the combination can result in a reduction in unnecessary intervention and has the potential to more
accurately identify fetuses at risk of neonatal morbidity.
The term 'monitoring' implies a degree of automatic surveillance but this is not the case as CTG and
ST+CTG records are subjectively interpreted, frequently by junior, inexperienced staff. The retrospective
review of cases in the trial revealed significant errors in the use of fetal blood sampling and the
interpretation of both CTG and ST+CTG recordings during the study. The feasibility of representing expert
clinical knowledge in a decision support tool to provide consistent, accurate interpretation of the CTG was
demonstrated in two clinical studies. The full potential of ST+CTG analysis may only be achieved with
some degree of automatic data processing and interpretation.
The randomised trial also demonstrated the lack of appropriate measures of neonatal outcome with which to
judge the effectiveness of fetal monitoring. Analysis of cord artery and vein blood gas status at delivery can
provide useful information about fetal oxygenation prior to delivery but currently the information is poorly
used, if at all. Use of erroneous data, inappropriate measures of 'acidemia', failure to distinguish between
respiratory and metabolic components and unphysiological expectations about relationships to other
measures of neonatal outcome were some of the problems highlighted. The use of generic terminology such
as 'birth asphyxia' or 'acidosis' which have varying definitions has caused much confusion and should be
avoided. There is unlikely to be one 'gold standard' measure of neonatal condition at delivery
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
Machine learning and disease prediction in obstetrics
Machine learning technologies and translation of artificial intelligence tools to enhance the patient experience are changing obstetric and maternity care. An increasing number of predictive tools have been developed with data sourced from electronic health records, diagnostic imaging and digital devices. In this review, we explore the latest tools of machine learning, the algorithms to establish prediction models and the challenges to assess fetal well-being, predict and diagnose obstetric diseases such as gestational diabetes, pre-eclampsia, preterm birth and fetal growth restriction. We discuss the rapid growth of machine learning approaches and intelligent tools for automated diagnostic imaging of fetal anomalies and to asses fetoplacental and cervix function using ultrasound and magnetic resonance imaging. In prenatal diagnosis, we discuss intelligent tools for magnetic resonance imaging sequencing of the fetus, placenta and cervix to reduce the risk of preterm birth. Finally, the use of machine learning to improve safety standards in intrapartum care and early detection of complications will be discussed. The demand for technologies to enhance diagnosis and treatment in obstetrics and maternity should improve frameworks for patient safety and enhance clinical practice
Intelligent monitoring and interpretation of preterm physiological signals using machine learning
Every year, more than one in ten babies are born prematurely. In Ireland of the 70000 babies delivered every year, 4500 are born too early. Premature babies are at a higher risk of complications, which may lead to both short-term and long-term adverse health outcomes. The neonatal population is especially vulnerable and a delay in the identification of medical conditions, as well as delays in the initiating the correct treatment, may be fatal. After birth, preterms are admitted to the neonatal intensive care unit (NICU), where a continuous flow of information in the form of physiological signals is available. Physiological signals can assist clinicians in decision making related to the diagnosis and treatment of various diseases. This information, however, can be highly complex, and usually requires expert analysis which may not be available at all times. The work conducted in this thesis develops a decision support systems for the intelligent monitoring of preterms in the NICU. This will allow for an accurate estimation of the current health status of the preterm neonate as well as the prediction of possible long-term complications. This thesis is comprised of three main work packages (WP), each addressing health complication of preterm on three different stages of life. At the first 12 hours of life the health status is quantified using the clinical risk index for babies (CRIB). This is followed by the assessment of the preterm’s well-being at discharge from the NICU using the clinical course score (CCS). Finally, the long-term neurodevelopmental follow-up is assessed using the Bayley III scales of development at two years. This is schematically represented in Figure 1 along with the main findings and contributions.
Low blood pressure (BP) or hypotension is a recognised problem in preterm infants particularly during the first 72 hours of life. Hypotension may cause decreased cerebral perfusion, resulting in deprived oxygen delivery to the brain. Deciding when and whether to treat hypotension relies on our understanding of the relation between BP, oxygenation and brain activity. The electroencephalogram (EEG) is the most commonly used technology to assess the ‘brain health’ of a newborn. The first WP investigates the relationship between short-term dynamics in BP and EEG energy in the preterm on a large dataset of continuous multi-channel unedited EEG recordings in the context of the health status measured by the CRIB score. The obtained results indicate that a higher risk of mortality for the preterm is associated with a lower level of nonlinear interaction between EEG and BP. The level of coupling between these two systems can potentially serve as an additional source of information when deciding whether or not to intervene in the preterm. The electrocardiogram (ECG) is also routinely recorded in preterm infants. Analysis of heart rate variability (HRV) provides a non-invasive assessment of both the sympathetic and parasympathetic control of the heart rate. A novel automated objective decision support tool for the prediction of the short-term outcome (CCS) in preterm neonates who may have low BP is proposed in the second WP. Combining multiple HRV features extracted during hypotensive episodes, the classifier achieved an AUC of 0.97 for the task of short-term outcome prediction, using a leave-one-patient-out performance assessment. The developed system is based on the boosted decision tree classifier and allows for the continuous monitoring of the preterm. The proposed system is validated on a large clinically collected dataset of multimodal recordings from preterm neonates.
If the correct treatment is initiated promptly after diagnosis, it can potentially improve the neurodevelopmental outcome of the preterm infant. The third WP presents a pilot study investigating the predictive capability of the early EEG recorded at discharge from the NICU with respect to the 2-year neurodevelopmental outcome using machine learning techniques. Two methods are used: 1) classical feature-based classifier, and 2) end-to-end deep learning. This is a fundamental study in this area, especially in the context of applying end-to-end learning to the preterm EEG for the problem of long-term outcome prediction. It is shown that for the available labelled dataset of 37 preterm neonates, the classical feature-based approach outperformed the end-to-end deep learning technique. A discussion of the obtained result as well as a section highlighting the possible limitations and areas that need to be investigated in the future are provided
Enhancing ECG Analysis of Implantable Cardiac Monitor Data: An Efficient Pipeline for Multi-Label Classification
Implantable Cardiac Monitor (ICM) devices are demonstrating as of today, the
fastest-growing market for implantable cardiac devices. As such, they are
becoming increasingly common in patients for measuring heart electrical
activity. ICMs constantly monitor and record a patient's heart rhythm and when
triggered - send it to a secure server where health care professionals (denote
HCPs from here on) can review it. These devices employ a relatively simplistic
rule-based algorithm (due to energy consumption constraints) to alert for
abnormal heart rhythms. This algorithm is usually parameterized to an
over-sensitive mode in order to not miss a case (resulting in relatively high
false-positive rate) and this, combined with the device's nature of constantly
monitoring the heart rhythm and its growing popularity, results in HCPs having
to analyze and diagnose an increasingly growing amount of data. In order to
reduce the load on the latter, automated methods for ECG analysis are nowadays
becoming a great tool to assist HCPs in their analysis. While state-of-the-art
algorithms are data-driven rather than rule-based, training data for ICMs often
consist of specific characteristics which make its analysis unique and
particularly challenging. This study presents the challenges and solutions in
automatically analyzing ICM data and introduces a method for its classification
that outperforms existing methods on such data. As such, it could be used in
numerous ways such as aiding HCPs in the analysis of ECGs originating from ICMs
by e.g. suggesting a rhythm type
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