3,122 research outputs found
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
A novel technique for fetal heart rate estimation from Doppler ultrasound signal
<p>Abstract</p> <p>Background</p> <p>The currently used fetal monitoring instrumentation that is based on Doppler ultrasound technique provides the fetal heart rate (FHR) signal with limited accuracy. It is particularly noticeable as significant decrease of clinically important feature - the variability of FHR signal. The aim of our work was to develop a novel efficient technique for processing of the ultrasound signal, which could estimate the cardiac cycle duration with accuracy comparable to a direct electrocardiography.</p> <p>Methods</p> <p>We have proposed a new technique which provides the true beat-to-beat values of the FHR signal through multiple measurement of a given cardiac cycle in the ultrasound signal. The method consists in three steps: the dynamic adjustment of autocorrelation window, the adaptive autocorrelation peak detection and determination of beat-to-beat intervals. The estimated fetal heart rate values and calculated indices describing variability of FHR, were compared to the reference data obtained from the direct fetal electrocardiogram, as well as to another method for FHR estimation.</p> <p>Results</p> <p>The results revealed that our method increases the accuracy in comparison to currently used fetal monitoring instrumentation, and thus enables to calculate reliable parameters describing the variability of FHR. Relating these results to the other method for FHR estimation we showed that in our approach a much lower number of measured cardiac cycles was rejected as being invalid.</p> <p>Conclusions</p> <p>The proposed method for fetal heart rate determination on a beat-to-beat basis offers a high accuracy of the heart interval measurement enabling reliable quantitative assessment of the FHR variability, at the same time reducing the number of invalid cardiac cycle measurements.</p
Non-invasive Detection and Compression of Fetal Electrocardiogram
Noninvasive detection of fetal electrocardiogram (FECG) from abdominal ECG recordings is highly dependent on typical statistical signal processing techniques such as independent component analysis (ICA), adaptive noise filtering, and multichannel blind deconvolution. In contrast to the previous multichannel FECG extraction methods, several recent schemes for single‐channel FECG extraction such as the extended Kalman filter (EKF), extended Kalman smoother (EKS), template subtraction (TS), and support vector regression (SVR) for detecting R waves on ECG, are evaluated via the quantitative metrics such as sensitivity (SE), positive predictive value (PPV), F‐score, detection error rate (DER), and range of accuracy. A correlation predictor that combines with multivariable gray model (GM) is also proposed for sequential ECG data compression, which displays better percent root mean-square difference (PRD) than those of Sabah’s scheme for fixed and predicted compression ratio (CR). Automatic calculation on fetal heart rate (FHR) on the reconstructed FECG from mixed signals of abdominal ECG recordings is also experimented with sample synthetic ECG data. Sample data on FHR and T/QRS for both physiological case and pathological case are simulated in a 10-min time sequence
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
Non Invasive Foetal Monitoring with a Combined ECG - PCG System
Although modern ultrasound provides remarkable images and biophysical measures, the technology is expensive and the observations are only available over a short time. Longer term monitoring is achieved in a clinical setting using ultrasonic Doppler cardiotocography (CTG) but this has a number of limitations. Some pathologies and some anomalies of cardiac functioning are not detectable with CTG. Moreover, although frequent and/or long-term foetal heart rate (FHR) monitoring is recommended, mainly in high risk pregnancies, there is a lack of established evidence for safe ultrasound irradiation exposure to the foetus for extended periods (Ang et al., 2006). Finally, high quality ultrasound devices are too expensive and not approved for home care use. In fact, there is a remarkable mismatch between ability to examine a foetus in a clinical setting, and the almost complete absence of technology that permits longer term monitoring of a foetus at home. Therefore, in the last years, many efforts (Hany et al., 1989; Jimenez et al., 1999; Kovacs et al., 2000; Mittra et al., 2008; Moghavvemi et al., 2003; Nagal, 1986; Ruffo et al., 2010; Talbert et al., 1986; Varady et al., 2003) have been attempted by the scientific community to find a suitable alternative
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