5,693 research outputs found
Computer Aided ECG Analysis - State of the Art and Upcoming Challenges
In this paper we present current achievements in computer aided ECG analysis
and their applicability in real world medical diagnosis process. Most of the
current work is covering problems of removing noise, detecting heartbeats and
rhythm-based analysis. There are some advancements in particular ECG segments
detection and beat classifications but with limited evaluations and without
clinical approvals. This paper presents state of the art advancements in those
areas till present day. Besides this short computer science and signal
processing literature review, paper covers future challenges regarding the ECG
signal morphology analysis deriving from the medical literature review. Paper
is concluded with identified gaps in current advancements and testing, upcoming
challenges for future research and a bullseye test is suggested for morphology
analysis evaluation.Comment: 7 pages, 3 figures, IEEE EUROCON 2013 International conference on
computer as a tool, 1-4 July 2013, Zagreb, Croati
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
The human ECG - nonlinear deterministic versus stochastic aspects
We discuss aspects of randomness and of determinism in electrocardiographic
signals. In particular, we take a critical look at attempts to apply methods of
nonlinear time series analysis derived from the theory of deterministic
dynamical systems. We will argue that deterministic chaos is not a likely
explanation for the short time variablity of the inter-beat interval times,
except for certain pathologies. Conversely, densely sampled full ECG recordings
possess properties typical of deterministic signals. In the latter case,
methods of deterministic nonlinear time series analysis can yield new insights.Comment: 6 pages, 9 PS figure
Response of Autonomic Nervous System to Body Positions: Fourier and Wavelet Analysis
Two mathematical methods, the Fourier and wavelet transforms, were used to
study the short term cardiovascular control system. Time series, picked from
electrocardiogram and arterial blood pressure lasting 6 minutes, were analyzed
in supine position (SUP), during the first (HD1), and the second parts (HD2) of
head down tilt and during recovery (REC). The wavelet transform
was performed using the Haar function of period (,2,,6) to
obtain wavelet coefficients. Power spectra components were analyzed within
three bands, VLF (0.003-0.04), LF (0.04-0.15) and HF (0.15-0.4) with the
frequency unit cycle/interval. Wavelet transform demonstrated a higher
discrimination among all analyzed periods than the Fourier transform. For the
Fourier analysis, the LF of R-R intervals and VLF of systolic blood pressure
show more evident difference for different body positions. For the wavelet
analysis, the systolic blood pressures show much more evident difference than
the R-R intervals. This study suggests a difference in the response of the
vessels and the heart to different body positions. The partial dissociation
between VLF and LF results is a physiologically relevant finding of this work.Comment: RevTex,8 figure
Novel hybrid extraction systems for fetal heart rate variability monitoring based on non-invasive fetal electrocardiogram
This study focuses on the design, implementation and subsequent verification of a new type of hybrid extraction system for noninvasive fetal electrocardiogram (NI-fECG) processing. The system designed combines the advantages of individual adaptive and non-adaptive algorithms. The pilot study reviews two innovative hybrid systems called ICA-ANFIS-WT and ICA-RLS-WT. This is a combination of independent component analysis (ICA), adaptive neuro-fuzzy inference system (ANFIS) algorithm or recursive least squares (RLS) algorithm and wavelet transform (WT) algorithm. The study was conducted on clinical practice data (extended ADFECGDB database and Physionet Challenge 2013 database) from the perspective of non-invasive fetal heart rate variability monitoring based on the determination of the overall probability of correct detection (ACC), sensitivity (SE), positive predictive value (PPV) and harmonic mean between SE and PPV (F1). System functionality was verified against a relevant reference obtained by an invasive way using a scalp electrode (ADFECGDB database), or relevant reference obtained by annotations (Physionet Challenge 2013 database). The study showed that ICA-RLS-WT hybrid system achieve better results than ICA-ANFIS-WT. During experiment on ADFECGDB database, the ICA-RLS-WT hybrid system reached ACC > 80 % on 9 recordings out of 12 and the ICA-ANFIS-WT hybrid system reached ACC > 80 % only on 6 recordings out of 12. During experiment on Physionet Challenge 2013 database the ICA-RLS-WT hybrid system reached ACC > 80 % on 13 recordings out of 25 and the ICA-ANFIS-WT hybrid system reached ACC > 80 % only on 7 recordings out of 25. Both hybrid systems achieve provably better results than the individual algorithms tested in previous studies.Web of Science713178413175
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