220 research outputs found
Heartbeat Classification in Wearables Using Multi-layer Perceptron and Time-Frequency Joint Distribution of ECG
Heartbeat classification using electrocardiogram (ECG) data is a vital
assistive technology for wearable health solutions. We propose heartbeat
feature classification based on a novel sparse representation using
time-frequency joint distribution of ECG. Fundamental to this is a multi-layer
perceptron, which incorporates these signatures to detect cardiac arrhythmia.
This approach is validated with ECG data from MIT-BIH arrhythmia database.
Results show that our approach has an average 95.7% accuracy, an improvement of
22% over state-of-the-art approaches. Additionally, ECG sparse distributed
representations generates only 3.7% false negatives, reduction of 89% with
respect to existing ECG signal classification techniques.Comment: 6 pages, 7 figures, published in IEEE/ACM International Conference on
Connected Health: Applications, Systems and Engineering Technologies (CHASE
Dualities in persistent (co)homology
We consider sequences of absolute and relative homology and cohomology groups
that arise naturally for a filtered cell complex. We establish algebraic
relationships between their persistence modules, and show that they contain
equivalent information. We explain how one can use the existing algorithm for
persistent homology to process any of the four modules, and relate it to a
recently introduced persistent cohomology algorithm. We present experimental
evidence for the practical efficiency of the latter algorithm.Comment: 16 pages, 3 figures, submitted to the Inverse Problems special issue
on Topological Data Analysi
Automatic Detection of ECG Abnormalities by using an Ensemble of Deep Residual Networks with Attention
Heart disease is one of the most common diseases causing morbidity and
mortality. Electrocardiogram (ECG) has been widely used for diagnosing heart
diseases for its simplicity and non-invasive property. Automatic ECG analyzing
technologies are expected to reduce human working load and increase diagnostic
efficacy. However, there are still some challenges to be addressed for
achieving this goal. In this study, we develop an algorithm to identify
multiple abnormalities from 12-lead ECG recordings. In the algorithm pipeline,
several preprocessing methods are firstly applied on the ECG data for
denoising, augmentation and balancing recording numbers of variant classes. In
consideration of efficiency and consistency of data length, the recordings are
padded or truncated into a medium length, where the padding/truncating time
windows are selected randomly to sup-press overfitting. Then, the ECGs are used
to train deep neural network (DNN) models with a novel structure that combines
a deep residual network with an attention mechanism. Finally, an ensemble model
is built based on these trained models to make predictions on the test data
set. Our method is evaluated based on the test set of the First China ECG
Intelligent Competition dataset by using the F1 metric that is regarded as the
harmonic mean between the precision and recall. The resultant overall F1 score
of the algorithm is 0.875, showing a promising performance and potential for
practical use.Comment: 8 pages, 2 figures, conferenc
A comparison of statistical machine learning methods in heartbeat detection and classification
In health care, patients with heart problems require quick responsiveness in a clinical setting or in the operating theatre. Towards that end, automated classification of heartbeats is vital as some heartbeat irregularities are time consuming to detect. Therefore, analysis of electro-cardiogram (ECG) signals is an active area of research. The methods proposed in the literature depend on the structure of a heartbeat cycle. In this paper, we use interval and amplitude based features together with a few samples from the ECG signal as a feature vector. We studied a variety of classification algorithms focused especially on a type of arrhythmia known as the ventricular ectopic fibrillation (VEB). We compare the performance of the classifiers against algorithms proposed in the literature and make recommendations regarding features, sampling rate, and choice of the classifier to apply in a real-time clinical setting. The extensive study is based on the MIT-BIH arrhythmia database. Our main contribution is the evaluation of existing classifiers over a range sampling rates, recommendation of a detection methodology to employ in a practical setting, and extend the notion of a mixture of experts to a larger class of algorithms
Event categories in the EDELWEISS WIMP search experiment
Four categories of events have been identified in the EDELWEISS-I dark matter
experiment using germanium cryogenic detectors measuring simultaneously charge
and heat signals. These categories of events are interpreted as electron and
nuclear interactions occurring in the volume of the detector, and electron and
nuclear interactions occurring close to the surface of the detectors(10-20 mu-m
of the surface). We discuss the hypothesis that low energy surface nuclear
recoils,which seem to have been unnoticed by previous WIMP searches, may
provide an interpretation of the anomalous events recorded by the UKDMC and
Saclay NaI experiments. The present analysis points to the necessity of taking
into account surface nuclear and electron recoil interactions for a reliable
estimate of background rejection factors.Comment: 11 pages, submitted to Phys. Lett.
Background discrimination capabilities of a heat and ionization germanium cryogenic detector
The discrimination capabilities of a 70 g heat and ionization Ge bolometer
are studied. This first prototype has been used by the EDELWEISS Dark Matter
experiment, installed in the Laboratoire Souterrain de Modane, for direct
detection of WIMPs. Gamma and neutron calibrations demonstrate that this type
of detector is able to reject more than 99.6% of the background while retaining
95% of the signal, provided that the background events distribution is not
biased towards the surface of the Ge crystal. However, the 1.17 kg.day of data
taken in a relatively important radioactive environment show an extra
population slightly overlapping the signal. This background is likely due to
interactions of low energy photons or electrons near the surface of the
crystal, and is somewhat reduced by applying a higher charge-collecting inverse
bias voltage (-6 V instead of -2 V) to the Ge diode. Despite this
contamination, more than 98% of the background can be rejected while retaining
50% of the signal. This yields a conservative upper limit of 0.7
event.day^{-1}.kg^{-1}.keV^{-1}_{recoil} at 90% confidence level in the 15-45
keV recoil energy interval; the present sensitivity appears to be limited by
the fast ambient neutrons. Upgrades in progress on the installation are
summarized.Comment: Submitted to Astroparticle Physics, 14 page
First Results of the EDELWEISS WIMP Search using a 320 g Heat-and-Ionization Ge Detector
The EDELWEISS collaboration has performed a direct search for WIMP dark
matter using a 320 g heat-and-ionization cryogenic Ge detector operated in a
low-background environment in the Laboratoire Souterrain de Modane. No nuclear
recoils are observed in the fiducial volume in the 30-200 keV energy range
during an effective exposure of 4.53 kg.days. Limits for the cross-section for
the spin-independent interaction of WIMPs and nucleons are set in the framework
of the Minimal Supersymmetric Standard Model (MSSM). The central value of the
signal reported by the experiment DAMA is excluded at 90% CL.Comment: 14 pages, Latex, 4 figures. Submitted to Phys. Lett.
Identification of backgrounds in the EDELWEISS-I dark matter search experiment
This paper presents our interpretation and understanding of the different
backgrounds in the EDELWEISS-I data sets. We analyze in detail the several
populations observed, which include gammas, alphas, neutrons, thermal sensor
events and surface events, and try to combine all data sets to provide a
coherent picture of the nature and localisation of the background sources. In
light of this interpretation, we draw conclusions regarding the background
suppression scheme for the EDELWEISS-II phase
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