1,080 research outputs found
Extended segmented beat modulation method for cardiac beat classification and electrocardiogram denoising
none4noBeat classification and denoising are two challenging and fundamental operations when processing digital electrocardiograms (ECG). This paper proposes the extended segmented beat modulation method (ESBMM) as a tool for automatic beat classification and ECG denoising. ESBMM includes four main steps: (1) beat identification and segmentation into PQRS and TU segments; (2) wavelet-based time-frequency feature extraction; (3) convolutional neural network-based classification to discriminate among normal (N), supraventricular (S), and ventricular (V) beats; and (4) a template-based denoising procedure. ESBMM was tested using the MIT–BIH arrhythmia database available at Physionet. Overall, the classification accuracy was 91.5% while the positive predictive values were 92.8%, 95.6%, and 83.6%, for N, S, and V classes, respectively. The signal-to-noise ratio improvement after filtering was between 0.15 dB and 2.66 dB, with a median value equal to 0.99 dB, which is significantly higher than 0 (p < 0.05). Thus, ESBMM proved to be a reliable tool to classify cardiac beats into N, S, and V classes and to denoise ECG tracings.openNasim A.; Sbrollini A.; Morettini M.; Burattini L.Nasim, A.; Sbrollini, A.; Morettini, M.; Burattini, L
A Personalized Zero-Shot ECG Arrhythmia Monitoring System: From Sparse Representation Based Domain Adaption to Energy Efficient Abnormal Beat Detection for Practical ECG Surveillance
This paper proposes a low-cost and highly accurate ECG-monitoring system
intended for personalized early arrhythmia detection for wearable mobile
sensors. Earlier supervised approaches for personalized ECG monitoring require
both abnormal and normal heartbeats for the training of the dedicated
classifier. However, in a real-world scenario where the personalized algorithm
is embedded in a wearable device, such training data is not available for
healthy people with no cardiac disorder history. In this study, (i) we propose
a null space analysis on the healthy signal space obtained via sparse
dictionary learning, and investigate how a simple null space projection or
alternatively regularized least squares-based classification methods can reduce
the computational complexity, without sacrificing the detection accuracy, when
compared to sparse representation-based classification. (ii) Then we introduce
a sparse representation-based domain adaptation technique in order to project
other existing users' abnormal and normal signals onto the new user's signal
space, enabling us to train the dedicated classifier without having any
abnormal heartbeat of the new user. Therefore, zero-shot learning can be
achieved without the need for synthetic abnormal heartbeat generation. An
extensive set of experiments performed on the benchmark MIT-BIH ECG dataset
shows that when this domain adaptation-based training data generator is used
with a simple 1-D CNN classifier, the method outperforms the prior work by a
significant margin. (iii) Then, by combining (i) and (ii), we propose an
ensemble classifier that further improves the performance. This approach for
zero-shot arrhythmia detection achieves an average accuracy level of 98.2% and
an F1-Score of 92.8%. Finally, a personalized energy-efficient ECG monitoring
scheme is proposed using the above-mentioned innovations.Comment: Software implementation: https://github.com/MertDuman/Zero-Shot-EC
Compressively Sensed Image Recognition
Compressive Sensing (CS) theory asserts that sparse signal reconstruction is
possible from a small number of linear measurements. Although CS enables
low-cost linear sampling, it requires non-linear and costly reconstruction.
Recent literature works show that compressive image classification is possible
in CS domain without reconstruction of the signal. In this work, we introduce a
DCT base method that extracts binary discriminative features directly from CS
measurements. These CS measurements can be obtained by using (i) a random or a
pseudo-random measurement matrix, or (ii) a measurement matrix whose elements
are learned from the training data to optimize the given classification task.
We further introduce feature fusion by concatenating Bag of Words (BoW)
representation of our binary features with one of the two state-of-the-art
CNN-based feature vectors. We show that our fused feature outperforms the
state-of-the-art in both cases.Comment: 6 pages, submitted/accepted, EUVIP 201
Quality Assessment of Ambulatory Electrocardiogram Signals by Noise Detection using Optimal Binary Classification
In order to improve the diagnostic capability in Ambulatory Electrocardiogram signal and to reduce the noise signal impacts, there is a need for more robust models in place. In terms of improvising to the existing solutions, this article explores a novel binary classifier that learns from the features optimized by fusion of diversity assessment measures, which performs Quality Assessment of Ambulatory Electrocardiogram Signals (QAAES) by Noise Detection. The performance of the proposed model QAAES has been scaled by comparing it with contemporary models. Concerning performance analysis, the 10-fold cross-validation has been carried on a benchmark dataset. The results obtained from experiments carried on proposed and other contemporary models for cross-validation metrics have been compared to signify the sensitivity, specificity, and noise detection accuracy
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