1,085 research outputs found
Patient-adapted and inter-patient ecg classification using neural network and gradient boosting
Heart disease diagnosis is an important non-invasive technique. Therefore, there exists an effort to increase the accuracy of arrhythmia classification based on ECG signals. In this work, we present a novel approach of heart arrhythmia detection. The model consists of two parts. The first part extracts important features from raw ECG signal using Auto-Encoder Neural Network. Extracted features obtained by Auto-Encoder represent an input for the second part of the model, the Gradient Boosting and Feedforward Neural Network classifiers. For comparison purposes, we evaluated our approach by using MIT-BIH ECG database and also following recommendations of the Association for the Advancement of Medical Instrumentation (AAMI) for ECG class labeling. We divided our experiment into two scenarios. The first scenario represents the classification task for the patient-adapted paradigm and the second one was dedicated to the inter-patient paradigm. We compared the measured results to the state-of-the-art methods and it shows that our method outperforms the state-of-the art methods in the Ventricular Ectopic (VEB) class for both paradigms and Supraventricular Ectopic (SVEB) class in the inter-patient paradigm.Web of Science28325424
Exploring ECG Signal Analysis Techniques for Arrhythmia Detection: A Review
The heart holds paramount importance in the human body as it serves the crucial function of supplying blood and nutrients to various organs. Thus, maintaining its health is imperative. Arrhythmia, a heart disorder, arises when the heart's rhythm becomes irregular. Electrocardiogram (ECG) signals are commonly utilized for analyzing arrhythmia due to their simplicity and cost-effectiveness. The peaks observed in ECG graphs, particularly the R peak, are indicative of heart conditions, facilitating arrhythmia diagnosis. Arrhythmia is broadly categorized into Tachycardia and Bradycardia for identification purposes. This paper explores diverse techniques such as Deep Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), Support Vector Machines (SVM), Neural Network (NN) classifiers, as well as Wavelet and Time–Frequency Transform (TQWT), which have been employed over the past decade for arrhythmia detection using various datasets. The study delves into the analysis of arrhythmia classification on ECG datasets, highlighting the effectiveness of data preprocessing, feature extraction, and classification techniques in achieving superior performance in classifying ECG signals for arrhythmia detection
Precision medicine and artificial intelligence : a pilot study on deep learning for hypoglycemic events detection based on ECG
Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patients, such as confusion, irritability, seizure and can even be fatal in specific conditions. Hypoglycemia affects the electrophysiology of the heart. However, due to strong inter-subject heterogeneity, previous studies based on a cohort of subjects failed to deploy electrocardiogram (ECG)-based hypoglycemic detection systems reliably. The current study used personalised medicine approach and Artificial Intelligence (AI) to automatically detect nocturnal hypoglycemia using a few heartbeats of raw ECG signal recorded with non-invasive, wearable devices, in healthy individuals, monitored 24 hours for 14 consecutive days. Additionally, we present a visualisation method enabling clinicians to visualise which part of the ECG signal (e.g., T-wave, ST-interval) is significantly associated with the hypoglycemic event in each subject, overcoming the intelligibility problem of deep-learning methods. These results advance the feasibility of a real-time, non-invasive hypoglycemia alarming system using short excerpts of ECG signal
Precision medicine and artificial intelligence : a pilot study on deep learning for hypoglycemic events detection based on ECG
Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patients, such as confusion, irritability, seizure and can even be fatal in specific conditions. Hypoglycemia affects the electrophysiology of the heart. However, due to strong inter-subject heterogeneity, previous studies based on a cohort of subjects failed to deploy electrocardiogram (ECG)-based hypoglycemic detection systems reliably. The current study used personalised medicine approach and Artificial Intelligence (AI) to automatically detect nocturnal hypoglycemia using a few heartbeats of raw ECG signal recorded with non-invasive, wearable devices, in healthy individuals, monitored 24 hours for 14 consecutive days. Additionally, we present a visualisation method enabling clinicians to visualise which part of the ECG signal (e.g., T-wave, ST-interval) is significantly associated with the hypoglycemic event in each subject, overcoming the intelligibility problem of deep-learning methods. These results advance the feasibility of a real-time, non-invasive hypoglycemia alarming system using short excerpts of ECG signal
Global ECG Classification by Self-Operational Neural Networks with Feature Injection
Objective: Global (inter-patient) ECG classification for arrhythmia detection
over Electrocardiogram (ECG) signal is a challenging task for both humans and
machines. The main reason is the significant variations of both normal and
arrhythmic ECG patterns among patients. Automating this process with utmost
accuracy is, therefore, highly desirable due to the advent of wearable ECG
sensors. However, even with numerous deep learning approaches proposed
recently, there is still a notable gap in the performance of global and
patient-specific ECG classification performances. This study proposes a novel
approach to narrow this gap and propose a real-time solution with shallow and
compact 1D Self-Organized Operational Neural Networks (Self-ONNs). Methods: In
this study, we propose a novel approach for inter-patient ECG classification
using a compact 1D Self-ONN by exploiting morphological and timing information
in heart cycles. We used 1D Self-ONN layers to automatically learn
morphological representations from ECG data, enabling us to capture the shape
of the ECG waveform around the R peaks. We further inject temporal features
based on RR interval for timing characterization. The classification layers can
thus benefit from both temporal and learned features for the final arrhythmia
classification. Results: Using the MIT-BIH arrhythmia benchmark database, the
proposed method achieves the highest classification performance ever achieved,
i.e., 99.21% precision, 99.10% recall, and 99.15% F1-score for normal (N)
segments; 82.19% precision, 82.50% recall, and 82.34% F1-score for the
supra-ventricular ectopic beat (SVEBs); and finally, 94.41% precision, 96.10%
recall, and 95.2% F1-score for the ventricular-ectopic beats (VEBs)
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
Deep Learning Models for Arrhythmia Classification Using Stacked Time-frequency Scalogram Images from ECG Signals
Electrocardiograms (ECGs), a medical monitoring technology recording cardiac
activity, are widely used for diagnosing cardiac arrhythmia. The diagnosis is
based on the analysis of the deformation of the signal shapes due to irregular
heart rates associated with heart diseases. Due to the infeasibility of manual
examination of large volumes of ECG data, this paper aims to propose an
automated AI based system for ECG-based arrhythmia classification. To this
front, a deep learning based solution has been proposed for ECG-based
arrhythmia classification. Twelve lead electrocardiograms (ECG) of length 10
sec from 45, 152 individuals from Shaoxing People's Hospital (SPH) dataset from
PhysioNet with four different types of arrhythmias were used. The sampling
frequency utilized was 500 Hz. Median filtering was used to preprocess the ECG
signals. For every 1 sec of ECG signal, the time-frequency (TF) scalogram was
estimated and stacked row wise to obtain a single image from 12 channels,
resulting in 10 stacked TF scalograms for each ECG signal. These stacked TF
scalograms are fed to the pretrained convolutional neural network (CNN), 1D
CNN, and 1D CNN-LSTM (Long short-term memory) models, for arrhythmia
classification. The fine-tuned CNN models obtained the best test accuracy of
about 98% followed by 95% test accuracy by basic CNN-LSTM in arrhythmia
classification.Comment: 4 pages,1 figure
Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review
The prevalence of cardiovascular diseases is increasing around the world. However, the technology is evolving and can be monitored with low-cost sensors anywhere at any time. This subject is being researched, and different methods can automatically identify these diseases, helping patients and healthcare professionals with the treatments. This paper presents a systematic review of disease identification, classification, and recognition with ECG sensors. The review was focused on studies published between 2017 and 2022 in different scientific databases, including PubMed Central, Springer, Elsevier, Multidisciplinary Digital Publishing Institute (MDPI), IEEE Xplore, and Frontiers. It results in the quantitative and qualitative analysis of 103 scientific papers. The study demonstrated that different datasets are available online with data related to various diseases. Several ML/DP-based models were identified in the research, where Convolutional Neural Network and Support Vector Machine were the most applied algorithms. This review can allow us to identify the techniques that can be used in a system that promotes the patient’s autonomy.N/
Advanced Biometrics with Deep Learning
Biometrics, such as fingerprint, iris, face, hand print, hand vein, speech and gait recognition, etc., as a means of identity management have become commonplace nowadays for various applications. Biometric systems follow a typical pipeline, that is composed of separate preprocessing, feature extraction and classification. Deep learning as a data-driven representation learning approach has been shown to be a promising alternative to conventional data-agnostic and handcrafted pre-processing and feature extraction for biometric systems. Furthermore, deep learning offers an end-to-end learning paradigm to unify preprocessing, feature extraction, and recognition, based solely on biometric data. This Special Issue has collected 12 high-quality, state-of-the-art research papers that deal with challenging issues in advanced biometric systems based on deep learning. The 12 papers can be divided into 4 categories according to biometric modality; namely, face biometrics, medical electronic signals (EEG and ECG), voice print, and others
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