Table 2 - uploaded by Damien François
Content may be subject to copyright.
Source publication
Supervised and interpatient classification of heart beats is primordial in many applications requiring long-term monitoring of the cardiac function. Several classification models able to cope with the strong class unbalance and a large variety of feature sets have been proposed for this task. In practice, over 200 features are often considered, and...
Context in source publication
Citations
... Given that HRV is computed using R-R intervals of an ECG wave, it may be the case that other parameters, arising also from other aspects of the ECG wave could be helpful as features, such as those used in detecting other pathologies like atrial fibrillation [55][56][57]. Further research could elucidate on this matter. ...
Virtual Reality (VR) allows users to interact with 3D immersive environments and has the potential to be a key technology across many domain applications, including access to a future metaverse. Yet, consumer adoption of VR technology is limited by cybersickness (CS)—a debilitating sensation accompanied by a cluster of symptoms, including nausea, oculomotor issues and dizziness. A leading problem is the lack of automated objective tools to predict or detect CS in individuals, which can then be used for resistance training, timely warning systems or clinical intervention. This paper explores the spatiotemporal brain dynamics and heart rate variability involved in cybersickness and uses this information to both predict and detect CS episodes. The present study applies deep learning of EEG in a spiking neural network (SNN) architecture to predict CS prior to using VR (85.9%, F7) and detect it (76.6%, FP1, Cz). ECG-derived sympathetic heart rate variability (HRV) parameters can be used for both prediction (74.2%) and detection (72.6%) but at a lower accuracy than EEG. Multimodal data fusion of EEG and sympathetic HRV does not change this accuracy compared to ECG alone. The study found that Cz (premotor and supplementary motor cortex) and O2 (primary visual cortex) are key hubs in functionally connected networks associated with both CS events and susceptibility to CS. F7 is also suggested as a key area involved in integrating information and implementing responses to incongruent environments that induce cybersickness. Consequently, Cz, O2 and F7 are presented here as promising targets for intervention.
... In some cases, the results obtained at this stage require a revision of the entire processing scheme as a whole. The most common classification methods are: discriminant function 25 30 ; Decision Trees (DT) 31 . Several different approaches for ECG analysis are based on a chaos theory 32 , a combination of statistical, geometric, and nonlinear heart rate variability features 32 , a semantic web ontology and heart failure expert system 33 At the same time, each of the above-mentioned methods has its drawbacks and limitations. ...
Decision support systems can seriously help medical doctors in the diagnosis of different diseases, especially in complicated cases. This article is devoted to recognizing and diagnosing heart disease based on automatic computer processing of the electrocardiograms (ECG) of patients. In the general case, the change of the ECG parameters can be presented as a random sequence of the signals under processing. Developing new computational methods for such signal processing is an important research problem in creating efficient medical decision support systems. Authors consider the possibility of increasing the diagnostic accuracy of cardiovascular diseases by implementing of the new proposed computational method of information processing. This method is based on the generalized nonlinear canonical decomposition of a random sequence of the change of cardiogram parameters. The use of a nonlinear canonical model makes it possible to significantly simplify the maximum likelihood criterion for classifying diseases. This simplification is provided by the transition from a multi-dimensional distribution density of cardiogram parameters to a product of one-dimensional distribution densities of independent random coefficients of a nonlinear canonical decomposition. The absence of any restrictions on the class of random sequences under study makes it possible to achieve maximum accuracy in diagnosing cardiovascular diseases. Functional diagrams for implementing the proposed method reflecting the features of its application are presented. The quantitative parameters of the core of the computational diagnostic procedure can be determined in advance based on the preliminary statistical data of the ECGs for different heart diseases. That is why the developed method is quite simple in terms of computation (computing complexity, accuracy, computing time, etc.) and can be implemented in medical computer decision systems for monitoring cardiovascular diseases and for their diagnosis in real time. The results of the numerical experiment confirm the high accuracy of the developed method for classifying cardiovascular diseases.
... The two feature choice strategies, i.e. filter and wrapper, were compared by Doquire et al. [95]. For experimentation, the authors employ 200 features. ...
Cardiovascular disease (CVD) remains the primary reason for illness and death throughout the world despite tremendous progress in diagnosis and treatment. Artificial intelligence (AI) technology can drastically revolutionize the way we perform cardiology to enhance and optimize CVD results. With boosting of information technology and the increased volume and complexity of data, aside from a large number of optimization problems that arise in clinical fields, AI approaches such as machine learning and optimization have become extremely popular. AI also can help improve medical expertise by uncovering clinically important information. Early on, the treatment of vast amounts of medical data was a significant task, leading to adaptations in the biological field of machine learning. Improvements are carried out and tested every day in algorithms for machine learning so that more accurate data may be analyzed and provided. Machine learning has been active in the realm of healthcare, from the extraction of information from medical papers to the prediction and diagnosis of a disease. In this perspective, this chapter provides an overview of how to use meta-heuristic algorithm on CVD’s classification process for enhancing feature selection process, and various parameters optimization.KeywordsFeature selectionMetaheuristics algorithmsCloudCVDEngineering design problems
... Le paramètre le plus couramment utilisé dans la littérature, pour la classification des battements cardiaques, est le rythme cardiaque, également appelé intervalle R-R[98]. À l'exception des patients porteurs d'un stimulateur cardiaque, les variations de la durée de cet intervalle sont corrélées aux variations de la morphologie des ondes[99].L'utilisation d'un intervalle R-R normalisé permet d'améliorer significativement les performances de classification[100,101]. D'autres distances entre les différents points de repère d'un battement cardiaque, illustrés à laFigure 2.5, sont également utilisées dans la littérature, comme la durée du complexe QRS ou les intervalles Q-T et P-R[102].Dans cette étude, nous ne chercherons pas à déterminer les valeurs de ces paramètres qui sont spécifiques aux signaux ECG. ...
Avec le fort développement de l'Internet des Objets (IoT), il devient nécessaire de converger vers de nouveaux capteurs dit intelligents. Ces capteurs doivent permettre d'analyser l'environnement extérieur, comprendre le contexte dans lequel ils sont utilisés et être conscient des besoins utilisateurs. Ils doivent cependant rester petits, fiables, bon marché et avoir une autonomie de plusieurs années. La conversion analogique-paramètre (Analog-to-Feature, A2F) est une nouvelle méthode d'acquisition pensée pour les appareils IoT, et semble être une solution adaptée pour de tels capteurs. Cette conversion consiste à extraire des paramètres directement sur le signal analogique. Une sélection pertinente des paramètres permet d'extraire uniquement l'information nécessaire à une tache particulière. Le convertisseur proposé est basé sur la technique de l'échantillonnage non-uniforme en ondelettes (NUWS). L'architecture mélange le signal analogique avec des ondelettes paramétrables avant d'intégrer et convertir le signal en données numériques. L'objectif de la thèse est de proposer une méthode pour concevoir un convertisseur A2F générique basé sur le NUWS. Il est ainsi nécessaire de définir les caractéristiques des ondelettes afin d'acquérir une large gamme de signaux basse fréquence (ECG, EMG, EEG, parole…). Cette étape nécessite l'utilisation d'algorithmes de sélection de paramètres et d'algorithmes d'apprentissage automatique pour sélectionner le meilleur ensemble d'ondelettes pour une application donnée et qui doit permettre de définir les spécifications du convertisseur. L'étape de sélection des paramètres doit tenir compte des contraintes de mise en œuvre pour optimiser au mieux la consommation d'énergie. Un algorithme de sélection de paramètres est proposé pour choisir des ondelettes pour une application donnée, afin de maximiser la précision de classification tout en diminuant la consommation d'énergie, grâce à un modèle de consommation réalisé dans une technologie CMOS 0.18μm.
... New issues develop as a result of the creation of large datasets. Consequently, reliable and unique feature selection approaches are required [38]. Feature selection can assist with data visualization and understanding, as well as minimizing measurement and storage needs, training and utilization times, and overcoming the curse of dimensionality to enhance prediction performance [39], [40]. ...
The new coronavirus (known as COVID-19) was first identified in Wuhan and quickly spread worldwide, wreaking havoc on the economy and people's everyday lives. Fever, cough, sore throat, headache, exhaustion, muscular aches, and difficulty breathing are all typical symptoms of COVID-19. A reliable detection technique is needed to identify affected individuals and care for them in the early stages of COVID-19 and reduce the virus's transmission. The most accessible method for COVID-19 identification is RT-PCR; however, due to its time commitment and false-negative results, alternative options must be sought. Indeed, compared to RT-PCR, chest CT scans and chest X-ray images provide superior results. Because of the scarcity and high cost of CT scan equipment, X-ray images are preferable for screening. In this paper, a pre-trained network, DenseNet169, was employed to extract features from X-ray images. Features were chosen by a feature selection method (ANOVA) to reduce computations and time complexity while overcoming the curse of dimensionality to improve predictive accuracy. Finally, selected features were classified by XGBoost. The ChestX-ray8 dataset, which was employed to train and evaluate the proposed method. This method reached 98.72% accuracy for two-class classification (COVID-19, healthy) and 92% accuracy for three-class classification (COVID-19, healthy, pneumonia).
... New issues develop as a result of the creation of large datasets. Consequently, reliable and unique feature selection approaches are required [38]. Feature selection can assist with data visualization and understanding, as well as minimizing measurement and storage needs, training and utilization times, and overcoming the curse of dimensionality to enhance prediction performance [39], [40]. ...
The new coronavirus (known as COVID-19) was first identified in Wuhan and quickly spread worldwide, wreaking havoc on the economy and people's everyday lives. Fever, cough, sore throat, headache, exhaustion, muscular aches, and difficulty breathing are all typical symptoms of COVID-19. A reliable detection technique is needed to identify affected individuals and care for them in the early stages of COVID-19 and reduce the virus's transmission. The most accessible method for COVID-19 identification is RT-PCR; however, due to its time commitment and false-negative results, alternative options must be sought. Indeed, compared to RT-PCR, chest CT scans and chest X-ray images provide superior results. Because of the scarcity and high cost of CT scan equipment, X-ray images are preferable for screening. In this paper, a pre-trained network, DenseNet169, was employed to extract features from X-ray images. Features were chosen by a feature selection method (ANOVA) to reduce computations and time complexity while overcoming the curse of dimensionality to improve predictive accuracy. Finally, selected features were classified by XGBoost. The ChestX-ray8 dataset, which was employed to train and evaluate the proposed method. This method reached 98.72% accuracy for two-class classification (COVID-19, healthy) and 92% accuracy for three-class classification (COVID-19, healthy, pneumonia). <br
... New issues develop as a result of the creation of large datasets. Consequently, reliable and unique feature selection approaches are required [38]. Feature selection can assist with data visualization and understanding, as well as minimizing measurement and storage needs, training and utilization times, and overcoming the curse of dimensionality to enhance prediction performance [39], [40]. ...
The new coronavirus (known as COVID-19) was first identified in Wuhan and quickly spread worldwide, wreaking havoc on the economy and people's everyday lives. Fever, cough, sore throat, headache, exhaustion, muscular aches, and difficulty breathing are all typical symptoms of COVID-19. A reliable detection technique is needed to identify affected individuals and care for them in the early stages of COVID-19 and reduce the virus's transmission. The most accessible method for COVID-19 identification is RT-PCR; however, due to its time commitment and false-negative results, alternative options must be sought. Indeed, compared to RT-PCR, chest CT scans and chest X-ray images provide superior results. Because of the scarcity and high cost of CT scan equipment, X-ray images are preferable for screening. In this paper, a pre-trained network, DenseNet169, was employed to extract features from X-ray images. Features were chosen by a feature selection method (ANOVA) to reduce computations and time complexity while overcoming the curse of dimensionality to improve predictive accuracy. Finally, selected features were classified by XGBoost. The ChestX-ray8 dataset, which was employed to train and evaluate the proposed method. This method reached 98.72% accuracy for two-class classification (COVID-19, healthy) and 92% accuracy for three-class classification (COVID-19, healthy, pneumonia). <br
... The ability of the classification algorithm and the features to accurately represent heartbeats are crucial for successful classification. Although many methods have been reported, their direct comparisons are questionable due to their differences in the types of heartbeats being classified [4][5][6][7][8][9][10][11][12][13][14][15], ECG features [4][5] [9][10] [6][7] [16] [17], and classification models [ [21]. Based on the the Association for the Advancement of Medical Instrumentation (AAMI) standard, most previous research have examined validation performance, but the results are below the average 99% for all classes [22]. ...
Automatic heartbeat classification is an important
stage in identifying cardiac arrhythmia. Several machine
learning (ML) techniques have been proposed to perform this,
but they produce an accuracy result of below 99%. In this
study, a deep neural network (DNN) structure is applied to
improve ML performance. The feature selection method is
based on the combination of discrete wavelet transform (DWT)
and principal component analysis (PCA). To avoid
computational complexity, the components of PCA are derived
by low-dimensional DWT coefficients. The results show that
the proposed ML model achieves good performance, producing
99.76% accuracy, 91.80% sensitivity, 99.78% specificity,
93.02% precision, and 92.31% F1-score. To benchmark the
proposed model, the support vector machine (SVM) and
random forest (RF) techniques are used as the baseline models.
The DNNs are 2.3% more sensitive than SVM, while the RF
fails to classify the ECG heartbeat. Four datasets are used to
analyze the robustness and generalization performance of the
proposed model: MIT-BIH, SVDB, MITDB, and IncartDB. All
testing results produce satisfying performance. The proposed
ML model offers a potential solution to improve the
generalizability of a DNN-based model in different cardiac
datasets for classifying tasks.
... In another context, feature selection aims to find the optimal subset consisting 's' number of features that are selected from the total 'r' number of features. In the past few decades, various FS methods were developed by the researchers in literature for obtaining the best feature pool [4,[12][13][14][15][16][17][18][19][20][21][22][23][24][25][26] for the ECG arrhythmia classification problems. Pattarin et al. [26] develop the Principal Component Analysis (PCA) based method which reduces the dimension of the model to a great extent with better classification performance. ...
... The author achieves the overall accuracy of 84.6% with the linear discrimination approach. Doquire et al. [16] compared the two feature selection techniques i.e., filter and wrapper. The authors use 200 features for experimentation. ...
In this paper, a novel feature selection method is proposed for the categorization of electrocardiogram (ECG) heartbeats. The proposed technique uses the Fisher ratio and BAT optimization algorithm to obtain the best feature set for ECG classification. The MIT-BIH arrhythmia database contains sixteen classes of the ECG heartbeats. The MIT-BIH ECG arrhythmia database divided into intra-patient and inter-patient schemes to be used in this study. The proposed feature selection methodology works in following steps: firstly, features are extracted using empirical wavelet transform (EWT) and then higher-order statistics, as well as symbolic features, are computed for each decomposed mode of EWT. Thereafter, the complete feature vector is obtained by the conjunction of EWT based features and RR interval features. Secondly, for feature selection, the Fisher ratio is utilized. It is optimized by using BAT algorithm so as to have maximal discrimination of the between classes. Finally, in the classification step, the k-nearest neighbor classifier is used to classify the heartbeats. The performance measures i.e., accuracy, sensitivity, positive predictivity, specificity for intra-patient scheme are 99.80%, 99.80%, 99.80%, 99.987% and for inter-patient scheme are 97.59%, 97.589%, 97.589%, 99.196% respectively. The proposed feature selection technique outperforms the other state of art feature selection methods.
... Feature correlation and effectiveness are important concerns for this type of methods. To avoid negative impacts of noisy data, techniques, like the floating sequential search [29] and the weighted LD model [18], must be employed to reduce the feature space. Regarding the selection of classifiers, the support vector machine (SVM) is the most widely used for its robustness, good generalization and computationally efficiency [1,14]. ...
Cardiac arrhythmia has been identified as a type of cardiovascular diseases (CVDs) that causes approximately 12% of all deaths globally. The development of Internet-of-Things has spawned novel ways for heart monitoring but also presented new challenges for manual arrhythmia detection. An automated method is highly demanded to provide support for physicians. Current attempts for automatic arrhythmia detection can roughly be divided as feature-engineering based and deep-learning based methods. Most of the feature-engineering based methods are suffering from adopting single classifier and use fixed features for classifying all five types of heartbeats. This introduces difficulties in identification of the problematic heartbeats and limits the overall classification performance. The deep-learning based methods are usually not evaluated in a realistic manner and report overoptimistic results which may hide potential limitations of the models. Moreover, the lack of consideration of frequency patterns and the heart rhythms can also limit the model performance. To fill in the gaps, we propose a framework for arrhythmia detection from IoT-based ECGs. The framework consists of two modules: a data cleaning module and a heartbeat classification module. Specifically, we propose two solutions for the heartbeat classification task, namely Dynamic Heartbeat Classification with Adjusted Features (DHCAF) and Multi-channel Heartbeat Convolution Neural Network (MCHCNN). DHCAF is a feature-engineering based approach, in which we introduce dynamic ensemble selection (DES) technique and develop a result regulator to improve classification performance. MCHCNN is deep-learning based solution that performs multi-channel convolutions to capture both temporal and frequency patterns from heartbeat to assist the classification. We evaluate the proposed framework with DHCAF and with MCHCNN on the well-known MIT-BIH-AR database, respectively. The results reported in this paper have proven the effectiveness of our framework.
