Classification of ECG Arrhythmia with Machine Learning Techniques

Blbl, Halil Ibrahim; Usta, Nese; Yildiz, M.

doi:10.1109/icmla.2017.0-104

Cited by 24 publications

(6 citation statements)

References 12 publications

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“…The one-stage classifier is incapable of classifying the arrhythmia types correctly. However, as mentioned earlier, because of the nature of the data, the way it has been used and the methods that apply to it, a fair comparison between the proposed two-stage classifier and other literature background is difficult [32][33][34][35][36][37][38][39][40].…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

A Novel Two-Stage Heart Arrhythmia Ensemble Classifier

et al. 2021

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Atrial fibrillation (AF) and ventricular arrhythmia (Arr) are among the most common and fatal cardiac arrhythmias in the world. Electrocardiogram (ECG) data, collected as part of the UK Biobank, represents an opportunity for analysis and classification of these two diseases in the UK. The main objective of our study is to investigate a two-stage model for the classification of individuals with AF and Arr in the UK Biobank dataset. The current literature addresses heart arrhythmia classification very extensively. However, the data used by most researchers lack enough instances of these common diseases. Moreover, by proposing the two-stage model and separation of normal and abnormal cases, we have improved the performance of the classifiers in detection of each specific disease. Our approach consists of two stages of classification. In the first stage, features of the ECG input are classified into two main classes: normal and abnormal. At the second stage, the features of the ECG are further categorised as abnormal and further classified into two diseases of AF and Arr. A diverse set of ECG features such as the QRS duration, PR interval and RR interval, as well as covariates such as sex, BMI, age and other factors, are used in the modelling process. For both stages, we use the XGBoost Classifier algorithm. The healthy population present in the data, has been undersampled to tackle the class imbalance present in the data. This technique has been applied and evaluated using an ECG dataset from the UKBioBank ECG taken at rest repository. The main results of our paper are as follows: The classification performance for the proposed approach has been measured using F1 score, Sensitivity (Recall) and Specificity (Precision). The results of the proposed system are 87.22%, 88.55% and 85.95%, for average F1 Score, average sensitivity and average specificity, respectively. Contribution and significance: The performance level indicates that automatic detection of AF and Arr in participants present in the UK Biobank is more precise and efficient if done in a two-stage manner. Automatic detection and classification of AF and Arr individuals this way would mean early diagnosis and prevention of more serious consequences later in their lives.

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Section: Comparison With Other Methodsmentioning

confidence: 99%

A Novel Two-Stage Heart Arrhythmia Ensemble Classifier

et al. 2021

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“…Classification is one of the final stages in analysing ECG signals. Most research develops systems for several tasks, such as disease classification (166), patient classification (167), ECG simulation (168), and emotion recognition (169). With this aim, supervised methods such as naive Bayes ( 170 Machine learning has contributed to various elements such as detection or classification of heartbeats (185), arrhythmias (129,186), and unexpected changes in heart morphology (187,188).…”

Section: Ecg/hrv Modellin-classification -Machine Learningmentioning

confidence: 99%

A Proposal for a Data-Driven Approach to the Influence of Music on Heart Dynamics

Idrobo-Ávila

Correa

Muñoz-Bolaños

et al. 2021

Front. Cardiovasc. Med.

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Electrocardiographic signals (ECG) and heart rate viability measurements (HRV) provide information in a range of specialist fields, extending to musical perception. The ECG signal records heart electrical activity, while HRV reflects the state or condition of the autonomic nervous system. HRV has been studied as a marker of diverse psychological and physical diseases including coronary heart disease, myocardial infarction, and stroke. HRV has also been used to observe the effects of medicines, the impact of exercise and the analysis of emotional responses and evaluation of effects of various quantifiable elements of sound and music on the human body. Variations in blood pressure, levels of stress or anxiety, subjective sensations and even changes in emotions constitute multiple aspects that may well-react or respond to musical stimuli. Although both ECG and HRV continue to feature extensively in research in health and perception, methodologies vary substantially. This makes it difficult to compare studies, with researchers making recommendations to improve experiment planning and the analysis and reporting of data. The present work provides a methodological framework to examine the effect of sound on ECG and HRV with the aim of associating musical structures and noise to the signals by means of artificial intelligence (AI); it first presents a way to select experimental study subjects in light of the research aims and then offers possibilities for selecting and producing suitable sound stimuli; once sounds have been selected, a guide is proposed for optimal experimental design. Finally, a framework is introduced for analysis of data and signals, based on both conventional as well as data-driven AI tools. AI is able to study big data at a single stroke, can be applied to different types of data, and is capable of generalisation and so is considered the main tool in the analysis.

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“…However, this algorithm is relatively complex and involves a substantial number of R-peak detection blocks [4]. Halil et al employed various machine learning techniques for classifying P, Q, R, S, and T waves in ECG signals, integrating the BP (Back Propagation) algorithm with the MLP classifier, as well as the KA (Kernel-Adatron) algorithm with SVM classifier [5]. Furthermore, Saira et al proposed a novel algorithm utilizing two-event-related moving averages (TERMA) and fractional Fourier transform (FRFT) for better analysis of ECG signals [6].…”

Section: Introductionmentioning

confidence: 99%

ECG Classification Exercise Health Analysis Algorithm Based on GRU and Convolutional Neural Network

Ji,

Zhu

2024

IEEE Access

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Sudden cardiac death (SCD) is one of the main causes of death among athletes during exercise and physical activity. By analyzing the results of ECG classification, doctors can promptly detect the presence of cardiovascular diseases and implement appropriate treatment measures, thereby reducing disease progression and the occurrence of complications. Previous studies typically us ed machine learning methods that often required manual feature selection, which could be subjective, time-consuming, and limited in scope. Additionally, the relationships between features may be overlooked, leading to a decrease in model performance. Therefore, our proposed approach automatically learns and selects relevant features, avoiding the issues associated with manual feature selection. In this paper, we introduce the Grunet deep learning network model, which utilizes convolutional neural networks to automatically extract features from ECG signals, thereby enhancing feature utilization and representation capabilities. Given the time-sensitive nature of ECG signals, we introduce gated recurrent units (GRU) to better capture these features. The gating mechanism of GRU helps manage information flow and facilitates capturing long-term dependencies. In our experiments conducted on the MIT-BIH Arrhythmia Database and the European ST-T Database, our proposed method achieved an overall classification accuracy of 99.47% on the European ST-T Database, with a precision of 98.76% and a recall of 97.92% on the MIT-BIH Arrhythmia Database. The classification accuracy for classes N and Q reached 100%. The experimental results demonstrate that our method outperforms existing techniques, significantly reducing the cost of manual intervention and improving the accuracy of heartbeat classification .

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Classification of ECG Arrhythmia with Machine Learning Techniques

Cited by 24 publications

References 12 publications

A Novel Two-Stage Heart Arrhythmia Ensemble Classifier

A Novel Two-Stage Heart Arrhythmia Ensemble Classifier

A Proposal for a Data-Driven Approach to the Influence of Music on Heart Dynamics

ECG Classification Exercise Health Analysis Algorithm Based on GRU and Convolutional Neural Network

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