Arrhythmic Beat Classification Research Articles

A novel LMS algorithm for ECG signal preprocessing and KNN classifier based abnormality detection

ECG signal abnormality detection is useful for identifying heart related problems. Two popular abnormality detection techniques are ischaemic beat classification and arrhythmic beat classification. In this work, ECG signal preprocessing and KNN based arrhythmic beat classification are performed to categorize into normal and abnormal subjects. LMS based adaptive filters are used in ECG signal preprocessing, but they consume more time for processing due to long critical path. To overcome this problem, a novel adaptive filter with delayed error normalized LMS algorithm is utilized to attain high speed and low latency design. Low power design is achieved in this design by applying pipelining concept in the error feedback path. R-peak detection is carried out in the preprocessed signal using wavelets for HRV feature extraction. Arrhythmic beat classification is carried out by KNN classifier on HRV feature extracted signal. Classification performance reveals that the proposed DWT with KNN classifier provides the accuracy of 97.5% which is better than other machine leaning techniques.

ECG Signal Preprocessing and SVM Classifier-Based Abnormality Detection in Remote Healthcare Applications

Medical expert systems are part of the portable and smart healthcare monitoring devices used in day-to-day life. Arrhythmic beat classification is mainly used in electrocardiogram (ECG) abnormality detection for identifying heart related problems. In this paper, ECG signal preprocessing and support vector machine-based arrhythmic beat classification are performed to categorize into normal and abnormal subjects. In ECG signal preprocessing, a delayed error normalized LMS adaptive filter is used to achieve high speed and low latency design with less computational elements. Since the signal processing technique is developed for remote healthcare systems, white noise removal is mainly focused. Discrete wavelet transform is applied on the preprocessed signal for HRV feature extraction and machine learning techniques are used for performing arrhythmic beat classification. In this paper, SVM classifier and other popular classifiers have been used on noise removed feature extracted signal for beat classification. Results indicate that the performance of SVM classifier is better than other machine learning-based classifiers.

An APN model for Arrhythmic beat classification

Changes in the normal rhythm of a human heart may result in different cardiac arrhythmias, which may be immediately fatal or cause irreparable damage to the heart sustained over long periods of time. Therefore, the ability to automatically identify arrhythmias from ECG recordings is important for clinical diagnosis and treatment. In this article, classification by using associative Petri net (APN) for personalized ECG-arrhythmia-pattern identification is proposed for the first time in literature. A rule-based classification model and reasoning algorithm of APN are created for ECG arrhythmias classification. The performance evaluation using MIT-BIH arrhythmia database shows that our approach compares well with other reported studies.

Fuzzy Unordered Rule Induction for evaluating cardiac Arrhythmia

Heart rate variations reveal current or impending heart/cardiac diseases. A non-stationary signal — heart rate — is measured using Electrocardiogram (ECG) to assess cardiac Arrhythmia. But studying ECG reports is both tedious and time consuming when you have to locate abnormalities from the collected data. This is overcome by computer based analytical tools for in-depth data study/classification for diagnosis. Automatic arrhythmia assessment is easy due to the existence of image processing techniques. Many algorithms exist for ECG signals detection/classification. This paper investigates RR interval based ECG classification procedures for arrhythmic beat classification, a process based on RR interval beat extraction using Symlet on ECG data. Extracted RR data is used as a classification feature with beats being classified through a boosting algorithm and Fuzzy Unordered Rule Induction Algorithm (FURIA). Classification efficiency evaluation was through the MITBIH arrhythmia database.

Investigating Cardiac Arrhythmia in ECG using Random Forest Classification

Electrocardiogram (ECG) is used to assess the heart arrhythmia. Accurate detection of beats helps determine different types of arrhythmia which are relevant to diagnose heart disease. Automatic assessment of arrhythmia for patients is widely studied. This paper presents an ECG classification method for arrhythmic beat classification using RR interval. The methodology is based on discrete cosine transform (DCT) conversion of RR interval. The RR interval of the beat is extracted from the ECG and used as feature. DCT conversion of RR interval is applied and the beats are classified using random tree. Experiments were conducted using MIT-BIH arrhythmia database.

A Framework for Fuzzy Expert System Creation—Application to Cardiovascular Diseases

A methodology for the automated development of fuzzy expert systems is presented. The idea is to start with a crisp model described by crisp rules and then transform them into a set of fuzzy rules, thus creating a fuzzy model. The adjustment of the model's parameters is performed via a stochastic global optimization procedure. The proposed methodology is tested by applying it to problems related to cardiovascular diseases, such as automated arrhythmic beat classification and automated ischemic beat classification, which, besides being well-known benchmarks, are of particular interest due to their obvious medical diagnostic importance. For both problems, the initial set of rules was determined by expert cardiologists, and the MIT-BIH arrhythmia database and the European ST-T database are used for optimizing the fuzzy model's parameters and evaluating the fuzzy expert system. In both cases, the results indicate an escalation of the performance from the simple initial crisp model to the more sophisticated fuzzy models, proving the scientific added value of the proposed framework. Also, the ability to interpret the decisions of the created fuzzy expert systems is a major advantage compared to "black box" approaches, such as neural networks and other techniques.

A methodology for the automated creation of fuzzy expert systems for ischaemic and arrhythmic beat classification based on a set of rules obtained by a decision tree

In the current work we propose a methodology for the automated creation of fuzzy expert systems, applied in ischaemic and arrhythmic beat classification. The proposed methodology automatically creates a fuzzy expert system from an initial training dataset. The approach consists of three stages: (a) extraction of a crisp set of rules from a decision tree induced from the training dataset, (b) transformation of the crisp set of rules into a fuzzy model and (c) optimization of the fuzzy model's parameters using global optimization. The above methodology is employed in order to create fuzzy expert systems for ischaemic and arrhythmic beat classification in ECG recordings. The fuzzy expert system for ischaemic beat detection is evaluated in a cardiac beat dataset that was constructed using recordings from the European Society of Cardiology ST-T database. The arrhythmic beat classification fuzzy expert system is evaluated using the MIT-BIH arrhythmia database. The fuzzy expert system for ischaemic beat classification reported 91% sensitivity and 92% specificity. The arrhythmic beat classification fuzzy expert system reported 96% average sensitivity and 99% average specificity for all categories. The proposed methodology provides high accuracy and the ability to interpret the decisions made. The fuzzy expert systems for ischaemic and arrhythmic beat classification compare well with previously reported results, indicating that they could be part of an overall clinical system for ECG analysis and diagnosis.

Time course reconstruction of fetal cardiac signals from fMCG: independent component analysis versus adaptive maternal beat subtraction

M-mode and pulsed Doppler echocardiography, cardiotocography and trans-abdominal fetal ECG are available in clinical practice to monitor fetal cardiac activity during advancing gestation, but none of these methods allows the direct measurement of morphological and temporal parameters for fetal rhythm assessment. Fetal magnetocardiograms (fMCGs) are noninvasive recordings of magnetic field variations associated with electrical activity of the fetal heart obtained with superconducting sensors positioned over the maternal abdomen inside a shielded room. Because of maternal cardiac activity, fMCGs are contaminated by maternal components that need to be eliminated to reconstruct fetal cardiac traces. The aim of the present work was to use two methods working in the time domain, an independent component analysis algorithm (FastICA) and an adaptive maternal beat subtraction technique (AMBS), for the retrieval of fetal cardiac signals from fMCGs. Detection rates of both methods were calculated, and FastICA and AMBS performances were compared in the context of clinical applications by estimating several temporal and morphological characteristics of the retrieved fetal traces, such as the shape and duration P-QRS-T waves, arrhythmic beat detection and classification, and noise reduction. Quantitative and qualitative comparison produced figures that always suggested that FastICA was superior to AMBS from the perspective of clinical use of the recovered fetal signals.

An arrhythmia classification system based on the RR-interval signal

Objective: This paper proposes a knowledge-based method for arrhythmic beat classification and arrhythmic episode detection and classification using only the RR-interval signal extracted from ECG recordings. Methodology: A three RR-interval sliding window is used in arrhythmic beat classification algorithm. Classification is performed for four categories of beats: normal, premature ventricular contractions, ventricular flutter/fibrillation and 2° heart block. The beat classification is used as input of a knowledge-based deterministic automaton to achieve arrhythmic episode detection and classification. Six rhythm types are classified: ventricular bigeminy, ventricular trigeminy, ventricular couplet, ventricular tachycardia, ventricular flutter/fibrillation and 2° heart block. Results: The method is evaluated by using the MIT-BIH arrhythmia database. The achieved scores indicate high performance: 98% accuracy for arrhythmic beat classification and 94% accuracy for arrhythmic episode detection and classification. Conclusion: The proposed method is advantageous because it uses only the RR-interval signal for arrhythmia beat and episode classification and the results compare well with more complex methods.