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1

A review of shockable arrhythmia detection of ECG signals using machine and deep learning techniques

Kavya Lakkakula, Karuna Yepuganti, Saritha Saladi, Prakash Allam Jaya, Patro Kiran Kumar, Sahoo Suraj Prakash, Tadeusiewicz Ryszard, Pławiak Paweł

International Journal of Applied Mathematics and Computer Science

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2024

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Vol. 34, no. 3

485--511

EN

An electrocardiogram (ECG) is an essential medical tool for analyzing the functioning of the heart. An arrhythmia is a deviation in the shape of the ECG signal from the normal sinus rhythm. Long-term arrhythmias are the primary sources of cardiac disorders. Shockable arrhythmias, a type of life-threatening arrhythmia in cardiac patients, are characterized by disorganized or chaotic electrical activity in the heart’s lower chambers (ventricles), disrupting blood flow throughout the body. This condition may lead to sudden cardiac arrest in most patients. Therefore, detecting and classifying shockable arrhythmias is crucial for prompt defibrillation. In this work, various machine and deep learning algorithms from the literature are analyzed and summarized, which is helpful in automatic classification of shockable arrhythmias. Additionally, the advantages of these methods are compared with existing traditional unsupervised methods. The importance of digital signal processing techniques based on feature extraction, feature selection, and optimization is also discussed at various stages. Finally, available databases, the performance of automated algorithms, limitations, and the scope for future research are analyzed. This review encourages researchers’ interest in this challenging topic and provides a broad overview of its latest developments.

2

A high-performance arrhythmic heartbeat classification using ensemble learning method and PSD based feature extraction approach

Yakut Önder, Bolat Emine Doğru

Biocybernetics and Biomedical Engineering

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2022

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Vol. 42, no. 2

667--680

EN

Health problems, directly or indirectly caused by cardiac arrhythmias, may threaten life. The analysis of electrocardiogram (ECG) signals is an important diagnostic tool for assessing cardiac function in clinical research and disease diagnosis. Until today various Soft Computing methods and techniques have been proposed for the analysis of ECG signals. In this study, a new Ensemble Learning based method is proposed that automatically classifies the arrhythmic heartbeats of ECG signal according to the category-based and patient-based evaluation plan. A two-stage median filter was used to remove the baseline wander from the ECG signal. The locations of fiducial points of the ECG signal were determined using the developed QRS complex detection method. Within the scope of this study, four different feature extraction methods were utilized. A new feature extraction technique based on the Power Spectral Density has been proposed. Hybrid sub-feature sets were constructed using a Wrapper-based feature selection algorithm. A new method based on Ensemble Learning (EL) has been proposed by using a stacking algorithm. Multi-layer Perceptron (MLP) and Random Forest (RF) as base learners and Linear Regression (LR) as meta learner were utilized. Average performance values for the category-based arrhythmic heartbeat classification of the proposed new method based on Ensemble Learning; accuracy was 99,88%, sensitivity was 99,08%, specificity was 99,94% and positive predictivity (+P) was 99,08%. Average performance values for patient-based arrhythmic heartbeat classification were 99,72% accuracy, 99,30% sensitivity, 99,83% specificity and 99,30% positive predictivity (+P). Thus, it is concluded that the proposed method has higher performance results than similar studies in the literature.

3

Parallel classification model of arrhythmia based on DenseNet-BiLSTM

Gan Yi, Shi Jun-Cheng, He Wei-Ming, Sun Fu-Jia

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 4

1548--1560

EN

In order to improve the classification performance of the model for different kinds of arrhythmias, a parallel classification model of arrhythmia based on DenseNet-BiLSTM is researched and proposed. Firstly, the model adopts a parallel structure. After wavelet denoising and heartbeat segmentation of ECG signals, this model can simultaneously capture the waveform features of small-scale heartbeat and large-scale heartbeat containing RR interval; Then, based on deep learning, Densely connected convolutional network (DenseNet) is applied to improve the model’s ability to extract local features of ECG signals, and bidirectional long short-term memory network (BiLSTM) is introduced to improve the performance of the model in extracting time series features of ECG signals; Finally, weighted cross entropy loss function is used to alleviate the class imbalance of arrhythmia, and Softmax function is applied to achieve 4 classifications of arrhythmia. Experiments based on MIT-BIH arrhythmia database show that under the intra-patient paradigm, training time for each epoch, overall accuracy, F1 and specificity are 42 s, 99.44%, 95.89% and 99.32%, respectively; Under the inter-patient paradigm, training time for each epoch, overall accuracy, F1 and specificity are 23 s, 92.37%, 63.49% and 94.51%, respectively. Compared with other classification models, the model proposed in this paper has a good classification effect and is expected to be used in clinical auxiliary diagnosis.

4

An improved cardiac arrhythmia classification using an RR interval-based approach

Rahul Jagdeep, Sora Marpe, Sharma Lakhan Dev, Bohat Vijay Kumar

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 2

656--666

EN

Accurate and early detection of cardiac arrhythmia present in an electrocardiogram (ECG) can prevent many premature deaths. Cardiac arrhythmia arises due to the improper conduction of electrical impulses throughout the heart. In this paper, we propose an improved RR interval-based cardiac arrhythmia classification approach. The Discrete Wavelet Transform (DWT) and median filters were used to remove high-frequency noise and baseline wander from the raw ECG. Next, the processed ECG was segmented after the determination of the QRS region. We extracted the primary feature RR interval and other statistical features from the beats to classify the Normal, Premature Ventricular Contraction (PVC), and Premature Atrial Contraction (PAC). The K-Nearest Neighbour (k-NN), Support Vector Machine (SVM), Decision Tree (DT), Naı¨ve Bayes (NB), and Random Forest (RF) classifier were utilised for classification. Overall performance of SVM with Gaussian kernel achieved Se % = 99.28, Sp % = 99.63, +P % = 99.28, and Acc % = 99.51, which is better than the other classifiers used in this method. The obtained results of the proposed method are significantly better and more accurate.

5

Spectral entropy and deep convolutional neural network for ECG beat classification

Asgharzadeh-Bonab Akbar, Amirani Mehdi Chehel, Mehri Alaeddin

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 2

691--700

EN

Sudden cardiac death is the result of abnormal heart conditions. Therefore, early detection of such abnormal conditions is vital to identify heart problems. Hence, in this paper, we aim to present a new computer-aided diagnosis (CAD) method based on time-frequency analysis of electrocardiogram (ECG) signals and deep neural networks for arrhythmia detection. Time-frequency transforms have the capability of providing spectral information at different times, which is very useful for analyzing non-stationary signals. On the other side, entropy is an attractive measurement from ECG signals which can distinguish different types of them. In this paper, time-frequency spectral entropy is proposed to extract the efficient features from ECG signals. All computed entropies cannot provide separability among different classes, two-directional two-dimensional principal component analysis (2D2PCA) can be used to reduce the dimension of the extracted features. Finally, the convolutional neural network (CNN) classifies the time-frequency features to diagnose the ECG beat signals and detect arrhythmias. The results show that the spectral entropy can provide good separation between different among ECG beats and the proposed method outperforms the recently introduced method for analyzing ECG signals.

6

SpEC: A system for patient specific ECG beat classification using deep residual network

Allam Jaya Prakash, Samantray Saunak, Ari Samit

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 4

1446--1457

EN

Electrocardiogram (ECG) is a non-invasive technique used to detect various cardiac disorders. One of the major causes of cardiac arrest is an arrhythmia. Furthermore, ECG beat classification is essential to detect life-threatening cardiac arrhythmias. The major limitations of the traditional ECG beat classification systems are the requirement of an extensive training dataset to train the model and inconsistent performance for the detection of ventricular and supraventricular ectopic (V and S) beats. To overcome these limitations, a system denoted as SpEC is proposed in this work based on Stockwell transform (ST) and two-dimensional residual network (2D-ResNet) for improvement of ECG beat classification technique with a limited amount of training data. ST, which is used to represent the ECG signal into a time-frequency domain, provides frequency invariant amplitude response and dynamic resolution. The resultant ST images are applied as input to the proposed 2D-ResNet to classify five different types of ECG beats in a patient-specific way as recommended by the Association for the Advancement of Medical Instrumentation (AAMI). The proposed SpEC system achieved an overall accuracy (Acc) of 99.73%, sensitivity (Sen) = 98.84%, Specificity (Spe) = 99.50%, Positive predictivity (Ppr) = 98.20% on MIT-BIH arrhythmia database, and shows an overall Acc of 89.87% on real-time acquired ECG dataset with classification time of single ECG beat image = 0.2365 (s) in detecting of five arrhythmia classes. The proposed method shows better performance on both the database compared to the earlier reported state-of-art techniques.

7

Characterization of cardiac arrhythmias by variational mode decomposition technique

Maji U., Mitra M., Pal S.

Biocybernetics and Biomedical Engineering

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2017

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Vol. 37, no. 3

578--589

EN

Automatic detection of cardiac abnormalities in early stage is a popular area of research for decades. In this work a novel algorithm for detection of cardiac arrhythmia is proposed using variational mode decomposition (VMD). Arrhythmia is a crucial abnormality of heart in which the rhythmic disorder may lead to sudden cardiac arrest. Existing algorithms for arrhythmia detection are based on accuracy of detection of fiducial points, parameter selection and extraction, quality of classifier and other factors. Unlike other works, proposed method tries to characterize both atrial and ventricular arrhythmias simultaneously and independently from the segmented sections of the signal. VMD, being able to separate closely spaced frequencies, has a good potential to be useful to provide significant features in transformed domain. Unique feature combinations are also proposed to characterize different arrhythmic events.

8

Cardiac arrhythmia classification using the phase space sorted by Poincare sections

Yaghoobi Karimui R., Azadi S.

Biocybernetics and Biomedical Engineering

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2017

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Vol. 37, no. 4

690--700

EN

Many methods for automatic heartbeat classification have been applied and reported in literature, but methods, which used the basin geometry of quasi-periodic oscillations of electrocardiogram (ECG) signal in the phase space for classifying cardiac arrhythmias, frequently extracted a limited amount of information of this geometry. Therefore, in this study, we proposed a novel technique based on Poincare section to quantify the basin of quasi-periodic oscillations, which can fill the mentioned gap to some extent. For this purpose, we first reconstructed the two-dimensional phase space of ECG signal. Then, we sorted this space using the Poincare sections in different angles. Finally, we evaluated the geometric features extracted from the sorted spaces of five heartbeat groups recommend by the association for the advancement of medical instrumentation (AAMI) by using the sequential forward selection (SFS) algorithm. The results of this algorithm indicated that a combination of nine features extracted from the sorted phase space along with per and post instantaneous heart rate could significantly separate the five heartbeat groups (99.23% and 96.07% for training and testing sets, respectively). Comparing these results with the results of earlier work also indicated that our proposed method had a figure of merit (FOM) about 32.12%. Therefore, this new technique not only can quantify the basin geometry of quasi-periodic oscillations of ECG signal in the phase space, but also its output can improve the performance of detection systems developed for the cardiac arrhythmias, especially in the five heartbeat groups recommend by the AAMI.

9

Cardiac arrhythmia alarm from optical interferometric signals during resting or sleeping for early intervention

Cikajlo I., Šprager S., Erjavec T., Zazula D.

Biocybernetics and Biomedical Engineering

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2016

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Vol. 36, no. 1

267--275

EN

Diagnostics of cardiac arrhythmias and frequent interventions may contribute to early detection of diseases or even prevent sudden death. Generally electrocardiograph with several on body electrodes at outpatient clinic is applied and the procedure requires a medical expert. We propose cardiac arrhythmia estimation on the basis of heartbeat detection with optical fibers integrated in the bedding. The modified Michelson's interferometer with error detection was used to measure and maximum a-posteriori probability was used to estimate the beat-to-beat intervals. The consistency of heartbeat intervals was examined with simultaneous measurement with clinical electrocardiograph in 10 healthy volunteers and 10 patients with diagnosed heart arrhythmias. Heart beat interval data obtained in patients were examined and irregularities/arrhythmias were identified from the medical guidelines. The current system enables assessment also in home environment without any on-body sensor placement or required assistance. Thus early intervention is possible as the irregularities are submitted to the nurse on duty and stored in the database for subsequent more detailed analysis.

10

Technika chłodnicza wykorzystywana do zabiegu krioablacji w leczeniu arytmii serca

Bisewska E.

Technika Chłodnicza i Klimatyzacyjna

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2014

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nr 6-7

234--241

PL

Arytmia, to zaburzony rytm pracy serca, w którym pobudzenie czynnościowe jego struktur następuje w sposób niemiarowy, opóźniony bądż przyśpieszony. Artykuł poświęcony jest charakterystyce zabiegu krioablacji, który jest zabiegiem kriochirurgicznym. Jego celem jest usunięcie ogniska arytmii serca przez zastosowanie punktowe temperatur kriogenicznych. Przedstawiono w nim biomechanizm uszkodzenia komórki przez jej zamrożenie, ogólną charakterystykę zabiegu krioablacji i jego ocenę w porównaniu do innych, stosowanych w praktyce metod leczenia arytmii serca.

EN

The paper deals with cryoablation treatment which is a kind of cryogenic surgery. Its purpose is to deactivate the centre of heartbeat irregularity by local application of cryogenic temperatures. The destruction mechanism in frozen cells is described and the cryoablation treatment is presented and compared to other therapies of irregular heartbeat.

11

Kardiologia interwencyjna : przegląd najczęściej stosowanych technik i zabiegów

Pałko T., Golnik N.

Inżynier i Fizyk Medyczny

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2013

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Vol. 2, nr 6

319--322

PL

W artykule opisano najczęściej stosowane techniki i zabiegi stosowane w kardiologii interwencyjnej, polegającej na łączeniu diagnostyki, w szczególności diagnostyki obrazowej, wykorzystującej technikę cewnikowania serca i naczyń, z małoinwazyjnymi zabiegami korygującymi wykryte patologie. Spośród najczęstszych zabiegów interwencyjnych przedstawiono kardiostymulację, koronaroplastykę, wszczepianie stentów naczyniowych, elektroablację oraz inne często stosowane zabiegi w obrębie serca z użyciem cewnikowania serca i naczyń.

EN

The paper describes technology and procedures commonly used in interventional cardiology, which is based on combining image diagnostics with cardiac and vessels catheterization and with minimally invasive procedures correcting the detected pathologies. Cardiostimulation, coronaroplasty, implantation of stents, electroablation and other interventional cardiac operations used with cardiac and vessel catheterization, were presented.

12

Comparison of T-wave alternans detection methods

Janusek D., Pawłowski Z., Karczmarewicz S., Przybylski A.

Biocybernetics and Biomedical Engineering

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2004

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Vol. 24, no. 4

31-41

EN

T-wave alternans (TWA), which has recently been detected at the micro-volt level, is a non-invasive marker of the vulnerability to ventricular arrhythmia. The aim of this study was to compare the presently used TWA detection methods and to determine the ECG signal acquisition and processing conditions ensuring the TWA detection at its lowest possible level. The results of the evaluation and comparison of seven methods are presented. The ECG signal analysis was performed in the time and frequency domain. The differential and correlation methods were applied as the time domain methods. The complex demodulation method and methods based on the FFT and Karhunen-Loeve transform were tested as the frequency domain methods. The T-wave alternans markers were measured in 28 patients, and the results were compared. The usefulness of TWA detection methods has been demonstrated to dependant on the signal properties, such as the nonstationarity of TWA and the level of noise. Strong correlation between the magnitude of the T-wave alternans and ventricular repolarization time is described. A new, spectral methods is proposed.