Automatic Arrhythmia Classification Research Articles

Review on spiking neural network-based ECG classification methods for low-power environments.

This paper reviews arrhythmia classification studies using electrocardiogram (ECG) signals. Research on automatically diagnosing arrhythmia in daily life has been actively underway for early detection and treatment of heart disease. Development of automatic arrhythmia classification using ECG signal began based on handcrafted morphological feature extraction and machine learning-based classification methods. As deep neural networks (DNN) show excellent performance in the signal processing field, studies using various types of DNN are also being conducted in ECG classification. However, these DNN-based studies have extremely high computational complexity, making it challenging to perform real-time classification, and are unsuitable for low-power environments such as wearable devices due to high power consumption. Currently, research based on spiking neural network (SNN), which mimics the low-power operation of the human nervous system, is attracting attention as a method that can dramatically reduce complexity and power consumption. The classification accuracy of the SNN-based ECG classification studies is close to that of the DNN-based studies. When combined with neuromorphic hardware, it shows ultra-low-power performance, suggesting the possibility of use in lightweight devices. In this paper, the SNN-based ECG classification studies for low-power environments are mainly reviewed, and prior to this, conventional and DNN-based ECG classification studies are also reviewed. We hope that this review will be helpful to researchers and engineers interested in the field of ECG classification.

The deep convolutional networks for the classification of multi-class arrhythmia

An arrhythmia is an irregular heartbeat. Many researchers in the AI field have carried out the automatic classification of arrhythmias, and the issue that has been widely discussed is imbalanced data. A popular technique for overcoming this problem is the synthetic minority oversampling technique (SMOTE) technique. In this paper, the author adds some sampling of data obtained from other datasets into the primary dataset. In this case, the main dataset is the Massachusetts Institute of Technology–Beth Israel Hospital (MIT-BIH) arrhythmia database and an additional dataset from the MIT-BIH supraventricular arrhythmia database. The classification process is carried out with one-dimensional convolutional neural network model (1D-CNN) to perform multiclass and subject-class advancement of medical instrumentation (AAMII) classifications. The results obtained from this study are an accuracy of 99.10% for multiclass and 99.25% for subject-class.

Study and analysis on detection, classification, and prediction of cardiac arrhythmia using soft computing tool

Cardiac Arrhythmia (CA) is a disorder of heartbeat or rhythm, that happens when the electrical signals that synchronize the heartbeats do not function properly. The Electro Cardio Gram (ECG) is the electrical realization of the expanding and contracting action of the heart and can be registered easily with the electrodes placed near the chest. Hence, due to the complexity of analyzing the huge number of signals in ECG records, it has become one of the major challenges to cardiologists to make early and accurate diagnoses and prognoses. Therefore, there is an essential need for accurate automatic arrhythmia classification. According to the records of the World Health Organization (WHO), 4.5 million CA patients are reporting alone in the United States. Therefore, it is stated as one of the most common reasons for death worldwide and it is very essential to the early diagnosis and prevention of CA. Hence, this research article mainly focuses to analyse the various methods used for the classification, early diagnosis, and prevention of CA. This research presents the overview of a few research articles suggesting different methods based on various fields like IOT, Machine Learning (ML) approaches, Deep Learning (DL) approaches, and so on for the automatic detection of Cardiac Arrhythmia. The literature work mainly focuses on various early Detection, prediction, and classification techniques for CA. The research gaps were also analyzed from these papers and elaborated for further research work which can be helpful for society.

An image classification method for arrhythmias based on Gramian angular summation field and improved Inception-ResNet-v2

Arrhythmia is a significant cardiovascular disease that poses a threat to human health, and its primary diagnosis relies on electrocardiogram (ECG). Implementing computer technology to achieve automatic classification of arrhythmia can effectively avoid human error, improve diagnostic efficiency, and reduce costs. However, most automatic arrhythmia classification algorithms focus on one-dimensional temporal signals, which lack robustness. Therefore, this study proposed an arrhythmia image classification method based on Gramian angular summation field (GASF) and an improved Inception-ResNet-v2 network. Firstly, the data was preprocessed using variational mode decomposition, and data augmentation was performed using a deep convolutional generative adversarial network. Then, GASF was used to transform one-dimensional ECG signals into two-dimensional images, and an improved Inception-ResNet-v2 network was utilized to implement the five arrhythmia classifications recommended by the AAMI (N, V, S, F, and Q). The experimental results on the MIT-BIH Arrhythmia Database showed that the proposed method achieved an overall classification accuracy of 99.52% and 95.48% under the intra-patient and inter-patient paradigms, respectively. The arrhythmia classification performance of the improved Inception-ResNet-v2 network in this study outperforms other methods, providing a new approach for deep learning-based automatic arrhythmia classification.

A deep neural network based on multi-model and multi-scale for arrhythmia classification

Automatic arrhythmia classification has been a cross research hot spot of artificial intelligence applied on biomedical engineering. In this study, a deep neural network based on multi-model and multi-scale net (MMnet) was proposed to classify arrhythmia. Firstly, the raw one-dimensional (1-D) ECG recordings from the public 2018 China Physiological Signal Challenge database (CPSC 2018 DB) was preprocessed to remove noise by a wavelet transform algorithm based on db6, and the clean 1-D recordings was transformed into their corresponding two-dimensional (2-D) time–frequency diagrams by a modified frequency slice wavelet transform (MFSWT). Then, the clean 1-D ECG recordings and the corresponding 2-D diagrams were used as model 1 and model 2 of the multi-model input of MMnet to feed into the multi-scale net of the MMnet which included a backbone module for extracting initially rough features and a dilated convolution module for further fine features. So, the more comprehensive and accurate information within the 1-D recordings and the 2-D diagrams was acquired for classifying all 9 arrhythmia categories (i.e., N, AF, I-AVB, RBBB, PAC, PVC, STD, LBBB and STE). Finally, the results for 9-class classification were precision 84.91 %, recall 82.64 % and F1 83.52 %. The results indicate the MMnet can obtain relatively more comprehensive information within the ECG recordings.

ECG Heartbeat Classification Using Machine Learning and Metaheuristic Optimization for Smart Healthcare Systems.

Early diagnosis and classification of arrhythmia from an electrocardiogram (ECG) plays a significant role in smart healthcare systems for the health monitoring of individuals with cardiovascular diseases. Unfortunately, the nonlinearity and low amplitude of ECG recordings make the classification process difficult. Thus, the performance of most traditional machine learning (ML) classifiers is questionable, as the interrelationship between the learning parameters is not well modeled, especially for data features with high dimensions. To address the limitations of ML classifiers, this paper introduces an automatic arrhythmia classification approach based on the integration of a recent metaheuristic optimization (MHO) algorithm and ML classifiers. The role of the MHO is to optimize the search parameters of the classifiers. The approach consists of three steps: the preprocessing of the ECG signal, the extraction of the features, and the classification. The learning parameters of four supervised ML classifiers were utilized for the classification task; support vector machine (SVM), k-nearest neighbors (kNNs), gradient boosting decision tree (GBDT), and random forest (RF) were optimized using the MHO algorithm. To validate the advantage of the proposed approach, several experiments were conducted on three common databases, including the Massachusetts Institute of Technology (MIT-BIH), the European Society of Cardiology ST-T (EDB), and the St. Petersburg Institute of Cardiological Techniques 12-lead Arrhythmia (INCART). The obtained results showed that the performance of all the tested classifiers were significantly improved after integrating the MHO algorithm, with the average ECG arrhythmia classification accuracy reaching 99.92% and a sensitivity of 99.81%, outperforming the state-of the-art methods.

Automatic classification of arrhythmias using multi-branch convolutional neural networks based on channel-based attention and bidirectional LSTM

Cardiac arrhythmia is an abnormal rhythm of the heartbeat and can be life-threatening Electrocardiogram (ECG) is a technology that uses an electrocardiograph machine to record a graph of the changes in electrical activity produced by the heart at each cardiac cycle. ECG can generally be used to check whether the examinee has arrhythmia, ion channel disease, cardiomyopathy, electrolyte disorder and other diseases. To reduce the workload of doctors and improve the accuracy of ECG signal recognition, a novel and lightweight automatic ECG classification method based on Convolutional Neural Network (CNN) is proposed. The multi-branch network with different receptive fields is used to extract the multi-spatial deep features of heartbeats. The Channel Attention Module (CAM) and Bidirectional Long Short-Term Memory neural network (BLSTM) module are used to filter redundant ECG features. CAM and BLSTM are beneficial for distinguishing different categories of heartbeats. In the experiments, a four-fold cross-validation technique is used to improve the generalization capability of the network, and it shows good performance on the testing set. This method divides heartbeats into five categories according to the American Advancement of Medical Instrumentation (AAMI) criteria, which is validated in the MIT-BIH arrhythmia database. The sensitivity of this method to Ventricular Ectopic Beat (VEB) is 98.5% and the F1 score is 98.2%. The precision of the Supraventricular Ectopic Beat (SVEB) is 91.1%, and the corresponding F1 score is 90.8%. The proposed method has high classification performance and a lightweight feature. In a word, it has broad application prospects in clinical medicine and health testing.

A Deep Neural Network Ensemble Classifier with Focal Loss for Automatic Arrhythmia Classification.

Automated electrocardiogram classification techniques play an important role in assisting physicians in diagnosing arrhythmia. Among these, the automatic classification of single-lead heartbeats has received wider attention due to the urgent need for portable ECG monitoring devices. Although many heartbeat classification studies performed well in intrapatient assessment, they do not perform as well in interpatient assessment. In particular, for supraventricular ectopic heartbeats (S), most models do not classify them well. To solve these challenges, this article provides an automated arrhythmia classification algorithm. There are three key components of the algorithm. First, a new heartbeat segmentation method is used, which improves the algorithm's capacity to classify S substantially. Second, to overcome the problems created by data imbalance, a combination of traditional sampling and focal loss is applied. Finally, using the interpatient evaluation paradigm, a deep convolutional neural network ensemble classifier is built to perform classification validation. The experimental results show that the overall accuracy of the method is 91.89%, the sensitivity is 85.37%, the positive productivity is 59.51%, and the specificity is 93.15%. In particular, for the supraventricular ectopic heartbeat(s), the method achieved a sensitivity of 80.23%, a positivity of 49.40%, and a specificity of 96.85%, exceeding most existing studies. Even without any manually extracted features or heartbeat preprocessing, the technique achieved high classification performance in the interpatient assessment paradigm.

Visualization deep learning model for automatic arrhythmias classification

Objective. With the improvement of living standards, heart disease has become one of the common diseases that threaten human health. Electrocardiography (ECG) is an effective way of diagnosing cardiovascular diseases. With the rapid growth of ECG examinations and the shortage of cardiologists, accurate and automatic arrhythmias classification has become a research hotspot. The main purpose of this paper is to improve accuracy in detecting abnormal ECG patterns. Approach. A hybrid 1D Resnet-GRU method, consisting of the Resnet and gated recurrent unit (GRU) modules, is proposed to implement classification of arrhythmias from 12-lead ECG recordings. In addition, the focal Loss function is used to solve the problem of unbalanced datasets. Based on the proposed 1D Resnet-GRU model, we use class-discriminative visualization to improve interpretability and transparency as an additional step. In this paper, the Grad-CAM++ mechanism has been employed to the trained network model and generate thermal images superimposed on raw signals to explore underlying explanations of various ECG segments. Main results. The experimental results show that the proposed method can achieve a high score of 0.821 (F1-score) in classifying 9 kinds of arrythmias, and Grad-CAM++ not only provides insight into the predictive power of the model, but is also consistent with the diagnostic approach of the arrhythmia classification. Significance. The proposed method can effectively select and integrate ECG features to achieve the goal of end-to-end arrhythmia classification by using 12-lead ECG signals, which can serve a promising and useful way for automatic arrhythmia classification, and can provide an explainable deep leaning model for clinical diagnosis.

Multiscale Laplacian graph kernel features combined with tree deep convolutional neural network for the detection of ECG arrhythmia

In this manuscript, Multiscale Laplacian graph kernel features combined with Tree Deep Convolutional Neural Network (MLGK-TDCNN) is proposed for the detection of Electrocardiogram (ECG) arrhythmia. Here, the input ECG signals are taken from two datasets: (i) MIT-BIH AF database (AFDB) (ii) MIT-BIH arrhythmia database (MBDB). These datasets are fully unbalanced dataset, and these datasets are balanced using Improved fuzzy c-means method for unbalanced dataset. Moreover, the de-noised ECG signals are given to the MLGK. The proposed MLGK is to combine the Multiscale kernel features from the Preprocessed ECG signals. The combined Multiscale kernel features given to the TDCNN classifier for the detection of AF with raw normal sinus rhythm (NSR). The proposed approach is activated in MATLAB platform, then the efficiency is analyzed with existing approaches. The experimental outcomes demonstrate that the proposed FFREWT-MLGK-TDCNN approach is compared with two databases. From the analysis, the accuracy of AFDB shows 9.40%, 16.44% and 23.20% better than the existing approaches, the accuracy of MBDB shows 14.67%, 21.42% and 7.54% better than the existing approaches, like novel intelligent approach depending on multi-scale convolution kernel (MCK) and Squeeze-and-Excitation network (SENet) for AF detection, automatic arrhythmia classification strategy using the optimization-based deep convolutional neural network (CNN-BaROA), deep learning method for classifying arrhythmia by using 2-second segments of 2D recurrence plot images of ECG signals (2D-CNN) respectively.

Classification of Arrhythmia Based on Convolutional Neural Networks and Encoder-Decoder Model

As a common and high-risk type of disease, heart disease seriously threatens people’s health. At the same time, in the era of the Internet of Thing (IoT), smart medical device has strong practical significance for medical workers and patients because of its ability to assist in the diagnosis of diseases. Therefore, the research of real-time diagnosis and classification algorithms for arrhythmia can help to improve the diagnostic efficiency of diseases. In this paper, we design an automatic arrhythmia classification algorithm model based on Convolutional Neural Network (CNN) and Encoder-Decoder model. The model uses Long Short-Term Memory (LSTM) to consider the influence of time series features on classification results. Simultaneously, it is trained and tested by the MIT-BIH arrhythmia database. Besides, Generative Adversarial Networks (GAN) is adopted as a method of data equalization for solving data imbalance problem. The simulation results show that for the inter-patient arrhythmia classification, the hybrid model combining CNN and Encoder-Decoder model has the best classification accuracy, of which the accuracy can reach 94.05%. Especially, it has a better advantage for the classification effect of supraventricular ectopic beats (class S) and fusion beats (class F).

Segment Origin Prediction: A Self-supervised Learning Method for Electrocardiogram Arrhythmia Classification.

The automatic arrhythmia classification system has made a significant contribution to reducing the mortality rate of cardiovascular diseases. Although the current deep-learning-based models have achieved ideal effects in arrhythmia classification, their performance still needs to be further improved due to the small scale of the dataset. In this paper, we propose a novel self-supervised pre-training method called Segment Origin Prediction (SOP) to improve the model's arrhythmia classification performance. We design a data reorganization module, which allows the model to learn ECG features by predicting whether two segments are from the same original signal without using annotations. Further, by adding a feed-forward layer to the pre-training stage, the model can achieve better performance when using labeled data for arrhythmia classification in the downstream stage. We apply the proposed SOP method to six representative models and evaluate the performances on the PhysioNet Challenge 2017 dataset. After using the SOP pre-training method, all baseline models gain significant improvement. The experimental results verify the effectiveness of the proposed SOP method.

An automatic arrhythmia classification model based on improved Marine Predators Algorithm and Convolutions Neural Networks

Preparation of Convolutional Neural Networks (CNNs) for classification purposes depends heavily on the knowledge of hyper-parameters tuning. This study aims, in particular in task of automated electrocardiograms (ECG), to minimize the user variability in the CNN training by searching and optimizing the CNN parameters automatically. In the clinical practice, the task of ECG classification analysis is restricted by existing models’ configuration. The hyper-parameters of the CNN model should be adjusted for the ECG classification problem. The best configuration for hyper-parameters is selected to have an impact on the production of the model. Deep knowledge of deep learning algorithms and suitable optimization techniques are also needed. Although there are many strategies for automated optimization, different benefits and disadvantages occur as they are applied to ECG classification problem. Here we present a CNN model for classification of non-ectopic (N), ventricular ectopic (V), supraventricular ectopic (S), and fusion (F) ECG rhythmias by the hybrid models based on modified version of Marine Predators algorithm (MPA) and CNN, known as the IMPA-CNN. The proposed model summarizes the feature extraction techniques of major features and, thus, outperforms other current classification models through automatically select the best hyper-parameters configuration of the CNN model. To reduce the time and complication complexity, optimum characteristics have been extracted directly from the raw signal using 1D-local binary pattern, higher order statistics, central moment, Hermite basis function discrete wavelet transform, and R–R intervals. Then, a modified version of MPA algorithm is used to select appropriate hyper-parameters for the CNN model like initial learning rate for the CNN model that is one of the major hyper parameters effect output performance, optimizer type which can be set to stochastic gradient descent (SGD), adaptive moment estimation (Adam), root mean square propagation (RMSprop), the activation function form used for modeling non-linear functions, set to ‘Rectified Linear Unit (ReLU), or ‘sigmoid’ and some other hyper-parameters are related to the optimization and training process of CNN model. Many available optimization algorithms for hyper-parameters optimization problems are provided. In addition, experiments with well know data sets like MIT-BIH arrhythmia, European ST-T database, and St. Petersburg INCART database are carried out to compare the performance of various optimization approaches and to provide practical illustration of the optimization of hyper-parameters for the proposed CNN model.

Arrhythmia Classification Using Biased Dropout and Morphology-Rhythm Feature With Incremental Broad Learning

In recent times, people have become increasingly health-conscious. To obtain timely and accurate information on the status of the heart, one of the most important organs of the human body, there is a growing demand among individuals and doctors for accurate and real-time automatic classification of arrhythmias. Consequently, this paper proposes a fast and accurate classification method for arrhythmias. In the proposed method, we build an incremental broad learning (IBL) classification model based on the biased dropout technique for arrhythmia-type recognition. In particular, we extract the morphological-rhythm features of the denoised signal as the input data of the IBL model in the electrocardiogram signal preprocessing. The IBL model enhances the classification effect of the node optimization model by using improved features. To the best of our knowledge, this study is the first application of the IBL model to the study of arrhythmia classification. The results of experiments conducted on the MIT-BIH database indicate that the proposed method is effective and achieves superior classification results. The average classification accuracy for six types of arrhythmias was 99%, and the training time required was only 2.7 s. In addition, based on the evaluation index recommended by the ANSI/AAMI EC57:2012 standard, our method is superior to existing methods on all indexes, except for the positive predictive rate of ventricular ectopic beats. Therefore, the proposed classification method outperforms state-of-the-art methods in terms of real-time performance and accuracy and provides a new approach for further improvements in arrhythmia classification.

Arrhythmia Classification with ECG signals based on the Optimization-Enabled Deep Convolutional Neural Network

Arrhythmia classification is the need of the hour as the world is reporting a higher death troll as a cause of cardiac diseases. Most of the existing methods developed for arrhythmia classification face a hectic challenge of classification accuracy and they raised the challenge of automatic monitoring and classification methods. Accordingly, the paper proposes the automatic arrhythmia classification strategy using the optimization-based deep convolutional neural network (deep CNN). The optimization algorithm named, Bat-Rider optimization algorithm (BaROA) is newly developed using the multi-objective bat algorithm (MOBA) and Rider Optimization Algorithm (ROA).At first, the wave and gabor features are extracted from the ECG signals in such a way that these features represent the individual ECG features. Finally, the signals are provided to the BaROA-based DCNN classifier that identifies conditions of the individual as arrhythmia and no-arrhythmia from the ECG signals. The methods are analyzed using the MIT-BIH Arrhythmia Database and the analysis is performed based on the evaluation parameters, like accuracy, specificity, and sensitivity, which are found to be 93.19 %, 95 %, and 93.98 %, respectively.

Key technologies in digital breast tomosynthesis system:theory, design, and optimization

To develop a digital breast tomosynthesis (DBT) imaging system with optimizes imaging chain. Based on 3D tomography and DBT imaging scanning, we analyzed the methods for projection data correction, geometric correction, projection enhancement, filter modulation, and image reconstruction, and established a hardware testing platform. In the experiment, the standard ACR phantom and high-resolution phantom were used to evaluate the system stability and noise level. The patient projection data of commercial equipment was used to test the effect of the imaging algorithm. In the high-resolution phantom study, the line pairs were clear without confusing artifacts in the images reconstructed with the geometric correction parameters. In ACR phantom study, the calcified foci, cysts, and fibrous structures were more clearly defined in the reconstructed images after filtering and modulation. The patient data study showed a high contrast between tissues, and the lesions were more clearly displayed in the reconstructed image. This DBT imaging system can be used for mammary tomography with an image quality comparable to that of commercial DBT systems to facilitate imaging diagnosis of breast diseases.

Automatic Cardiac Arrhythmia Classification Using Combination of Deep Residual Network and Bidirectional LSTM

Cardiac arrhythmia is associated with abnormal electrical activities of the heart, which can be reflected by altered characteristics of electrocardiogram (ECG). Due to the simplicity and non-invasive nature, the ECG has been widely used for detecting arrhythmias and there is an urgent need for automatic ECG detection. Up to date, some algorithms have been proposed for automatic classification of cardiac arrhythmias based on the features of the ECG; however, their stratification rate is still poor due to unreliable features of signal characteristics or limited generalization capability of the classifier, and therefore, it remains a challenge for automatic diagnosis of arrhythmias. In this paper, we propose a new method for automatic classification of arrhythmias based on deep neural networks (DNNs). The two DNN models constitutive of residual convolutional modules and bidirectional long short-term memory (LSTM) layers are trained to extract features from raw ECG signals. The extracted features are concatenated to form a feature vector which is trained to do the final classification. The algorithm is evaluated based on the test set of China Physiological Signal Challenge (CPSC) dataset with F1 measure regarded as the harmonic mean between the precision and recall. The resulting overall F1 score is 0.806, FAF score is 0.914 for atrial fibrillation (AF), FBlock score is 0.879 for block, FPC and FST scores are 0.801 and 0.742 for premature contraction and ST-segment change, which demonstrates a good performance that may have potential practical applications.

Inter-Patient ECG Heartbeat Classification with Temporal VCG Optimized by PSO

Classifying arrhythmias can be a tough task for a human being and automating this task is highly desirable. Nevertheless fully automatic arrhythmia classification through Electrocardiogram (ECG) signals is a challenging task when the inter-patient paradigm is considered. For the inter-patient paradigm, classifiers are evaluated on signals of unknown subjects, resembling the real world scenario. In this work, we explore a novel ECG representation based on vectorcardiogram (VCG), called temporal vectorcardiogram (TVCG), along with a complex network for feature extraction. We also fine-tune the SVM classifier and perform feature selection with a particle swarm optimization (PSO) algorithm. Results for the inter-patient paradigm show that the proposed method achieves the results comparable to state-of-the-art in MIT-BIH database (53% of Positive predictive (+P) for the Supraventricular ectopic beat (S) class and 87.3% of Sensitivity (Se) for the Ventricular ectopic beat (V) class) that TVCG is a richer representation of the heartbeat and that it could be useful for problems involving the cardiac signal and pattern recognition.

A Multistage Deep Belief Networks Application on Arrhythmia Classification

An electrocardiogram (ECG) is a biomedical signal type that determines the normality and abnormality of heart beats using the electrical activity of the heart and has a great importance for cardiac disorders. The computer-aided analysis of biomedical signals has become a fabulous utilization method over the last years. This study introduces a multistage deep learning classification model for automatic arrhythmia classification. The proposed model includes a multi-stage classification system that uses ECG waveforms and the Second Order Difference Plot (SODP) features using a Deep Belief Network (DBN) classifier which has a greedy layer wise training with Restricted Boltzmann Machines algorithm. The multistage DBN model classified the MIT-BIH Arrhythmia Database heartbeats into 5 main groups defined by ANSI/AAMI standards. All ECG signals are filtered with median filters to remove the baseline wander. ECG waveforms were segmented from long-term ECG signals using a window with a length of 501 data points (R wave centered). The extracted waveforms and elliptical features from the SODP are utilized as the input of the model. The proposed DBN-based multistage arrhythmia classification model has discriminated five types of heartbeats with a high accuracy rate of 96.10%.

A NEW OPTIMIZED WAVELET TRANSFORM FOR HEART BEAT CLASSIFICATION

A method for automatic classification of Arrhythmias from Electrocardiogram based on features generated from a new Continuous Wavelet Transform (CWT) is presented in this paper. The classification performance was studied using the most commonly available database, the MIT-BIH arrhythmia database. The new wavelet for classification was evolved using Genetic Algorithm (GA). The optimum wavelet for classification was obtained after several runs of the GA algorithm. The class labeling was followed according to the Association for the Advancement of Medical Instrumentation (AAMI). The wavelet scales corresponding to the different frequency levels giving maximum classification performance was identified. Probabilistic Neural Network (PNN) classifier was used for classification. The proposed classification system offered an overall sensitivity of 97% for Normal beats (N), 75% for Supraventricular beats (Sv) and 93% for Ventricular beats (V) which is better than existing results reported in literature. This technique could exclusively identify some of the isolated abnormalities compared to other results reported.