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Papers by Pinjala N Malleswari

Nowadays heart diseases and their diagnosis have emerged as a prominent subject in health care sy... more Nowadays heart diseases and their diagnosis have emerged as a prominent subject in health care systems, given that the heart performs a crucial role in the human body. Several computational techniques have been explored for the recognition and classification of cardiac diseases using Electrocardiogram (ECG) signals. Deep Learning (DL) is a present focus in healthcare solicitations, particularly in the classification of heartbeats in ECG signals. Many studies have utilized dissimilar DL models, including RNN (Recurrent Neural Networks), GRU (Gated Recurrent Unit), and CNN (Convolutional Neural Networks), to classify heartbeats using the MIT-BIH arrhythmia dataset. This article presents a methodical exploration of Bi-LSTM (Bi directional Long Short-Term Memory) based DL models for heartbeat classification using various quality metrics. Proposed variants include the Bi-LSTM model, demonstrating remarkable accuracy in classifying the heartbeats into five classes: Normal (N) beat, Supraventricular (S) beat, Ventricular contraction (V), Fusion beats (F), and Unclassifiable Beat (Q). The proposed technique outperforms present classifiers with Accuracy, Sensitivity, Specificity, and F1 score values of 98%, 96.9%, 97.4%, and 97.5% respectively. The simulations are conducted using MATLAB 2020a.

International Journal on Recent and Innovation Trends in Computing and Communication

Arrhythmia classification is a prominent research problem due to the computational complexities o... more Arrhythmia classification is a prominent research problem due to the computational complexities of learning the morphology of various ECG patterns and its wide prevalence in the medical field, particularly during the COVID-19 pandemic. In this article, we used Empirical Mode Decomposition and Discrete Wavelet Transform for preprocessing and then the modified signal is classified using various classifiers such as Decision Tree, Logistic Regression, Gaussian Naïve Bayes, Random Forest, Linear SVM, Polynomial SVM, RBF SVM, Sigmoid SVM and Convolutional Neural Networks. The proposed method classify the data into five classes N (Normal), S (Supraventricular premature) beat, (V) Premature ventricular contraction, F (Fusion of ventricular and normal), and Q, (Unclassifiable Beat) using softmax regressor at the end of the network. The proposed approach performs well in terms of classification accuracy when tested using ECG signals acquired from the MIT-BIH database. In comparison to existi...

166 Published By: Blue Eyes Intelligence Engineering & Sciences Publication Retrieval Number A298... more 166 Published By: Blue Eyes Intelligence Engineering & Sciences Publication Retrieval Number A2988058119/19©BEIESP  Abstract : Electrocardiography is a technology used to identify the abnormalities in heart and noise free ECG data is often required for correct medication of cardiac disorders. Generally ECG signals are contaminated by noise and human artifacts during data acquisition. The denoising signal plays a major role in medical field. Electrocardiogram (ECG) signals represent important characteristics for diagnosing the disease or how the treatment works on the heart, which makes it necessary to design filters to weaken and eliminate these noises. This paper describes the denoising of ECG signal from baseline wander noise using digital filters and wavelet transform. The function of the filters has been tested on different cardiac signals. The results show that wavelet transform has the best performance in denoising ECG signals than digital filters such as IIR (Infinite Impuls...

Journal of Biomimetics, Biomaterials and Biomedical Engineering, 2021

Electrocardiogram (ECG) is the most important signal in the biomedical field for the diagnosis of... more Electrocardiogram (ECG) is the most important signal in the biomedical field for the diagnosis of Cardiac Arrhythmia (CA). ECG signal often interrupted with various noises due to non-stationary nature which leads to poor diagnosis. Denoising process helps the physicians for accurate decision making in treatment. In many papers various noise elimination techniques are tried to enhance the signal quality. In this paper a novel hybrid denoising technique using EMD-DWT for the removal of various noises such as Additive White Gaussian Noise (AWGN), Baseline Wander (BW) noise, Power Line Interference (PLI) noise at various concentrations are compared to the conventional methods in terms of Root Mean Square Error (RSME), Signal to Noise Ratio (SNR), Peak Signal to Noise Ratio (PSNR), Cross-Correlation (CC) and Percent Root Square Difference (PRD). The average values of RMSE, SNR, PSNR, CC and PRD are 0.0890, 9.8821, 14.4464, 0.9872 and 10.9036 for the EMD approach, respectively, and 0.0707...

Journal of Ambient Intelligence and Humanized Computing, 2021

Interdisciplinary Sciences: Computational Life Sciences

Nowadays heart diseases and their diagnosis have emerged as a prominent subject in health care sy... more Nowadays heart diseases and their diagnosis have emerged as a prominent subject in health care systems, given that the heart performs a crucial role in the human body. Several computational techniques have been explored for the recognition and classification of cardiac diseases using Electrocardiogram (ECG) signals. Deep Learning (DL) is a present focus in healthcare solicitations, particularly in the classification of heartbeats in ECG signals. Many studies have utilized dissimilar DL models, including RNN (Recurrent Neural Networks), GRU (Gated Recurrent Unit), and CNN (Convolutional Neural Networks), to classify heartbeats using the MIT-BIH arrhythmia dataset. This article presents a methodical exploration of Bi-LSTM (Bi directional Long Short-Term Memory) based DL models for heartbeat classification using various quality metrics. Proposed variants include the Bi-LSTM model, demonstrating remarkable accuracy in classifying the heartbeats into five classes: Normal (N) beat, Supraventricular (S) beat, Ventricular contraction (V), Fusion beats (F), and Unclassifiable Beat (Q). The proposed technique outperforms present classifiers with Accuracy, Sensitivity, Specificity, and F1 score values of 98%, 96.9%, 97.4%, and 97.5% respectively. The simulations are conducted using MATLAB 2020a.

International Journal on Recent and Innovation Trends in Computing and Communication

Arrhythmia classification is a prominent research problem due to the computational complexities o... more Arrhythmia classification is a prominent research problem due to the computational complexities of learning the morphology of various ECG patterns and its wide prevalence in the medical field, particularly during the COVID-19 pandemic. In this article, we used Empirical Mode Decomposition and Discrete Wavelet Transform for preprocessing and then the modified signal is classified using various classifiers such as Decision Tree, Logistic Regression, Gaussian Naïve Bayes, Random Forest, Linear SVM, Polynomial SVM, RBF SVM, Sigmoid SVM and Convolutional Neural Networks. The proposed method classify the data into five classes N (Normal), S (Supraventricular premature) beat, (V) Premature ventricular contraction, F (Fusion of ventricular and normal), and Q, (Unclassifiable Beat) using softmax regressor at the end of the network. The proposed approach performs well in terms of classification accuracy when tested using ECG signals acquired from the MIT-BIH database. In comparison to existi...

166 Published By: Blue Eyes Intelligence Engineering & Sciences Publication Retrieval Number A298... more 166 Published By: Blue Eyes Intelligence Engineering & Sciences Publication Retrieval Number A2988058119/19©BEIESP  Abstract : Electrocardiography is a technology used to identify the abnormalities in heart and noise free ECG data is often required for correct medication of cardiac disorders. Generally ECG signals are contaminated by noise and human artifacts during data acquisition. The denoising signal plays a major role in medical field. Electrocardiogram (ECG) signals represent important characteristics for diagnosing the disease or how the treatment works on the heart, which makes it necessary to design filters to weaken and eliminate these noises. This paper describes the denoising of ECG signal from baseline wander noise using digital filters and wavelet transform. The function of the filters has been tested on different cardiac signals. The results show that wavelet transform has the best performance in denoising ECG signals than digital filters such as IIR (Infinite Impuls...

Journal of Biomimetics, Biomaterials and Biomedical Engineering, 2021

Electrocardiogram (ECG) is the most important signal in the biomedical field for the diagnosis of... more Electrocardiogram (ECG) is the most important signal in the biomedical field for the diagnosis of Cardiac Arrhythmia (CA). ECG signal often interrupted with various noises due to non-stationary nature which leads to poor diagnosis. Denoising process helps the physicians for accurate decision making in treatment. In many papers various noise elimination techniques are tried to enhance the signal quality. In this paper a novel hybrid denoising technique using EMD-DWT for the removal of various noises such as Additive White Gaussian Noise (AWGN), Baseline Wander (BW) noise, Power Line Interference (PLI) noise at various concentrations are compared to the conventional methods in terms of Root Mean Square Error (RSME), Signal to Noise Ratio (SNR), Peak Signal to Noise Ratio (PSNR), Cross-Correlation (CC) and Percent Root Square Difference (PRD). The average values of RMSE, SNR, PSNR, CC and PRD are 0.0890, 9.8821, 14.4464, 0.9872 and 10.9036 for the EMD approach, respectively, and 0.0707...

Journal of Ambient Intelligence and Humanized Computing, 2021

Interdisciplinary Sciences: Computational Life Sciences