RETRACTED ARTICLE: Cardiac arrhythmia detection using dual-tree wavelet transform and convolutional neural network

Abstract

The non-stationary ECG signals are used as a key tools in screening coronary diseases. ECG recording is collected from millions of cardiac cells' and depolarization and re-polarization conducted in a synchronized manner as: The P-wave occurs first, followed by the QRScomplex and the T-wave, which will repeat in each beat. The signal is altered in a cardiac beat period for different heart conditions. This change can be observed in order to diagnose the patient's heart status. There are life-threatening (critical) and non-life -threatening (noncritical) arrhythmia (abnormal Heart). Critical arrhythmia gives little time for surgery, whereas non-critical needs additional life-saving care. Simple naked eye diagnosis can mislead the detection. At that point, Computer Assisted Diagnosis (CAD) is therefore required. In this paper Dual Tree Wavelet Transform (DTWT) used as a feature extraction technique along with Convolution Neural Network (CNN) to detect abnormal Heart. The findings of this research and associated studies are without any cumbersome artificial environments. The CAD method proposed has high generalizability; it can help doctors efficiently identify diseases and decrease misdiagnosis.

Smart Arrhythmia Detection Using Single Lead ECG Signal and Hybridized Deep Neural Network Model

“…K Reddy Madhavi et al (2022) presented the dual-tree wavelet transform and CNN model that identified the irregular heartbeat effectively. In the end, a sophisticated neural network model autonomously identified refined features and provided precise classification.…”

“…K Reddy Madhavi et al. (2022) presented the dual-tree wavelet transform and CNN model that identified the irregular heartbeat effectively.…”

Smart Arrhythmia Detection Using Single Lead ECG Signal and Hybridized Deep Neural Network Model

“…K Reddy Madhavi et al (2022) presented the dual-tree wavelet transform and CNN model that identified the irregular heartbeat effectively. In the end, a sophisticated neural network model autonomously identified refined features and provided precise classification.…”

“…K Reddy Madhavi et al. (2022) presented the dual-tree wavelet transform and CNN model that identified the irregular heartbeat effectively.…”

Optimizing Ischemic Stroke Diagnosis: Enhanced Performance with MobileNetV2 in Automated Image Segmentation

“…Deep learning algorithms have demonstrated exceptional performance in several tasks recently [12][13], particularly in the analysis of medical pictures. CNN are deep learning algorithms that are mostly utilized in radiology and other computer vision tasks.…”

Detection and Classification of MRI Images using Multistage Classifier for Early Prediction of Alzheimer’s Disease

“…Since there is no conclusive proof as to which biomarker is the most dependable, it is crucial to choose the correct one [6]. The diagnostic value of biomarkers is enhanced by the ability to automatically extract spatial characteristics, such as cortical CT measurements, brain volume, and brain surface area, through processing that incorporates both handcrafted as well as deep learning techniques [7].…”

Smart Arrhythmia Detection Using Single Lead ECG Signal and Hybridized Deep Neural Network Model

“…K Reddy Madhavi et al (2022) presented the dual-tree wavelet transform and CNN model that identified the irregular heartbeat effectively. In the end, a sophisticated neural network model autonomously identified refined features and provided precise classification.…”

“…K Reddy Madhavi et al. (2022) presented the dual-tree wavelet transform and CNN model that identified the irregular heartbeat effectively.…”

Optimizing Ischemic Stroke Diagnosis: Enhanced Performance with MobileNetV2 in Automated Image Segmentation

“…Deep learning algorithms have demonstrated exceptional performance in several tasks recently [12][13], particularly in the analysis of medical pictures. CNN are deep learning algorithms that are mostly utilized in radiology and other computer vision tasks.…”

Detection and Classification of MRI Images using Multistage Classifier for Early Prediction of Alzheimer’s Disease

“…Since there is no conclusive proof as to which biomarker is the most dependable, it is crucial to choose the correct one [6]. The diagnostic value of biomarkers is enhanced by the ability to automatically extract spatial characteristics, such as cortical CT measurements, brain volume, and brain surface area, through processing that incorporates both handcrafted as well as deep learning techniques [7].…”

Smart Arrhythmia Detection Using Single Lead ECG Signal and Hybridized Deep Neural Network Model

“…K Reddy Madhavi et al (2022) presented the dual-tree wavelet transform and CNN model that identified the irregular heartbeat effectively. In the end, a sophisticated neural network model autonomously identified refined features and provided precise classification.…”

“…K Reddy Madhavi et al. (2022) presented the dual-tree wavelet transform and CNN model that identified the irregular heartbeat effectively.…”

Optimizing Ischemic Stroke Diagnosis: Enhanced Performance with MobileNetV2 in Automated Image Segmentation

“…Deep learning algorithms have demonstrated exceptional performance in several tasks recently [12][13], particularly in the analysis of medical pictures. CNN are deep learning algorithms that are mostly utilized in radiology and other computer vision tasks.…”

Detection and Classification of MRI Images using Multistage Classifier for Early Prediction of Alzheimer’s Disease

“…Since there is no conclusive proof as to which biomarker is the most dependable, it is crucial to choose the correct one [6]. The diagnostic value of biomarkers is enhanced by the ability to automatically extract spatial characteristics, such as cortical CT measurements, brain volume, and brain surface area, through processing that incorporates both handcrafted as well as deep learning techniques [7].…”