sabir muhammad - Academia.edu (original) (raw)
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Papers by sabir muhammad
IJARST, 2023
is a non-invasive diagnostic tool essential for evaluating heart health and diagnosing a range of... more is a non-invasive diagnostic tool essential for evaluating heart health and diagnosing a range of cardiovascular conditions. Despite its widespread use, accurate interpretation and analysis of ECG signals remain challenging due to the complex nature of cardiovascular diseases and the subtle variances in ECG waveforms[1]. This paper explores the application of deep learning techniques, including convolutional neural networks (CNNs) and long shortterm memory (LSTM) networks, to enhance the accuracy and efficiency of ECG signal analysis[2]. The significance of this research is underscored by the global burden of cardiovascular diseases, which are leading causes of morbidity and mortality[3]. By advancing ECG interpretation through automated deep learning models[4], this study aims to contribute to the early detection, diagnosis, and management of cardiac conditions. Furthermore, the paper introduces novel approaches to ECG segmentation and arrhythmia classification, leveraging the strengths of deep learning to handle the variability and complexity inherent in ECG data[5].
IJARST, 2023
is a non-invasive diagnostic tool essential for evaluating heart health and diagnosing a range of... more is a non-invasive diagnostic tool essential for evaluating heart health and diagnosing a range of cardiovascular conditions. Despite its widespread use, accurate interpretation and analysis of ECG signals remain challenging due to the complex nature of cardiovascular diseases and the subtle variances in ECG waveforms[1]. This paper explores the application of deep learning techniques, including convolutional neural networks (CNNs) and long shortterm memory (LSTM) networks, to enhance the accuracy and efficiency of ECG signal analysis[2]. The significance of this research is underscored by the global burden of cardiovascular diseases, which are leading causes of morbidity and mortality[3]. By advancing ECG interpretation through automated deep learning models[4], this study aims to contribute to the early detection, diagnosis, and management of cardiac conditions. Furthermore, the paper introduces novel approaches to ECG segmentation and arrhythmia classification, leveraging the strengths of deep learning to handle the variability and complexity inherent in ECG data[5].