https://ijsr.internationaljournallabs.com/index.php/ijsr Journal of Social Research International Journal Labs en-US Journal of Social Research 2827-9832 <p><a href="http://creativecommons.org/licenses/by-sa/4.0/" rel="license"><img src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png" alt="Creative Commons License"></a><br>This work is licensed under a&nbsp;<a href="http://creativecommons.org/licenses/by-sa/4.0/" rel="license">Creative Commons Attribution-ShareAlike 4.0 International.</a></p> <p>Authors who publish with this journal agree to the following terms:</p> <ul> <li class="show">Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a<a href="https://creativecommons.org/licenses/by-sa/4.0">&nbsp;Creative Commons Attribution-ShareAlike 4.0 International (CC-BY-SA).</a>&nbsp;that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.</li> <li class="show">Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.</li> <li class="show">Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.</li> </ul> https://ijsr.internationaljournallabs.com/index.php/ijsr/article/view/2362 <p><em>Recent advancements in analyzing methods for the identification of arrhythmia based on deep learning have revealed great promise towards improving cardiac care. Probabilistic models have been used effectively to detect a number of arrhythmic disorders from ECG signals with the help of convolutional neural networks and Long Short Term Memory neural network. These models are more precise and quicker than conventional approaches to deal with the ailment in the initial stages and with diseases such as bradycardia, ventricular tachycardia, or atrial fibrillation. However, barriers such as class distribution, data sanitization, interpretability, and generalization across different types of patients remain, which hinders their clinical utilization. Actually, deep learning is used in clinical practice, especially in wearable devices and remote patient monitoring for the unceasing and real-time continuous rheological evaluation of the cardiovascular system. The subsequent advancements in this area will focus on the proper combination of the data from multiple subject areas and the application of specific treatment approaches, including the use of artificial intelligence in a more extensive medical system. Other than the diagnosis of arrhythmias, deep learning has the chances of enhancing patient prognoses, preliminary assessment, and tailor-made treatments. It is likely that deep learning-based systems will have a possibility to evolve into powerful aid for diagnosing and setting further treatment in cases of arrhythmias, though there are issues on the way to the enhance the availability and quality of the care. This will probably be facilitated by continued research and integration between academicians, practitioners, and policy makers.</em></p> Arbaz Haider Khan Hira Zainab Roman Khan Hafiz Khawar Hussain Copyright (c) 2024 Arbaz Haider Khan, Hira Zainab, Roman Khan, Hafiz Khawar Hussain https://creativecommons.org/licenses/by-sa/4.0 2024-12-06 2024-12-06 4 1 10.55324/josr.v4i1.2362