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@article{163663,
        author = {SIMRAN SULTANA and YESHASWINI G and AYESHA ZAHEEN and FIZA FATHIMA and DR.HEENA KOUSER and PROF.UMME AYMUN},
        title = {Advanced Diagnostic Approaches for the Cardiac Arrythmia},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {11},
        pages = {2416-2423},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=163663},
        abstract = {One important category of cardiovascular disorders is arrhythmias, and early identification and diagnosis are essential to averting high-risk incidents like sudden cardiac death. Even while automatic arrhythmia identification based on electrocardiogram (ECG) patches ,heart-beat sensor and body temperature sensors has garnered interest, Many heart patients in rural areas are unable to get the appropriate emergency treatment on time due to the limitations of the IoT wearable sensor connections with a reliable and accurate network. This paper provides a review that aimed to analyze Connectivity, Scheduling and Backup (CSB) components of IoT wearable sensors for smart healthcare. The IoT technology helps to connect the remote patients reliably by establishing the best network connection. This study compares important techniques with different IoT sensors used to detect the vital situations for remote cardiac health monitoring. In IoT health monitoring, The stored data in cloud will be of capable classifying the collected data from the sensors and generate two different signals as emergency and normal for the accurate data transmission. Moreover, WI-Fi is used to connect to the health caregivers with reliable connections. Finally, suitable algorithms will be taken for filtering the data before forwarding to a health center database. This provides appropriate analysis for network connection, data accuracy with scheduling and backup module for IoT health monitoring.},
        keywords = {Deep learning (DL), Random forest (RF), Support Vector Machine (SVM), K-nearest Neighbor (KNN), Electrophysiological, CSB.},
        month = {},
        }