• Mr. RAMBABU ATMAKURI
• Ms. YAMAVARAM UNNATHI
• Mr. SRIRAMULA GANESH CHARAN
• Mr. ALETI SAI RAM

Timely detection of cardiac arrhythmia characterized by abnormal heartbeats can help in the early diagnosis and treatment of cardiovascular diseases. Wearable healthcare devices typically use neural networks to provide the most convenient way of continuously monitoring heart activity for arrhythmia detection. However, it is challenging to achieve high accuracy and energy efficiency in these smart wearable healthcare devices. In this work, we provide architecture-level solutions to deploy neural networks for cardiac arrhythmia classification. We have created a hierarchical structure after analyzing various neural network topologies where only required network components are activated to improve energy efficiency while maintaining high accuracy. In our proposed architecture, we introduce a severity-based classification approach to directly help the users of the wearable healthcare device as well as the medical professionals. The principle motivation for this project is to develop a method of monitoring heart activity for patients with heart disease, pacemakers, and other special heart conditions so the patient can lead a relatively active life without being confined to a specific region. By being able to monitor sickly patients remotely, peace of mind can be offered to extended family knowing that emergency services can be dispatched in the event of cardiac arrest, or severe irregular heart patterns.

SEVERITY BASED HIERARCHIAL ECG CLASSIFICATION USING NEURAL NETWORKS

"SEVERITY BASED HIERARCHIAL ECG CLASSIFICATION USING NEURAL NETWORKS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 3, page no.d49-d53, March-2024, Available :http://www.jetir.org/papers/JETIR2403307.pdf

"SEVERITY BASED HIERARCHIAL ECG CLASSIFICATION USING NEURAL NETWORKS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 3, page no. ppd49-d53, March-2024, Available at : http://www.jetir.org/papers/JETIR2403307.pdf