Design and Analysis of IoT Health Monitoring Based on Low Power Protocol

Abstract

Wearable electrocardiogram (ECG) devices have revolutionized cardiac monitoring by newlineproviding a cost-effective and comfortable alternative to continuous medical supervision. To newlineaddress the demand for real-time ECG data classification, a groundbreaking ensemble-based newlineframework has been proposed. This novel approach combines Convolutional Neural newlineNetworks (CNN) for efficient feature extraction with linear and bio-inspired classifiers, such newlineas Random Forests (RF), Support Vector Classifier (SVC), and kNearest Neighbors (kNN), newlineto enhance classification performance. The key feature utilized for accurate classification is newlinethe RR intervals in ECG signals, which are the periods between two successive R-waves in newlinethe QRS complex, enabling precise differentiation between normal and arrhythmia signals. newlineImpressively, this innovative approach has achieved an outstanding accuracy of over 99.7% newlinefor eight different disease types, making it highly desirable for on-field ECG analysis newlineapplications. newlineMoreover, to address data security concerns during transmission between ECG sensing newlinedevices and cloud data centers, a Reinforcement Transfer Learning-based low-power newlineclassification model has been introduced. This model incorporates context-aware feature newlineanalysis and trains a deep learning classifier, combining Long Short-Term Memory (LSTM) newlineand Gated Recurrent Unit (GRU) based Recurrent Neural Network (RNN) on standard MIT newlineBIH and PTB XL datasets. To ensure enhanced security with high efficiency, the model newlineintegrates blockchain-based systems and a side-chain approach. newlineThe side-chain-based high-security model integrates the Whale Optimization Model (WOA) newlinewith a consensus-independent performance-optimization engine to determine the need for new newlinesidechains. Timestamp-based processing and mining delay-based fitness function designs newlinecontribute to reduced processing delay by 8.5%, communication delay by 3.2%, and energy newlinerequirements by 15.3%, all while maintaining improved security performance compared to newlineother fut