An Effective Internet of Things based Assessment of ANN and ANFIS algorithms for Cardiac Arrhythmia

Reducing the influence of significant noise signal components on the obtained raw ECG signal is ess ential for precise identification of cardiac arrhythmias (CA), which frequently present as irregularitie s in heart rate or rhythm. Preprocessing is used to remove noise signals and baseline drift from the E CG wave that is recorded using the internet of things (IoT). After that, the denoised signal is subjecte d to dimensionality reduction and feature extraction. In order to determine whether classification met hod is more effective in detecting cardiac arrhythmias, this study compares two methods: an adaptive neuro-fuzzy inference system and artificial feed-forward neural networks trained with the back-prop agation learning algorithm. An Adaptive Neuro Fuzzy Inference System analyses ICA features obtai ned by non-parametric power spectral estimates, and an Artificial Neural Network (ANN) classifier u ses the ECG signal's morphological and statistical aspects to identify patterns. The creation of artifici al feed-forward neural networks provides a rich framework for studying the Back Propagation Algor ithm. Sensitivity, specificity, accuracy, and positive predictiveivity are some of the performance char acteristics that are thoroughly examined. An overall accuracy of 97.79%, sensitivity of 99.82%, spec ificity of 99.68%, and positive predictivity of 98.58% were seen in the results of the Artificial Neural Feed Forward Network (ANFFN). The Adaptive Neuro Fuzzy Inference System (ANFIS) outperfor ms these metrics with an astounding overall accuracy of 99.62%, specificity of 98.63%, and positive predictivity of 99.46%. With a classification accuracy of 99.82%, ANFIS demonstrates to be the mos t effective classifier for identifying cardiac arrhythmias.