Low intricate digital twin method to predict cardiac arrhythmia

The ECG and PPG signals contribute for the state machine learning logic and enables prompt diagnostic interpretation both in spatial and frequency domain. Analysis of electrocardiogram (ECG) signals and simultaneously PPG analysis is the prime research motive in clinical data processing. The valuation flow of diagnostic values for these bio signals mostly depends on quantity, accuracy, precision, speed, and real time signal evaluation useful for clinical diagnosis and treatment. Peak parameters of ECG signals like Heart rate (HR), Heart rate variability (HRV), Heart rate turbulence (HRT), T – wave alternans (TWA), Pulse rate (PR), Pulse rate variability (PRV), Pulse rate turbulence (PRT), Pulse transit time (PTT) are extracted using peak detection algorithm. They provide useful information about the cardiac vascular parameters. In this paper we have introduced a method for analysis of ECG signals in the mode of Peak detection. This method provides an excellent online diagnose pattern for cardiovascular diseases like myocardial infarction and cardiac arrest. By the fact that ECG signals shows a peak detected. The evaluated parameters both in Time and Frequency domain provide an accurate evaluation of the disorders. The machine learning algorithm for efficient power consumption is analyzed. Comparison of different kernels of Support Vector Machine (SVM) with K-Nearest Neighbors (KNN) algorithm is also handled. The cloud-based analysis can be handled efficiently with the linear classifier such as SVM kernels and KNN classifiers. Comparing the performance metrics between the SVM kernels the Laplace RBF has more accuracy and precision than the other kernels. The KNN classifier is more precise and accurate compared with other classifiers. The energy, power, and time consumption of the kernels also compared the Laplace RBF takes consumes moderate energy and time and has higher efficient than other kernels.