Authors :

Sampath Rajaram

Address :

Mohamed Sathak A.J. College of Engineering, India

Abstract :

Data acquired by sensor-based Internet of Things devices has recently reached huge proportions, including bioinformation. Different kinds of health big data are crated using different methods, and the gathered data is categorized accordingly. The design is possible to evaluate potential dangers associated with one's own concerns with the heart in real time. This paper's goal is to provide a model for individualized heart condition categorization that processes collected biosensor input data in real-time using a deep neural network and a quick and efficient preprocessing approach, incorporating pattern mining techniques. Learning input data and developing an approximation function are two potential uses for the model, and it may also assist users in identifying danger scenarios. In the preliminary processing, a quick Fourier transform is used for the purpose of analyzing the pulse frequency. Data reduction strategies include using the recovered power spectrum's frequency-by-frequency ratio information. Preprocessed data is analyzed using a neural network approach. Among its many applications is the analysis and evaluation of linear data by means of a deep neural network. A network of deep neural networks can employ gradient descent to build multiple layers, each of which represents a node's process. There is a standard, oversight, and noisy category in the trained model that makes use of the pre-collected ECG data. Subsequently, both peaceful and chaotic electrocardiogram (ECG) data were fed into the newly implemented deep neural network system in real time. This research assessed the suggested method by calculating F-measure ratios and knowledge operation cost reductions.This led to a reduction in ECG size to 1:32 with the use of cumulative frequency % and rapidly Fourier transform. An evaluation of the deep neural network's F-measure revealed an accuracy of 83.83% for the model. Results show that the modified deep neural network method, with the integration of pattern mining, can effectively decrease operating time and minimize the amount of massive data in terms of computing labor.

Keywords :

Real-time control; Big Data; Pattern Mining; Deep Neural Network; Healthcare; Data Mining; Heart Rate.

DOI :

https://doi.org/10.70023/piqm241