Cargando…
A Frequency Pattern Mining Model Based on Deep Neural Network for Real-Time Classification of Heart Conditions
Recently, a massive amount of big data of bioinformation is collected by sensor-based IoT devices. The collected data are also classified into different types of health big data in various techniques. A personalized analysis technique is a basis for judging the risk factors of personal cardiovascula...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7551638/ https://www.ncbi.nlm.nih.gov/pubmed/32722657 http://dx.doi.org/10.3390/healthcare8030234 |
_version_ | 1783593226404364288 |
---|---|
author | Yoo, Hyun Han, Soyoung Chung, Kyungyong |
author_facet | Yoo, Hyun Han, Soyoung Chung, Kyungyong |
author_sort | Yoo, Hyun |
collection | PubMed |
description | Recently, a massive amount of big data of bioinformation is collected by sensor-based IoT devices. The collected data are also classified into different types of health big data in various techniques. A personalized analysis technique is a basis for judging the risk factors of personal cardiovascular disorders in real-time. The objective of this paper is to provide the model for the personalized heart condition classification in combination with the fast and effective preprocessing technique and deep neural network in order to process the real-time accumulated biosensor input data. The model can be useful to learn input data and develop an approximation function, and it can help users recognize risk situations. For the analysis of the pulse frequency, a fast Fourier transform is applied in preprocessing work. With the use of the frequency-by-frequency ratio data of the extracted power spectrum, data reduction is performed. To analyze the meanings of preprocessed data, a neural network algorithm is applied. In particular, a deep neural network is used to analyze and evaluate linear data. A deep neural network can make multiple layers and can establish an operation model of nodes with the use of gradient descent. The completed model was trained by classifying the ECG signals collected in advance into normal, control, and noise groups. Thereafter, the ECG signal input in real time through the trained deep neural network system was classified into normal, control, and noise. To evaluate the performance of the proposed model, this study utilized a ratio of data operation cost reduction and F-measure. As a result, with the use of fast Fourier transform and cumulative frequency percentage, the size of ECG reduced to 1:32. According to the analysis on the F-measure of the deep neural network, the model had 83.83% accuracy. Given the results, the modified deep neural network technique can reduce the size of big data in terms of computing work, and it is an effective system to reduce operation time. |
format | Online Article Text |
id | pubmed-7551638 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75516382020-10-14 A Frequency Pattern Mining Model Based on Deep Neural Network for Real-Time Classification of Heart Conditions Yoo, Hyun Han, Soyoung Chung, Kyungyong Healthcare (Basel) Article Recently, a massive amount of big data of bioinformation is collected by sensor-based IoT devices. The collected data are also classified into different types of health big data in various techniques. A personalized analysis technique is a basis for judging the risk factors of personal cardiovascular disorders in real-time. The objective of this paper is to provide the model for the personalized heart condition classification in combination with the fast and effective preprocessing technique and deep neural network in order to process the real-time accumulated biosensor input data. The model can be useful to learn input data and develop an approximation function, and it can help users recognize risk situations. For the analysis of the pulse frequency, a fast Fourier transform is applied in preprocessing work. With the use of the frequency-by-frequency ratio data of the extracted power spectrum, data reduction is performed. To analyze the meanings of preprocessed data, a neural network algorithm is applied. In particular, a deep neural network is used to analyze and evaluate linear data. A deep neural network can make multiple layers and can establish an operation model of nodes with the use of gradient descent. The completed model was trained by classifying the ECG signals collected in advance into normal, control, and noise groups. Thereafter, the ECG signal input in real time through the trained deep neural network system was classified into normal, control, and noise. To evaluate the performance of the proposed model, this study utilized a ratio of data operation cost reduction and F-measure. As a result, with the use of fast Fourier transform and cumulative frequency percentage, the size of ECG reduced to 1:32. According to the analysis on the F-measure of the deep neural network, the model had 83.83% accuracy. Given the results, the modified deep neural network technique can reduce the size of big data in terms of computing work, and it is an effective system to reduce operation time. MDPI 2020-07-26 /pmc/articles/PMC7551638/ /pubmed/32722657 http://dx.doi.org/10.3390/healthcare8030234 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Yoo, Hyun Han, Soyoung Chung, Kyungyong A Frequency Pattern Mining Model Based on Deep Neural Network for Real-Time Classification of Heart Conditions |
title | A Frequency Pattern Mining Model Based on Deep Neural Network for Real-Time Classification of Heart Conditions |
title_full | A Frequency Pattern Mining Model Based on Deep Neural Network for Real-Time Classification of Heart Conditions |
title_fullStr | A Frequency Pattern Mining Model Based on Deep Neural Network for Real-Time Classification of Heart Conditions |
title_full_unstemmed | A Frequency Pattern Mining Model Based on Deep Neural Network for Real-Time Classification of Heart Conditions |
title_short | A Frequency Pattern Mining Model Based on Deep Neural Network for Real-Time Classification of Heart Conditions |
title_sort | frequency pattern mining model based on deep neural network for real-time classification of heart conditions |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7551638/ https://www.ncbi.nlm.nih.gov/pubmed/32722657 http://dx.doi.org/10.3390/healthcare8030234 |
work_keys_str_mv | AT yoohyun afrequencypatternminingmodelbasedondeepneuralnetworkforrealtimeclassificationofheartconditions AT hansoyoung afrequencypatternminingmodelbasedondeepneuralnetworkforrealtimeclassificationofheartconditions AT chungkyungyong afrequencypatternminingmodelbasedondeepneuralnetworkforrealtimeclassificationofheartconditions AT yoohyun frequencypatternminingmodelbasedondeepneuralnetworkforrealtimeclassificationofheartconditions AT hansoyoung frequencypatternminingmodelbasedondeepneuralnetworkforrealtimeclassificationofheartconditions AT chungkyungyong frequencypatternminingmodelbasedondeepneuralnetworkforrealtimeclassificationofheartconditions |