Flamingo-Optimization-Based Deep Convolutional Neural Network for IoT-Based Arrhythmia Classification

被引:4
|
作者
Kumar, Ashwani [1 ]
Kumar, Mohit [2 ]
Mahapatra, Rajendra Prasad [1 ]
Bhattacharya, Pronaya [3 ]
Le, Thi-Thu-Huong [4 ]
Verma, Sahil [5 ]
Kavita [5 ]
Mohiuddin, Khalid [6 ]
机构
[1] SRM Inst Sci & Technol, Fac Engn & Technol, Dept Comp Sci & Engn, NCR Campus, Ghaziabad 201204, India
[2] MIT Art Design & Technol Univ, Pune 412201, India
[3] Amity Univ, Amity Sch Engn & Technol, Dept Comp Sci & Engn, Res & Innovat Cell, Kolkata 700135, India
[4] Pusan Natl Univ, Blockchain Platform Res Ctr, Busan 609735, South Korea
[5] Uttaranchal Univ Univ, Fac Comp Sci & Engn, Dehra Dun 248007, India
[6] King Khalid Univ, Fac Informat Syst, Abha 62529, Saudi Arabia
关键词
arrhythmia classification; DCNN; ECG signal; flamingo optimization; IoT nodes; ALZHEIMERS-DISEASE;
D O I
10.3390/s23094353
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Cardiac arrhythmia is a deadly disease that threatens the lives of millions of people, which shows the need for earlier detection and classification. An abnormal signal in the heart causing arrhythmia can be detected at an earlier stage when the health data from the patient are monitored using IoT technology. Arrhythmias may suddenly lead to death and the classification of arrhythmias is considered a complicated process. In this research, an effective classification model for the classification of heart disease is developed using flamingo optimization. Initially, the ECG signal from the heart is collected and then it is subjected to the preprocessing stage; to detect and control the electrical activity of the heart, the electrocardiogram (ECG) is used. The input signals collected using IoT nodes are collectively presented in the base station for the classification using flamingo-optimization-based deep convolutional networks, which effectively predict the disease. With the aid of communication technologies and the contribution of IoT, medical professionals can easily monitor the health condition of patients. The performance is analyzed in terms of accuracy, sensitivity, and specificity.
引用
收藏
页数:14
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