Literature DB >> 31226858

Wearable Real-Time Heart Attack Detection and Warning System to Reduce Road Accidents.

Muhammad E H Chowdhury1, Khawla Alzoubi2, Amith Khandakar3, Ridab Khallifa4, Rayaan Abouhasera5, Sirine Koubaa6, Rashid Ahmed7, Md Anwarul Hasan8.   

Abstract

Heart attack is one of the leading causes of human death worldwide. Every year, about 610,000 people die of heart attack in the United States alone-that is one in every four deaths-but there are well understood early symptoms of heart attack that could be used to greatly help in saving many lives and minimizing damages by detecting and reporting at an early stage. On the other hand, every year, about 2.35 million people get injured or disabled from road accidents. Unexpectedly, many of these fatal accidents happen due to the heart attack of drivers that leads to the loss of control of the vehicle. The current work proposes the development of a wearable system for real-time detection and warning of heart attacks in drivers, which could be enormously helpful in reducing road accidents. The system consists of two subsystems that communicate wirelessly using Bluetooth technology, namely, a wearable sensor subsystem and an intelligent heart attack detection and warning subsystem. The sensor subsystem records the electrical activity of the heart from the chest area to produce electrocardiogram (ECG) trace and send that to the other portable decision-making subsystem where the symptoms of heart attack are detected. We evaluated the performance of dry electrodes and different electrode configurations and measured overall power consumption of the system. Linear classification and several machine algorithms were trained and tested for real-time application. It was observed that the linear classification algorithm was not able to detect heart attack in noisy data, whereas the support vector machine (SVM) algorithm with polynomial kernel with extended time-frequency features using extended modified B-distribution (EMBD) showed highest accuracy and was able to detect 97.4% and 96.3% of ST-elevation myocardial infarction (STEMI) and non-ST-elevation MI (NSTEMI), respectively. The proposed system can therefore help in reducing the loss of lives from the growing number of road accidents all over the world.

Entities:  

Keywords:  heart attack; machine learning algorithm; portable device; real time system; support vector machine

Mesh:

Year:  2019        PMID: 31226858      PMCID: PMC6632021          DOI: 10.3390/s19122780

Source DB:  PubMed          Journal:  Sensors (Basel)        ISSN: 1424-8220            Impact factor:   3.576


  10 in total

1.  Automated Detection of COVID-19 Cases on Radiographs using Shape-Dependent Fibonacci-p Patterns.

Authors:  Karen Panetta; Foram Sanghavi; Sos Agaian; Neel Madan
Journal:  IEEE J Biomed Health Inform       Date:  2021-06-03       Impact factor: 7.021

2.  Estimating Blood Pressure from the Photoplethysmogram Signal and Demographic Features Using Machine Learning Techniques.

Authors:  Moajjem Hossain Chowdhury; Md Nazmul Islam Shuzan; Muhammad E H Chowdhury; Zaid B Mahbub; M Monir Uddin; Amith Khandakar; Mamun Bin Ibne Reaz
Journal:  Sensors (Basel)       Date:  2020-06-01       Impact factor: 3.576

3.  Special Issue "Advanced Signal Processing in Intelligent Systems for Health Monitoring".

Authors:  Maysam Abbod; Jiann-Shing Shieh
Journal:  Sensors (Basel)       Date:  2019-10-31       Impact factor: 3.576

Review 4.  Integration of novel monitoring devices with machine learning technology for scalable cardiovascular management.

Authors:  Chayakrit Krittanawong; Albert J Rogers; Kipp W Johnson; Zhen Wang; Mintu P Turakhia; Jonathan L Halperin; Sanjiv M Narayan
Journal:  Nat Rev Cardiol       Date:  2020-10-09       Impact factor: 32.419

5.  Smart access development for classifying lung disease with chest x-ray images using deep learning.

Authors:  Tarunika Kumaraguru; P Abirami; K M Darshan; S P Angeline Kirubha; S Latha; P Muthu
Journal:  Mater Today Proc       Date:  2021-04-16

6.  Performance Analysis of Conventional Machine Learning Algorithms for Diabetic Sensorimotor Polyneuropathy Severity Classification Using Nerve Conduction Studies.

Authors:  Fahmida Haque; Mamun B I Reaz; Muhammad E H Chowdhury; Serkan Kiranyaz; Sawal H M Ali; Mohammed Alhatou; Rumana Habib; Ahmad A A Bakar; Norhana Arsad; Geetika Srivastava
Journal:  Comput Intell Neurosci       Date:  2022-04-25

Review 7.  Review on Medical Implantable Antenna Technology and Imminent Research Challenges.

Authors:  Md Mohiuddin Soliman; Muhammad E H Chowdhury; Amith Khandakar; Mohammad Tariqul Islam; Yazan Qiblawey; Farayi Musharavati; Erfan Zal Nezhad
Journal:  Sensors (Basel)       Date:  2021-05-02       Impact factor: 3.576

8.  Deep convolutional neural networks for COVID-19 automatic diagnosis.

Authors:  Heba M Emara; Mohamed R Shoaib; Mohamed Elwekeil; Walid El-Shafai; Taha E Taha; Adel S El-Fishawy; El-Sayed M El-Rabaie; Saleh A Alshebeili; Moawad I Dessouky; Fathi E Abd El-Samie
Journal:  Microsc Res Tech       Date:  2021-06-14       Impact factor: 2.893

9.  Wavelet Transform Artificial Intelligence Algorithm-Based Data Mining Technology for Norovirus Monitoring and Early Warning.

Authors:  Xucheng Fan; Na Xue; Zhiguo Han; Chao Wang; Heer Ma; Yaoqin Lu
Journal:  J Healthc Eng       Date:  2021-09-17       Impact factor: 2.682

10.  Thermal Change Index-Based Diabetic Foot Thermogram Image Classification Using Machine Learning Techniques.

Authors:  Amith Khandakar; Muhammad E H Chowdhury; Mamun Bin Ibne Reaz; Sawal Hamid Md Ali; Tariq O Abbas; Tanvir Alam; Mohamed Arselene Ayari; Zaid B Mahbub; Rumana Habib; Tawsifur Rahman; Anas M Tahir; Ahmad Ashrif A Bakar; Rayaz A Malik
Journal:  Sensors (Basel)       Date:  2022-02-24       Impact factor: 3.576

  10 in total

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