Artificial neural networks for the diagnosis of atrial fibrillation

Hau

Computer Assisted Surgery

et al. 2016

Background: Atrial fibrillation (AFIB) is one of the most common types of arrhythmia, which leads to heart failure and stroke to public. As AFIB has the high potential to cause permanent disability in patients, its early detection is extremely important. There are different types of AFIB classification algorithm that have been proposed by researchers in recent years. Methods: This paper reviews the features of AFIB in terms of ECG morphological features and heart rate variability (HRV) analysis on different methods. The existing classification method, particularly focusing on Artificial Intelligence technique, is also comprehensively described. Other than that, the existing implementation technology of arrhythmia detection platforms such as smart phone and System-on-Chip-based embedded device are also elaborated in terms of their design trade-offs. Conclusion: Current existing AFIB detection algorithm cannot compromise for high accuracy and low complexity. Due to the limitation of embedded system, design trade off should be considered to strike the balance between the performance of algorithm and the limitation.

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Artificial intelligence classification methods of atrial fibrillation with implementation technology

Hau

Computer Assisted Surgery

et al. 2016

“…ANNs have been used to interpret plain radiographs, 27 ultrasound, 28 CT, 29 MRI, 30 and radioisotope scans. of ECGs to diagnose myocardial infarction, 32 atrial fibrillation, 33 and ventricular arrythmias. 34 Analysis of electro-enchalograms (EEG) by neural networks has led to its application in the diagnosis of epilepsy 35 and sleep disorders.…”

Section: Diagnosismentioning

confidence: 99%

Artificial intelligence in medicine

Ramesh

Kambhampati²,

Monson³

et al. 2004

Ann R Coll Surg Engl

745

396

A rtificial intelligence (AI) is defined as 'a field of science and engineering concerned with the computational understanding of what is commonly called intelligent behaviour, and with the creation of artefacts that exhibit such behaviour'.1 Aristotle attempted to formalise 'right thinking' (logic) through his syllogisms (a three part deductive reasoning). Much of the work in the modern era was inspired by this and the early studies on the operation of mind helped to establish contemporary logical thinking. Programs which enable computers to function in the ways, that make people seem intelligent are called artificial intelligent systems. The British mathematician Alan Turing (1950) was one of the founders of modern computer science and AI. He defined intelligent behaviour in a computer as the ability to achieve human-level performance in cognitive tasks, this later became popular as the 'Turing test'.2 Since the middle of the last century, researchers have explored the potential applications of intelligent techniques in every field of medicine.3,4 The application of AI technology in the field of surgery was first successively investigated by Gunn in 1976, when he explored the possibility of diagnosing acute abdominal pain with computer analysis. 5 The last two decades have seen a surge in the interest in medical AI.Modern medicine is faced with the challenge of acquiring, analysing and applying the large amount of knowledge necessary to solve complex clinical problems. The development of medical artificial intelligence has been related to the development of AI programs intended Artificial intelligence in medicine AN

“…The detection of arrhythmia is an important task in clinical reasons which can initiate life saving operations. Quick availability of ECG signal from remote location and providing proper filtering circuit on time can help in analyzing the signal for arrhythmia [1].From early times several detection algorithms have been proposed, such as the sequential hypothesis testing [3], the threshold-crossing intervals [4], algorithms based on neural-networks [6], and wavelets [7]. The classification of detected arrhythmias is also a research field of great interest.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Classification of Cardiac Arrhythmia Using ECG Signals

Bhardwaj¹,

Choudhary²,

Dayama³

2012

IJCA

ECG is a graphical record of the electrical tension of heart and has established as one the most important bio-signal used by cardiologists for diagnostic purposes and further to adopt an appropriate course of treatment. The difficulties faced in interpretation of ECG signals forced researchers to study about automatic detection of cardiac arrhythmia disorders. The data analysis techniques using specific computer software could easily interpret complex ECG signals, predict presence or absence of cardiac arrhythmia. This provides real time analysis and further facilitates for timely diagnosis. In this paper, Support Vector Machine (SVM) technique, using LibSVM3.1 has been applied to ECG dataset for arrhythmia classification in five categories. Out of these five categories, one is normal and four are arrhythmic beat categories. The dataset used in this study is 3003 arrhythmic beats out of which 2101 beats (70%) are used for training and remaining 902 beats (30%) have been used for testing purpose. Total performance accuracy is found to be around 95.21 % in this case.