Introduction

Atrial Fibrillation(AF) is the most common cardiac

arrhythmia[1]. Palpitations, chest pain, dyspnea, fatigue,

lightheadedness, syncope and congestive heart failure

are most common symptoms associated with existence

of AF[2]. AF increases the risk of stroke; this risk factor

increases seven times with clinical conditions such as

hypertension[1].

Mortality risk is almost double in patients with presence

of AF who also have pre-existing cardiovascular

condition[3].AF can be classified into three categories

namely Proximal AF, Persistent AF and Longstanding

permanent AF.

Algorithms for improved detection of Atrial

Fibrillation from the

Electrocardiogram(ECG)

Rohit Hadia

ESR 9

Supervised by : Dr Dewar Finlay, Dr Daniel Guldenring,

Prof. Jim McLaughlin

Faculty of Computing and Engineering

University of Ulster, UK

This project has received funding from the European Union's Horizon 2020 research and innovation programme under the

Marie Sklodowska-Curie grant agreement No 676201

Existing Algorithm And Issues

Most of commercially available AF detection devices

use algorithm which detects AF by measuring the

variance between R-R interval statistically[1].

However, some cardiac activities leads to false

detection of AF as a result of similar amplitude-time

characteristics as shown in fig.3.

Research Work

Early stage research work includes development of

algorithm to detect cardiac activities such as

PAC(Premature Atrial Contraction),PVC(Premature

Ventricular contraction), Atrial Flutter which to some

extent differs from normal sinus node conduction

characteristics. Furthermore background technical and

clinical literature research will be done during this time

to get more familiarised with the research subject .At

later stages these learning will be used to develop an

advanced algorithm to detect AF from ECG.

Fig.1 Electrical conduction in(A) Normal heart and (B) heart with Atrial fibrillation

ECG With AF

Normal ECG

Fig.2 Normal ECG and ECG with AF, Missing P wave ,Irregular RR interval [4].

Irregular R-R Interval

Regular R-R interval

Fig.3

Fig.3 Anomalies leading to False detection of AF[5].

[1]A.Kennedy,D.Finlay,D.Guldenring,R.Bond,J.McLaughlin,The accuracy of beat-interval based algorithms for detecting atrial fibrillation,Computing in Cardiology Conference (CinC), 2015, Nice, France. IEEE. 4 pp.

[2]V.Fuster, L.Rydén, D.Cannom, H.Crijns, A.Curtis, K.Ellenbogen, J.Halperin, J.Heuzey, G.Neal Kay, J.Lowe, S.Bertil Olsson, E.Prystowsky, J.Tamargo, S.Wann, S.Smith, A.Jacobs, C.Adams, J.Anderson, E.Antman, J.Halperin,

S.Hunt, R.Nishimura, J.Ornato, R.Page, B.Riegel, S.Priori, J.Blanc, A.Budaj, A.Camm, V.Dean, J.Deckers, C.Despres, K.Dickstein, J.Lekakis, K.McGregor, M.Metra, J.Morais, A.Osterspey, J.Zamorano,ACC/AHA/ESC 2006

guidelines for the management of patients with atrial fibrillation

Europace Sep 2006, 8 (9) 651-745;

[3]E.Benjamin, P.Wolf, R.D’Agostino, H.Silbershatz, W.Kannel, D.Levy,Impact of Atrial Fibrillation on the Risk of Death,American heart association journal,Circulation. 1998;98:946-952.

[4]J.Heuser,https://commons.wikimedia.org/wiki/File:Afib_ecg.jpg

[5]D.Lake,J.Moorman,Accurate estimation of entropy in very short physiological time series: the problem of atrial fibrillation detection in implanted ventricular devices,Am J Physiol Heart Circ Physiol. 2011 Jan;300(1):H319-25. doi:

10.1152/ajpheart.00561.2010. Epub 2010 Oct 29.

Introduction Problem Statement

Research Work