Diagnosis of cardiac arrhythmia using Swarm-intelligence based Metaheuristic Techniques: A comparative analysis

EAI Endorsed Transactions on Pervasive Health and Technology

Gurvinder Singh¹  Samriti Sharma¹ 

[1] Dept. of Comp. Sc., GNDU Amritsar, India;

关键词: cardiacarrhythmia;    featureselection;    butterflyalgorithm;    dragonflyalgorithm;    grey-wolfoptimization;    ant-lion optimization;    satin-bowerbird optimization;    chaotic maps;   

DOI  :  10.4108/eai.22-9-2020.166357

来源: DOAJ

【 摘 要 】

INTRODUCTION:Heartdiseasesaretheprominenthumandisordersthathavesignificantlyaffectedthelifestyleandlivesofthevictims.Cardiacarrhythmia(heartarrhythmia)isoneofthecriticalheartdisordersthatreflectsthestateofheartbeat among individuals. ECG (Electrocardiogram) signals are commonly used in the diagnostic process of this cardiac disorder. OBJECTIVES: In this manuscript,an effort has been made to employ and examine the performance of emerging Swarm Intelligence (SI) techniques in finding an optimal set of features used for cardiac arrhythmia diagnosis. METHODS: A standard benchmark UCI dataset set comprises of 279 attributes and 452 instances have been considered. FivedifferentSI-based meta-heuristic techniquesviz.binaryGrey-WolfOptimizer(bGWO),AntLionOptimization(ALO),Butterflyoptimizationalgorithm(BOA),Dragonfly Algorithm(DA),andSatin-B irdOptimization(SBO) havebeenalsoemployedforthesame.Additionally, fivenovelchaoticvariantsofSBOhavebeendesignedtosolve the featureselectionproblemfordiagnosinga cardiacarrhythmia.Differentperformancemetricslikeaccuracy,fitness value, optimal set of features and execution time have been computed. CONCLUSION:Ithasbeenobservedfromtheexperimentationthatintermsofaccuracyandfitnessvalueofcardiacarrhythmia,theSBOoutperformedotherSIalgorithmsviz.bGWO,DA,BOA,andALO.Additionally,BOAandALOseem to be the best fit when the emphasis is on dimension size only.

【 授权许可】

Unknown