Cardiovascular Disease Is The Leading Cause Of Death Globally. It Accounts For 17.3 Million Deaths/year, And By 2030, This Is Expected To Rise To 23.6 Million [1]. Electrocardiogram (ECG) Signals Are An Important Source Of Information To The Cardiologist For Cardiovascular Disease Diagnosis. Analysis Of Electrocardiogram (ECG) Signals Is Challenging Due To The Complexity Of Their Signal Morphology. However, Analyzing Or Processing These Signal Records Manually To Discover Heart Diseases Is Time-consuming And Sensitive To Human Errors. To Solve This Issue, An Automatic Diagnostic Classification System Is Required, And Many Studies Have Been Proposed. CSP Is A Statistical Method That Has Been Proven To Be An Effective Feature Extraction Method To Discriminate Between Two Classes. The Aim Of Using CSP In This Work Is To Distinguish Between Normal And Abnormal ECG Signals
