This study proposes Fuzzy Logic Method (FLM) to analyze ECG signals for determining the heartbeat case. The proposed method can accurately classify and distinguish both normal heartbeats (NORM) and abnormal heartbeats. The so called abnormal heartbeats include the left bundle branch block (LBBB), the right bundle branch block (RBBB), the ventricular premature contractions (VPC), and the atrial premature contractions (APC). ECG signal analysis comprises three main stages: (i) the qualitative features stage for qualitative feature selection of an ECG signal; (ii) fuzzy rules base establishment; and (iii) the classification stage for determining patient heartbeat cases. The fuzzy rules base receives four qualitative features of an ECG signal as its inputs and generates one output “heartbeat case”. Through fuzzy inference engine and defuzzification operations, we can make a decision to determine the heartbeat case of the patient’s heart disease. The ECG records available in the MIT-BIH arrhythmia database are utilized to illustrate the effectiveness of the proposed method. In the experiments, the sensitivities were 95.06%, 91.03%, 90.50%, 92.63% and 93.77% for NORM, LBBB, RBBB, VPC and APC, respectively. The total classification accuracy (TCA) was approximately 93.78%.