A multi-adaptive neuro-fuzzy inference system with variable thresholds for heartbeat classification

Various heart disorders are non-invasively diagnosed using electrocardiograms (ECGs). An ECG records a variety of waveforms, including P, QRS, and T waves, which represent the electrical activity of the human heart. Cardiovascular diseases are diagnosed by examining the length, form, and spacing of these waveforms. This research develops a multi-adaptive, neuro-fuzzy inference system (MANFIS), enhanced by a variable threshold approach, to enhance heartbeat classification accuracy. The MIT-BIH arrhythmia database was utilized, and seven features were extracted from each record. A subtractive clustering method was employed to prepare the inputs for the MANFIS, enabling heartbeat classification. By applying a variable threshold to the MANFIS outputs, classification accuracy was further enhanced. The proposed method, termed variable-threshold MANFIS, can separately detect normal sinus rhythm, left bundle branch block, right bundle branch block, premature ventricular contraction, atrial premature condition, and paced beat. This is achieved using six different ANFIS classifiers, each with its own threshold. The system was evaluated, achieving an accuracy of 98.33%, a sensitivity of 93.12%, a specificity of 99.66%, a precision of 98.33, and an F1-score of 95.44. A distinct feature of this machine-learning-based model is its controllable threshold, which delivers promising results across all training, testing, and validation datasets. The proposed diagnostic system is applicable in new automated medical instrumentation and serves as a valuable tool in cardiology.