ANovel Severity Index of Heart Disease from Beat-wise Analysis of ECG Using Fuzzy Logic for Smart-Health

Continuous monitoring of Electrocardiogram (ECG) sensor signals using smart-health devices can significantly improve the ability and efficacy of clinicians to diagnose and monitor heart disease. Monitoring Premature Ventricular Contraction (PVC) beat in an ECG signal can help individuals in the early detection of heart disease. This paper presents a Simulinko model-based approach of using fuzzy logic for accurate detection of PVC beats in ECG signals. Clinical diagnostic criteria were used to develop the ECG fuzzy rule set that classified each ECG beat into the two beat-groups: Normal and PVC. A severity index of the PVC was also formulated. The novel severity index could help in quantifying the percentage of PVC beats into groups that can be useful for disease diagnosis and progression monitoring. The metric correlates well to ECG QRS interval feature which is clinically known to associate with patients’ heart conditions. The model was tested using ECG signals from the MIT-BIH database (Physionet). The accuracy of the proposed model is 96.6%. The sensitivity and specificity of the model are 95.6% and 96.8%, respectively. Our proposed model is computationally efficient, clinical-based, intuitive, can be easily ported to smart-health devices, and will be able to provide feedback in real-time.