m-CADE: A mobile based cardiovascular abnormality detection engine using efficient multi-domain feature combinations

Health care administration is very poor in rural areas and the scenario is predicted to persist so. Due to the ubiquitous usage, mobile phones are greatly employed in telecardiology systems to bridge this gap. Towards accomplishing Electrocardiogram (ECG) analysis on such resource-constrained devices, we propose mobile-Cardiovascular Abnormality Detection Engine (m-CADE) encompassing a low computational feature extraction method, feature selection for identifying highly discriminative feature groups and an integrated classifier for abnormality detection. Novel features were extracted from time, frequency and statistical domains. Three feature combinations were analyzed using the integrated classifier for detection of cardiovascular abnormality. The combination using morphological descriptors from time domain, Daubechies Wavelet coefficients from the frequency domain and statistical domain features achieved highest performance. The most efficient feature subset is extracted using information theoretic Dynamic Weighting based Feature Selection. These methods were incorporated in an Android Application (App size 547 KB) which detects abnormality with an Average Accuracy of 98.9%, Sensitivity of 99.2% and Specificity of 97.6% in 1.3 sec. The robustness of the proposed system is demonstrated using the benchmarked MITBIH Arrhythmia database and the proposed method significantly outperforms existing methods for mobile based Telecardiology systems.