Abstract—Heart disease is one of the main causes of global death, and instant diagnosis of this condition is significant for health improvement. This condition can be classified using the electrocardiogram (ECG) signal information. Application of artificial neural network (ANN) as a medical diagnostic classifier has been suggested by various studies in signal recognition. Collaboration with the recent advances in mobile technology for processing and transmission of medical data where medical feedback can be delivered promptly. This study presents a method of ECG signal classification for the patient coming from remote areas using ANN modeled algorithm used in a smartphone. The system is composed of monitoring device that accepts ECG signal from the patient and transmit this signal through a General Packet Radio Service (GPRS) technology. And a smartphone that receives and processes the information for medical ECG classification. Five type of ECG signals obtained from the selected arrhythmia database were classified with the sensitivity of 96.67%, specificity of 99.17% and correctness rate of 98.67% by the proposed method.

Index Terms—Algorithm, artificial neural network (ANN), electrocardiogram (ECG), general packet radio service (GPRS), smartphone.

Vincent D. Montaño is with the College of Engineering, Samar State University, Catbalogan City, Samar, Philippines. Noel B. Linsangan and Noel D. Ramos are with the Computer Engineering Department, Mapúa Institute of Technology, Intramuros, Manila, Philippines.

Cite:Vincent D. Montaño, Noel B. Linsangan, Noel D. Ramos, "ECG Signal Classification for Remote Area Patients Using Artificial Neural Networks in Smartphone," International Journal of Computer and Electrical Engineering vol. 7, no. 3, pp. 215-222, 2015.