@inproceedings{4ac5145308994dd788bb406f150ac2f6,
title = "A biomedical SoC architecture for predicting ventricular arrhythmia",
abstract = "Electrocardiography (ECG) represents the hearts electrical activity and has features such as QRS complex, P-wave and T-wave that provide critical clinical information for detection and prediction of cardiac diseases. This paper presents a novel ECG processing architecture for the prediction of ventricular arrhythmia (VA). The architecture implements a novel ECG feature extraction which is optimized for ultra-low power applications. The architecture is based on Curve Length Transform (CLT) for the detection of QRS complex and Discrete Wavelet Transform (DWT) for the delineation of TP waves. Features extracted from two consecutive ECG cycles are used to set innovative parameters for VA prediction up to 3 hours before VA onset. Two databases of the heart signal recordings from the American Heart Association (AHA) and the MIT PhysioNet were used as training, test and validation sets to evaluate the performance of the proposed system.",
keywords = "Curve length Transform, Discrete Wavelet Transform, ECG features, Ventricular Arrythmia",
author = "Temesghen Tekeste and Hani Saleh and Baker Mohammad and Ahsan Khandoker and Mohammed Ismail",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE International Symposium on Circuits and Systems, ISCAS 2016 ; Conference date: 22-05-2016 Through 25-05-2016",
year = "2016",
month = jul,
day = "29",
doi = "10.1109/ISCAS.2016.7539034",
language = "British English",
series = "Proceedings - IEEE International Symposium on Circuits and Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2262--2265",
booktitle = "ISCAS 2016 - IEEE International Symposium on Circuits and Systems",
address = "United States",
}