Pandit, D.; Liu, C.; Zhang, L. Automated Hertbeat Abnormality Detection Using Realtime R-Assisted Lightweight Feature Extraction Algorithm. Preprints2018, 2018040192. https://doi.org/10.20944/preprints201804.0192.v1
APA Style
Pandit, D., Liu, C., & Zhang, L. (2018). Automated Hertbeat Abnormality Detection Using Realtime R-Assisted Lightweight Feature Extraction Algorithm. Preprints. https://doi.org/10.20944/preprints201804.0192.v1
Chicago/Turabian Style
Pandit, D., Chengyu Liu and Li Zhang. 2018 "Automated Hertbeat Abnormality Detection Using Realtime R-Assisted Lightweight Feature Extraction Algorithm" Preprints. https://doi.org/10.20944/preprints201804.0192.v1
Abstract
Automated Electrocardiogram (ECG) processing is an important technique which helps in identifying abnormalities in the heart before any formal diagnosis. This research presents a real-time and lightweight R-assisted feature extraction algorithm and a heartbeat classification scheme which achieves highly accurate abnormality detection. In the proposed algorithm, we extract fifteen features from each heartbeat taken from raw Lead-II ECG signals. The features carry medically valuable information such as locations, amplitude and energy of ECG waves (P, Q, R, S, T waves) which are then used for detection of any abnormality that might be present in the heartbeat using various classification algorithms. We have used four popular databases from Physionet and extracted ten thousand ECG signals from each for training the models and benchmarking results. Four classification models i.e. Naïve Bays, k-Nearest Neighbor, Neural Network, Decision Tree were used for abnormality detection validating the efficiency of the system.
Computer Science and Mathematics, Data Structures, Algorithms and Complexity
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
