TY - GEN
T1 - Input Feature Selection in ECG Signal Data Modelling using Long Short Term Memory
AU - Saikhu, Ahmad
AU - Hudiyanti, Cinthia Vairra
AU - Wijaya, Arya Yudhi
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - One of the diseases that are a significant burden worldwide is cardiovascular disorders, diseases related to the work of the heart have a high probability of causing death. So we need a tool or model to detect the patient's heart signal against the risk of cardiovascular disorders. Electrocardiogram (ECG) recordings are often used to capture the propagation or propagation of electrical signals in the heart from the patient's body surface. Reading the ECG signal data is very tiring because every second, there are around 180 points that are captured which consist of the patient's pulse, movement, and breath. In this research, input feature selection will be carried out using the Long Short Term Memory method for ECG signal data. The results of the prediction of the ECG signal can be used to predict and treat cardiovascular disorders. Furthermore, the results of the model performance that the Long Short Term Memory model with one input, namely (t-1), is the best compared to using two or four input features.
AB - One of the diseases that are a significant burden worldwide is cardiovascular disorders, diseases related to the work of the heart have a high probability of causing death. So we need a tool or model to detect the patient's heart signal against the risk of cardiovascular disorders. Electrocardiogram (ECG) recordings are often used to capture the propagation or propagation of electrical signals in the heart from the patient's body surface. Reading the ECG signal data is very tiring because every second, there are around 180 points that are captured which consist of the patient's pulse, movement, and breath. In this research, input feature selection will be carried out using the Long Short Term Memory method for ECG signal data. The results of the prediction of the ECG signal can be used to predict and treat cardiovascular disorders. Furthermore, the results of the model performance that the Long Short Term Memory model with one input, namely (t-1), is the best compared to using two or four input features.
KW - ECG signal
KW - Long Short Term Memory
KW - cardiovascular disorder
KW - electrocardiogram
KW - feature selection
KW - model performance
UR - http://www.scopus.com/inward/record.url?scp=85126686630&partnerID=8YFLogxK
U2 - 10.1109/ISRITI54043.2021.9702810
DO - 10.1109/ISRITI54043.2021.9702810
M3 - Conference contribution
AN - SCOPUS:85126686630
T3 - 2021 4th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2021
SP - 229
EP - 234
BT - 2021 4th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2021
Y2 - 16 December 2021
ER -