TY - JOUR
T1 - Developing smart tele-ECG system for early detection and monitoring heart diseases based on ecg signal
T2 - Progress and challenges
AU - Jatmiko, Wisnu
AU - Ma'sum, M. Anwar
AU - Wisesa, Hanif Arief
AU - Sanabila, Hadaiq Rolis
N1 - Funding Information:
This study is supported by Insentif Riset Sistem Inovasi Nasiona (INSINAS) Research Grant No. 5459/ UN2.R3.1/HKP05.00/2018, entitled ‘Pengembangan Sistem Telehealth Cerdas Terintegrasi Berbasiskan Perangkat Portable dan Big Data Platform untuk Meningkatkan Pelayanan Kesehatan di Indonesia’ from Ministry of Research and Higher Education, Republic of Indonesia.
Publisher Copyright:
© 2019 Authors.
PY - 2019/4/1
Y1 - 2019/4/1
N2 - Technology is developed to benefit society. One of the applications of technology in the healthcare sector is telehealth monitoring system. The system proposes a new way of communication between the doctor and the patient, even in a very remote location. In this paper, we elaborate the progress and challenges regarding the development of Tele-ECG in Indonesia, which includes data acquisition, feature extraction, data compression, classification algorithm, mobile and web development system and small device implementation on an FPGA board. The classification is conducted by using LVQ, GLVQ, FNLVQ, FNLVQ-PSO, FNGLVQ, and AM-GLVQ. The compression is conducted by using SPIHT algorithm. Tele-ECG can assist in monitoring heartbeat anomalies and reduce the risk of heart attack. It could also be a solution for infrastructure discrepancy in healthcare.
AB - Technology is developed to benefit society. One of the applications of technology in the healthcare sector is telehealth monitoring system. The system proposes a new way of communication between the doctor and the patient, even in a very remote location. In this paper, we elaborate the progress and challenges regarding the development of Tele-ECG in Indonesia, which includes data acquisition, feature extraction, data compression, classification algorithm, mobile and web development system and small device implementation on an FPGA board. The classification is conducted by using LVQ, GLVQ, FNLVQ, FNLVQ-PSO, FNGLVQ, and AM-GLVQ. The compression is conducted by using SPIHT algorithm. Tele-ECG can assist in monitoring heartbeat anomalies and reduce the risk of heart attack. It could also be a solution for infrastructure discrepancy in healthcare.
KW - AM-GLVQ
KW - ECG
KW - FNGLVQ
KW - FNLVQ
KW - FNLVQ-PSO
KW - FPGA
KW - GLVQ
KW - LVQ
KW - SPIHT
KW - Telehealth
UR - http://www.scopus.com/inward/record.url?scp=85083987319&partnerID=8YFLogxK
U2 - 10.21307/IJSSIS-2019-009
DO - 10.21307/IJSSIS-2019-009
M3 - Article
AN - SCOPUS:85083987319
SN - 1178-5608
VL - 12
SP - 1
EP - 28
JO - International Journal on Smart Sensing and Intelligent Systems
JF - International Journal on Smart Sensing and Intelligent Systems
IS - 1
ER -