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Developing smart Tele-ECG system for early detection and monitoring heart diseases based on ECG signal: progress and challenges Cover

Developing smart Tele-ECG system for early detection and monitoring heart diseases based on ECG signal: progress and challenges

Open Access
|Nov 2019

Figures & Tables

Figure 1:

A telehealth system architecture.

Figure 2:

The illustration of QRS complex in an ECG signal.

Figure 3:

The ECG data before and after the cubic spline interpolation in order to remove the baseline wander.

Figure 4:

The individual beat segmentation.

Figure 5:

Outlier removal using IQR.

Figure 6:

Beat features before and after outlier removal using IQR.

Figure 7:

Daubechies 8 5-level decomposition (Setiawan et al., 2011).

Figure 8:

The ECG decomposed signal.

Figure 9:

The architecture of the LVQ algorithm.

Figure 10:

FNLVQ architecture (Kusumoputro et al., 2002).

Figure 11:

Computing similarity in FNLVQ (Kusumoputro et al., 2002).

Figure 12:

FNLVQ-PSO architecture (Jatmiko et al., 2009).

Figure 13:

FNGLVQ architecture (Setiawan et al., 2011).

Figure 14:

AM-GLVQ architecture (Imah et al., 2012).

Figure 15:

The process of two-dimensional SPIHT as proposed by Isa et al.

Figure 16:

The ECG data before and after the beat reordering method.

Figure 17:

The process of two-dimensional SPIHT as proposed by Jati et al.

Figure 18:

The process of predictive coding by Jati et al.

Figure 19:

The spatial orientation tree of 3D SPIHT.

Figure 20:

3D Residual array of the 3D SPIHT method.

Figure 21:

The architecture of Tele-ECG.

Figure 22:

The ECG sensor.

Figure 23:

Multi-lead and single-lead ECG machines.

Figure 24:

The mobile system on Tele-EG.

Figure 25:

The ECG classifier on FPGA.

Figure 26:

The Daubechies Wavelet architecture.

Figure 27:

The Convolution Unit Architecture.

Figure 28:

The design of FLVQ architecture.

Figure 29:

The design of FNGLVQ in FPGA.

Figure 30:

The State machine of FNGLVQ.

Figure 31:

The top-level design of AFNGLVQ in FPGA.

Figure 32:

The top-level design of AFNGLVQ in FPGA.

Figure 33:

Accuracy of Arrhythmias classification using LVQ-based classifier.

Figure 34:

Impact of Round Robin in Arrhythmias classification.

Figure 35:

Arrhythmias classification with unknown class.

Figure 36:

Arrhythmias classification in FPGA.

ECG compression performance_

NoURLMean response time (ms)
1/RegisterPatient129.6
2/RegisterDoctor218.9
3/LookHistory206.6
4/UploadHistory556.4
5/GetHospitalData231.7
6/GetDoctorData192.3
7/VerifyHistory160.7
8/GetUnverifiedHistory227.8
9/RegisterAffiliation201.8
10/RegisterHospital215.0
11/GetDoctorAffiliation423.9
Mean251.3

Tele-ECG services_

No.URLService
1/RegsiterPatienRegisters user data as patient, then the data are saved in patient table of the database
2/RegsiterDoctorRegisters user data as doctor, then the data are saved in doctor table of the database
3/UploadHistoryUploads patient heartbeat history, then the heartbeat data are saved in history table of the
4/LookHistoryChecks status of patient heartbeat history if it is verified by a doctor (cardiologist)
5/GetDoctorDataDownloads doctor (cardiologists) information
6/GetHospitalDataDownloads hospital information
7/GetUnverifiedHistoryDownloads unverified patient heartbeat history to be verified by doctor (cardiologist)
8/VerifyHistoryVerifies patient heartbeat history by doctor (cardiologist)
9/RegisterHospitalRegisters hospital data, then the data are saved in hospital table of the database
10/RegisterAffiliationAffiliates the doctor (cardiologist) into hospital
11/GetDoctorAffiliationDownloads doctor’s (cardiologists’) affiliations.
Language: English
Page range: 1 - 28
Submitted on: Apr 2, 2018
Published on: Nov 29, 2019
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2019 Wisnu Jatmiko, M. Anwar Ma’sum, Hanif Arief Wisesa, Hadaiq Rolis Sanabila, published by Macquarie University, Australia
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.