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

Wisnu Jatmiko
Wisnu Jatmiko
, 
M. Anwar Ma’sum
M. Anwar Ma’sum
, 
Hanif Arief Wisesa
Hanif Arief Wisesa
 und 
Hadaiq Rolis Sanabila
Hadaiq Rolis Sanabila
   | 29. Nov. 2019
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Article Category: research-article
Online veröffentlicht: 29. Nov. 2019
Seitenbereich: 1 - 28
Eingereicht: 02. Apr. 2018
DOI: https://doi.org/10.21307/ijssis-2019-009
Schlüsselwörter
© 2019 Wisnu Jatmiko et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1:

A telehealth system architecture.
A telehealth system architecture.

Figure 2:

The illustration of QRS complex in an ECG signal.
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.
The ECG data before and after the cubic spline interpolation in order to remove the baseline wander.

Figure 4:

The individual beat segmentation.
The individual beat segmentation.

Figure 5:

Outlier removal using IQR.
Outlier removal using IQR.

Figure 6:

Beat features before and after outlier removal using IQR.
Beat features before and after outlier removal using IQR.

Figure 7:

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

Figure 8:

The ECG decomposed signal.
The ECG decomposed signal.

Figure 9:

The architecture of the LVQ algorithm.
The architecture of the LVQ algorithm.

Figure 10:

FNLVQ architecture (Kusumoputro et al., 2002).
FNLVQ architecture (Kusumoputro et al., 2002).

Figure 11:

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

Figure 12:

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

Figure 13:

FNGLVQ architecture (Setiawan et al., 2011).
FNGLVQ architecture (Setiawan et al., 2011).

Figure 14:

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

Figure 15:

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

Figure 16:

The ECG data before and after the beat reordering method.
The ECG data before and after the beat reordering method.

Figure 17:

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

Figure 18:

The process of predictive coding by Jati et al.
The process of predictive coding by Jati et al.

Figure 19:

The spatial orientation tree of 3D SPIHT.
The spatial orientation tree of 3D SPIHT.

Figure 20:

3D Residual array of the 3D SPIHT method.
3D Residual array of the 3D SPIHT method.

Figure 21:

The architecture of Tele-ECG.
The architecture of Tele-ECG.

Figure 22:

The ECG sensor.
The ECG sensor.

Figure 23:

Multi-lead and single-lead ECG machines.
Multi-lead and single-lead ECG machines.

Figure 24:

The mobile system on Tele-EG.
The mobile system on Tele-EG.

Figure 25:

The ECG classifier on FPGA.
The ECG classifier on FPGA.

Figure 26:

The Daubechies Wavelet architecture.
The Daubechies Wavelet architecture.

Figure 27:

The Convolution Unit Architecture.
The Convolution Unit Architecture.

Figure 28:

The design of FLVQ architecture.
The design of FLVQ architecture.

Figure 29:

The design of FNGLVQ in FPGA.
The design of FNGLVQ in FPGA.

Figure 30:

The State machine of FNGLVQ.
The State machine of FNGLVQ.

Figure 31:

The top-level design of AFNGLVQ in FPGA.
The top-level design of AFNGLVQ in FPGA.

Figure 32:

The top-level design of AFNGLVQ in FPGA.
The top-level design of AFNGLVQ in FPGA.

Figure 33:

Accuracy of Arrhythmias classification using LVQ-based classifier.
Accuracy of Arrhythmias classification using LVQ-based classifier.

Figure 34:

Impact of Round Robin in Arrhythmias classification.
Impact of Round Robin in Arrhythmias classification.

Figure 35:

Arrhythmias classification with unknown class.
Arrhythmias classification with unknown class.

Figure 36:

Arrhythmias classification in FPGA.
Arrhythmias classification in FPGA.

ECG compression performance.

No URL Mean response time (ms)
1 /RegisterPatient 129.6
2 /RegisterDoctor 218.9
3 /LookHistory 206.6
4 /UploadHistory 556.4
5 /GetHospitalData 231.7
6 /GetDoctorData 192.3
7 /VerifyHistory 160.7
8 /GetUnverifiedHistory 227.8
9 /RegisterAffiliation 201.8
10 /RegisterHospital 215.0
11 /GetDoctorAffiliation 423.9
Mean 251.3

Tele-ECG services.

No. URL Service
1 /RegsiterPatien Registers user data as patient, then the data are saved in patient table of the database
2 /RegsiterDoctor Registers user data as doctor, then the data are saved in doctor table of the database
3 /UploadHistory Uploads patient heartbeat history, then the heartbeat data are saved in history table of the
4 /LookHistory Checks status of patient heartbeat history if it is verified by a doctor (cardiologist)
5 /GetDoctorData Downloads doctor (cardiologists) information
6 /GetHospitalData Downloads hospital information
7 /GetUnverifiedHistory Downloads unverified patient heartbeat history to be verified by doctor (cardiologist)
8 /VerifyHistory Verifies patient heartbeat history by doctor (cardiologist)
9 /RegisterHospital Registers hospital data, then the data are saved in hospital table of the database
10 /RegisterAffiliation Affiliates the doctor (cardiologist) into hospital
11 /GetDoctorAffiliation Downloads doctor’s (cardiologists’) affiliations.
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