Foreword |
|
xiii | |
About the editors |
|
xv | |
Preface |
|
xvii | |
|
1 Critical factors in the development, implementation and evaluation of telemedicine |
|
|
1 | (14) |
|
|
|
|
1 | (8) |
|
1.1.1 Critical factor 1: address a gap in service |
|
|
3 | (2) |
|
1.1.2 Critical factor 2: clearly defining the purpose of the telemedicine service |
|
|
5 | (1) |
|
1.1.3 Critical factor 3: integrate into the organizational structure |
|
|
5 | (1) |
|
1.1.4 Critical factor 4: the role of change management |
|
|
6 | (1) |
|
1.1.5 Critical factor 5: the crucial role of infrastructure |
|
|
6 | (1) |
|
1.1.6 Critical factor 6: buy-in from stakeholders |
|
|
7 | (1) |
|
1.1.7 Critical factor 7: financial sustainability |
|
|
7 | (1) |
|
1.1.8 Critical factor 8: legislative and policy requirements |
|
|
8 | (1) |
|
1.1.9 Critical factor 9: intersectoral collaboration |
|
|
8 | (1) |
|
1.1.10 Critical factor 10: review and re-focus |
|
|
9 | (1) |
|
|
9 | (2) |
|
|
11 | (4) |
|
|
12 | (3) |
|
2 Surgical tele-mentoring |
|
|
15 | (16) |
|
|
|
15 | (1) |
|
|
16 | (1) |
|
|
17 | (2) |
|
2.2 History of tele-mentoring |
|
|
19 | (2) |
|
2.3 Applications of tele-mentoring systems |
|
|
21 | (2) |
|
2.3.1 Videoconferencing techniques |
|
|
21 | (1) |
|
2.3.2 Wearable technology |
|
|
22 | (1) |
|
2.3.3 Robotic tele-mentoring platforms |
|
|
22 | (1) |
|
|
23 | (1) |
|
|
23 | (1) |
|
|
24 | (1) |
|
2.6 Conclusion and future directions |
|
|
25 | (6) |
|
|
26 | (5) |
|
3 Technologies in medical information processing |
|
|
31 | (24) |
|
|
|
|
|
|
|
|
|
32 | (1) |
|
|
33 | (3) |
|
|
34 | (1) |
|
|
34 | (1) |
|
|
35 | (1) |
|
|
35 | (1) |
|
3.2.5 Blood oxygen saturation |
|
|
36 | (1) |
|
3.3 Bio-signal transmission and processing |
|
|
36 | (6) |
|
|
37 | (3) |
|
3.3.2 Medical image transmission and analysis |
|
|
40 | (1) |
|
|
41 | (1) |
|
3.3.4 Biopotential electrode sensing |
|
|
42 | (1) |
|
3.4 Data mining and knowledge management |
|
|
42 | (2) |
|
3.5 Virtual collaboration framework for information interpretation |
|
|
44 | (6) |
|
3.5.1 The interpretation framework |
|
|
45 | (2) |
|
3.5.2 Components of interpretation layer |
|
|
47 | (1) |
|
3.5.3 How the framework works |
|
|
48 | (1) |
|
|
49 | (1) |
|
3.6 Conclusions and future work |
|
|
50 | (5) |
|
|
51 | (4) |
|
4 A comparative note on recent advances of signal/image processing techniques in healthcare |
|
|
55 | (22) |
|
Bala Subramanian Chockalingam |
|
|
|
|
|
55 | (2) |
|
4.2 Data-driven cardiac gating signal extraction method for PET |
|
|
57 | (3) |
|
|
59 | (1) |
|
4.3 3-D subject-specific shape and density estimation of the lumbar spine |
|
|
60 | (6) |
|
|
63 | (3) |
|
4.4 Abnormality detection based on ECG signal preprocessing in remote healthcare application |
|
|
66 | (2) |
|
4.4.1 Preprocessing using DENLMS algorithm |
|
|
67 | (1) |
|
4.5 Breast cancer classification using histology images |
|
|
68 | (2) |
|
|
69 | (1) |
|
|
70 | (1) |
|
|
70 | (7) |
|
|
70 | (7) |
|
5 A real-time ECG-processing platform for telemedicine applications |
|
|
77 | (26) |
|
|
|
78 | (2) |
|
|
80 | (2) |
|
5.2.1 Stockwell transform (ST) |
|
|
80 | (1) |
|
5.2.2 Twin support vector machines (TSVMs) |
|
|
81 | (1) |
|
5.2.3 Particle swarm optimization (PSO) |
|
|
82 | (1) |
|
|
82 | (9) |
|
|
83 | (1) |
|
|
84 | (2) |
|
5.3.3 R-wave localization and ECG segmentation |
|
|
86 | (1) |
|
|
87 | (2) |
|
5.3.5 CST feature recognition using TSVMs |
|
|
89 | (2) |
|
5.4 Hardware implementation on Wi-Fi integrated embedded platform |
|
|
91 | (3) |
|
5.4.1 Performance metrics |
|
|
94 | (1) |
|
5.5 Results and discussion |
|
|
94 | (4) |
|
5.5.1 Comparison with literature |
|
|
97 | (1) |
|
5.6 Conclusion and future scope |
|
|
98 | (5) |
|
|
99 | (4) |
|
6 Data mining in telemedicine |
|
|
103 | (30) |
|
|
|
|
|
|
6.1 Introduction to data mining |
|
|
103 | (1) |
|
6.2 Data mining in telemedicine |
|
|
104 | (5) |
|
6.2.1 Role of data mining in telemedicine |
|
|
105 | (3) |
|
6.2.2 Big data sources and characterization |
|
|
108 | (1) |
|
6.3 Integration of data mining techniques into telemedicine |
|
|
109 | (13) |
|
6.3.1 Data mining framework |
|
|
109 | (2) |
|
6.3.2 Data mining techniques |
|
|
111 | (11) |
|
|
122 | (4) |
|
6.4.1 Heart diseases prediction |
|
|
123 | (2) |
|
6.4.2 Breast cancer prediction |
|
|
125 | (1) |
|
6.5 Challenges of deploying data mining techniques into telemedicine |
|
|
126 | (2) |
|
|
128 | (5) |
|
|
128 | (5) |
|
7 Social work and tele-mental health services for rural and remote communities |
|
|
133 | (16) |
|
|
|
|
|
|
133 | (1) |
|
7.2 Rural heterogeneity and complexity challenge mental health service provision |
|
|
134 | (1) |
|
7.3 Bridging the rural/urban divide using ICT for mental health service provision |
|
|
135 | (1) |
|
7.4 An ambivalent engagement: social work and ICT |
|
|
136 | (2) |
|
7.5 Sustainable engagement with ICT to meet rural community mental health needs |
|
|
138 | (3) |
|
|
141 | (8) |
|
|
142 | (7) |
|
8 Technology-enhanced social work practice and education |
|
|
149 | (14) |
|
|
|
|
|
|
149 | (1) |
|
8.2 Tele-social work practice |
|
|
150 | (2) |
|
8.3 Tele-social work education |
|
|
152 | (5) |
|
8.3.1 Teaching tele-social work in group work |
|
|
153 | (2) |
|
8.3.2 Applying tele-social work in field education |
|
|
155 | (2) |
|
|
157 | (1) |
|
|
158 | (5) |
|
|
158 | (5) |
|
9 Advanced telemedicine system for remote healthcare monitoring |
|
|
163 | (24) |
|
|
|
|
|
163 | (6) |
|
9.1.1 Monitoring of remotely located epileptic patients |
|
|
166 | (1) |
|
|
167 | (1) |
|
|
168 | (1) |
|
9.1.4 Organization of the chapter |
|
|
168 | (1) |
|
9.2 Monitoring of remotely located patients |
|
|
169 | (4) |
|
9.2.1 Remote patients' chronic wound monitoring |
|
|
169 | (1) |
|
9.2.2 Remote patients monitoring related to heart patients |
|
|
170 | (2) |
|
9.2.3 Remote patients monitoring related to diabetic patients |
|
|
172 | (1) |
|
9.2.4 Remote monitoring for intensive care unit (ICU) patients |
|
|
172 | (1) |
|
9.3 Standards for telemedicine system |
|
|
173 | (1) |
|
9.4 Types of a telemedicine system |
|
|
173 | (1) |
|
9.5 Special features of the telemedicine system |
|
|
174 | (2) |
|
9.6 Cloud-based workflow model of a telemedicine system for remote patient monitoring |
|
|
176 | (1) |
|
9.7 Advantage and disadvantage of the telemedicine system |
|
|
177 | (2) |
|
9.8 Challenges for designing the telemedicine system |
|
|
179 | (1) |
|
|
180 | (1) |
|
|
180 | (7) |
|
|
181 | (6) |
|
10 Impact of tone-mapping operators and viewing devices on visual quality of experience of colour and grey-scale HDR images |
|
|
187 | (26) |
|
|
|
|
|
|
187 | (2) |
|
10.2 Comparison of HDR images with traditional images (LDR) |
|
|
189 | (1) |
|
10.3 Characteristics of SSDs |
|
|
190 | (1) |
|
|
191 | (7) |
|
10.4.1 Subjective assessment of the impact of TMOS and viewing devices |
|
|
192 | (4) |
|
10.4.2 Objective assessment of the impact of TMOs and viewing devices |
|
|
196 | (2) |
|
|
198 | (7) |
|
10.5.1 Results from subjective assessments |
|
|
198 | (3) |
|
10.5.2 Results from objective assessments |
|
|
201 | (4) |
|
10.6 Comparison of subjective and objective assessments of HDR image quality |
|
|
205 | (1) |
|
|
206 | (2) |
|
10.8 Conclusions and future work |
|
|
208 | (5) |
|
|
209 | (4) |
|
11 Modeling the relationships between changes in EEC features and subjective quality of HDR images |
|
|
213 | (26) |
|
|
|
|
|
|
213 | (3) |
|
|
216 | (2) |
|
|
218 | (4) |
|
11.3.1 Tone-mapping operators |
|
|
218 | (1) |
|
|
218 | (1) |
|
|
219 | (1) |
|
|
220 | (1) |
|
|
220 | (1) |
|
|
221 | (1) |
|
11.3.7 Feature extraction |
|
|
222 | (1) |
|
11.4 EEG signal acquisition |
|
|
222 | (1) |
|
|
223 | (5) |
|
11.5.1 Subjective rating analysis |
|
|
223 | (1) |
|
11.5.2 EEG signal analysis |
|
|
223 | (3) |
|
11.5.3 Correlation and analysis of variance |
|
|
226 | (1) |
|
11.5.4 The coupling measurements |
|
|
227 | (1) |
|
11.6 A mobile EEG-based QoE model |
|
|
228 | (2) |
|
11.6.1 EEG-based QoE model based on regression technique |
|
|
228 | (1) |
|
|
229 | (1) |
|
|
230 | (3) |
|
11.7.1 Experimental set-up |
|
|
230 | (1) |
|
11.7.2 Limitations using mobile devices |
|
|
230 | (3) |
|
11.7.3 Limitations using the EEG device |
|
|
233 | (1) |
|
|
233 | (6) |
|
|
233 | (6) |
|
12 IoMT and healthcare delivery in chronic diseases |
|
|
239 | (20) |
|
|
12.1 IoMT and healthcare delivery |
|
|
239 | (1) |
|
12.2 Impact of the IoMT in chronic disease treatment protocols/functional areas |
|
|
240 | (7) |
|
12.2.1 Remote clinical diagnosis and communication |
|
|
241 | (2) |
|
12.2.2 Product procurement |
|
|
243 | (1) |
|
12.2.3 Imaging and post-processing |
|
|
243 | (3) |
|
12.2.4 Drug/treatment planning |
|
|
246 | (1) |
|
12.2.5 Preventive health, wellness and patient education |
|
|
247 | (1) |
|
12.3 Chronic disease-specific implementation |
|
|
247 | (8) |
|
12.3.1 Chronic disease monitoring as the lucrative application of IoMT |
|
|
247 | (3) |
|
12.3.2 Implementation in diabetes |
|
|
250 | (4) |
|
12.3.3 Implementation challenges |
|
|
254 | (1) |
|
12.3.4 Future for IoMT in chronic disease monitoring |
|
|
254 | (1) |
|
|
255 | (4) |
|
|
255 | (4) |
|
13 Transform domain robust watermarking method using Riesz wavelet transform for medical data security and privacy |
|
|
259 | (26) |
|
|
|
|
|
|
259 | (4) |
|
|
263 | (2) |
|
13.3 Proposed medical image watermarking algorithm using RWT |
|
|
265 | (1) |
|
13.3.1 Watermark embedding steps |
|
|
265 | (1) |
|
13.3.2 Watermark extraction steps |
|
|
266 | (1) |
|
|
266 | (2) |
|
13.4.1 Generalized Riesz wavelet transform (GRWT) |
|
|
266 | (1) |
|
13.4.2 Singular value decomposition |
|
|
267 | (1) |
|
13.5 Simulation results and discussions |
|
|
268 | (12) |
|
|
280 | (5) |
|
|
281 | (4) |
|
|
285 | (2) |
Index |
|
287 | |