Big-ECG: Cardiographic Predictive Cyber-Physical System for Stroke Management Article Swipe
Iqram Hussain
,
Se Jin Park
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/access.2021.3109806
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/access.2021.3109806
Electrocardiogram (ECG) is sensitive to autonomic dysfunction and cardiac complications derived from ischemic or hemorrhage stroke and is supposed to be a potential prognostic tool in stroke identification and post-stroke treatment. ECG data generated cannot be real-time accumulated, processed, and used for enterprise-level healthcare and wellness services with the existing cardiovascular monitoring system used in hospitals. This study aims to assess the feasibility of a cyber-physical cardiac monitoring system to classify stroke patients with altered cardiac activity and healthy adults. Here, we propose Big-ECG, a cyber-physical cardiac monitoring system for stroke management, consisting of a wearable ECG sensor, data storage and data analysis in a big data platform, and health advisory services using data analytics and medical ontology. We investigated our proposed ECG-based patient monitoring system with 45 stroke patients (average age 70.8 years old, 68% men) admitted to the rehabilitation center of the hospital and 40 healthy elderly volunteers (average age 75.4 years old, 38% men). We recorded ECG at resting state using a single-channel ECG patch within three months of diagnosis of ischemic stroke (clinically confirmed). In statistical results, ECG fiducial features, RR-I, QRS, QT, ST, and heart rate variability (HRV) features, SDSD, LF/HF, LF/(LF + HF), and HF/(LF + HF) are observed as significantly distinctive biomarkers for the stroke group relative to the healthy control group. The Random Trees model presented the best classification performance (overall accuracy: 95.6%) utilizing ECG fiducial variables. This system may assist healthcare enterprises in prognosis and rehabilitation management during post-stroke treatment.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2021.3109806
- https://ieeexplore.ieee.org/ielx7/6287639/6514899/09527216.pdf
- OA Status
- gold
- Cited By
- 94
- References
- 64
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3197552172
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3197552172Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2021.3109806Digital Object Identifier
- Title
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Big-ECG: Cardiographic Predictive Cyber-Physical System for Stroke ManagementWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-01-01Full publication date if available
- Authors
-
Iqram Hussain, Se Jin ParkList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2021.3109806Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/6287639/6514899/09527216.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://ieeexplore.ieee.org/ielx7/6287639/6514899/09527216.pdfDirect OA link when available
- Concepts
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Medicine, Stroke (engine), Electrocardiography, Internal medicine, Cardiology, QRS complex, Medical emergency, Physical therapy, Physical medicine and rehabilitation, Engineering, Mechanical engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
94Total citation count in OpenAlex
- Citations by year (recent)
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2025: 7, 2024: 14, 2023: 28, 2022: 36, 2021: 9Per-year citation counts (last 5 years)
- References (count)
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64Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.a | 21, 64, 84, 94, 104, 164 |
| abstract_inverted_index.40 | 146 |
| abstract_inverted_index.45 | 127 |
| abstract_inverted_index.In | 178 |
| abstract_inverted_index.We | 118, 157 |
| abstract_inverted_index.as | 205 |
| abstract_inverted_index.at | 160 |
| abstract_inverted_index.be | 20, 35 |
| abstract_inverted_index.in | 25, 54, 103, 241 |
| abstract_inverted_index.is | 2, 17 |
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| abstract_inverted_index.to | 4, 19, 59, 69, 138, 214 |
| abstract_inverted_index.we | 81 |
| abstract_inverted_index.ECG | 31, 96, 159, 166, 181, 232 |
| abstract_inverted_index.HF) | 202 |
| abstract_inverted_index.QT, | 186 |
| abstract_inverted_index.ST, | 187 |
| abstract_inverted_index.The | 219 |
| abstract_inverted_index.age | 131, 151 |
| abstract_inverted_index.and | 7, 16, 28, 39, 44, 77, 100, 108, 115, 145, 188, 199, 243 |
| abstract_inverted_index.are | 203 |
| abstract_inverted_index.big | 105 |
| abstract_inverted_index.for | 41, 89, 209 |
| abstract_inverted_index.may | 237 |
| abstract_inverted_index.our | 120 |
| abstract_inverted_index.the | 48, 61, 139, 143, 210, 215, 224 |
| abstract_inverted_index.70.8 | 132 |
| abstract_inverted_index.75.4 | 152 |
| abstract_inverted_index.HF), | 198 |
| abstract_inverted_index.QRS, | 185 |
| abstract_inverted_index.This | 56, 235 |
| abstract_inverted_index.aims | 58 |
| abstract_inverted_index.best | 225 |
| abstract_inverted_index.data | 32, 98, 101, 106, 113 |
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| abstract_inverted_index.old, | 134, 154 |
| abstract_inverted_index.rate | 190 |
| abstract_inverted_index.tool | 24 |
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| abstract_inverted_index.with | 47, 73, 126 |
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| abstract_inverted_index.Here, | 80 |
| abstract_inverted_index.RR-I, | 184 |
| abstract_inverted_index.SDSD, | 194 |
| abstract_inverted_index.Trees | 221 |
| abstract_inverted_index.group | 212 |
| abstract_inverted_index.heart | 189 |
| abstract_inverted_index.men). | 156 |
| abstract_inverted_index.model | 222 |
| abstract_inverted_index.patch | 167 |
| abstract_inverted_index.state | 162 |
| abstract_inverted_index.study | 57 |
| abstract_inverted_index.three | 169 |
| abstract_inverted_index.using | 112, 163 |
| abstract_inverted_index.years | 133, 153 |
| abstract_inverted_index.HF/(LF | 200 |
| abstract_inverted_index.LF/(LF | 196 |
| abstract_inverted_index.LF/HF, | 195 |
| abstract_inverted_index.Random | 220 |
| abstract_inverted_index.assess | 60 |
| abstract_inverted_index.assist | 238 |
| abstract_inverted_index.cannot | 34 |
| abstract_inverted_index.center | 141 |
| abstract_inverted_index.during | 246 |
| abstract_inverted_index.group. | 218 |
| abstract_inverted_index.health | 109 |
| abstract_inverted_index.months | 170 |
| abstract_inverted_index.stroke | 15, 26, 71, 90, 128, 175, 211 |
| abstract_inverted_index.system | 52, 68, 88, 125, 236 |
| abstract_inverted_index.within | 168 |
| abstract_inverted_index.adults. | 79 |
| abstract_inverted_index.altered | 74 |
| abstract_inverted_index.cardiac | 8, 66, 75, 86 |
| abstract_inverted_index.control | 217 |
| abstract_inverted_index.derived | 10 |
| abstract_inverted_index.elderly | 148 |
| abstract_inverted_index.healthy | 78, 147, 216 |
| abstract_inverted_index.medical | 116 |
| abstract_inverted_index.patient | 123 |
| abstract_inverted_index.propose | 82 |
| abstract_inverted_index.resting | 161 |
| abstract_inverted_index.sensor, | 97 |
| abstract_inverted_index.storage | 99 |
| abstract_inverted_index.+ | 197, 201 |
| abstract_inverted_index.(average | 130, 150 |
| abstract_inverted_index.(overall | 228 |
| abstract_inverted_index.Big-ECG, | 83 |
| abstract_inverted_index.activity | 76 |
| abstract_inverted_index.admitted | 137 |
| abstract_inverted_index.advisory | 110 |
| abstract_inverted_index.analysis | 102 |
| abstract_inverted_index.classify | 70 |
| abstract_inverted_index.existing | 49 |
| abstract_inverted_index.fiducial | 182, 233 |
| abstract_inverted_index.hospital | 144 |
| abstract_inverted_index.ischemic | 12, 174 |
| abstract_inverted_index.observed | 204 |
| abstract_inverted_index.patients | 72, 129 |
| abstract_inverted_index.proposed | 121 |
| abstract_inverted_index.recorded | 158 |
| abstract_inverted_index.relative | 213 |
| abstract_inverted_index.results, | 180 |
| abstract_inverted_index.services | 46, 111 |
| abstract_inverted_index.supposed | 18 |
| abstract_inverted_index.wearable | 95 |
| abstract_inverted_index.wellness | 45 |
| abstract_inverted_index.ECG-based | 122 |
| abstract_inverted_index.accuracy: | 229 |
| abstract_inverted_index.analytics | 114 |
| abstract_inverted_index.autonomic | 5 |
| abstract_inverted_index.diagnosis | 172 |
| abstract_inverted_index.features, | 183, 193 |
| abstract_inverted_index.generated | 33 |
| abstract_inverted_index.ontology. | 117 |
| abstract_inverted_index.platform, | 107 |
| abstract_inverted_index.potential | 22 |
| abstract_inverted_index.presented | 223 |
| abstract_inverted_index.prognosis | 242 |
| abstract_inverted_index.real-time | 36 |
| abstract_inverted_index.sensitive | 3 |
| abstract_inverted_index.utilizing | 231 |
| abstract_inverted_index.38% | 155 |
| abstract_inverted_index.68% | 135 |
| abstract_inverted_index.biomarkers | 208 |
| abstract_inverted_index.consisting | 92 |
| abstract_inverted_index.healthcare | 43, 239 |
| abstract_inverted_index.hemorrhage | 14 |
| abstract_inverted_index.hospitals. | 55 |
| abstract_inverted_index.management | 245 |
| abstract_inverted_index.monitoring | 51, 67, 87, 124 |
| abstract_inverted_index.processed, | 38 |
| abstract_inverted_index.prognostic | 23 |
| abstract_inverted_index.treatment. | 30, 248 |
| abstract_inverted_index.variables. | 234 |
| abstract_inverted_index.volunteers | 149 |
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| abstract_inverted_index.management, | 91 |
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| abstract_inverted_index.investigated | 119 |
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| abstract_inverted_index.significantly | 206 |
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| abstract_inverted_index.cyber-physical | 65, 85 |
| abstract_inverted_index.identification | 27 |
| abstract_inverted_index.rehabilitation | 140, 244 |
| abstract_inverted_index.single-channel | 165 |
| abstract_inverted_index.enterprise-level | 42 |
| abstract_inverted_index.Electrocardiogram | 0 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 99 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.99329977 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |