Changes
On December 3, 2024 at 10:34:16 AM UTC,
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Changed title to MIT-BIH arrhythmia database (previously MIT-BIH Arrhythmia Database)
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Set author of MIT-BIH arrhythmia database to Jianning Li (previously Ali Razaa)
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Updated description of MIT-BIH arrhythmia database from
The proposed framework aims to address the limitations of deep learning applications for ECG signal classification. Firstly, we proposed a CNN-based autoencoder in a federated architecture to denoise the raw ECG signal from patients. When trained on the baseline dataset, The proposed autoencoder provided an excellent reconstruction of the raw input signals and improved the overall performance when applied in federated settings.
toFive open ECG databases from PhysioNet are involved in this study namely the MIT-BIH arrhythmia database,St-Petersburg Institute of Cardiological Technics 12-lead Arrhythmia Database,The MIT-BIH Normal Sinus Rhythm Database,The MIT-BIH Long Term Database and European ST-T Database.
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Removed the following tags from MIT-BIH arrhythmia database
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Changed value of field
defined_in
tohttps://doi.org/10.48550/arXiv.1907.09504
in MIT-BIH arrhythmia database -
Changed value of field
citation
to['https://doi.org/10.48550/arXiv.1806.04564', 'https://doi.org/10.48550/arXiv.1803.06441']
in MIT-BIH arrhythmia database -
Changed value of field
landing_page
tohttps://physionet.org/content/miibih-arrhythmia-database/1.0.0/
in MIT-BIH arrhythmia database -
Deleted resource Original Metadata from MIT-BIH arrhythmia database
f | 1 | { | f | 1 | { |
2 | "access_rights": "", | 2 | "access_rights": "", | ||
n | 3 | "author": "Ali Razaa", | n | 3 | "author": "Jianning Li", |
4 | "author_email": "", | 4 | "author_email": "", | ||
5 | "citation": [ | 5 | "citation": [ | ||
n | 6 | "https://doi.org/10.48550/arXiv.2208.10463", | n | 6 | "https://doi.org/10.48550/arXiv.1806.04564", |
7 | "https://doi.org/10.48550/arXiv.2106.12498", | ||||
8 | "https://doi.org/10.1109/ECAI58194.2023.10193930", | ||||
9 | "https://doi.org/10.48550/arXiv.2005.08689", | ||||
10 | "https://doi.org/10.1109/TBCAS.2019.2953001", | ||||
11 | "https://doi.org/10.1016/j.knosys.2021.107763", | ||||
12 | "https://doi.org/10.48550/arXiv.2301.09496", | ||||
13 | "https://doi.org/10.48550/arXiv.1812.07421" | 7 | "https://doi.org/10.48550/arXiv.1803.06441" | ||
14 | ], | 8 | ], | ||
15 | "creator_user_id": "17755db4-395a-4b3b-ac09-e8e3484ca700", | 9 | "creator_user_id": "17755db4-395a-4b3b-ac09-e8e3484ca700", | ||
n | 16 | "defined_in": "https://doi.org/10.48550/arXiv.2404.15333", | n | 10 | "defined_in": "https://doi.org/10.48550/arXiv.1907.09504", |
17 | "doi": "10.57702/ye06lfce", | 11 | "doi": "10.57702/ye06lfce", | ||
18 | "doi_date_published": "2024-12-02", | 12 | "doi_date_published": "2024-12-02", | ||
19 | "doi_publisher": "TIB", | 13 | "doi_publisher": "TIB", | ||
20 | "doi_status": true, | 14 | "doi_status": true, | ||
21 | "domain": "https://service.tib.eu/ldmservice", | 15 | "domain": "https://service.tib.eu/ldmservice", | ||
n | 22 | "extra_authors": [ | n | ||
23 | { | ||||
24 | "extra_author": "Kim Phuc Tran", | ||||
25 | "orcid": "" | ||||
26 | }, | ||||
27 | { | ||||
28 | "extra_author": "Ludovic Koehla", | ||||
29 | "orcid": "" | ||||
30 | }, | ||||
31 | { | ||||
32 | "extra_author": "Shujun Li", | ||||
33 | "orcid": "" | ||||
34 | } | ||||
35 | ], | ||||
36 | "groups": [ | 16 | "groups": [ | ||
37 | { | 17 | { | ||
38 | "description": "", | 18 | "description": "", | ||
39 | "display_name": "Arrhythmia", | 19 | "display_name": "Arrhythmia", | ||
40 | "id": "955d7ab7-aa60-41d3-aed0-5a1ba1bd2eb4", | 20 | "id": "955d7ab7-aa60-41d3-aed0-5a1ba1bd2eb4", | ||
41 | "image_display_url": "", | 21 | "image_display_url": "", | ||
42 | "name": "arrhythmia", | 22 | "name": "arrhythmia", | ||
43 | "title": "Arrhythmia" | 23 | "title": "Arrhythmia" | ||
44 | }, | 24 | }, | ||
45 | { | 25 | { | ||
46 | "description": "", | 26 | "description": "", | ||
n | 47 | "display_name": "Arrhythmia Detection", | n | ||
48 | "id": "3f3ff023-1729-4464-93c3-c80695c932ce", | ||||
49 | "image_display_url": "", | ||||
50 | "name": "arrhythmia-detection", | ||||
51 | "title": "Arrhythmia Detection" | ||||
52 | }, | ||||
53 | { | ||||
54 | "description": "", | ||||
55 | "display_name": "Biomedical", | ||||
56 | "id": "e45c67ec-5404-494c-81b9-1bed2c205c48", | ||||
57 | "image_display_url": "", | ||||
58 | "name": "biomedical", | ||||
59 | "title": "Biomedical" | ||||
60 | }, | ||||
61 | { | ||||
62 | "description": "", | ||||
63 | "display_name": "Database", | ||||
64 | "id": "5913c3ea-ed3c-435a-89e4-6539c33591c8", | ||||
65 | "image_display_url": "", | ||||
66 | "name": "database", | ||||
67 | "title": "Database" | ||||
68 | }, | ||||
69 | { | ||||
70 | "description": "", | ||||
71 | "display_name": "ECG", | 27 | "display_name": "ECG", | ||
72 | "id": "e60fa1d6-8886-4696-aae0-baccbb62b6c2", | 28 | "id": "e60fa1d6-8886-4696-aae0-baccbb62b6c2", | ||
73 | "image_display_url": "", | 29 | "image_display_url": "", | ||
74 | "name": "ecg", | 30 | "name": "ecg", | ||
75 | "title": "ECG" | 31 | "title": "ECG" | ||
76 | }, | 32 | }, | ||
77 | { | 33 | { | ||
78 | "description": "", | 34 | "description": "", | ||
n | 79 | "display_name": "ECG Analysis", | n | 35 | "display_name": "ECG Database", |
80 | "id": "ae151f89-b30e-41f5-9882-6afc70a4dece", | 36 | "id": "14feac32-ebaa-4d7f-acb3-750266e2f1ed", | ||
81 | "image_display_url": "", | 37 | "image_display_url": "", | ||
n | 82 | "name": "ecg-analysis", | n | 38 | "name": "ecg-database", |
83 | "title": "ECG Analysis" | 39 | "title": "ECG Database" | ||
84 | }, | 40 | }, | ||
85 | { | 41 | { | ||
86 | "description": "", | 42 | "description": "", | ||
n | 87 | "display_name": "ECG Heartbeat Classification", | n | 43 | "display_name": "ECG recordings", |
88 | "id": "50e09818-e7b4-4aba-8aaa-eb48f380ae87", | 44 | "id": "1bf9daba-ca53-4558-840e-8bbd7e25e6cc", | ||
89 | "image_display_url": "", | 45 | "image_display_url": "", | ||
n | 90 | "name": "ecg-heartbeat-classification", | n | 46 | "name": "ecg-recordings", |
91 | "title": "ECG Heartbeat Classification" | 47 | "title": "ECG recordings" | ||
92 | }, | 48 | }, | ||
93 | { | 49 | { | ||
94 | "description": "", | 50 | "description": "", | ||
n | 95 | "display_name": "ECG Signal Analysis", | n | 51 | "display_name": "Heart conditions", |
96 | "id": "565dbf23-c655-4176-97ba-651c91a0a53c", | 52 | "id": "59cc6d2d-b002-4c29-9c67-f07b1ed53643", | ||
97 | "image_display_url": "", | 53 | "image_display_url": "", | ||
n | 98 | "name": "ecg-signal-analysis", | n | 54 | "name": "heart-conditions", |
99 | "title": "ECG Signal Analysis" | 55 | "title": "Heart conditions" | ||
100 | }, | 56 | }, | ||
101 | { | 57 | { | ||
102 | "description": "", | 58 | "description": "", | ||
n | 103 | "display_name": "Electrocardiography", | n | 59 | "display_name": "PhysioNet", |
104 | "id": "842dc05a-7e0c-4acc-8bd4-6e3b49bad603", | 60 | "id": "145fcebc-5b55-4869-aa7a-2fdbaf8f78cf", | ||
105 | "image_display_url": "", | 61 | "image_display_url": "", | ||
n | 106 | "name": "electrocardiography", | n | ||
107 | "title": "Electrocardiography" | ||||
108 | }, | ||||
109 | { | ||||
110 | "description": "", | ||||
111 | "display_name": "Heartbeat", | ||||
112 | "id": "ed381359-5d8f-4286-afd8-5200ac289bb8", | ||||
113 | "image_display_url": "", | ||||
114 | "name": "heartbeat", | 62 | "name": "physionet", | ||
115 | "title": "Heartbeat" | 63 | "title": "PhysioNet" | ||
116 | }, | ||||
117 | { | ||||
118 | "description": "", | ||||
119 | "display_name": "Heartbeat Arrhythmias", | ||||
120 | "id": "a357660d-b794-4c8f-a4c9-8314cec8348d", | ||||
121 | "image_display_url": "", | ||||
122 | "name": "heartbeat-arrhythmias", | ||||
123 | "title": "Heartbeat Arrhythmias" | ||||
124 | } | 64 | } | ||
125 | ], | 65 | ], | ||
126 | "id": "9ef88f83-756f-42d3-a62e-d17633057cb7", | 66 | "id": "9ef88f83-756f-42d3-a62e-d17633057cb7", | ||
127 | "isopen": false, | 67 | "isopen": false, | ||
n | 128 | "landing_page": "https://physionet.org/content/miib/1.0/", | n | 68 | "landing_page": |
69 | "https://physionet.org/content/miibih-arrhythmia-database/1.0.0/", | ||||
129 | "license_title": null, | 70 | "license_title": null, | ||
130 | "link_orkg": "", | 71 | "link_orkg": "", | ||
131 | "metadata_created": "2024-12-02T21:53:13.484629", | 72 | "metadata_created": "2024-12-02T21:53:13.484629", | ||
n | 132 | "metadata_modified": "2024-12-02T21:53:14.066998", | n | 73 | "metadata_modified": "2024-12-03T10:34:15.190239", |
133 | "name": "mit-bih-arrhythmia-database", | 74 | "name": "mit-bih-arrhythmia-database", | ||
n | 134 | "notes": "The proposed framework aims to address the limitations of | n | 75 | "notes": "Five open ECG databases from PhysioNet are involved in |
135 | deep learning applications for ECG signal classi\ufb01cation. Firstly, | 76 | this study namely the MIT-BIH arrhythmia database,St-Petersburg | ||
136 | we proposed a CNN-based autoencoder in a federated architecture to | 77 | Institute of Cardiological Technics 12-lead Arrhythmia Database,The | ||
137 | denoise the raw ECG signal from patients. When trained on the baseline | 78 | MIT-BIH Normal Sinus Rhythm Database,The MIT-BIH Long Term Database | ||
138 | dataset, The proposed autoencoder provided an excellent reconstruction | 79 | and European ST-T Database.", | ||
139 | of the raw input signals and improved the overall performance when | ||||
140 | applied in federated settings.", | ||||
141 | "num_resources": 1, | 80 | "num_resources": 0, | ||
142 | "num_tags": 18, | 81 | "num_tags": 5, | ||
143 | "organization": { | 82 | "organization": { | ||
144 | "approval_status": "approved", | 83 | "approval_status": "approved", | ||
145 | "created": "2024-11-25T12:11:38.292601", | 84 | "created": "2024-11-25T12:11:38.292601", | ||
146 | "description": "", | 85 | "description": "", | ||
147 | "id": "079d46db-32df-4b48-91f3-0a8bc8f69559", | 86 | "id": "079d46db-32df-4b48-91f3-0a8bc8f69559", | ||
148 | "image_url": "", | 87 | "image_url": "", | ||
149 | "is_organization": true, | 88 | "is_organization": true, | ||
150 | "name": "no-organization", | 89 | "name": "no-organization", | ||
151 | "state": "active", | 90 | "state": "active", | ||
152 | "title": "No Organization", | 91 | "title": "No Organization", | ||
153 | "type": "organization" | 92 | "type": "organization" | ||
154 | }, | 93 | }, | ||
155 | "owner_org": "079d46db-32df-4b48-91f3-0a8bc8f69559", | 94 | "owner_org": "079d46db-32df-4b48-91f3-0a8bc8f69559", | ||
156 | "private": false, | 95 | "private": false, | ||
157 | "relationships_as_object": [], | 96 | "relationships_as_object": [], | ||
158 | "relationships_as_subject": [], | 97 | "relationships_as_subject": [], | ||
n | 159 | "resources": [ | n | 98 | "resources": [], |
160 | { | ||||
161 | "cache_last_updated": null, | ||||
162 | "cache_url": null, | ||||
163 | "created": "2024-12-02T22:29:38", | ||||
164 | "data": [ | ||||
165 | "dcterms:title", | ||||
166 | "dcterms:accessRights", | ||||
167 | "dcterms:creator", | ||||
168 | "dcterms:description", | ||||
169 | "dcterms:issued", | ||||
170 | "dcterms:language", | ||||
171 | "dcterms:identifier", | ||||
172 | "dcat:theme", | ||||
173 | "dcterms:type", | ||||
174 | "dcat:keyword", | ||||
175 | "dcat:landingPage", | ||||
176 | "dcterms:hasVersion", | ||||
177 | "dcterms:format", | ||||
178 | "mls:task", | ||||
179 | "datacite:isDescribedBy" | ||||
180 | ], | ||||
181 | "description": "The json representation of the dataset with its | ||||
182 | distributions based on DCAT.", | ||||
183 | "format": "JSON", | ||||
184 | "hash": "", | ||||
185 | "id": "d28d8804-3635-4aa9-90d3-05548a8e8d9f", | ||||
186 | "last_modified": "2024-12-02T21:53:14.057184", | ||||
187 | "metadata_modified": "2024-12-02T21:53:14.069701", | ||||
188 | "mimetype": "application/json", | ||||
189 | "mimetype_inner": null, | ||||
190 | "name": "Original Metadata", | ||||
191 | "package_id": "9ef88f83-756f-42d3-a62e-d17633057cb7", | ||||
192 | "position": 0, | ||||
193 | "resource_type": null, | ||||
194 | "size": 2047, | ||||
195 | "state": "active", | ||||
196 | "url": | ||||
197 | resource/d28d8804-3635-4aa9-90d3-05548a8e8d9f/download/metadata.json", | ||||
198 | "url_type": "upload" | ||||
199 | } | ||||
200 | ], | ||||
201 | "services_used_list": "", | 99 | "services_used_list": "", | ||
202 | "state": "active", | 100 | "state": "active", | ||
203 | "tags": [ | 101 | "tags": [ | ||
204 | { | 102 | { | ||
n | 205 | "display_name": "Anomaly Detection", | n | ||
206 | "id": "772b074e-4795-4f11-80b4-362b2f8a0dca", | ||||
207 | "name": "Anomaly Detection", | ||||
208 | "state": "active", | ||||
209 | "vocabulary_id": null | ||||
210 | }, | ||||
211 | { | ||||
212 | "display_name": "Arrhythmia", | 103 | "display_name": "Arrhythmia", | ||
213 | "id": "79b4436f-58df-46e7-b799-e37e44861ea4", | 104 | "id": "79b4436f-58df-46e7-b799-e37e44861ea4", | ||
214 | "name": "Arrhythmia", | 105 | "name": "Arrhythmia", | ||
n | 215 | "state": "active", | n | ||
216 | "vocabulary_id": null | ||||
217 | }, | ||||
218 | { | ||||
219 | "display_name": "Arrhythmia database", | ||||
220 | "id": "4725bbed-c868-4d19-9da6-bd11433c926e", | ||||
221 | "name": "Arrhythmia database", | ||||
222 | "state": "active", | ||||
223 | "vocabulary_id": null | ||||
224 | }, | ||||
225 | { | ||||
226 | "display_name": "Biomedical", | ||||
227 | "id": "7fb8c492-58a3-4b81-8449-eb4e6cbc1e37", | ||||
228 | "name": "Biomedical", | ||||
229 | "state": "active", | ||||
230 | "vocabulary_id": null | ||||
231 | }, | ||||
232 | { | ||||
233 | "display_name": "Classification", | ||||
234 | "id": "cc82e2f5-be18-4e27-9bd8-0cb307b8a455", | ||||
235 | "name": "Classification", | ||||
236 | "state": "active", | 106 | "state": "active", | ||
237 | "vocabulary_id": null | 107 | "vocabulary_id": null | ||
238 | }, | 108 | }, | ||
239 | { | 109 | { | ||
240 | "display_name": "Database", | 110 | "display_name": "Database", | ||
241 | "id": "9ecb6609-5ce3-4852-9ccb-a1c51ecba2a9", | 111 | "id": "9ecb6609-5ce3-4852-9ccb-a1c51ecba2a9", | ||
242 | "name": "Database", | 112 | "name": "Database", | ||
243 | "state": "active", | 113 | "state": "active", | ||
244 | "vocabulary_id": null | 114 | "vocabulary_id": null | ||
245 | }, | 115 | }, | ||
246 | { | 116 | { | ||
n | 247 | "display_name": "Deep Learning", | n | ||
248 | "id": "3feb7b21-e049-4dca-9372-0d438c483f6a", | ||||
249 | "name": "Deep Learning", | ||||
250 | "state": "active", | ||||
251 | "vocabulary_id": null | ||||
252 | }, | ||||
253 | { | ||||
254 | "display_name": "ECG", | 117 | "display_name": "ECG", | ||
255 | "id": "ccfae47f-089b-4315-bf52-aac716b895bb", | 118 | "id": "ccfae47f-089b-4315-bf52-aac716b895bb", | ||
256 | "name": "ECG", | 119 | "name": "ECG", | ||
n | 257 | "state": "active", | n | ||
258 | "vocabulary_id": null | ||||
259 | }, | ||||
260 | { | ||||
261 | "display_name": "ECG signal analysis", | ||||
262 | "id": "1f98e5dd-d07d-45d4-a089-e9e2e0266fbf", | ||||
263 | "name": "ECG signal analysis", | ||||
264 | "state": "active", | ||||
265 | "vocabulary_id": null | ||||
266 | }, | ||||
267 | { | ||||
268 | "display_name": "Federated Learning", | ||||
269 | "id": "457a40d1-65a2-4186-b368-30d39191ea94", | ||||
270 | "name": "Federated Learning", | ||||
271 | "state": "active", | 120 | "state": "active", | ||
272 | "vocabulary_id": null | 121 | "vocabulary_id": null | ||
273 | }, | 122 | }, | ||
274 | { | 123 | { | ||
275 | "display_name": "Heartbeat", | 124 | "display_name": "Heartbeat", | ||
276 | "id": "9d769c8b-7e6f-4e03-a1ff-e93cb50503a9", | 125 | "id": "9d769c8b-7e6f-4e03-a1ff-e93cb50503a9", | ||
277 | "name": "Heartbeat", | 126 | "name": "Heartbeat", | ||
278 | "state": "active", | 127 | "state": "active", | ||
279 | "vocabulary_id": null | 128 | "vocabulary_id": null | ||
280 | }, | 129 | }, | ||
281 | { | 130 | { | ||
n | 282 | "display_name": "Heartbeat Arrhythmias", | n | ||
283 | "id": "85491bab-285a-482d-aa01-4a362fde95b9", | ||||
284 | "name": "Heartbeat Arrhythmias", | ||||
285 | "state": "active", | ||||
286 | "vocabulary_id": null | ||||
287 | }, | ||||
288 | { | ||||
289 | "display_name": "Machine Learning", | ||||
290 | "id": "c4f3defc-ca48-45a9-9217-ce35bd3ed73c", | ||||
291 | "name": "Machine Learning", | ||||
292 | "state": "active", | ||||
293 | "vocabulary_id": null | ||||
294 | }, | ||||
295 | { | ||||
296 | "display_name": "PhysioNet", | 131 | "display_name": "PhysioNet", | ||
297 | "id": "58c83228-5d24-46b6-8098-8cfbba9008f5", | 132 | "id": "58c83228-5d24-46b6-8098-8cfbba9008f5", | ||
298 | "name": "PhysioNet", | 133 | "name": "PhysioNet", | ||
299 | "state": "active", | 134 | "state": "active", | ||
300 | "vocabulary_id": null | 135 | "vocabulary_id": null | ||
n | 301 | }, | n | ||
302 | { | ||||
303 | "display_name": "Time Series", | ||||
304 | "id": "cb3036eb-2914-4e95-9ed3-111e2f4d13d6", | ||||
305 | "name": "Time Series", | ||||
306 | "state": "active", | ||||
307 | "vocabulary_id": null | ||||
308 | }, | ||||
309 | { | ||||
310 | "display_name": "arrhythmia", | ||||
311 | "id": "2a151665-ef43-4ff7-9186-62953fa665a0", | ||||
312 | "name": "arrhythmia", | ||||
313 | "state": "active", | ||||
314 | "vocabulary_id": null | ||||
315 | }, | ||||
316 | { | ||||
317 | "display_name": "electrocardiography", | ||||
318 | "id": "b6db96d8-a6f7-45ce-b91e-d8856181c746", | ||||
319 | "name": "electrocardiography", | ||||
320 | "state": "active", | ||||
321 | "vocabulary_id": null | ||||
322 | }, | ||||
323 | { | ||||
324 | "display_name": "heartbeat classification", | ||||
325 | "id": "4038bff4-a901-428d-b3f0-9acee855b757", | ||||
326 | "name": "heartbeat classification", | ||||
327 | "state": "active", | ||||
328 | "vocabulary_id": null | ||||
329 | } | 136 | } | ||
330 | ], | 137 | ], | ||
t | 331 | "title": "MIT-BIH Arrhythmia Database", | t | 138 | "title": "MIT-BIH arrhythmia database", |
332 | "type": "dataset", | 139 | "type": "dataset", | ||
333 | "version": "" | 140 | "version": "" | ||
334 | } | 141 | } |