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  "access_rights": "",
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  "access_rights": "",
            
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  "author": "Ali Razaa",
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  "author": "Jianning Li",
            
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  "author_email": "",
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  "author_email": "",
            
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  "citation": [
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  "citation": [
            
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    "https://doi.org/10.48550/arXiv.2208.10463",
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    "https://doi.org/10.48550/arXiv.1806.04564",
            
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    "https://doi.org/10.48550/arXiv.2106.12498",

            
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    "https://doi.org/10.1109/ECAI58194.2023.10193930",

            
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    "https://doi.org/10.48550/arXiv.2005.08689",

            
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    "https://doi.org/10.1109/TBCAS.2019.2953001",

            
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    "https://doi.org/10.1016/j.knosys.2021.107763",

            
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    "https://doi.org/10.48550/arXiv.2301.09496",

            
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    "https://doi.org/10.48550/arXiv.1812.07421"
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    "https://doi.org/10.48550/arXiv.1803.06441"
            
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  ],
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  "defined_in": "https://doi.org/10.48550/arXiv.1907.09504",
            
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  "doi": "10.57702/ye06lfce",
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  "doi": "10.57702/ye06lfce",
            
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  "doi_date_published": "2024-12-02",
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  "doi_date_published": "2024-12-02",
            
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  "doi_publisher": "TIB",
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  "doi_publisher": "TIB",
            
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  "domain": "https://service.tib.eu/ldmservice",
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  "domain": "https://service.tib.eu/ldmservice",
            
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  "extra_authors": [
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    {

            
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      "extra_author": "Kim Phuc Tran",

            
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      "orcid": ""

            
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    },

            
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    {

            
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      "extra_author": "Ludovic Koehla",

            
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      "orcid": ""

            
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    },

            
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    {

            
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      "extra_author": "Shujun Li",

            
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      "orcid": ""

            
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    }

            
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  ],

            
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  "groups": [
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  "groups": [
            
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    {
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      "description": "",
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      "description": "",
            
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      "display_name": "Arrhythmia",
            
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      "image_display_url": "",
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      "name": "arrhythmia",
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      "name": "arrhythmia",
            
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      "title": "Arrhythmia"
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      "title": "Arrhythmia"
            
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    },
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      "description": "",
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      "description": "",
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      "display_name": "ECG Heartbeat Classification",
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      "name": "ecg-heartbeat-classification",
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      "name": "ecg-recordings",
            
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      "title": "ECG Heartbeat Classification"
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      "title": "ECG recordings"
            
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    },
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    {
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      "description": "",
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      "description": "",
            
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      "display_name": "ECG Signal Analysis",
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      "display_name": "Heart conditions",
            
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      "name": "ecg-signal-analysis",
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      "name": "heart-conditions",
            
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      "title": "ECG Signal Analysis"
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      "title": "Heart conditions"
            
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    },
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    {
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      "description": "",
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      "display_name": "Electrocardiography",
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      "display_name": "PhysioNet",
            
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      "id": "842dc05a-7e0c-4acc-8bd4-6e3b49bad603",
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      "id": "145fcebc-5b55-4869-aa7a-2fdbaf8f78cf",
            
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      "image_display_url": "",
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      "image_display_url": "",
            
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      "name": "electrocardiography",
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      "title": "Electrocardiography"

            
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    },

            
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      "description": "",

            
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      "display_name": "Heartbeat",

            
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      "id": "ed381359-5d8f-4286-afd8-5200ac289bb8",

            
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      "image_display_url": "",

            
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      "name": "heartbeat",
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      "name": "physionet",
            
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      "title": "Heartbeat"
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      "title": "PhysioNet"
            
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    },

            
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    {

            
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      "description": "",

            
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      "display_name": "Heartbeat Arrhythmias",

            
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      "id": "a357660d-b794-4c8f-a4c9-8314cec8348d",

            
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      "image_display_url": "",

            
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      "name": "heartbeat-arrhythmias",

            
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      "title": "Heartbeat Arrhythmias"

            
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    }
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  "landing_page": 
            
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  "link_orkg": "",
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  "metadata_created": "2024-12-02T21:53:13.484629",
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  "metadata_created": "2024-12-02T21:53:13.484629",
            
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  "metadata_modified": "2024-12-03T10:34:15.190239",
            
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  "name": "mit-bih-arrhythmia-database",
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  "name": "mit-bih-arrhythmia-database",
            
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  "notes": "The proposed framework aims to address the limitations of 
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  "notes": "Five open ECG databases from PhysioNet are involved in 
            
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deep learning applications for ECG signal classi\ufb01cation. Firstly, 
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this study namely the MIT-BIH arrhythmia database,St-Petersburg 
            
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we proposed a CNN-based autoencoder in a federated architecture to 
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Institute of Cardiological Technics 12-lead Arrhythmia Database,The 
            
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denoise the raw ECG signal from patients. When trained on the baseline 
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MIT-BIH Normal Sinus Rhythm Database,The MIT-BIH Long Term Database 
            
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dataset, The proposed autoencoder provided an excellent reconstruction 
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and European ST-T Database.",
            
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of the raw input signals and improved the overall performance when 

            
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applied in federated settings.",

            
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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",
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      "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",

            
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      "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
}