Decision Support Systems for Cardio Vascular Disease using Deep Learning Techniques
| dc.contributor.author | AKSA, DJemai | |
| dc.contributor.author | OMARI, FEth ALlah WAlid | |
| dc.date.accessioned | 2022-03-31T08:35:48Z | |
| dc.date.available | 2022-03-31T08:35:48Z | |
| dc.date.issued | 2020 | |
| dc.description | Mr A. Rahmoun Encadreur Mr H. Bensenan Co-encadreur | en_US |
| dc.description.abstract | On thisgraduationproject,weinvestigatetheuseofDSSforcardiovasculardiseasesdiag- nostic, focusingonmultipletypesofarrhythmia.wealsoexploretheuseofLSTMasaclas- sification modelusinglongertermECGsignal(10s),aswellasbuildingaplatformtodisplay and acquirenewsignalsusingIoTdevice(MySignals). The useofLSTMprovestobeperformingverywellonthetaskofclassifying17categories of rhythm(15arrhythmia+normalsinusrhythm+pacemakerrhythm)comparedtootherstate- of-the-art methods,yieldinganaccuracyof93%,sensitivity95%,andaspecificityof99.46%. As wellasareal-timeresponsiveplatformallowingthevisualizationofnewlyobtainedsignals and outputofthecomputer-aideddiagnostic. This projectdepictsthatthetopicofstudystillfacesmanyobstacles,challengesandlim- itations suchas(clinicalimplication,healthydata...etc)andothers.Despiteallthelater,the LSTM anddeeplearningmechanismsdemonstratedthatitiscapableofrecognizinglong-term patterns withinourtime-seriesdataalongwithgeneralizingthediagnosticprocess. | en_US |
| dc.identifier.uri | https://repository.esi-sba.dz/jspui/handle/123456789/76 | |
| dc.language.iso | en | en_US |
| dc.subject | LSTM | en_US |
| dc.subject | Deep Learning | en_US |
| dc.subject | Arrhythmia | en_US |
| dc.subject | DSS | en_US |
| dc.subject | Visualization Platform | en_US |
| dc.subject | Iot | en_US |
| dc.subject | time-series | en_US |
| dc.title | Decision Support Systems for Cardio Vascular Disease using Deep Learning Techniques | en_US |
| dc.type | Thesis | en_US |