| 000 | 03750naaaa2201093uu 4500 | ||
|---|---|---|---|
| 001 | https://directory.doabooks.org/handle/20.500.12854/76753 | ||
| 005 | 20220714184456.0 | ||
| 020 | _abooks978-3-0365-1691-2 | ||
| 020 | _a9783036516929 | ||
| 020 | _a9783036516912 | ||
| 024 | 7 |
_a10.3390/books978-3-0365-1691-2 _cdoi |
|
| 041 | 0 | _aEnglish | |
| 042 | _adc | ||
| 072 | 7 |
_aKNTX _2bicssc |
|
| 100 | 1 |
_aJović, Alan _4edt _91605693 |
|
| 700 | 1 |
_aJović, Alan _4oth _91605693 |
|
| 245 | 1 | 0 | _aIntelligent Biosignal Analysis Methods |
| 260 |
_aBasel, Switzerland _bMDPI - Multidisciplinary Digital Publishing Institute _c2021 |
||
| 300 | _a1 electronic resource (256 p.) | ||
| 506 | 0 |
_aOpen Access _2star _fUnrestricted online access |
|
| 520 | _aThis book describes recent efforts in improving intelligent systems for automatic biosignal analysis. It focuses on machine learning and deep learning methods used for classification of different organism states and disorders based on biomedical signals such as EEG, ECG, HRV, and others. | ||
| 540 |
_aCreative Commons _fhttps://creativecommons.org/licenses/by/4.0/ _2cc _4https://creativecommons.org/licenses/by/4.0/ |
||
| 546 | _aEnglish | ||
| 650 | 7 |
_aInformation technology industries _2bicssc _91081039 |
|
| 653 | _asleep stage scoring | ||
| 653 | _aneural network-based refinement | ||
| 653 | _aresidual attention | ||
| 653 | _aT-end annotation | ||
| 653 | _asignal quality index | ||
| 653 | _atSQI | ||
| 653 | _aoptimal shrinkage | ||
| 653 | _aemotion | ||
| 653 | _aEEG | ||
| 653 | _aDEAP | ||
| 653 | _aCNN | ||
| 653 | _asurgery image | ||
| 653 | _adisgust | ||
| 653 | _aautonomic nervous system | ||
| 653 | _aelectrocardiogram | ||
| 653 | _agalvanic skin response | ||
| 653 | _aolfactory training | ||
| 653 | _apsychophysics | ||
| 653 | _asmell | ||
| 653 | _awearable sensors | ||
| 653 | _awine sensory analysis | ||
| 653 | _aaccuracy | ||
| 653 | _aconvolution neural network (CNN) | ||
| 653 | _aclassifiers | ||
| 653 | _aelectrocardiography | ||
| 653 | _ak-fold validation | ||
| 653 | _amyocardial infarction | ||
| 653 | _asensitivity | ||
| 653 | _asleep staging | ||
| 653 | _aelectroencephalography (EEG) | ||
| 653 | _abrain functional connectivity | ||
| 653 | _afrequency band fusion | ||
| 653 | _aphase-locked value (PLV) | ||
| 653 | _awearable device | ||
| 653 | _aemotional state | ||
| 653 | _amental workload | ||
| 653 | _astress | ||
| 653 | _aheart rate | ||
| 653 | _aeye blinks rate | ||
| 653 | _askin conductance level | ||
| 653 | _aemotion recognition | ||
| 653 | _aelectroencephalogram (EEG) | ||
| 653 | _aphotoplethysmography (PPG) | ||
| 653 | _amachine learning | ||
| 653 | _afeature extraction | ||
| 653 | _afeature selection | ||
| 653 | _adeep learning | ||
| 653 | _anon-stationarity | ||
| 653 | _aindividual differences | ||
| 653 | _ainter-subject variability | ||
| 653 | _acovariate shift | ||
| 653 | _across-participant | ||
| 653 | _ainter-participant | ||
| 653 | _adrowsiness detection | ||
| 653 | _aEEG features | ||
| 653 | _adrowsiness classification | ||
| 653 | _afatigue detection | ||
| 653 | _aresidual network | ||
| 653 | _aMish | ||
| 653 | _aspatial transformer networks | ||
| 653 | _anon-local attention mechanism | ||
| 653 | _aAlzheimer's disease | ||
| 653 | _afall detection | ||
| 653 | _aevent-centered data segmentation | ||
| 653 | _aaccelerometer | ||
| 653 | _awindow duration | ||
| 653 | _an/a | ||
| 856 | 4 | 0 |
_awww.oapen.org _uhttps://mdpi.com/books/pdfview/book/4202 _70 _zDOAB: download the publication |
| 856 | 4 | 0 |
_awww.oapen.org _uhttps://directory.doabooks.org/handle/20.500.12854/76753 _70 _zDOAB: description of the publication |
| 999 |
_c3006275 _d3006275 |
||