LEADER 04971nam 2201237z- 450 001 9910566473903321 005 20220506 035 $a(CKB)5680000000037640 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/81209 035 $a(oapen)doab81209 035 $a(EXLCZ)995680000000037640 100 $a20202205d2022 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aSignal Processing Using Non-invasive Physiological Sensors 210 $aBasel$cMDPI - Multidisciplinary Digital Publishing Institute$d2022 215 $a1 online resource (222 p.) 311 08$a3-0365-3720-1 311 08$a3-0365-3719-8 330 $aNon-invasive biomedical sensors for monitoring physiological parameters from the human body for potential future therapies and healthcare solutions. Today, a critical factor in providing a cost-effective healthcare system is improving patients' quality of life and mobility, which can be achieved by developing non-invasive sensor systems, which can then be deployed in point of care, used at home or integrated into wearable devices for long-term data collection. Another factor that plays an integral part in a cost-effective healthcare system is the signal processing of the data recorded with non-invasive biomedical sensors. In this book, we aimed to attract researchers who are interested in the application of signal processing methods to different biomedical signals, such as an electroencephalogram (EEG), electromyogram (EMG), functional near-infrared spectroscopy (fNIRS), electrocardiogram (ECG), galvanic skin response, pulse oximetry, photoplethysmogram (PPG), etc. We encouraged new signal processing methods or the use of existing signal processing methods for its novel application in physiological signals to help healthcare providers make better decisions. 606 $aMedical equipment and techniques$2bicssc 610 $aacoustic 610 $aAMR voice 610 $aauscultation sites 610 $abiomedical signal processing 610 $ablink 610 $abrain-computer interface 610 $abrain-computer interface 610 $aBrain-Computer Interface 610 $achannel of interest 610 $achannel selection 610 $aclassification 610 $acomputer aided diagnosis 610 $acongenital heart disease 610 $aconvolution neural network (CNN) 610 $aconvolutional neural network (CNN) 610 $adeep neural network 610 $adiscrete wavelet transform 610 $aECG 610 $aECG derived respiration (EDR) 610 $aEEG 610 $aElectrocardiogram (ECG) 610 $aelectroencephalogram (EEG) 610 $aelectroencephalography 610 $aEMG 610 $aemotion recognition 610 $aempirical mode decomposition 610 $aeye blink 610 $afeature extraction 610 $afeature selection and reduction 610 $afunctional near-infrared spectroscopy 610 $aGSR 610 $ahome automation 610 $ahuman machine interface (HMI) 610 $ahybrid brain-computer interface (BCI) 610 $ahypertension 610 $aimage gradient 610 $aimage processing 610 $along short-term memory (LSTM) 610 $amachine learning 610 $amel-frequency cepstral coefficients 610 $amental imagery 610 $amobile 610 $amotor imagery 610 $amovement intention 610 $amovement-related cortical potential 610 $amultiscale principal component analysis 610 $amyoelectric control 610 $aneurorehabilitation 610 $aOpen-CV 610 $apattern recognition 610 $aphonocardiogram 610 $aphysiological signals 610 $apulse plethysmograph 610 $aquadriplegia 610 $aRaspberry Pi 610 $areaction 610 $areflex 610 $aregion of interest 610 $arehabilitation 610 $arespiratory rate (RR) 610 $aresponse 610 $ashort-time Fourier transform (STFT) 610 $asound 610 $astartle 610 $astatistical analysis 610 $asteady-state visually evoked potential (SSVEP) 610 $astroke 610 $asuccessive decomposition index 610 $asupport vector machines 610 $awheelchair 610 $az-score method 615 7$aMedical equipment and techniques 700 $aNiazi$b Imran Khan$4edt$01314109 702 $aNaseer$b Noman$4edt 702 $aSantosa$b Hendrik$4edt 702 $aNiazi$b Imran Khan$4oth 702 $aNaseer$b Noman$4oth 702 $aSantosa$b Hendrik$4oth 906 $aBOOK 912 $a9910566473903321 996 $aSignal Processing Using Non-invasive Physiological Sensors$93031716 997 $aUNINA