PMID | Title | Journal | Publication Year | Author | View in RVdb |
---|---|---|---|---|---|
22606663 | R peak detection in electrocardiogram signal based on an optimal combination of wavelet transform, hilbert transform, and adaptive thresholding. | J Med Signals Sens | 2011 | Rabbani H;Mahjoob MP;Farahabadi E;Farahabadi A | View More |
23895941 | Automatic sleep staging using empirical mode decomposition, discrete wavelet transform, time-domain, and nonlinear dynamics features of heart rate variability signals. | Comput Methods Programs Biomed | 2013 | Ebrahimi F;Setarehdan SK;Ayala-Moyeda J;Nazeran H | View More |
24259846 | Frequency Band Analysis of Electrocardiogram (ECG) Signals for Human Emotional State Classification Using Discrete Wavelet Transform (DWT). | J Phys Ther Sci | 2013 | Murugappan M;Murugappan S;Zheng BS | View More |
24480166 | Investigating the performance improvement of HRV Indices in CHF using feature selection methods based on backward elimination and statistical significance. | Comput Biol Med | 2014 | Narin A;Isler Y;Ozer M | View More |
27598465 | Assessing heart rate variability through wavelet-based statistical measures. | Comput Biol Med | 2016 | Wachowiak MP;Hay DC;Johnson MJ | View More |
30104037 | Design of high performance QRS complex detector for wearable healthcare devices using biorthogonal spline wavelet transform. | ISA Trans | 2018 | Berwal D;Kumar A;Kumar Y | View More |
30568932 | Heart Rate Variability Classification using Support Vector Machine and Genetic Algorithm. | J Biomed Phys Eng | 2018 | Ashtiyani M;Navaei Lavasani S;Asgharzadeh Alvar A;Deevband MR | View More |
32570360 | Continuous Monitoring and Statistical Modelling of Heart Rate Variability. | Stud Health Technol Inform | 2020 | Nitulescu A;Crisan-Vida M;Stoicu-Tivadar L | View More |
33036135 | Continuous m-Health Data Authentication Using Wavelet Decomposition for Feature Extraction. | Sensors (Basel) | 2020 | Enamamu T;Otebolaku A;Marchang J;Dany J | View More |
33653263 | Heart rate variability-derived features based on deep neural network for distinguishing different anaesthesia states. | BMC Anesthesiol | 2021 | Zhan J;Wu ZX;Duan ZX;Yang GY;Du ZY;Bao XH;Li H | View More |