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Wavelet basis functions in biomedical signal processing - SPAN LAB

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J. Rafiee et al. / Expert Systems with Applications 38 (2011) 6190–6201 6199Table 1AStudied wavelet families <strong>in</strong> this research (Rafiee et al., 2010).No. Family (short form) Order1 Haar (db1) db 12–45 Daubechies(db) db 2–db 4546–50 Coiflet (coif) coif 1–coif 551 Morlet (Morl) morl52–147 Complex Morlet (cmor Fb-Fc) a Included Table 1B148 Discrete Meyer (dmey) dmey149 Meyer (meyr) meyr150 Mexican Hat (mexh) mexh151–200 Shannon (Shan Fb-Fc) a Included Table 1B201–260 Frequency B-Spl<strong>in</strong>e (fbsp M-Fb- Fc) a Included Table 1B261–267 Gaussian (gaus) gaus 1–gaus 7268–275 Complex Gaussian (cgau) cgau 1–cgau 8276–290 Biorthogonal (bior Nr.Nd) b Included Table 1B291–305 Reverse Biorthogonal (rbio Nr.Nd) b Included Table 1B306–324 Symlet (sym) sym 2–sym 20aFb is a bandwidth parameter, Fc is a wavelet center frequency, and M is an<strong>in</strong>teger order parameter.b Nr and Nd are orders: r for reconstruction/d for decomposition.5. ConclusionsIn summary, db44 appears to be the most similar function toEMG, EEG, and VPA <strong>signal</strong>s among 324 mother wavelet <strong>functions</strong>.Symmetric mother wavelets have been shown more proper resultswith bio<strong>signal</strong>s <strong>in</strong> prior research. Daubechies are orthogonal compactsupport <strong>functions</strong> and are not symmetric. However, db44 possessesa near-symmetric attribute (see Fig. 9) that well-matchedthe bio<strong>signal</strong>s. Most bio<strong>signal</strong>s possess sharp spikes that could beappropriately analyzed us<strong>in</strong>g Daubechies’ wavelets to reflect sharp,asymmetric spikes seen <strong>in</strong> these <strong>signal</strong>s. These features of db44suggest that it would be effective for many bio<strong>signal</strong> process<strong>in</strong>gmethods based on the resemblance of the <strong>signal</strong> and mother waveletfunction.The similarity between <strong>signal</strong> and mother wavelet function isnot always proper for <strong>signal</strong> process<strong>in</strong>g based on wavelet transformand it is just appropriate for those wavelet-based process<strong>in</strong>gmethods based on the resemblance between <strong>signal</strong>s and mother<strong>functions</strong>.Table 1BStudied wavelet families <strong>in</strong> detail (Rafiee et al., 2010).No Wave No Wave No Wave No Wave No Wave52 1–1.5 100 3–1.1 148 dmey 196 2–0.6 244 2–0.2–0.453 1–1 101 3–1.2 149 meyr 197 2–0.7 245 2–0.2–0.554 1–0.5 102 3–1.3 150 mexh 198 2–0.8 246 2–0.2–0.655 1–0.3 103 3–1.4 151 0.1–0.1 199 2–0.9 247 2–0.2–0.756 1–0.2 104 3–1.5 152 0.1–0.2 200 1–1 248 2–0.2–0.857 1–0.1 105 3–1.6 153 0.1–0.3 201 1–0.1–0.1 249 2–0.2–0.958 1–0.05 106 3–1.8 154 0.1–0.4 202 1–0.1–0.2 250 2–0.2–159 1–0.02 107 3–1.9 155 0.1–0.5 203 1–0.1–0.3 251 3–0.2–0.160 1–0.01 108 3–2 156 0.1–0.6 204 1–0.1–0.4 252 3–0.2–0.261 2–0.1 109 3–2.1 157 0.1–0.7 205 1–0.1–0.5 253 3–0.2–0.362 2–0.2 110 3–2.2 158 0.1–0.8 206 1–0.1–0.6 254 3–0.2–0.463 2–0.3 111 3–2.3 159 0.1–0.9 207 1–0.1–0.7 255 3–0.2–0.564 2–0.4 112 3–2.4 160 0.1–1 208 1–0.1–0.8 256 3–0.2–0.665 2–0.5 113 3–2.5 161 0.2–0.1 209 1–0.1–0.9 257 3–0.2–0.766 2–0.6 114 3–2.6 162 0.2–0.2 210 1–0.1–1 258 3–0.2–0.867 2–0.7 115 3–2.7 163 0.2–0.3 211 2–0.1–0.1 259 3–0.2–0.968 2–0.8 116 3–2.8 164 0.2–0.4 212 2–0.1–0.2 260 3–0.2–169 2–0.9 117 3–2.9 165 0.2–0.5 213 2–0.1–0.3 276 1.170 2–1 118 3–3 166 0.2–0.6 214 2–0.1–0.4 277 1.371 2–1.1 119 4–0.1 167 0.2–0.7 215 2–0.1–0.5 278 1.572 2–1.2 120 4–0.2 168 0.2–0.8 216 2–0.1–0.6 279 2.273 2–1.3 121 4–0.3 169 0.2–0.9 217 2–0.1–0.7 280 2.474 2–1.4 122 4–0.4 170 0.2–1 218 2–0.1–0.8 281 2.675 2–1.5 123 4–0.5 171 0.5–0.1 219 2–0.1–0.9 282 2.876 2–1.6 124 4–0.6 172 0.5–0.2 220 2–0.1–1 283 3.177 2–1.8 125 4–0.7 173 0.5–0.3 221 3–0.1–0.1 284 3.378 2–1.9 126 4–0.8 174 0.5–0.4 222 3–0.1–0.2 285 3.579 2–2 127 4–0.9 175 0.5–0.5 223 3–0.1–0.3 286 3.780 2–2.1 128 4–1 176 0.5–0.6 224 3–0.1–0.4 287 3.981 2–2.2 129 4–1.1 177 0.5–0.7 225 3–0.1–0.5 288 4.482 2–2.3 130 4–1.2 178 0.5–0.8 226 3–0.1–0.6 289 5.583 2–2.4 131 4–1.3 179 0.5–0.9 227 3–0.1–0.7 290 6.884 2–2.5 132 4–1.4 180 0.5–1 228 3–0.1–0.8 291 1.185 2–2.6 133 4–1.5 181 1–0.1 229 3–0.1–0.9 292 1.386 2–2.7 134 4–1.6 182 1–0.2 230 3–0.1–1 293 1.587 2–2.8 135 4–1.8 183 1–0.3 231 1–0.2–0.1 294 2.288 2–2.9 136 4–1.9 184 1–0.4 232 1–0.2–0.2 295 2.489 2–3 137 4–2 185 1–0.5 233 1–0.2–0.3 296 2.690 3–0.1 138 4–2.1 186 1–0.6 234 1–0.2–0.4 297 2.891 3–0.2 139 4–2.2 187 1–0.7 235 1–0.2–0.5 298 3.192 3–0.3 140 4–2.3 188 1–0.8 236 1–0.2–0.6 299 3.393 3–0.4 141 4–2.4 189 1–0.9 237 1–0.2–0.7 300 3.594 3–0.5 142 4–2.5 190 1–1 238 1–0.2–0.8 301 3.795 3–0.6 143 4–2.6 191 2–0.1 239 1–0.2–0.9 302 3.996 3–0.7 144 4–2.7 192 2–0.2 240 1–0.2–1 303 4.497 3–0.8 145 4–2.8 193 2–0.3 241 2–0.2–0.1 304 5.598 3–0.9 146 4–2.9 194 2–0.4 242 2–0.2–0.2 305 6.899 3–1 147 4–3 195 2–0.5 243 2–0.2–0.3

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