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A wavelet neural network algorithm of EEG signals data compression and spikes recognition

Zhang, Y.; Liu, A.; Yu, K.

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 16(2): 172-176

1999

ISSN/ISBN: 1001-5515
PMID: 12552657
Document Number: 498794
A novel method of EEG signals compression representation and epileptiform spikes recognition based on wavelet neural network and its algorithm is presented. The wavelet network not only can compress data effectively but also can recover original signal. In addition, the characters of the spikes and the spike-slow rhythm are auto-detected from the time-frequency isoline of EEG signal. This method is well worth using in the field of the electrophysiological signal processing and time-frequency analyzing.

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Foo, T.T.; Hull, S.S.; Cheung, J.Y. 1995: A PC based neural network algorithm for measurement of heart rate variability Biomedical Sciences Instrumentation 31: 35-40
Hang, X.; Greenberg, N.L.; Shiota, T.; Firstenberg, M.S.; Thomas, J.D.; Thomas, J.D. 2000: Compression of real time volumetric echocardiographic data using modified SPIHT based on the three-dimensional wavelet packet transform Computers in Cardiology 27: 123-126
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Shah, S.; Faller, W.E.; Luttges, M.W. 1991: Neural network analyses of stochastic information: application to neurobiological data Biomedical Sciences Instrumentation 27: 231-238
Ayala, M.; Adjouadi, M.; Yaylali, I.; Jayakar, P. 2004: An optimization approach to recognition of epileptogenic data using neural networks with simplified input layers Biomedical Sciences Instrumentation 40: 181-186
Ala-Korpela, M.; Hiltunen, Y.; Bell, J.D. 1996: Artificial neural network analysis of 1H nuclear magnetic resonance spectroscopic data from human plasma Anticancer Research 16(3b): 1473-1477
Zhang, M.; Zhang, B.; Chen, Y. 2014: Wavelet entropy analysis of spontaneous EEG signals in Alzheimer's disease Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 31(4): 755
Okamoto, Y.; Nakano, H.; Yoshikawa, M.; Matsuoka, H.; Sakamoto, T.; Tsujii, T. 1994: Study on decision support system for the interpretation of laboratory data by an artificial neural network--with a special reference to estimation for histological diagnosis of liver diseases with laboratory data on liver function Rinsho Byori. Japanese Journal of Clinical Pathology 42(2): 195-199
Gotman, J. 1985: Automatic recognition of interictal spikes Electroencephalography and Clinical Neurophysiology. Suppl 37: 93-114
Chen, Y.H. 2000: Wavelet packet-based ECG compression Zhongguo Yi Liao Qi Xie Za Zhi 24(5): 270-273
Seker, H.; Odetayo, M.O.; Petrovic, D.; Naguib, R.N.G.; Bartoli, C.; Alasio, L.; Lakshmi, M.S.; Sherbet, G.V. 2002: Assessment of nodal involvement and survival analysis in breast cancer patients using image cytometric data: statistical, neural network and fuzzy approaches Anticancer Research 22(1a): 433-438
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Tenhagen, A.; Feuring, T.; Lippe, W.M. 1998: Testing the identity of hashish samples with ICP-AES and NAA and data handling with neural networks. 2. Data verification with the use of artificial neural networks Die Pharmazie 53(1): 39-42
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Stavrovskaia, E.D.; Makeev, V.I.; Mironov, A.A. 2006: ClusterTree-RS: the binary tree algorithm for identification of co-regulated genes by clustering regulatory signals Molekuliarnaia Biologiia 40(3): 524-532
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Tossavainen, T.; Juhola, M.; Grönfors, T. 2000: Lossy compression of auditory brainstem response signals Studies in Health Technology and Informatics 77: 1250-1255
Defilippi, C.; Gómez, E.; Quinteros, E. 1988: Electric and motor activity at the level of the small intestine in the dog: contractions without spikes and spikes without contractions Revista Medica de Chile 116(12): 1246-1251