Sleep Stages Classification Using WT And ANN: MIT College of Engineering Pune, India (Affiliated to University of Pune)

 
9783659212871: Sleep Stages Classification Using WT And ANN: MIT College of Engineering Pune, India (Affiliated to University of Pune)
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EEG signal processing is one of the hottest areas of research in digital signal processing applications and biomedical research. Analysis of EEG signals provides a crucial tool for diagnosis of neurobiological diseases. The problem of EEG signal classification into different sleep stages is primarily a pattern recognition problem using extracted features. Many methods of feature extraction have been applied to extract the relevant characteristics from a given EEG data. The EEG data was collected from publicly available source. The data consists of different age male & female recordings for a whole night of 8 hrs. The feature extraction was done by computing the Discrete Wavelet Transform and ANN using BP algorithm.The wavelet transform coefficients compress the number of data points into few features. The Approximation & Detailed coefficients obtained from Sub-band coding method provide important features of the EEG signals. In this project we have applied optimization techniques to reduce the computation complexity of the network without affecting the accuracy of the classification. Classification of the EEG data using neural network provides robust and improved Performance

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Ms. Vijaylaxmi P. Jain M.E.(VLSI & Embedded), from MIT COE Pune,affiliated to University of Pune(India),her area of interest are VLSI & Embedded System and Neural Network.Currently working with Dr. D. Y. Patil School of Engg. Pune,Prof.V.V.Shete HOD(E&TC)(MITCOE Pune, India) & Prof.Bahubali K.Shiragapur HOD(E&TC)Dr.D.Y.Patil School of Engg Pune.

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Book Description Condition: New. Publisher/Verlag: LAP Lambert Academic Publishing | MIT College of Engineering Pune, India (Affiliated to University of Pune) | EEG signal processing is one of the hottest areas of research in digital signal processing applications and biomedical research. Analysis of EEG signals provides a crucial tool for diagnosis of neurobiological diseases. The problem of EEG signal classification into different sleep stages is primarily a pattern recognition problem using extracted features. Many methods of feature extraction have been applied to extract the relevant characteristics from a given EEG data. The EEG data was collected from publicly available source. The data consists of different age male & female recordings for a whole night of 8 hrs. The feature extraction was done by computing the Discrete Wavelet Transform and ANN using BP algorithm.The wavelet transform coefficients compress the number of data points into few features. The Approximation & Detailed coefficients obtained from Sub-band coding method provide important features of the EEG signals. In this project we have applied optimization techniques to reduce the computation complexity of the network without affecting the accuracy of the classification. Classification of the EEG data using neural network provides robust and improved Performance | Format: Paperback | Language/Sprache: english | 76 pp. Seller Inventory # K9783659212871

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Book Description LAP Lambert Academic Publishing Aug 2012, 2012. Taschenbuch. Condition: Neu. Neuware - EEG signal processing is one of the hottest areas of research in digital signal processing applications and biomedical research. Analysis of EEG signals provides a crucial tool for diagnosis of neurobiological diseases. The problem of EEG signal classification into different sleep stages is primarily a pattern recognition problem using extracted features. Many methods of feature extraction have been applied to extract the relevant characteristics from a given EEG data. The EEG data was collected from publicly available source. The data consists of different age male & female recordings for a whole night of 8 hrs. The feature extraction was done by computing the Discrete Wavelet Transform and ANN using BP algorithm.The wavelet transform coefficients compress the number of data points into few features. The Approximation & Detailed coefficients obtained from Sub-band coding method provide important features of the EEG signals. In this project we have applied optimization techniques to reduce the computation complexity of the network without affecting the accuracy of the classification. Classification of the EEG data using neural network provides robust and improved Performance 76 pp. Englisch. Seller Inventory # 9783659212871

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Book Description LAP Lambert Academic Publishing Aug 2012, 2012. Taschenbuch. Condition: Neu. Neuware - EEG signal processing is one of the hottest areas of research in digital signal processing applications and biomedical research. Analysis of EEG signals provides a crucial tool for diagnosis of neurobiological diseases. The problem of EEG signal classification into different sleep stages is primarily a pattern recognition problem using extracted features. Many methods of feature extraction have been applied to extract the relevant characteristics from a given EEG data. The EEG data was collected from publicly available source. The data consists of different age male & female recordings for a whole night of 8 hrs. The feature extraction was done by computing the Discrete Wavelet Transform and ANN using BP algorithm.The wavelet transform coefficients compress the number of data points into few features. The Approximation & Detailed coefficients obtained from Sub-band coding method provide important features of the EEG signals. In this project we have applied optimization techniques to reduce the computation complexity of the network without affecting the accuracy of the classification. Classification of the EEG data using neural network provides robust and improved Performance 76 pp. Englisch. Seller Inventory # 9783659212871

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Book Description LAP Lambert Academic Publishing. Paperback. Condition: New. 76 pages. Dimensions: 8.7in. x 5.9in. x 0.2in.EEG signal processing is one of the hottest areas of research in digital signal processing applications and biomedical research. Analysis of EEG signals provides a crucial tool for diagnosis of neurobiological diseases. The problem of EEG signal classification into different sleep stages is primarily a pattern recognition problem using extracted features. Many methods of feature extraction have been applied to extract the relevant characteristics from a given EEG data. The EEG data was collected from publicly available source. The data consists of different age male and female recordings for a whole night of 8 hrs. The feature extraction was done by computing the Discrete Wavelet Transform and ANN using BP algorithm. The wavelet transform coefficients compress the number of data points into few features. The Approximation and Detailed coefficients obtained from Sub-band coding method provide important features of the EEG signals. In this project we have applied optimization techniques to reduce the computation complexity of the network without affecting the accuracy of the classification. Classification of the EEG data using neural network provides robust and improved Performance This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN. Paperback. Seller Inventory # 9783659212871

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Book Description LAP Lambert Academic Publishing, Germany, 2012. Paperback. Condition: New. Aufl.. Language: English . Brand New Book ***** Print on Demand *****.EEG signal processing is one of the hottest areas of research in digital signal processing applications and biomedical research. Analysis of EEG signals provides a crucial tool for diagnosis of neurobiological diseases. The problem of EEG signal classification into different sleep stages is primarily a pattern recognition problem using extracted features. Many methods of feature extraction have been applied to extract the relevant characteristics from a given EEG data. The EEG data was collected from publicly available source. The data consists of different age male female recordings for a whole night of 8 hrs. The feature extraction was done by computing the Discrete Wavelet Transform and ANN using BP algorithm.The wavelet transform coefficients compress the number of data points into few features. The Approximation Detailed coefficients obtained from Sub-band coding method provide important features of the EEG signals. In this project we have applied optimization techniques to reduce the computation complexity of the network without affecting the accuracy of the classification. Classification of the EEG data using neural network provides robust and improved Performance. Seller Inventory # AAV9783659212871

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