a method to detect the AD patients from NC subjects using a novel
approach of channel optimization and TQWT based features extraction. Initially, the number of channels are
optimized using Shannon entropy to signal energy ratio. The EEG signals from 10 optimized channels were
decomposed into nine different SBs. The four different features are extracted from nine SBs to train and test
various classification models. The extracted features from SB7 with EBT produced the highest classification
accuracy of 96.20% with ten-fold cross-validation and it outperforms the state of the art methods.
Introduction
Proposed Method
	EEG dataset
		12-AD and 11-NC subjects
		16-electrods used named, Fz, Cz, Pz, O1, O2, F3, F8, Fp1, Fp2, F4, F7, C3, C4, T4, T3, T6 and T5
		captured eye-closed and resting state during day time in multiple sessions
		five seconds, 256Hz using 12-bit Analog to digital converter
		Flow diagram
			1. EEG Data
			2. Channel optimization
				1. Subband filtering
				2. Shannon Entropy (ShEn) and Signal Energy (E)
				3. ShEn by E ratio calculation
				4. Backward Elimination
			3. TQWT decomposition
			4. feature extraction
				1. Tsallis entropy (TsEn)
				2. Renyi's entropy (ReEn)
				3. Katz;s fractal dimension (KFD)
				4. Kurtosis (K)
			5. Classification
				1. AD
				or
				2. NC
	Channel selection method
		desire for optimal channel selection to reduce data redundancy and computational complexity
		from each channel is filtered into delta (δ: 0.5-4 Hz), theta (θ: 4-8 Hz), alpha (α: 8-13 Hz), and beta (β: 13-30 Hz) EEG SBs 
		SEREN is measured
			S E S E R=\frac{S h E n}{E}=\frac{-\sum P_m \log \left(P_m\right)}{\sum s(n)^2} ; Pm = probability of datum being in bin & s(n) = samples in EEG data sequence
		Out of 16 EEG channels only ten most significant channels ( Cz, Pz, O1, O2, Fp1, Fp2, C3, T4, T3 and T5) were selected for feature extraction followed by classification
		Pseudo code for channel optimization 
			1: Filter EEG signal into EEG SBs. Find ShEn/E ratio. Get max(ACC) = T H, channel count j = 16;
			2: while (1 ≤ i ≤ 16) do
			3: Remove Cj
			4: if ShEn/E ≥ TH then
			5: Reject the channel Ci;
			6: j ← j − 1;
			7: else
			8: j ← j − 1, Don’t reject Cj
			9: end if
			10: i ← i + 1;
			11: end while
			12: The value of j gives the number of optimal channels.
	Tunable Q-walelet transform
		TQWT consits of two variable parameters named Redundancy (R) and quality factor (Q), which can be changed to get required time-frequency resolution
		TQWT consist filter filter bank with two channels;
			1st: Lowpass filer (LPS) 
				H_0(w)=\left\{\begin{array}{cc} 1, & |w| \leq(1-\beta) \pi \\ \Theta\left(\frac{w+(\beta-1) \pi}{\alpha+\beta-1}\right), & (1-\beta) \pi<|w|<\alpha \pi \\ 0 & \alpha \pi \leq|w| \leq \pi \end{array}\right.
			and 2nd : Highpass filter (HPF)
				H_1(w)=\left\{\begin{array}{cc} 0, & |w| \leq(1-\beta) \pi \\ \Theta\left(\frac{\alpha \pi-w}{\alpha+\beta-1}\right), & (1-\beta) \pi<|w|<\alpha \pi \\ 1 & \alpha \pi \leq|w| \leq \pi \end{array}\right.
			expression for Θ(w) is
				\Theta(w)=\frac{1}{2}[\cos (w)+1][2-\cos (w)]^{0.5},|w| \leq \pi
			α and β are scaling parameter of LPF & HPF in range of 0 to 1, α+β>1
		Q is depending on SB central frequency F0 and Bandwith Bw; Q=F0/Bw
		β=2/Q+1 and α=1-β/R
		In decomposition of EEG using TQWT, authors selected parameters of Q=1, R=3 and J=8 after doing some testing
		Thus 9 different SBs have been evaluated
	Feature extraction
		decomposed in 9 different SBs using TQWT
		feature 1:TsEn measure has proved to be more robus compared to ShEn which detects spikes, bursts and EEG rhythms
			T s E n=\frac{1}{q-1}\left(1-\sum_{m=1}^N p_m(v)^q\right)
			where, 	q>0, 
					Pm are the probabilities of a datum being in bin
					N is number of samples in EEG
		feature 2:ReEn is used for epilepsy, drowsiness etc. Generalized the Hartley entropy, collision entropy, ShEn and min-entropy. Utilised to calculate entanglement
			\operatorname{ReEn}=\frac{1}{q-1}\left(\log \sum_{m=1}^N p_m(v)^q\right)
			where,	q is parameter value between 0 and 1.
					N is maximum number of segments in EEG
		feature 3: KFD is most important type of fractal dimension used to measure complexity. which can be defined as the sum and average of Euclidean distances between successive points of a sample D and b respectively, are measured also teh maximum distance between the first point and other point of the sample left
			K F D=\frac{\log (D / b)}{\log (l / a)}=\frac{\log (m)}{\log (m)+\log (l / b)}
			where, m=D/a
		feature 4: Kurtosis (K) can be expressed as a measure of how outlier-prone a distribution is
			K=\frac{E(x-\mu)^4}{\sigma^4}
			where, µ is mean and σ is standard deviation (STD)
Results and Discussion
	The statistical analysis of extracted feature sets has been performed using the KW test. KW test provides the value of p probability. If values of p  0.05, are significant to discriminate the different classes
	EBT method was used with highest classification accuracy of 96.20% with ten-fold cross-validation.