## 1 Introduction
In [35], the authors proposed multi-scale PE for the classification of epileptic events in EEG signals and distinguishing healthy controls from patients. The underlying idea of MSEn is based on coarse graining (computation of the average over an increasing number of samples), which computes a smaller value of entropy following the increment of the time scale [36]. The MSEn approach, proposed in [33], introduces oscillations due to the presence of significant side-lobes in the finite impulse response (FIR) filter of varying transfer function length. In [36], the authors replaced the FIR filter by the low-pass Butterworth filter to rectify the problem of aliasing associated with FIR filter frequency response. As an improvement of MSEn, intrinsic mode entropy (IMEn) [37] is proposed based on EMD, which computes complexity in high frequency scales of the signal, as well as being robust to dominating low-frequency components by combining the group of IMFs. The concept of multilevel filtering has been applied in the EMD domain [38] and in the flexible analytic wavelet transform (FAWT) [39] domain [40]. However, in some cases, the EMD method failed to extract low-energy components from the analyzed time series [41], and hence, low-energy components are absent in the time-frequency plane. Secondly, the EMD method may not be suitable for signals consisting of singularities and localized waves [42]. Keeping in mind the above constraints of existing multi-scale entropy measures, we have proposed a novel multi-scale K-NN entropy measure based on the multi-level filtering approach using the TQWT [43]. The K-NN entropy has been used as a discriminative feature in the area of biomedical signal processing [9; 25]. However, in the current study, it has been presented that the conventional K-NN entropy feature is not sufficient enough to provide good classification accuracy in classifying three EEG classes, namely seizure, seizure-free and normal. Thus, in this work, the conventional K-NN entropy has been enhanced with multi-scale filtering using TQWT, which provided a significant discrimination among seizure, seizure-free and normal EEG signals. Recently, in [44], the authors proposed multivariate sub-band fuzzy entropy based on TQWT and applied their proposed entropy for the discrimination of focal and non-focal EEG signals. In this paper, the TQWT-based multi-scale K-NN entropy is computed by generating sub-bands (SBs) of the signal to be analyzed by proper selection of the quality factor (\(Q\)), the redundancy parameter (\(R\)) and the number of levels of decomposition (\(J\)) using TQWT, followed by computation of K-NN entropy for the cumulative addition of each sub-band signal. Optimal performance can be achieved in this method by optimal selection of the \(Q\) and \(R\) parameters. The MSEn measures are used to distinguish seizure EEG signals from normal and seizure-free EEG signals automatically. Figure 1 presents the block diagram of the proposed method. The remainder of the paper is organized as follows. A brief description of K-NN entropy, TQWT-based multi-scale K-NN entropy and the details of the EEG database used are provided in Sections 2-4, respectively. Section 5 briefly describes the feature selection method and classifier used in this study. Section 6 presents the results obtained using the proposed method, Section 7 discusses the effectiveness of the proposed method in discriminating the normal, seizure-free and seizure classes of EEG signals, and the paper concludes in Section 8.