Among these, electrical signals recorded using an EEG are believed to provide more information about brain activity and can be collected in a non-invasive manner [7; 8; 9]. The placement of electrodes for recording the EEG signal is guided either by the benchmark set by the American Academy of Sleep Medicine (AASM) [10; 11]; or by the international 10-20 system for the placement of electrodes [10; 12]. The EEG signals considered for the purpose of this research were recorded for three different regions of the brain by placing the electrodes at three different lobes, i.e., Fp2-A1 (pre-frontal), Cz2-A1v (central), and O2-A1 (occipital), following the international 10-20 system. The EEG data was recorded separately for both male and female subjects due to biological differences between the two that could result in variations in their brain activities [13; 14; 15; 16]. Many sleep stage classification techniques have been proposed that use different features of the EEG signal for classification [17; 18; 19]. However, these techniques mostly use single or dual channel EEG data that is recorded from either one or two regions of the brain. Moreover, the choice of the brain lobes for recording EEG data is mostly arbitrary. Several studies have indicated that multi-channel EEG analysis is vital for accurate sleep stage detection [2; 11; 20]. Hence, in this study, we consider multi-channel EEG signals (Fp2-A1, Cz2-A1, and O2-A1) for sleep stage classification. Furthermore, existing techniques are not very robust in sleep stage identification, mostly due to the non-stationary nature of the EEG signals, which is not easily captured by the extracted features. Hence, instead of crafting our own features of the EEG signal in time, frequency or time-frequency domains [21; 22], a more promising approach would be to use an appropriate deep learning model that would automatically learn distinguishing features from the EEG data to uniquely identify each sleep stage [23]. Visual inspection of EEG recordings has also been considered to uncover unusual patterns in the data, however, it has proved to be difficult and time consuming [24]. Methods based on neural networks [25] and graph based similarity [26] have also been considered for sleep state identification. Neural networks, particularly, deep learning models are efficient at learning a hierarchy of distinguishing features due to their layered architectures [27], but they have their own problems. For instance, conventional deep learning algorithms that use the sigmoid activation functions often encounter the vanishing gradient problem during training [28]. Similarly, deeper network architectures mean more tunable parameters and hence a more complex optimization problem to solve. Hence, stacking more layers may not guarantee better performance [29]. Residual neural networks (ResNet) were proposed to address these shortcomings and were found to be more efficient than the general convolutional neural networks (CNN) [30; 31]. ResNet uses several identity shortcuts, also known as residual connections, with the convolution architecture to backpropagate the errors through multiple intermediate layers. Thus, a ResNet can effectively train networks, even with 1001 layers [32]. Moreover, ResNet incorporates many techniques for better training of neural networks, such as momentum, batch normalization, regularization, and weight initialization [30; 33]. In this study, a ResNet architecture is employed for automatic sleep state classification. Moreover, instead of the raw EEG signal, its Welch power spectral density (PSD) is calculated and then used as input to the ResNet based classifier. The Welch PSD determines the distribution of signal power over frequency [34], which reduces the estimated power spectra at the cost of reduced frequency resolution [35]. Human EEG studies have revealed that aging reduces the power of lower frequency bands, i.e., delta, theta, and sigma, while increasing that of the higher frequency bands, i.e., beta [36; 37].