Anurag Nishad

Bio: Anurag Nishad is an academic researcher from Indian Institute of Technology Indore. The author has contributed to research in topics: Wavelet transform & Fundamental frequency. The author has an hindex of 5, co-authored 8 publications receiving 173 citations.

Application of TQWT based filter-bank for sleep apnea screening using ECG signals

Abstract: The sleep apnea is a disease in which there is the absence of airflow during respiration for at least 10 s. It may occur several times during the night sleep. This disease can lead to many types of cardiovascular diseases. To detect this disease, signals obtained from many channels of polysomnography are to be observed visually by physicians for the long duration. This procedure is expensive, time-consuming, and subjective. Hence, it is required to build an automated system to detect the sleep apnea with few channels. This paper uses single-lead electrocardiogram (ECG) signal to detect apneic and non-apneic events. The proposed method uses tunable-Q wavelet transform (TQWT) based filter-bank instead of TQWT to decompose the segment of ECG signal into several constant bandwidth sub-band signals. Then centered correntropies are computed from the various sub-band signals. The obtained features are then fed to the various classifiers to select the optimum performing classifier. In this work, we have obtained the highest classification accuracy, specificity, and sensitivity of 92.78%, 93.91%, and 90.95% respectively using random forest classifier. Hence, our developed prototype is ready for validation with the huge database and clinical usage.

Classification of epileptic electroencephalogram signals using tunable-Q wavelet transform based filter-bank

Abstract: The epilepsy is a neurological disorder and the seizure events frequently appear in epileptic patients. This disorder can be analysed through electroencephalogram (EEG) signals. In this paper, we propose a novel approach for automated identification of seizure EEG signals. The proposed method in this paper decomposes EEG signal into set of sub-band signals by applying tunable-Q wavelet transform (TQWT) based filter-bank. The sub-bands in TQWT based filter-bank have different value of quality (Q)-factor and have nearly constant bandwidth (BW). The features are computed by applying cross-information potential (CIP) on $$N_s$$ number of sub-band signals, for $$N_s$$ values varying from two to maximum number of sub-band signals obtained from TQWT based filter-bank. The features are computed for various values of $$N_s$$ and fed as input to random forest (RF) classifier. We have observed that, with the increase in the $$N_s$$, the number of computed features increases and hence the classification accuracy (ACC) depends on $$N_s$$. In this work, we have obtained ACC of $$99 \%$$ in the classification of normal, seizure-free, and seizure EEG signals using our proposed method. The developed algorithm is ready to be tested using huge database and can be employed to aid the epileptologists to screen the seizure-free and seizure patients accurately.

Classification of emotions from EEG signals using time-order representation based on the S-transform and convolutional neural network

Abstract: Emotions are the most powerful information source to study the cognition, behaviour, and medical conditions of a person. Accurate identification of emotions helps in the development of affective computing, brain–computer interface, medical diagnosis system, etc. Electroencephalogram (EEG) signals are one such source to capture and study human emotions. In this Letter, a novel time-order representation based on the S-transform and convolutional neural network (CNN) is proposed for the identification of human emotions. EEG signals are transformed into time-order representation (TOR) based on the S-transform. This TOR is given as an input to CNN to automatically extract and classify the deep features. Emotional states of happiness, fear, sadness, and relax are classified with an accuracy of 94.58%. The superiority of the method is judged by evaluating four performance parameters and comparing it with existing state-of-the-art on the same dataset.

Recent advances in Wigner function approaches

Abstract: The Wigner function was formulated in 1932 by Eugene Paul Wigner, at a time when quantum mechanics was in its infancy. In doing so, he brought phase space representations into quantum mechanics. However, its unique nature also made it very interesting for classical approaches and for identifying the deviations from classical behavior and the entanglement that can occur in quantum systems. What stands out, though, is the feature to experimentally reconstruct the Wigner function, which provides far more information on the system than can be obtained by any other quantum approach. This feature is particularly important for the field of quantum information processing and quantum physics. However, the Wigner function finds wide-ranging use cases in other dominant and highly active fields as well, such as in quantum electronics—to model the electron transport, in quantum chemistry—to calculate the static and dynamical properties of many-body quantum systems, and in signal processing—to investigate waves passing through certain media. What is peculiar in recent years is a strong increase in applying it: Although originally formulated 86 years ago, only today the full potential of the Wigner function—both in ability and diversity—begins to surface. This review, as well as a growing, dedicated Wigner community, is a testament to this development and gives a broad and concise overview of recent advancements in different fields.

Cross-Subject Emotion Recognition Using Flexible Analytic Wavelet Transform From EEG Signals

Abstract: Human emotion is a physical or psychological process which is triggered either consciously or unconsciously due to perception of any object or situation. The electroencephalogram (EEG) signals can be used to record ongoing neuronal activities in the brain to get the information about the human emotional state. These complicated neuronal activities in the brain cause non-stationary behavior of the EEG signals. Thus, emotion recognition using EEG signals is a challenging study and it requires advanced signal processing techniques to extract the hidden information of emotions from EEG signals. Due to poor generalizability of features from EEG signals across subjects, recognizing cross-subject emotion has been difficult. Thus, our aim is to comprehensively investigate the channel specific nature of EEG signals and to provide an effective method based on flexible analytic wavelet transform (FAWT) for recognition of emotion. FAWT decomposes the EEG signal into different sub-band signals. Furthermore, we applied information potential to extract the features from the decomposed sub-band signals of EEG signal. The extracted feature values were smoothed and fed to the random forest and support vector machine classifiers that classified the emotions. The proposed method is applied to two different publicly available databases which are SJTU emotion EEG dataset and database for emotion analysis using physiological signal. The proposed method has shown better performance for human emotion classification as compared to the existing method. Moreover, it yields channel specific subject classification of emotion EEG signals when exposed to the same stimuli.

Time–frequency representation using IEVDHM–HT with application to classification of epileptic EEG signals

Abstract: Time–frequency representation (TFR) is useful for non-stationary signal analysis as it provides information about the time-varying frequency components. This study proposes a novel TFR based on the improved eigenvalue decomposition of Hankel matrix and Hilbert transform (IEVDHM–HT). In the proposed method, first the authors decompose non-stationary signals using the IEVDHM with suitably defined criterion for eigenvalue selection, requirement of number of iterations, and new component merging criteria. Furthermore, the HT is applied on extracted components in order to obtain the TFR of non-stationary signals. The performance of proposed TFR has been evaluated on synthetic signals in clean and white noise environment with different signal-to-noise ratios. The proposed method gives good performance in terms of Renyi entropy measure in comparison with other existing methods. Application of the proposed TFR is also shown for the classification of epileptic seizure electroencephalogram (EEG) signals. The least-square support vector machine (LS-SVM) with radial basis function kernel is used for classification of seizure and seizure-free EEG signals obtained from the publicly available database by the University of Bonn, Germany. The proposed method has achieved classification accuracy 100% for the studied EEG database.