Ahnaf Rashik Hassan

Computer-aided sleep staging using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and bootstrap aggregating

Abstract Computer-aided sleep staging based on single channel electroencephalogram (EEG) is a prerequisite for a feasible low-power wearable sleep monitoring system. It can also eliminate the burden of the clinicians during analyzing a high volume of data by making sleep scoring less onerous, time-consuming and error-prone. Most of the prior studies focus on multichannel EEG based methods which hinder the aforementioned goals. Among the limited number of single-channel based methods, only a few yield good performance in automatic sleep staging. In this article, a single-channel EEG based method for sleep staging using recently introduced Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Bootstrap Aggregating (Bagging) is proposed. At first, EEG signal segments are decomposed into intrinsic mode functions. Higher order statistical moments computed from these functions are used as features. Bagged decision trees are then employed to classify sleep stages. This is the first time that CEEMDAN is employed for automatic sleep staging. Experiments are carried out using the well-known Sleep-EDF database and the results show that the proposed method is superior as compared to the state-of-the-art methods in terms of accuracy. In addition, the proposed scheme gives high detection accuracy for sleep stages S1 and REM.

A decision support system for automatic sleep staging from EEG signals using tunable Q-factor wavelet transform and spectral features

BACKGROUND Automatic sleep scoring is essential owing to the fact that conventionally a large volume of data have to be analyzed visually by the physicians which is onerous, time-consuming and error-prone. Therefore, there is a dire need of an automated sleep staging scheme. NEW METHOD In this work, we decompose sleep-EEG signal segments using tunable-Q factor wavelet transform (TQWT). Various spectral features are then computed from TQWT sub-bands. The performance of spectral features in the TQWT domain has been determined by intuitive and graphical analyses, statistical validation, and Fisher criteria. Random forest is used to perform classification. Optimal choices and the effects of TQWT and random forest parameters have been determined and expounded. RESULTS Experimental outcomes manifest the efficacy of our feature generation scheme in terms of p-values of ANOVA analysis and Fisher criteria. The proposed scheme yields 90.38%, 91.50%, 92.11%, 94.80%, 97.50% for 6-stage to 2-stage classification of sleep states on the benchmark Sleep-EDF data-set. In addition, its performance on DREAMS Subjects Data-set is also promising. COMPARISON WITH EXISTING METHODS The performance of the proposed method is significantly better than the existing ones in terms of accuracy and Cohens kappa coefficient. Additionally, the proposed scheme gives high detection accuracy for sleep stages non-REM 1 and REM. CONCLUSIONS Spectral features in the TQWT domain can discriminate sleep-EEG signals corresponding to various sleep states efficaciously. The proposed scheme will alleviate the burden of the physicians, speed-up sleep disorder diagnosis, and expedite sleep research.

Automatic identification of epileptic seizures from EEG signals using linear programming boosting

BACKGROUND AND OBJECTIVE Computerized epileptic seizure detection is essential for expediting epilepsy diagnosis and research and for assisting medical professionals. Moreover, the implementation of an epilepsy monitoring device that has low power and is portable requires a reliable and successful seizure detection scheme. In this work, the problem of automated epilepsy seizure detection using singe-channel EEG signals has been addressed. METHODS At first, segments of EEG signals are decomposed using a newly proposed signal processing scheme, namely complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Six spectral moments are extracted from the CEEMDAN mode functions and train and test matrices are formed afterward. These matrices are fed into the classifier to identify epileptic seizures from EEG signal segments. In this work, we implement an ensemble learning based machine learning algorithm, namely linear programming boosting (LPBoost) to perform classification. RESULTS The efficacy of spectral features in the CEEMDAN domain is validated by graphical and statistical analyses. The performance of CEEMDAN is compared to those of its predecessors to further inspect its suitability. The effectiveness and the appropriateness of LPBoost are demonstrated as opposed to the commonly used classification models. Resubstitution and 10 fold cross-validation error analyses confirm the superior algorithm performance of the proposed scheme. The algorithmic performance of our epilepsy seizure identification scheme is also evaluated against state-of-the-art works in the literature. Experimental outcomes manifest that the proposed seizure detection scheme performs better than the existing works in terms of accuracy, sensitivity, specificity, and Cohens Kappa coefficient. CONCLUSION It can be anticipated that owing to its use of only one channel of EEG signal, the proposed method will be suitable for device implementation, eliminate the onus of clinicians for analyzing a large bulk of data manually, and expedite epilepsy diagnosis.

Computer-aided obstructive sleep apnea detection using normal inverse Gaussian parameters and adaptive boosting

Abstract Automatic sleep apnea detection using single lead ECG is a precondition for the implementation of a sleep apnea monitoring device. Computerized sleep apnea screening is also essential for expediting sleep apnea research and alleviating the onus of physicians of analyzing a large volume of data by visual inspection. However, most of the state-of-the-art works on automated sleep apnea identification are either based on multiple leads and multiple physiological signals or yield poor performance. In this article, normal inverse Gaussian (NIG) pdf modeling in the recently proposed tunable-Q factor wavelet transform (TQWT) domain is introduced for computer-assisted sleep apnea diagnosis from single-lead ECG signals. First, ECG signal segments are decomposed into sub-bands using TQWT. Afterwards, the corresponding NIG parameters are computed from each of the sub-bands. These parameters are used as features in the proposed apnea detection algorithm. Adaptive boosting (AdaBoost), an eminent ensemble learning based classification scheme is employed to perform classification. The suitability of TQWT is analyzed. The effectiveness of the selected features is validated by intuitive, statistical, and graphical analyses. The performance of the proposed feature extraction scheme is evaluated for various choices of classifiers. Optimal choices of TQWT and AdaBoost parameters are also determined. The performance of the proposed method, as compared to the state-of-the-art algorithms, is comparable or superior in terms of various performance metrics.

Computer-aided gastrointestinal hemorrhage detection in wireless capsule endoscopy videos

BACKGROUND AND OBJECTIVE Wireless Capsule Endoscopy (WCE) can image the portions of the human gastrointestinal tract that were previously unreachable for conventional endoscopy examinations. A major drawback of this technology is that a large volume of data are to be analyzed in order to detect a disease which can be time-consuming and burdensome for the clinicians. Consequently, there is a dire need of computer-aided disease detection schemes to assist the clinicians. In this paper, we propose a real-time, computationally efficient and effective computerized bleeding detection technique applicable for WCE technology. METHODS The development of our proposed technique is based on the observation that characteristic patterns appear in the frequency spectrum of the WCE frames due to the presence of bleeding region. Discovering these discriminating patterns, we develop a texture-feature-descriptor-based-algorithm that operates on the Normalized Gray Level Co-occurrence Matrix (NGLCM) of the magnitude spectrum of the images. A new local texture descriptor called difference average that operates on NGLCM is also proposed. We also perform statistical validation of the proposed scheme. RESULTS The proposed algorithm was evaluated using a publicly available WCE database. The training set consisted of 600 bleeding and 600 non-bleeding frames. This set was used to train the SVM classifier. On the other hand, 860 bleeding and 860 non-bleeding images were selected from the rest of the extracted images to form the test set. The accuracy, sensitivity and specificity obtained from our method are 99.19%, 99.41% and 98.95% respectively which are significantly higher than state-of-the-art methods. In addition, the low computational cost of our method makes it suitable for real-time implementation. CONCLUSION This work proposes a bleeding detection algorithm that employs textural features from the magnitude spectrum of the WCE images. Experimental outcomes backed by statistical validations prove that the proposed algorithm is superior to the existing ones in terms of accuracy, sensitivity, specificity and computational cost.

On the classification of sleep states by means of statistical and spectral features from single channel Electroencephalogram

Traditional sleep scoring based on visual inspection of Electroencephalogram (EEG) signals is onerous for sleep scorers because of the gargantuan volume of data that have to be analyzed per examination. Computer-aided sleep staging can alleviate the onus of the sleep scorers. Again, most of the existing works on automatic sleep staging are multichannel based. Multichannel based sleep scoring is not pragmatic for the implementation of a wearable and portable sleep quality evaluation device. Due to all these factors, automatic sleep scoring based on single channel EEG is garnering increasing attention of sleep researchers. In this work, we propound a single channel based solution to sleep scoring. First, we decompose the EEG signals into segments. We then compute various statistical and spectral features from the signal segments. After performing statistical analyses, we perform classification using artificial neural network. Results of various experiments perspicuously manifest that the proposed scheme is superior to state-of-the-art ones in accuracy.

Automatic screening of Obstructive Sleep Apnea from single-lead Electrocardiogram

Obstructive Sleep Apnea (OSA) is traditionally diagnosed using multiple channel physiological signal. This often leads to incorrect apnea event detection and weakens the performance of OSA diagnosis. Furthermore, there is a dire need of an automatic OSA screening system in order to alleviate the burden of the clinicians and to make a portable home sleep monitoring system feasible. In this work, an algorithm that uses single lead Electrocardiogram (ECG) to detect OSA events is propounded. The contribution of this work is twofold. First, it proposes an automatic OSA detection algorithm using Empirical Mode Decomposition, higher order statistical features and Extreme Learning Machine (ELM). Second, ELM is introduced in this work and this is the first time ELM has been applied to OSA detection. Experimental outcomes backed by statistical validation evinces that the proposed algorithm is superior to existing ones in accuracy.

Epilepsy and seizure detection using statistical features in the Complete Ensemble Empirical Mode Decomposition domain

In this paper, we introduce Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to devise an effective feature extraction scheme for physiological signal analysis. Unlike its predecessors- Empirical Mode Decomposition and Ensemble Empirical Mode Decomposition, CEEMDAN resolves mode mixing problem and gives better spectral separation of the modes. To demonstrate the effectiveness of CEEMDAN based features, we apply CEEMDAN to propose an automatic epileptic seizure detection algorithm. In this work, various statistical features are extracted from the EEG signal segments decomposed by CEEMDAN and seizure classification is performed using artificial neural network. The efficacy of our feature extraction scheme is validated by statistical and graphical analyses. The overall performance of our seizure detection scheme as compared to the state-of-the-art ones is also promising.

Automatic classification of sleep stages from single-channel electroencephalogram

A portable and wearable yet low-power sleep monitoring system necessitates an automatic sleep scoring algorithm with the use of minimum number of recording channels. Computer-aided sleep staging is also important to eradicate the onus of sleep scorers of analyzing an enormous volume of data. The existing works on sleep scoring are either multichannel based or yield poor performance. Therefore, an automatic sleep scoring algorithm based on single channel EEG signals is yet to emerge. In this work, we utilize spectral features to extract discriminatory information from EEG signal segments. We then perform statistical analyses to find out the efficacy and the discriminatory capability of the selected features for various sleep states. Afterwards, we employ Adaptive Boosting to perform classification. The experimental outcomes perspicuously manifest that the proposed scheme is superior to state-of-the-art ones in accuracy.

An automated method for sleep staging from EEG signals using normal inverse Gaussian parameters and adaptive boosting

Sleep stage screening based on visual inspection is burdensome, time-consuming, subjective, and error-prone owing to the large bulk of data which have to be screened. Consequently, automatic sleep scoring is essential for both sleep research and sleep disorder diagnosis. In this work, we present the application of newly proposed tunable-Q factor wavelet transform (TQWT) to devise a single channel EEG based computerized sleep staging algorithm. First, we decompose the sleep-EEG signal segments into TQWT sub-bands. Then we perform normal inverse Gaussian (NIG) pdf modeling of TQWT sub-bands wherein NIG parameters are used as features. The effects of various TQWT parameters are also studied. The suitability of NIG parameters in the TQWT domain is inspected. In this study, we employ adaptive boosting (AdaBoost) for sleep stage classification. To assess the performance of the classification model and to determine the optimal choices of AdaBoost parameters, 10 fold cross-validation is performed. The performance of the proposed scheme is promising in terms of sensitivity, specificity, accuracy, and Cohens Kappa co-efficient. Comparative analysis of performance suggests that the algorithmic performance of the proposed scheme, as opposed to that of the state-of-the-art ones is better. Further, the proposed algorithm also gives superior S1 and REM stage detection accuracy. The computerized sleep scoring scheme propounded herein can expedite sleep disorder diagnosis, contribute to the device implementation of a sleep monitoring system, and benefit sleep research.