AUTOMATED ARTIFACT REMOVAL AND DETECTION OF MILD COGNITIVE IMPAIRMENT FROM SINGLE CHANNEL ELECTROENCEPHALOGRAPHY SIGNALS FOR REAL-TIME IMPLEMENTATIONS ON WEARABLES

Electroencephalogram (EEG) is a technique for recording asynchronous activation of neuronal firing inside the brain with non-invasive scalp electrodes. EEG signal is well studied to evaluate the cognitive state, detect brain diseases such as epilepsy, dementia, coma, autism spectral disorder (ASD), etc. In this dissertation, the EEG signal is studied for the early detection of the Mild Cognitive Impairment (MCI). MCI is the preliminary stage of Dementia that may ultimately lead to Alzheimers disease (AD) in the elderly people. Our goal is to develop a minimalistic MCI detection system that could be integrated to the wearable sensors. This contribution has three major aspects: 1) cleaning the EEG signal, 2) detecting MCI, and 3) predicting the severity of the MCI using the data obtained from a single-channel EEG electrode. Artifacts such as eye blink activities can corrupt the EEG signals. We investigate unsupervised and effective removal of ocular artifact (OA) from single-channel streaming raw EEG data. Wavelet transform (WT) decomposition technique was systematically evaluated for effectiveness of OA removal for a single-channel EEG system. Discrete Wavelet Transform (DWT) and Stationary Wavelet Transform (SWT), is studied with four WT basis functions: haar, coif3, sym3, and bior4.4. The performance of the artifact removal algorithm was evaluated by the correlation coefficients (CC), mutual information (MI), signal to artifact ratio (SAR), normalized mean square error (NMSE), and time-frequency analysis. It is demonstrated that WT can be an effective tool for unsupervised OA removal from single channel EEG data for real-time applications.For the MCI detection from the clean EEG data, we collected the scalp EEG data, while the subjects were stimulated with five auditory speech signals. We extracted 590 features from the Event-Related Potential (ERP) of the collected EEG signals, which included time and spectral domain characteristics of the response. The top 25 features, ranked by the random forest method, were used for classification models to identify subjects with MCI. Robustness of our model was tested using leave-one-out cross-validation while training the classifiers. Best results (leave-one-out cross-validation accuracy 87.9%, sensitivity 84.8%, specificity 95%, and F score 85%) were obtained using support vector machine (SVM) method with Radial Basis Kernel (RBF) (sigma = 10, cost = 102). Similar performances were also observed with logistic regression (LR), further validating the results. Our results suggest that single-channel EEG could provide a robust biomarker for early detection of MCI. We also developed a single channel Electro-encephalography (EEG) based MCI severity monitoring algorithm by generating the Montreal Cognitive Assessment (MoCA) scores from the features extracted from EEG. We performed multi-trial and single-trail analysis for the algorithm development of the MCI severity monitoring. We studied Multivariate Regression (MR), Ensemble Regression (ER), Support Vector Regression (SVR), and Ridge Regression (RR) for multi-trial and deep neural regression for the single-trial analysis. In the case of multi-trial, the best result was obtained from the ER. In our single-trial analysis, we constructed the time-frequency image from each trial and feed it to the convolutional deep neural network (CNN). Performance of the regression models was evaluated by the RMSE and the residual analysis. We obtained the best accuracy with the deep neural regression method

Life-history traits of the Threatened Freshwater Fish Cirrhinus reba (Hamilton 1822) (Cypriniformes: Cyprinidae) in the Ganges River, Northwestern Bangladesh

The threatened Reba carp, Cirrhinus reba is a freshwater fish species found in ponds, rivers, canals and tanks of Bangladesh, India, Myanmar, Nepal and Pakistan. The present study describes the first complete and inclusive description of life-history traits including sex ratio, length-frequency distributions (LFD), length-weight relationships (LWR), condition factors (Allometric, KA; Fulton’s, KF; Relative condition, KR; Relative weight, WR), form factor (a3.0) and size at first sexual maturity of C. reba in the Ganges River, NW Bangladesh. Sampling was done using traditional fishing gears including cast net, square lift net and conical trap from April 2011 to March 2012. The total length (TL), fork length (FL) and standard length (SL) were measured to the nearest 0.01 cm using digital slide calipers and total body weight (BW) was measured using an electronic balance with 0.01 g accuracy. The LWR was calculated using the expression: W= a Lb, where W is the BW, L the TL. The size at first sexual maturity of C. reba was estimated using the empirical equation by Binohlan and Froese (2009) for male and female, separately. A total of 250 specimens ranging from 8.00 cm – 23.40 cm TL and 4.30 g – 200 g BW were analyzed in this study. The overall sex ratio did not differ significantly from the expected value of 1:1 (χ2 = 3.38, p 3.00) in male and female and there was significant differences in the intercepts (ancova, p< 0.001) and in the slopes (ancova, p< 0.001) between the sexes. In addition, the Mann-Whitney U-test showed significant differences in the Fulton’s condition factor between male and female (p< 0.001). The one sample t-test showed that the mean WR (actual mean = 99.50) did not differ from 100 for male (p= 0.523) and female (p= 0.197) in this study, indicating the habitat was still in good condition for C. reba. Moreover, the size at sexual maturity of male and female C. reba were estimated as 11.50 cm TL and 13.50 cm TL, respectively. The results of this study would be useful for the sustainable conservation of this threatened carp fishery in Bangladesh and also neighboring countries

Single Channel EEG Based Score Generation to Monitor the Severity and Progression of Mild Cognitive Impairment

Mild Cognitive Impairment (MCI) is a preliminary stage of Dementia. MCI is determined by behavioral screening measures such as Montreal Cognitive Assessment (MoCA) and Mini-Mental Status Examination (MMSE). Therefore, monitoring the progression of MCI and predicting MoCA scores from objective physiological measures like the EEG is crucial as it will not only help to improve the mental healthcare of the aging population but also to reduce healthcare costs. In this study, we demonstrate a single channel EEG based MoCA score generation method, which is cost-effective and suitable for continuous patient monitoring in the longitudinal study. We collected scalp EEG data while subjects were stimulated with five auditory speech signals. We extracted 590 features from Event-Related Brain Potentials (ERPs), which included time and spectral domain characteristics of the response. The top 11 features, ranked by mutual information, were used for building regression models to generate MoCA scores of the subjects. Robustness of our model was tested using R-squared value, mean square error (MSE), residual\u27s quantile plot, and cook\u27s distance. The analysis shows R-squared=0.78 with MSE=1.63, and residual analysis suggests that the model is acceptable in terms of quantile plot, leverage, and Cook\u27s distance. The outcomes indicate that single-channel based EEG can be used to estimate cognitive scores automatically for severity detection and progression monitoring, which will help us to efficaciously assess the mental health status of elderly people to improve the prognosis and rehabilitation of age-related cognitive impairments

A Single-channel EEG-based approach to detect mild cognitive impairment via speech-evoked brain responses

Mild cognitive impairment (MCI) is the preliminary stage of dementia, which may lead to Alzheimer\u27s disease (AD) in the elderly people. Therefore, early detection of MCI has the potential to minimize the risk of AD by ensuring the proper mental health care before it is too late. In this paper, we demonstrate a single-channel EEG-based MCI detection method, which is cost-effective and portable, and thus suitable for regular home-based patient monitoring. We collected the scalp EEG data from 23 subjects, while they were stimulated with five auditory speech signals. The cognitive state of the subjects was evaluated by the Montreal cognitive assessment test (MoCA). We extracted 590 features from the event-related potential (ERP) of the collected EEG signals, which included time and spectral domain characteristics of the response. The top 25 features, ranked by the random forest method, were used for classification models to identify subjects with MCI. Robustness of our model was tested using leave-one-out cross-validation while training the classifiers. Best results (leave-one-out cross-validation accuracy 87.9%, sensitivity 84.8%, specificity 95%, and F score 85%) were obtained using support vector machine (SVM) method with radial basis kernel (RBF) (sigma = 10/cost = 10^{2}). Similar performances were also observed with logistic regression (LR), further validating the results. Our results suggest that single-channel EEG could provide a robust biomarker for early detection of MCI

Single channel EEG time-frequency features to detect Mild Cognitive Impairment

Detection of Mild Cognitive Impairment (MCI), a possible biomarker for the Alzheimer disease (AD), has a huge importance for the management of the elderly\u27s healthcare. As the treatment of AD is very costly and the early discovery is beneficial, a low-cost, early detection mechanism is needed. In this study, Electroencephalography (EEG) data from seventeen subjects was used to determine if brain activity could be used to distinguish MCI with cognitively normal individuals and compare classification performance. Event-related brain potentials (ERP) were recorded in response to auditory speech stimuli. We extracted spectral and temporal features of the ERPs and built a MCI detection process with Support vector machine (SVM), Logistic Regression (LR), and Random Forest (RF). We have compared behavioral response against EEG based time domain features, frequency domain features, and top-ranked features of time-frequency domains. Four feature groups of our study demonstrate that the ranked time and frequency domain features of the EEG perform better than behavioral features and other EEG/ERP response metrics. Our results demonstrate the performance of the detection of MCI with a cross-validation accuracy of 87.9%, sensitivity 85%, specificity 90%, and F-score 94%. Ability to objectively and reliably detect MCI at early might lead to efficacious treatment of AD and related disorders

Biological aspects of the critically endangered fish, Labeo boga in the Ganges River, Northwestern Bangladesh

The present study reports the first complete and inclusive description of some biological parameters including length-frequency distribution (LFD), sex ratio (SR), length-weight relationship (LWR), condition factors (allometric, KA; Fulton’s, KF; relative, KR and relative weight, WR) and form factor (a3.0) of Labeo boga in the Ganges River, northwestern Bangladesh. Sampling was conducted using traditional fishing gears during April 2011 to March 2012. For each specimen, total length (TL) was measured to the nearest 0.01 cm using digital slide calipers and total body weight (BW) was measured using an electronic balance with 0.01 g accuracy. The LWR was calculated using the expression: W= a Lb, where W is the BW and L is the TL. A total of 211 specimens ranging from 9.78-27.50 cm TL and 10.00 to 276.10 g BW were studied. BW of females was significantly higher than that of males (Mann-Whitney U-test, p>0.001). However the overall sex ratio did not differ significantly from the expected value of 1:1 (χ2 = 0.12, p<0.05). The calculated b for the LWR indicated isometric growth (» 3.00) in males, females and combined gender and there were significant differences in the intercepts but not in the slopes between the sexes of L. boga in the Ganges River. KF of females was significantly higher than that for males (p<0.001). In addition, the Wilcoxon signed rank test showed that the WR did not differ from 100 for males and females in this study indicating good condition of habitat for L. boga. The results of this study would be an effective tool for fishery specialists to initiate early management strategies and regulations for the sustainable management of the remaining stocks of this species within the Padma River and surrounding ecosystems

NeuroMonitor ambulatory EEG device: Comparative analysis and its application for cognitive load assessment

We have previously presented a wireless ambulatory EEG device (NeuroMonitor) to non-invasively monitor prefrontal cortex scalp EEG activity in real-life settings. This paper discusses analysis and application of data acquired using this device. We assess the device data against a commercially available, clinical grade Neuroscan SynAmps RT EEG system. For the comparison, temporal statistical measures and Power Spectral Density (PSD) are computed for the simultaneous recordings from both devices from (nearly) identical electrode locations. Although the analog signal processing, sampling, and data recording specifications are slightly different for these devices (e.g., filter specifications, ADC - NeuroMonitor: 16 bit and Neuroscan: 24 bit, electrodes - NeuroMonitor: GS26 Pre-gelled Disposable, Neuroscan: Ag/AgCl reusable EEG disc electrodes), the temporal signals and the PSD of two devices had sufficient correlation. The paper also describes pilot data collection for a test protocol to determine cognitive load using the NeuroMonitor device. For analyzing attention levels for 5 different tasks, EEG rhythms (Alpha, Beta and Theta) are extracted and cognitive load index (CLI) is computed. Results show variations in the PSD of these rhythms with respect to corresponding expected cognitive loads in attention-related and relaxed tasks. This study validates the NeuroMonitor ambulatory EEG device data and shows a use-case for real-life cognitive load studies