Determination and evaluation of clinically efficient stopping criteria for the multiple auditory steady-state response technique

Background: Although the auditory steady-state response (ASSR) technique utilizes objective statistical detection algorithms to estimate behavioural hearing thresholds, the audiologist still has to decide when to terminate ASSR recordings introducing once more a certain degree of subjectivity. Aims: The present study aimed at establishing clinically efficient stopping criteria for a multiple 80-Hz ASSR system. Methods: In Experiment 1, data of 31 normal hearing subjects were analyzed off-line to propose stopping rules. Consequently, ASSR recordings will be stopped when (1) all 8 responses reach significance and significance can be maintained for 8 consecutive sweeps; (2) the mean noise levels were ≤ 4 nV (if at this “≤ 4-nV” criterion, p-values were between 0.05 and 0.1, measurements were extended only once by 8 sweeps); and (3) a maximum amount of 48 sweeps was attained. In Experiment 2, these stopping criteria were applied on 10 normal hearing and 10 hearing-impaired adults to asses the efficiency. Results: The application of these stopping rules resulted in ASSR threshold values that were comparable to other multiple-ASSR research with normal hearing and hearing-impaired adults. Furthermore, in 80% of the cases, ASSR thresholds could be obtained within a time-frame of 1 hour. Investigating the significant response-amplitudes of the hearing-impaired adults through cumulative curves indicated that probably a higher noise-stop criterion than “≤ 4 nV” can be used. Conclusions: The proposed stopping rules can be used in adults to determine accurate ASSR thresholds within an acceptable time-frame of about 1 hour. However, additional research with infants and adults with varying degrees and configurations of hearing loss is needed to optimize these criteria

Automatic Localization of Epileptic Spikes in EEGs of Children with Infantile Spasms

Infantile Spasms (ISS) characterized by electroencephalogram (EEG) recordings exhibiting hypsarrythmia (HYPS) are a severe form of epilepsy. Many clinicians have been trying to improve ISS outcomes; however, quantification of discharges from hypsarrythmic EEG readings remains challenging. This thesis describes the development of a novel method that assists clinicians to successfully localize the epileptic discharges associated with ISS in HYPS. The approach includes: construct the time-frequency domain (TFD) of the EEG recording using matching pursuit TFD (MP-TFD), decompose the TFD matrix into two submatrices using nonnegative matrix factorizations (NMF), and employ the decomposed vectors to locate the spikes. The proposed method was employed to an EEG dataset of five ISS individuals, and identification of spikes was compared with those which were identified by the epileptologists and those obtained using clinical software (Persyst). Performance evaluations showed results based on classification techniques: thresholdings, and support vector machine (SVM). Using the thresholdings, average true positive (TP) and false negative (FN) percentages of 86% and 14% were achieved, which represented a significant improvement over the use of Persyst, which only achieved average TP and FN percentages of 4% and 96%, respectively. Using SVM, the percentage of area under curve (AUC) of receiver operating characteristic (ROC) was significantly improved up to 98.56%. In summary, the proposed novel algorithm based on MP-TFD and NMF was able to successfully detect the epileptic discharges from the dataset. The development of the proposed automated method can potentially assist clinicians to successfully localize the epileptic discharges associated with ISS in HYPS. The quantitative assessment of spike detection, as well as other features of HYPS, is expected to allow a more accurate assessment of the relevance of EEG to clinical outcomes, which is significant in therapy management of ISS

Human Behavior-based Personalized Meal Recommendation and Menu Planning Social System

The traditional dietary recommendation systems are basically nutrition or health-aware where the human feelings on food are ignored. Human affects vary when it comes to food cravings, and not all foods are appealing in all moods. A questionnaire-based and preference-aware meal recommendation system can be a solution. However, automated recognition of social affects on different foods and planning the menu considering nutritional demand and social-affect has some significant benefits of the questionnaire-based and preference-aware meal recommendations. A patient with severe illness, a person in a coma, or patients with locked-in syndrome and amyotrophic lateral sclerosis (ALS) cannot express their meal preferences. Therefore, the proposed framework includes a social-affective computing module to recognize the affects of different meals where the person's affect is detected using electroencephalography signals. EEG allows to capture the brain signals and analyze them to anticipate affective toward a food. In this study, we have used a 14-channel wireless Emotive Epoc+ to measure affectivity for different food items. A hierarchical ensemble method is applied to predict affectivity upon multiple feature extraction methods and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) is used to generate a food list based on the predicted affectivity. In addition to the meal recommendation, an automated menu planning approach is also proposed considering a person's energy intake requirement, affectivity, and nutritional values of the different menus. The bin-packing algorithm is used for the personalized menu planning of breakfast, lunch, dinner, and snacks. The experimental findings reveal that the suggested affective computing, meal recommendation, and menu planning algorithms perform well across a variety of assessment parameters

International Federation of Clinical Neurophysiology (IFCN) – EEG research workgroup: Recommendations on frequency and topographic analysis of resting state EEG rhythms. Part 1: Applications in clinical research studies

In 1999, the International Federation of Clinical Neurophysiology (IFCN) published “IFCN Guidelines for topographic and frequency analysis of EEGs and EPs” (Nuwer et al., 1999). Here a Workgroup of IFCN experts presents unanimous recommendations on the following procedures relevant for the topographic and frequency analysis of resting state EEGs (rsEEGs) in clinical research defined as neurophysiological experimental studies carried out in neurological and psychiatric patients: (1) recording of rsEEGs (environmental conditions and instructions to participants; montage of the EEG electrodes; recording settings); (2) digital storage of rsEEG and control data; (3) computerized visualization of rsEEGs and control data (identification of artifacts and neuropathological rsEEG waveforms); (4) extraction of “synchronization” features based on frequency analysis (band-pass filtering and computation of rsEEG amplitude/power density spectrum); (5) extraction of “connectivity” features based on frequency analysis (linear and nonlinear measures); (6) extraction of “topographic” features (topographic mapping; cortical source mapping; estimation of scalp current density and dura surface potential; cortical connectivity mapping), and (7) statistical analysis and neurophysiological interpretation of those rsEEG features. As core outcomes, the IFCN Workgroup endorsed the use of the most promising “synchronization” and “connectivity” features for clinical research, carefully considering the limitations discussed in this paper. The Workgroup also encourages more experimental (i.e. simulation studies) and clinical research within international initiatives (i.e., shared software platforms and databases) facing the open controversies about electrode montages and linear vs. nonlinear and electrode vs. source levels of those analyses

Do we dance because we walk? The impact of regular vestibular experience on the early development of beat production and perception

Movement to music is a universal human behaviour (Savage, Brown, Sakai & Currie, 2015). Whilst the strong link between music and movement is clearly bidirectional, the origins are not clear. Studying the emergence of rhythmic skills through infancy provides a window into the perceptual and physical attributes, experience, and contexts necessary, to attain the basics of human musicality. This thesis asks whether the human experience of bipedal locomotion, as a primary source of regular vestibular information, is crucial for sensorimotor synchronisation (SMS), spontaneous motor tempo (SMT), and impacts rhythm perception. The first experiment evidences the emergence of tempo-flexibility when moving to music between 10- and 18-months-of-age. The following study is the first to show that experience of locomotion, including from infant carrying, predicts the temporal matching of infant movement to music. Curious if carrying practices influence the very rhythms that we naturally produce, a large-scale correlational study finds infant SMT is predicted by parent height, but not infant’s own body size, such that infants with taller caregivers show a slower SMT than those with shorter caregivers. We contend that this reflects infant experience of being carried by their caregiver. The fourth experiment confirms that experience of being carried at a novel tempo can alter the rhythms infant spontaneously produce. Finally, we asked how information from being carried during locomotion might be changing rhythm perception; specifically, if infants show greater activation of their sensorimotor system when hearing rhythms that match the tempo at which they were carried. Combined, these studies present a highly original piece of research into the ways in which early experiences of locomotion may impact fundamental musical skill

Patient-Specific Epileptic Seizure Onset Detection via Fused Eeg and Ecg Signals

Epilepsy is a neurological disorder that is associated with sudden and recurrent seizures. Epilepsy affects 65 million people world-wide and is the third most common neurological disorder, after stroke and Alzheimer disease. During an epileptic seizure, the brain endures a transient period of abnormally excessive synchronous activity, leading to a state of havoc for many epileptic patients. Seizures can range from being mild and unnoticeable to extremely violent and life threating. Many epileptic individuals are not able to control their seizures with any form of treatment or therapy. These individuals often experience serious risk of injury, limited independence and mobility, and social isolation. In an attempt to increase the quality of life of epileptic individuals, much research has been dedicated to developing seizure onset detection systems that are capable of accurately and rapidly detecting signs of seizures. This thesis presents a novel seizure onset detection system that is based on the fusion of independent electroencephalogram (EEG) and electrocardiogram (ECG) based decisions. The EEG-based detector relies on a on a common spatial pattern (CSP)-based feature enhancement stage that enables better discrimination between seizure and non-seizure features. The EEG-based detector also introduces a novel classification system that uses logical operators to pool support vector machine (SVM) seizure onset detections made independently across different relevant EEG spectral bands. In the ECG-based detector, heart rate variability (HRV) is extracted and analyzed using a Matching-Pursuit and Wigner-Ville Distribution algorithm in order to effectively extract meaningful HRV features representative of seizure and non-seizure states. Two fusion systems are adopted to fuse the EEG- and ECG-based decisions. In the first system, EEG- and ECG-based decisions are directly fused to obtain a final decision. The second fusion system adopts an over-ride option that allows for the EEG-based decision to over-ride the fusion-based decision in an event that the detector observes a string of EEG-based seizure decisions. The proposed detectors exhibit an improved performance, with respect to sensitivity and detection latency, compared with the state-of-the-art detectors. Experimental results demonstrate that the second detector achieves a sensitivity of 100%, detection latency of 2.6 seconds, and a specificity of 99.91% for the MAJORITY fusion case. In addition, a novel method to calculate the amount of neural synchrony that exists between the channels of an EEG matrix is carried out. This method is based on extracting the condition number from multi-channel EEG at a particular time instant to indicate the level of neural synchrony at that particular time instant. The proposed method of neural synchrony calculation is implemented in two detection systems. The first system uses only neural synchrony as the feature for seizure classification whereas the second system fuses energy and synchrony based decision to make a final classification decision. Both systems show promising results when tested on a set of clinical patients

Ability of early neurological assessment and continuous EEG to predict long term neurodevelopmental outcome at 5 years in infants following hypoxic-ischaemic encephalopathy

Hypoxic-ischaemic encephalopathy (HIE) symptoms evolve during the first days of life and their monitoring is critical for treatment decisions and long-term outcome predictions. This thesis aims to report the five-year outcome of a HIE cohort born in the pre-therapeutic hypothermia era and to evaluate the predictive value of (a) neonatal neurological and EEG markers and (b) development in the first 24 months, for outcome. Methods: Participants were recruited at age five from two birth cohorts; HIE and Comparison. Repeated neonatal neurological assessments using the Amiel-TisonNeurological-Assessment-at-Term, continuous video EEG monitoring in the first 72 hours, and Sarnat grading at 24 hours were recorded. EEG severity grades were assigned at 6, 12 and 24 hours. Development was assessed in the HIE cohort at 6, 12 and 24 months using the Griffiths Mental Development (0-2) Revised Scales. At age five, intellectual (WPPSI-IIIUK scale), neuropsychological (NEPSY-II scales), neurological and ophthalmic testing was completed. Results: 5-year outcomes were available for 81.5% (n=53) of HIE and 71.4% (n=30) of Comparison cohorts. In HIE, 47.2% (27% mild, 47% moderate, 83% severe Sarnat), had non-intact outcome vs. 3.3% of the Comparison cohort. Non-intact outcome rates by 6-hour EEG-grade were: grade0=3%, grade1=25%, grade2=54%, grade3/4=79%. In HIE, processing speed (p=0.01) and verbal short-term memory (p=0.005) were below test norms. No significant differences were found in IQ, NEPSY-II or ocular biometry scores between children following mild and moderate HIE. Median IQ scores for mild (99(94-112),p=grade 2) at 24hours had superior positive predictive value (74%; AUROC(95%CI)=0.70(0.55-0.85) for non-intact 5-year outcome than abnormal EEG at 6 hours (68%; AUROC(95%CI)=0.71(0.56-0.87). Within-child development scores were inconsistent across the first 24 months. Although all children with intact 24-month Griffiths quotient (n=30) had intact 5-year IQ, 8/30 had non-intact overall outcome. Conclusion: Predictive value of neonatal neurological assessments and an EEG grading system for outcome was confirmed. Intact early childhood outcomes post-HIE may mask subtle adverse neuropsychological sequelae into the school years. This thesis supports emerging evidence that mild-grade HIE is not a benign condition and its inclusion in studies of neuroprotective treatments for HIE is warranted