Classification of Diabetes and Cardiac Arrhythmia using Deep Learning

Master's thesis Information- and communication technology IKT591 - University of Agder 2018Deep Learning (DL) is a research area that has ourished signi cantly in the recent years and has shown remarkable potential for arti cial intelligence in the eld of medical applications. The reasons for success are the ability of DL algorithms to model high-level abstractions in the data by using automatic feature extraction property as well as signi cant amount of medical data that is available for training these algorithms. DL algorithms can learn features from a large volume of healthcare data, and then use the procured insights to assist clinical practice. We have implement DL algorithm for the classi cation of two diseases in the medical domain: Diabetes and Cardiac Arrhythmia. Diabetes is often considered as one of the world's major health problems according to the World Health Organization. Recent surveys indicate that there is an increase in the number of diabetic patients resulting in the increase in serious complications such as heart attacks and deaths. This thesis presents a Multi-Layer Feed Forward Neural Networks (MLFNN) for the classi cation of diabetes on publicly available Pima Indian Diabetes (PID) dataset. A series of experiments are conducted on this dataset with variation in learning algorithms, activation units, techniques to handle missing data and their impact on classi cation accuracy have been discussed. Finally, the results are compared with other machine learning algorithms like Na ve Bayes, Random Forest, and Logistic Regression. The achieved classi cation accuracy by MLFNN (82.5%) is the best of all the other classi ers. The term arrhythmia refers to any variation in the usual sequence of the heartbeat. There are many types of cardiac arrhythmia ranging in severity, including Premature Atrial Contractions (PACs), Atrial Fibrillation, and Premature Ventricular Contractions (PVCs). This thesis focuses on the use of DL algorithms: Convolutional Neural Network (CNN) and Long Short- Term Memory (LSTM) to classify arrhythmia with minimum possible data pre-processing on MIT-BIH Arrhythmia Database (MIT dataset). Furthermore, we study the in uence of di erent hyperparameters like L2 regularization and number of epochs on the classi cation accuracy of LSTM. We achieved a classi cation accuracy of 99.19% and 98.40% with CNN and LSTM models respectively. From our research, we believe that CNN model can assist the doctors in the classi cation of arrhythmia

Adaptive extreme edge computing for wearable devices

Wearable devices are a fast-growing technology with impact on personal healthcare for both society and economy. Due to the widespread of sensors in pervasive and distributed networks, power consumption, processing speed, and system adaptation are vital in future smart wearable devices. The visioning and forecasting of how to bring computation to the edge in smart sensors have already begun, with an aspiration to provide adaptive extreme edge computing. Here, we provide a holistic view of hardware and theoretical solutions towards smart wearable devices that can provide guidance to research in this pervasive computing era. We propose various solutions for biologically plausible models for continual learning in neuromorphic computing technologies for wearable sensors. To envision this concept, we provide a systematic outline in which prospective low power and low latency scenarios of wearable sensors in neuromorphic platforms are expected. We successively describe vital potential landscapes of neuromorphic processors exploiting complementary metal-oxide semiconductors (CMOS) and emerging memory technologies (e.g. memristive devices). Furthermore, we evaluate the requirements for edge computing within wearable devices in terms of footprint, power consumption, latency, and data size. We additionally investigate the challenges beyond neuromorphic computing hardware, algorithms and devices that could impede enhancement of adaptive edge computing in smart wearable devices

2017 Touro College & University System Faculty Publications

This is the 2017 edition of the Faculty Publications Book of the Touro College & University System. It includes all eligible 2017 publication citations of faculty within the Touro College & University System, including New York Medical College (NYMC). It was produced as a joint effort of the Touro College Libraries and the Health Sciences Library at NYMC.https://touroscholar.touro.edu/facpubs/1007/thumbnail.jp

Topics in Neuromodulation Treatment

"Topics in Neuromodulation Treatment" is a book that invites to the reader to make an update in this important and well-defined area involved in the Neuroscience world. The book pays attention in some aspects of the electrical therapy and also in the drug delivery management of several neurological illnesses including the classic ones like epilepsy, Parkinson's disease, pain, and other indications more recently incorporated to this important tool like bladder incontinency, heart ischemia and stroke. The manuscript is dedicated not only to the expert, but also to the scientist that begins in this amazing field. The authors are physicians of different specialties and they guarantee the clinical expertise to provide to the reader the best guide to treat the patient

Protein Kinases

Proteins are the work horses of the cell. As regulators of protein function, protein kinases are involved in the control of cellular functions via intricate signalling pathways, allowing for fine tuning of physiological functions. This book is a collaborative effort, with contribution from experts in their respective fields, reflecting the spirit of collaboration - across disciplines and borders - that exists in modern science. Here, we review the existing literature and, on occasions, provide novel data on the function of protein kinases in various systems. We also discuss the implications of these findings in the context of disease, treatment, and drug development

Alpha asymmetry index of prefrontal and temporal regions predicts treatment response to repetitive transcranial magnetic stimulation

Aims: Electroencephalography (EEG) measures could be a potential markers for prediction of repetitive transcranial magnetic stimulation (rTMS) outcomes in depression. The aim of this study was to investigate the value of alpha asymmetric index (AAI) in predicting treatment response in two different protocols of rTMS in patients with intractable major depression. Methods: Patients with intractable major depression (n=34) were divided into two treatment groups underwent two different rTMS protocols. The first group received ten sessions 20 Hz rTMS The second group received 10 Hz rTMS . The EEGs were recorded in all subjects pre- and post-intervention using a 19-channel, 10-20 electrodes placement protocol. Hamilton depression rating scale-17 items (HDRS-17) was used to divide the patients into responders and non-responder. The AAI in prefrontal (Fp1-Fp2), frontal (F3-F4 and F7-F8), and temporal (T3-T4) regions were calculated and compared between pre- and post-intervention in each group and between the responder and non-responder groups. Results: In the 20 Hz rTMS group responders, 6 patients responded to the treatment, whereas 10 Hz rTMS showed 8 responders. In the responders of 20 Hz rTMS, the AAI at Fp1-Fp2, F3-F4, and T3-T4significantly decreased after the intervention (P=0.011, P=0.042, and P=0.035). The responders of 10 Hz rTMS showed significant reduction in the AAI at Fp1-Fp2 and T3-T4 regions after intervention (P= 0.023 and P=0.044). Conclusions: It seems AAI at prefrontal and temporal region scould be used for prediction of treatment response, regardless of rTMS protocol

Single session neurofeedback treatment alters theta/beta-1 and theta/alpha ratios but not sufficient to induce clinical enhancement in attention

Background and Aims: Neurofeedback treatment (NFT) may enhance attention performance in healthy individuals. This study investigated the effects of single session NFT on attention in healthy individuals performing a visual attention task. Methods: This was an open-label single-blinded trial conducted on 14 healthy university students (n=14; mean=23.35±0.58 years) of a major medical university in Iran. The subjects underwent a single session NFT while performing attentional network task (ANT). The NFT protocol was theta suppression/beta-1enhancement applied at Cz for 20 min. Before and immediately after NFT, EEG signals were recorded in subjects while performing ANT. EEGs were recorded using a 19 channel device and 10-20 placement protocol. Results: The single session NFT increased the theta/beta-1 ratio in most of the electrode sites and the increase was statistically significant compared to the pre-intervention in the T6 site (p=0.011). The ratio decreased in just three sites of C3, Fz, and Cz, of them Fz showed a significant reduction (p=0.026). Contrary, the theta/alpha ratio decreased in most of the electrodes where the reductions were statistically significant in P3, P4, Cz, Pz (p<0.05), and C3 (p<0.01). The F7, F8, T3, and T4 showed no significant increased theta/alpha ratio. The central, temporal and occipital regions were involved in the NFT induced changes. Single NFT did not significantly change alerting, executive, or orienting networks of ANT. Conclusions: Theta/beta-1 and theta/alpha ratios can be reliably used to assess NFT induced attention enhancement. However, single NFT did not induce clinical outcomes and repeated sessions seem necessary to modulate alerting, executive, or orienting networks of ANT

Addictions

Addiction, increasingly perceived as a heterogeneous brain disorder, is one of the most peculiar psychiatric pathologies in that its management involves various, often non-overlapping, resources from the biological, psychological, medical, economical, social, and legal realms. Despite extensive efforts from the players of these various fields, to date there are no reliably effective treatments of addiction. This may stem from a lack of understanding of the etiology and pathophysiology of this disorder as well as from the lack of interest into the potential differences among patients in the way they interact compulsively with their drug. This book offers an overview of the psychobiology of addiction and its current management strategies from pharmacological, social, behavioural, and psychiatric points of view

Serotonergic modulation of the ventral pallidum by 5HT1A, 5HT5A, 5HT7 AND 5HT2C receptors

Introduction: Serotonin's involvement in reward processing is controversial. The large number of serotonin receptor sub-types and their individual and unique contributions have been difficult to dissect out, yet understanding how specific serotonin receptor sub-types contribute to its effects on areas associated with reward processing is an essential step. Methods: The current study used multi-electrode arrays and acute slice preparations to examine the effects of serotonin on ventral pallidum (VP) neurons. Approach for statistical analysis: extracellular recordings were spike sorted using template matching and principal components analysis, Consecutive inter-spike intervals were then compared over periods of 1200 seconds for each treatment condition using a student’s t test. Results and conclusions: Our data suggests that excitatory responses to serotonin application are pre-synaptic in origin as blocking synaptic transmission with low-calcium aCSF abolished these responses. Our data also suggests that 5HT1a, 5HT5a and 5HT7 receptors contribute to this effect, potentially forming an oligomeric complex, as 5HT1a antagonists completely abolished excitatory responses to serotonin application, while 5HT5a and 5HT7 only reduced the magnitude of excitatory responses to serotonin. 5HT2c receptors were the only serotonin receptor sub-type tested that elicited inhibitory responses to serotonin application in the VP. These findings, combined with our previous data outlining the mechanisms underpinning dopamine's effects in the VP, provide key information, which will allow future research to fully examine the interplay between serotonin and dopamine in the VP. Investigation of dopamine and serotonins interaction may provide vital insights into our understanding of the VP's involvement in reward processing. It may also contribute to our understanding of how drugs of abuse, such as cocaine, may hijack these mechanisms in the VP resulting in sensitization to drugs of abuse

Melatonin and anticancer therapy interactions with 5-Fluorouracil

On the basis of clinical studies, some researchers have advocated that the neurohormone and antioxidant melatonin, shown to possess intrinsic anticancer properties, be used as co-therapy in cancer patients being treated with the antineoplastic agent 5-fluorouracil, as increased patient survival times and enhanced quality of life have been observed. The focus of this research was thus to investigate the mechanisms of this seemingly beneficial drug interaction between 5-fluorouracil and melatonin. Metabolism studies were undertaken, in which it was established that there is no hepatic metabolic drug interaction between these agents by cytochrome P450, and that neither agent alters the activity of this enzyme system. Co-therapy with melatonin is thus unlikely to alter plasma levels of 5-fluorouracil by this mechanism. Novel mechanisms by which 5-fluorouracil is toxic were elucidated, such as the induction of lipid peroxidation, due to the formation of reactive oxygen species; decreases in brain serotonin, dopamine and norepinephrine levels, possibly leading to depression; hippocampal shrinkage and morphological alterations and lysis of hippocampal cells, which may underlie cognitive impairment; and a reduction in the nociceptive threshold when administered acutely. All these deleterious effects are attenuated by the co-administration of melatonin, suggesting that the agent exhibits antidepressive and analgesic properties, in addition to its known antioxidative and free radical-scavenging abilities. This suggests that melatonin cotherapy can significantly decrease 5-fluorouracil-induced toxicity, but this may also exert a protective effect on cancer cells and thus compromise the anticancer efficacy of 5-fluorouracil. It was, furthermore, found that stimulation of indoleamine 2,3-dioxygenase activity, mediated by increases in superoxide anion and interferon-γ levels, may underlie resistance to 5-fluorouracil therapy. Melatonin was shown to increase superoxide anion levels in vivo, and this is believed to be by conversion to the metabolite and known oxidant 6- hydroxymelatonin. This highlights that the possible deleterious effects of melatonin metabolites should be studied further. Serum corticosterone levels and cytokine profiles are unaltered by both 5-FU and melatonin, suggesting that these agents may be used by HIV infected individuals without promoting the progression to AIDS. It can thus be concluded that melatonin co-therapy is potentially useful in countering 5-fluorouracil toxicity