Quantitative Electroencephalography Analysis (qEEG) of Neuro-Electro-Adaptive Therapy 12™ [NEAT12] Up-Regulates Cortical Potentials in an Alcoholic during Protracted Abstinence: Putative Anti-Craving Implications

DNA Directed Pro-Dopamine Regulation Coupling Subluxation Repair, H-Wave® and Other Neurobiologically Based Modalities to Address Complexities of Chronic Pain in a Female Diagnosed with Reward Deficiency Syndrome (RDS): Emergence of Induction of “Dopamine Homeostasis” in the Face of the Opioid Crisis

Addiction is a complex multifactorial condition. Established genetic factors can provide clear guidance in assessing the risk of addiction to substances and behaviors. Chronic stress can accumulate, forming difficult to recognize addiction patterns from both genetic and epigenetic (environmental) factors. Furthermore, psychological/physical/chemical stressors are typically categorized linearly, delaying identification and treatment. The patient in this case report is a Caucasian female, aged 36, who presented with chronic pain and partial disability following a surgically repaired trimalleolar fracture. The patient had a history of unresolved attention deficit disorder and an MRI scan of her brain revealed atrophy and functional asymmetry. In 2018, the patient entered the Bajaj Chiropractic Clinic, where initial treatment focused on re-establishing integrity of the spine and lower extremity biomechanics and graduated into cognitive behavior stabilization assisted by DNA pro-dopamine regulation guided by Genetic Addiction Risk Severity testing. During treatment (2018–2021), progress achieved included: improved cognitive clarity, focus, sleep, anxiety, and emotional stability in addition to pain reduction (75%); elimination of powerful analgesics; and reduced intake of previously unaddressed alcoholism. To help reduce hedonic addictive behaviors and pain, coupling of H-Wave with corrective chiropractic care seems prudent. We emphasize the importance of genetic assessment along with attempts at inducing required dopaminergic homeostasis via precision KB220PAM. It is hypothesized that from preventive care models, a new standard is emerging including self-awareness and accountability for reward deficiency as a function of hypodopaminergia. This case study documents the progression of a patient dealing with the complexities of an injury, pain management, cognitive impairment, anxiety, depression, and the application of universal health principles towards correction versus palliative care.

Analysis of alcoholic EEG signals based on horizontal visibility graph entropy

This paper proposes a novel horizontal visibility graph entropy (HVGE) approach to evaluate EEG signals from alcoholic subjects and controlled drinkers and compare with a sample entropy (SaE) method. Firstly, HVGEs and SaEs are extracted from 1,200 recordings of biomedical signals, respectively. A statistical analysis method is employed to choose the optimal channels to identify the abnormalities in alcoholics. Five group channels are selected and forwarded to a K-Nearest Neighbour (K-NN) and a support vector machine (SVM) to conduct classification, respectively. The experimental results show that the HVGEs associated with left hemisphere, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$C$$\end{document}C1, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$C$$\end{document}C3 and FC5 electrodes, of alcoholics are significantly abnormal. The accuracy of classification with 10-fold cross-validation is 87.5 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}% with about three HVGE features. By using just optimal 13-dimension HVGE features, the accuracy is 95.8 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}%. In contrast, SaE features associated cannot identify the left hemisphere disorder for alcoholism and the maximum classification ratio based on SaE is just 95.2 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}% even using all channel signals. These results demonstrate that the HVGE method is a promising approach for alcoholism identification by EEG signals.