PSG DYNAMIC CHANGES IN METHAMPHETAMINE ABUSE USING RECURRENCE QUANTIFICATION ANALYSIS

ABSTRACT: Polysomnography (PSG) is a standard approach based on comprehensive monitoring of cardiorespiratory signals during sleep. This study has been conducted on subjects with a record of methamphetamine abuse. The significance of this work is methamphetamine abuse detection and measurement without the use of blood tests. With regard to the nonlinear and chaotic dynamic of vital signals and the richness of PSG, the tool employed to carry out the study is Recurrence Qualification Analysis. The objective behind this is to observe and quantify nonlinear dynamic changes of vital signals caused by methamphetamine abuse. Results reveal that: 1) chaotic signals, in other words, system complexity has decreased; 2) under the influence of methamphetamine, signal entropy has increased, bringing about the irregularity of the signals; 3) methamphetamine consumption prompts signal compression to overtake signal expansion which means signal information has declined. ABSTRAK: Polisomnografi (PSG) adalah pendekatan piawai berdasarkan pengawasan menyeluruh signal kardiorespiratori ketika tidur. Kajian ini telah dijalankan ke atas subjek yang mempunyai rekod salah guna methapitamin. Kepentingan kajian ini adalah bagi mengesan salah guna methapitamin dan mengukurnya tanpa menggunakan ujian darah. Dengan mengambil kira ketidak-linearan dan signal penting dinamik dan PSG yang berharga, kaedah yang digunakan bagi menjalankan kajian ini adalah Analisis Kelayakan Berulang. Objektif di sebalik kajian ini adalah bagi melihat dan mengkuantiti perubahan dinamik tidak linear ke atas signal penting disebabkan salah guna methapitamin. Hasil menunjukkan: 1) Signal huru-hara, atau kata lain, kesulitan sistem telah berkurang; 2) di bawah pengaruh methapitamin, signal entropi telah bertambah, menjadikan signal tidak normal; 3) pengambilan methapitamin menyebabkan signal mampat mengambil alih signal kembang bermaksud informasi signal telah berkurang

Study of Interactive Variation Between Brain and Heart Signals While Listening to the Holy Quran by Fusion Technique

Background: In recent years, much attention has been paid to the impact of spirituality on people’s health. Some signals can alter brain function and affects the autonomic nervous system to reduce blood pressure, heart rate, and anxiety levels. Objectives: This study aimed to investigate the effect of listening to the Holy Quran on the electrocardiogram (ECG) and electroencephalogram (EEG) signals of healthy people with the fusion technique. Materials & Methods: Cardiac signal recording and two brain signal channels in the C3 and C4 areas of 25 female students between 20 and 23 years old were performed in three stages: silence, listening to the Holy Quran, and silence again. We used standard complementary plots, then we matched the circles with different radii (0.1 to 1) on the complementary diagram and extracted the number of intersection points with the hypothetical lines of the complementary plot as a feature. We then examined all possible modes with the support vector machine classifier. A new data fusion technique was used to study the interactions between the heart and the brain. Results: The best accuracy of 98.75% was obtained for a distinction between pre and no-voice using the brain signal. Conclusion: The results of the present study show the effect of listening to the Holy Quran on physiological signals with the fusion technique

Application of machine learning in predicting non-alcoholic fatty liver disease using anthropometric and body composition indices

Abstract Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease, which can progress from simple steatosis to advanced cirrhosis and hepatocellular carcinoma. Clinical diagnosis of NAFLD is crucial in the early stages of the disease. The main aim of this study was to apply machine learning (ML) methods to identify significant classifiers of NAFLD using body composition and anthropometric variables. A cross-sectional study was carried out among 513 individuals aged 13 years old or above in Iran. Anthropometric and body composition measurements were performed manually using body composition analyzer InBody 270. Hepatic steatosis and fibrosis were determined using a Fibroscan. ML methods including k-Nearest Neighbor (kNN), Support Vector Machine (SVM), Radial Basis Function (RBF) SVM, Gaussian Process (GP), Random Forest (RF), Neural Network (NN), Adaboost and Naïve Bayes were examined for model performance and to identify anthropometric and body composition predictors of fatty liver disease. RF generated the most accurate model for fatty liver (presence of any stage), steatosis stages and fibrosis stages with 82%, 52% and 57% accuracy, respectively. Abdomen circumference, waist circumference, chest circumference, trunk fat and body mass index were among the most important variables contributing to fatty liver disease. ML-based prediction of NAFLD using anthropometric and body composition data can assist clinicians in decision making. ML-based systems provide opportunities for NAFLD screening and early diagnosis, especially in population-level and remote areas