Recording Lifetime Behavior and Movement in an Invertebrate Model

Characterization of lifetime behavioral changes is essential for understanding aging and aging-related diseases. However, such studies are scarce partly due to the lack of efficient tools. Here we describe and provide proof of concept for a stereo vision system that classifies and sequentially records at an extremely fine scale six different behaviors (resting, micro-movement, walking, flying, feeding and drinking) and the within-cage (3D) location of individual tephritid fruit flies by time-of-day throughout their lives. Using flies fed on two different diets, full sugar-yeast and sugar-only diets, we report for the first time their behavioral changes throughout their lives at a high resolution. We have found that the daily activity peaks at the age of 15–20 days and then gradually declines with age for flies on both diets. However, the overall daily activity is higher for flies on sugar-only diet than those on the full diet. Flies on sugar-only diet show a stronger diurnal localization pattern with higher preference to staying on the top of the cage during the period of light-off when compared to flies on the full diet. Clustering analyses of age-specific behavior patterns reveal three distinct young, middle-aged and old clusters for flies on each of the two diets. The middle-aged groups for flies on sugar-only diet consist of much younger age groups when compared to flies on full diet. This technology provides research opportunities for using a behavioral informatics approach for understanding different ways in which behavior, movement, and aging in model organisms are mutually affecting

Guest editorial: special issue on pervasive sensing and machine learning for mental health

The seven papers included in this special section focus on machine learning applications for the mental health industry. Mental health is one of the major global health issues affecting substantially more people than other noncommunicable diseases. Much research has been focused on developing novel technologies for tackling this global health challenge, including the development of advanced analytical techniques based on extensive datasets and multimodal acquisition for early detection and treatment of mental illnesses. The papers in this issue are dedicated to cover the related topics on technological advancements for mental health care and diagnosis with a focus on pervasive sensing and machine learning

Neuroimaging and machine learning for dementia diagnosis: recent advancements and future prospects

Dementia, a chronic and progressive cognitive declination of brain function caused by disease or impairment, is becoming more prevalent due to the aging population. A major challenge in dementia is achieving accurate and timely diagnosis. In recent years, neuroimaging with computer-aided algorithms have made remarkable advances in addressing this challenge. The success of these approaches is mostly attributed to the application of machine learning techniques for neuroimaging. In this review paper, we present a comprehensive survey of automated diagnostic approaches for dementia using medical image analysis and machine learning algorithms published in the recent years. Based on the rigorous review of the existing works, we have found that, while most of the studies focused on Alzheimer's disease, recent research has demonstrated reasonable performance in the identification of other types of dementia remains a major challenge. Multimodal imaging analysis deep learning approaches have shown promising results in the diagnosis of these other types of dementia. The main contributions of this review paper are as follows. 1) Based on the detailed analysis of the existing literature, this paper discusses neuroimaging procedures for dementia diagnosis. 2) It systematically explains the most recent machine learning techniques and, in particular, deep learning approaches for early detection of dementia

Simulation of ECG, blood pressure and ballistocardiographic signals

The blood flow in human arterial system can be considered as a fluid dynamics problem. Simulation of blood flow will provide a better understanding of the physiology of human body. Simulation studies of blood flow in the diseased condition can help to diagnose the health problem easily and also have many applications in the areas such as surgical planning and design of medical devices. This paper presents a synthetic electrocardiogram (ECG), blood pressure signals (BP) and ballistocardiographic signal (BCG). Dynamical models of electrocardiogram and cardiovascular system are important in medicine because they can be used as approximation of the real patient. An example is the Windkessel model, which is often used for simulation. ECG, BP and BCG signals can be generated with different sampling frequencies, with different noise levels, with different shapes, filters etc. The paper is based on real data (Real data and identification methods can be used to create models), which are then used for models based on coupled oscillators. Models of the above-mentioned signals are generated by a microcontroller, which allows easy control and adjustment of the output signal and other experiments. The presented paper describes a device that was developed and used for educational purposes, especially for biomedical engineering