Machine Learning Algorithms and Statistical Approaches for Alzheimer's Disease Analysis Based on Resting-State EEG Recordings: A Systematic Review.

Alzheimer's Disease (AD) is a neurodegenerative disorder and the most common type of dementia with a great prevalence in western countries. The diagnosis of AD and its progression is performed through a variety of clinical procedures including neuropsychological and physical examination, Electroencephalographic (EEG) recording, brain imaging and blood analysis. During the last decades, analysis of the electrophysiological dynamics in AD patients has gained great research interest, as an alternative and cost-effective approach. This paper summarizes recent publications focusing on (a) AD detection and (b) the correlation of quantitative EEG features with AD progression, as it is estimated by Mini Mental State Examination (MMSE) score. A total of 49 experimental studies published from 2009 until 2020, which apply machine learning algorithms on resting state EEG recordings from AD patients, are reviewed. Results of each experimental study are presented and compared. The majority of the studies focus on AD detection incorporating Support Vector Machines, while deep learning techniques have not yet been applied on large EEG datasets. Promising conclusions for future studies are presented.

EEG Window Length Evaluation for the Detection of Alzheimer's Disease over Different Brain Regions.

Alzheimer's Disease (AD) is a neurogenerative disorder and the most common type of dementia with a rapidly increasing world prevalence. In this paper, the ability of several statistical and spectral features to detect AD from electroencephalographic (EEG) recordings is evaluated. For this purpose, clinical EEG recordings from 14 patients with AD (8 with mild AD and 6 with moderate AD) and 10 healthy, age-matched individuals are analyzed. The EEG signals are initially segmented in nonoverlapping epochs of different lengths ranging from 5 s to 12 s. Then, a group of statistical and spectral features calculated for each EEG rhythm (δ, θ, α, ß, and γ) are extracted, forming the feature vector that trained and tested a Random Forests classifier. Six classification problems are addressed, including the discrimination from whole-brain dynamics and separately from specific brain regions in order to highlight any alterations of the cortical regions. The results indicated a high accuracy ranging from 88.79% to 96.78% for whole-brain classification. Also, the classification accuracy was higher at the posterior and central regions than at the frontal area and the right side of temporal lobe for all classification problems.

The hackathon model to spur innovation around global mHealth.

The challenge of providing quality healthcare to underserved populations in low- and middle-income countries (LMICs) has attracted increasing attention from information and communication technology (ICT) professionals interested in providing societal impact through their work. Sana is an organisation hosted at the Institute for Medical Engineering and Science at the Massachusetts Institute of Technology that was established out of this interest. Over the past several years, Sana has developed a model of organising mobile health bootcamp and hackathon events in LMICs with the goal of encouraging increased collaboration between ICT and medical professionals and leveraging the growing prevalence of cellphones to provide health solutions in resource limited settings. Most recently, these events have been based in Colombia, Uganda, Greece and Mexico. The lessons learned from these events can provide a framework for others working to create sustainable health solutions in the developing world.

CAROTID - a web-based platform for optimal personalized management of atherosclerotic patients.

Carotid atherosclerosis is the main cause of fatal cerebral ischemic events, thereby posing a major burden for public health and state economies. We propose a web-based platform named CAROTID to address the need for optimal management of patients with carotid atherosclerosis in a twofold sense: (a) objective selection of patients who need carotid-revascularization (i.e., high-risk patients), using a multifaceted description of the disease consisting of ultrasound imaging, biochemical and clinical markers, and (b) effective storage and retrieval of patient data to facilitate frequent follow-ups and direct comparisons with related cases. These two services are achieved by two interconnected modules, namely the computer-aided diagnosis (CAD) tool and the intelligent archival system, in a unified, remotely accessible system. We present the design of the platform and we describe three main usage scenarios to demonstrate the CAROTID utilization in clinical practice. Additionally, the platform was evaluated in a real clinical environment in terms of CAD performance, end-user satisfaction and time spent on different functionalities. CAROTID classification of high- and low-risk cases was 87%; the corresponding stenosis-degree-based classification would have been 61%. Questionnaire-based user satisfaction showed encouraging results in terms of ease-of-use, clinical usefulness and patient data protection. Times for different CAROTID functionalities were generally short; as an example, the time spent for generating the diagnostic decision was 5min in case of 4-s ultrasound video. Large datasets and future evaluation sessions in multiple medical institutions are still necessary to reveal with confidence the full potential of the platform.

Personalised physical exercise regime for chronic patients through a wearable ICT platform.

Today's state of the art in exercise physiology, professional athletics and sports practice in general clearly shows that the best results depend on the personalisation and continuous update of the recommendations provided to an athlete training, a sports lover or a person whose medical condition demands regular physical exercise. The vital signs information gathered in telemonitoring systems can be better evaluated and exploited if processed along with data from the subject's electronic health records, training history and performance statistics. In this context, the current paper intends to exploit modern smart miniaturised systems and advanced information systems towards the development of an infrastructure for continuous, non-invasive acquisition and advanced processing of vital signs information. In particular, it will look into wearable electronics embedded in textile capable of performing regular or exceptional measurements of vital physiological parameters and communicating them to an application server for further processing.

Cost-effective Ambulatory Monitoring.

The Mobinet service concept emerged, as points of care move closer to the patient and the citizen/patient undertakes a more active role in healthcare monitoring and prevention. Today's advances in monitoring devices and telecommunication networks have made possible a viable solution regarding the provision of continuous health monitoring services, seamlessly from the patients' point of view. The Mobinet concept has been tested under various clinical, technical and business pilots throughout Europe and is currently set for commercial launch in Greece.

Cost-effective health services for interactive continuous monitoring of vital signs parameters--the e-Vital concept.

The objective of the e-Vital project is the validation of the market concerning the provision of a novel remote telemedicine service aimed at large sensitive parts of the European population, the "at-risk" citizens, who are usually patients with a stable medical condition that allow a near normal life but may suddenly deteriorate and put life at risk. This service will increase their quality of life and their feeling of safety concerning their health. The e-Vital project focuses on the implementation and exploitation of a modular and ambulatory secure telemedicine platform, which is using easily wearable vital signs monitoring devices, causing minimal discomfort to patients, and which transfer in real time and on-line critical vital parameters to doctors and/or medical experts/consultants, regardless of their location, while getting feedback to increase their feeling of comfort or in case of alarm. The interactive continuous monitoring promises cost effective health services, more active involvement of patients in their own care, and a new sense of realism in making a diagnosis.

Ambulatory monitoring for chronic cardiac and pulmonary patients.

In the Greek pilot of the e-Vital project, remote telemedicine services are provided to chronic cardiac and pulmonary patients who are not confined to a hospital (i.e. receiving home/ambulatory health care) with the aim of exploring the dynamics of interactive continuous chronic patient monitoring, particularly focusing on the impact on patient's quality of life, the patient's active involvement in their own care and according impact on the overall quality of healthcare provision, as well as the benefits for healthcare providers (time management, patient management, savings, etc). The current trial in progress validates the business potential of remote monitoring services and demonstrates the technical environment that enables patient-doctor interaction regardless of location and the according communication modes and protocols. The domain for applying the envisaged service is the private healthcare sector and the users groups include individual chronic patients with cardiac and pulmonary diseases. So far, from the patient's point of view, the service mainly appeals to patients with arrhythmias due to lightweight technology and easy processes involved in the transmission of 1-lead ECG. Asthma and COPD patients are also enthusiastic, as they can receive doctor advice in real time. From the health professional's point of view the service has real added value in the fields of diagnosis, prevention, monitoring and follow up. In the case of asthma and COPD the value of the service in diagnosis is even more apparent as these patients may be asymptomatic, when they visit their doctor and have exacerbations when they are at home. e-Vital in this case ensures that the healthcare professional will acquire the complete image of the patient condition. Finally, the experiences gained so far indicate that e-Vital could be particularly efficient in isolated areas, with shortage of experienced scientific personnel.