Diagnosis of Alzheimers Disease from EEG Signals: Where Are We Standing?

Abstract

This paper reviews recent progress in the diagnosis of Alzheimer's disease (AD) from electroencephalograms (EEG). Three major effects of AD on EEG have been observed: slowing of the EEG, reduced complexity of the EEG signals, and perturbations in EEG synchrony. In recent years, a variety of sophisticated computational approaches has been proposed to detect those subtle perturbations in the EEG of AD patients. The paper first describes methods that try to detect slowing of the EEG. Next the paper deals with several measures for EEG complexity, and explains how those measures have been used to study fluctuations in EEG complexity in AD patients. Then various measures of EEG synchrony are considered in the context of AD diagnosis.Also the issue of EEG pre-processing is briefly addressed. Before one can analyze EEG, it is necessary to remove artifacts due to for example head and eye movement or interference from electronic equipment. Pre-processing of EEG has in recent years received much attention. In this paper, several state-of-the-art pre-processing techniques are outlined, for example, based on blind source separation and other non-linear filtering paradigms.In addition, the paper outlines opportunities and limitations of computational approaches for diagnosing AD based on EEG. At last, future challenges and open problems are discussed.

From Auditory and Visual to Immersive Neurofeedback: Application to Diagnosis of Alzheimer’s Disease

“…• Monitor alertness, coma and brain death [54,55] • Locate areas of damage following head injuries [56,57], stroke [58,59] or brain haemorrhage [60,61] • Detect Alzheimer's disease [62][63][64][65] and brain tumours [66,67] • Investigate sleep disorders [68,69] and epilepsy [70,71] • Monitor human brain development [72,73] • Measure the depth of anaesthesia [74] • Test the effect of drugs [75,76].…”

Optimization of EEG frequency bands for improved diagnosis of Alzheimer disease

“…Many studies have shown that the EEG of AD patients has an abnormal spectrum [3,6,10,13,15,[19][20][21]. In particular, the EEG signals of AD patients tend to "slow down": they contain more power in low-frequency bands compared to healthy age-matched subjects.…”

“…To date, the classification performance of these algorithms is about 80%-90% [5], and hence there is still significant room for improvement. A promising strategy to improve the diagnostic power of EEG for AD is to carefully design and optimise time-domain and frequency-domain EEG features [6]. We follow that approach in this paper: we investigate the use of relative power within different EEG frequency bands as a feature to distinguish mild AD from healthy control subjects.…”

“…• 1.5-6.5Hz and 6.5-8.5Hz [5,14] • 0.5-8Hz and 8-30Hz [6,10] • 1-4Hz, 4-8Hz, 8-12Hz, 12-25Hz [15] In contrast to those studies, in this paper we systematically explore various frequency bands, with the aim of improving the diagnosis of AD from EEG. This paper is structured as follows.…”

IMMERSIVE NEUROFEEDBACK - A New Paradigm

“…• Monitor alertness, coma and brain death [2,3] • Locate areas of damage following head injuries [4,5], stroke [6,7] or brain haemorrhage [8,9] • Detect Alzheimer's disease [10][11][12][13] and brain tumour [14,15] • Investigate sleep disorders [16,17] and epilepsy [18,19] • Monitor human brain development [20,21] • Measure the depth of anaesthesia [22] • Test drug effects [23,24] Both the progressive developments in electrical engineering and the fascination with the human brain have attracted researchers from different background to investigate EEG recordings.…”