Alzheimer's Disease Diagnosis and Severity Level Detection Based on Electroencephalography Modulation Spectral “Patch” Features

Cassani, Raymundo; Falk, Tiago H.

doi:10.1109/jbhi.2019.2953475

Cited by 16 publications

(26 citation statements)

References 55 publications

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“…In the context of Alzheimer's disease, four different features were computed from the acquired EEG signals based on insights from Cassani et al ( 2017 ) and Cassani and Falk ( 2020 ). These included: spectral power, magnitude squared coherence, amplitude modulation rate-of-change, and modulation frequency “patches” features.…”

Section: Methodsmentioning

confidence: 99%

“…Recently, the work by Cassani and Falk ( 2020 ) showed that improved AD diagnostics could be achieved if non-conventional bands were used in the calculation of the above-mentioned amplitude modulation rate-of-change features. These so-called “patches,” as seen in Figure 4 , were shown to be important in discriminating mild cognitive impairment from AD, as well as moderate AD from severe AD.…”

Section: Methodsmentioning

confidence: 99%

“…In preview, this paper reports the development of multimodal markers of AD severity for patients in the early stages of the disease (minimal to mild dementia; MMSE scores between 21 and 26). We explore the fusion of cortical, subcortical and white matter features extracted from MRI, as well as conventional EEG features alongside ones recently developed, such as the spectrotemporal energy patch features (Cassani and Falk, 2020 ). Analyses on data from 89 patients show the usefulness of multimodal AD severity level models for patients at these stages of AD, and access to an automated tool could be useful for clinicians to help with better diagnostics and monitoring.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, earlier work showed that changes in specific frequency subbands could be seen with disease progression (e.g., Kuskowski et al, 1993;Kowalski et al, 2001). More recently, spectrotemporal energy patches were shown to provide improved accuracy (relative to conventional subband powers) when predicting disease severity levels (Cassani and Falk, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…FIGURE 4 | Three regions (patches) identified in the modulation spectral domain to provide best AD diagnostic accuracy, adapted fromCassani and Falk (2020).…”

mentioning

confidence: 99%

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Multimodal Prediction of Alzheimer's Disease Severity Level Based on Resting-State EEG and Structural MRI

Jesus

Cassani

McGeown

et al. 2021

Front. Hum. Neurosci.

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While several biomarkers have been developed for the detection of Alzheimer's disease (AD), not many are available for the prediction of disease severity, particularly for patients in the mild stages of AD. In this paper, we explore the multimodal prediction of Mini-Mental State Examination (MMSE) scores using resting-state electroencephalography (EEG) and structural magnetic resonance imaging (MRI) scans. Analyses were carried out on a dataset comprised of EEG and MRI data collected from 89 patients diagnosed with minimal-mild AD. Three feature selection algorithms were assessed alongside four machine learning algorithms. Results showed that while MRI features alone outperformed EEG features, when both modalities were combined, improved results were achieved. The top-selected EEG features conveyed information about amplitude modulation rate-of-change, whereas top-MRI features comprised information about cortical area and white matter volume. Overall, a root mean square error between predicted MMSE values and true MMSE scores of 1.682 was achieved with a multimodal system and a random forest regression model.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…FIGURE 4 | Three regions (patches) identified in the modulation spectral domain to provide best AD diagnostic accuracy, adapted fromCassani and Falk (2020).…”

mentioning

confidence: 99%

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Multimodal Prediction of Alzheimer's Disease Severity Level Based on Resting-State EEG and Structural MRI

Jesus

Cassani

McGeown

et al. 2021

Front. Hum. Neurosci.

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Interval‐based features of auditory ERPs for diagnosis of early Alzheimer's disease

Sabbaghi

Sheikhani

Noroozıan

et al. 2021

Alz & Dem Diag Ass & Dis Mo

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Introduction It has been demonstrated that event‐related potentials (ERPs) mirror the neurodegenerative process of Alzheimer's disease (AD) and may therefore qualify as diagnostic markers. The aim of this study was to explore the potential of interval‐based features as possible ERP biomarkers for early detection of AD patients. Methods The current results are based on 7‐channel ERP recordings of 95 healthy controls (HCs) and 75 subjects with mild AD acquired during a three‐stimulus auditory oddball task. To evaluate interval‐based features as diagnostic biomarkers in AD, two classifiers were applied to the selected features to distinguish AD and healthy control ERPs: RBFNN (radial basis function neural network) and MLP (multilayer perceptron). Results Using extracted features and a radial basis function neural network, a high overall diagnostic accuracy of 98.3% was achieved. Discussion Our findings demonstrate the great promise for scalp ERP and interval‐based features as non‐invasive, objective, and low‐cost biomarkers for early AD detection.

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Exploring Imaging Biomarkers for Early Detection of Alzheimer’s Disease Using Deep Learning: A Comprehensive Analysis

Sami,

Makkar,

Meziane

et al. 2024

Communications in Computer and Information Science

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Alzheimer's Disease Diagnosis and Severity Level Detection Based on Electroencephalography Modulation Spectral “Patch” Features

Cited by 16 publications

References 55 publications

Multimodal Prediction of Alzheimer's Disease Severity Level Based on Resting-State EEG and Structural MRI

Multimodal Prediction of Alzheimer's Disease Severity Level Based on Resting-State EEG and Structural MRI

Interval‐based features of auditory ERPs for diagnosis of early Alzheimer's disease

Exploring Imaging Biomarkers for Early Detection of Alzheimer’s Disease Using Deep Learning: A Comprehensive Analysis

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