On the use of saliency maps and convolutional neural networks for improved Alzheimer's disease assessment
Bibliographic record
Abstract
Abstract Background Alzheimer’s disease (AD) is a progressive neurodegenerative disease accounting for 60–80∖% of dementia cases worldwide[2]. Early diagnosis can decrease the severity of the disorder in addition to improving the quality of life of patients. Biomarkers based on electroencephalography (EEG) have emerged as a promising tool in the study of AD, with the advantage of being non‐invasive, less expensive, and potentially portable when compared to other biomarkers. One such biomarker has been the so‐called "modulation‐spectral‐patch‐feature" proposed in [1]. Such features were found based on visual inspection of modulation spectra from healthy‐controls and age‐matched AD patients. Over the last few years, however, innovations in machine learning, in particular in deep‐neural‐networks, have revolutionized many biomedical image and signal processing applications[4,5,6]. In this paper, we explore their use in building better biomarkers for AD assessment. In particular, we explore the use of saliency‐maps(SM)[3] obtained from classification using convolutional‐neural‐networks(CNN) to extract optimal feature patches in a data‐driven sense. Method The study collected data from fifty‐four participants, including 20‐healthy‐controls, 19‐Mild‐Cognitive‐Impairment, and 15‐moderate‐to‐severe‐AD, all age‐matched. Twenty‐channel EEG signals were acquired during eyes‐closed‐resting‐state sessions of eight minutes. The EEG recordings were pre‐processed to remove artifacts. The model performance was evaluated using 5‐fold‐cross‐validation(CV). The average SM was extracted from the validation‐set in order to highlight the relevant modulation "patches" in a data‐driven manner. The CNN model has two convolutional‐layers and three fully‐connected‐layers. Result The tested CNN resulted in the following figures‐of‐merit over the 5‐fold‐CV: 89.4%+/‐2.3% for accuracy and 89.3%+/‐2.3% for f1. From the average SM of each fold, it can be seen that theta‐modulated‐by‐delta and beta‐modulated‐by‐theta were important modulation regions . Others regions such as beta‐modulated‐by‐alpha, alpha‐modulated‐by‐delta, and gamma‐modulated‐by‐beta were also important. While some of these regions match those found visually in[1], other provide more insights into AD assessment. Conclusion In this paper, we show the efficacy of EEG modulation‐spectrograms coupled CNNs saliency‐maps to extract data specific features useful for Alzheimer's disease diagnosis. In particular, several main regions of interest were identified, thus building on previous work that relied on visual inspection. As future work, we will also explore the importance of each EEG channel, thus potentially leading to a more low‐cost and portable solution.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".