Can Digital Technology Advance the Development of Treatments for Alzheimer’s Disease?

Carthy, Marie Mc; Schueler, Peter

doi:10.14283/jpad.2019.32

Cited by 14 publications

(14 citation statements)

References 16 publications

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“…Passive monitoring with digital tools that measure gait, sleep, tremor, word cadence, and location might also be explored as novel endpoints. Digital tools, unlike other COAs, more intimately adhere to the patient's experience and could reveal meaningful and relevant treatment effects that go unobserved in everyday life 60 …”

Section: Discussionmentioning

confidence: 99%

Building clinically relevant outcomes across the Alzheimer's disease spectrum

Rentz

Wessels

Annapragada

et al. 2021

A&D Transl Res & Clin Interv

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Demonstrating that treatments are clinically meaningful across the Alzheimer's disease (AD) continuum is critical for meeting our goals of accelerating a cure by 2025. While this topic has been a focus of several Alzheimer's Association Research Roundtable (AARR) meetings, there remains no consensus as to what constitutes a “clinically meaningful outcome” in the eyes of patients, clinicians, care partners, policymakers, payers, and regulatory bodies. Furthermore, the field has not come to agreement as to what constitutes a clinically meaningful treatment effect at each stage of disease severity. The AARR meeting on November 19–20, 2019, reviewed current approaches to defining clinical meaningfulness from various perspectives including those of patients and care partners, clinicians, regulators, health economists, and public policymakers. Participants discussed approaches that may confer clinical relevance at each stage of the disease continuum and fostered discussion about what should guide us in the future.

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Section: Discussionmentioning

confidence: 99%

Building clinically relevant outcomes across the Alzheimer's disease spectrum

Rentz

Wessels

Annapragada

et al. 2021

A&D Transl Res & Clin Interv

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“…These healthcare solutions may include elderly care [9], remote healthcare [10], fitness programs [11], detection and prognosis of neurological and mental disorders like Alzheimer, epilepsy, autism spectrum disorder and schizophrenia, etc. [12][13][14][15][16][17]. Deep learning [18] is another paradigm in this regard, which is capable of handling the large volume of signal data generated by wearable IoT sensing devices like EEG headsets for epilepsy [19].…”

Section: Introductionmentioning

confidence: 99%

Two-layer LSTM network-based prediction of epileptic seizures using EEG spectral features

Singh

Malhotra

2022

Complex Intell. Syst.

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Epilepsy is a chronic nervous disorder, which disturbs the normal daily routine of an epileptic patient due to sudden seizure onset. In this era of smart healthcare, automated seizure prediction techniques could assist the patients, their family, and medical personnel to control and manage these seizures. This paper proposes a spectral feature-based two-layer LSTM network model for automatic prediction of epileptic seizures using long-term multichannel EEG signals. This model makes use of spectral power and mean spectrum amplitude features of delta, theta, alpha, beta, and gamma bands of 23-channel EEG spectrum for this task. Initially, the proposed single-layer and two-layer LSTM models have been evaluated for EEG segments having durations in the range of 5–50 s for 24 epileptic subjects, out of which EEG segments of 30 s duration are found to be useful for accurate seizure prediction using two-layer LSTM model. Afterwards, to validate the performance of this classifier, the spectral features of 30 s duration EEG segments are fed to random forest, decision tree, k-nearest neighbour, support vector machine, and naive Bayes classifiers, which are empowered with grid search-based parameter estimation. Finally, the iterative simulation results and comparison with recently published existing techniques firmly reveal that the proposed two-layer LSTM model with EEG spectral features is an effective technique for accurately predicting seizures in real time with an average classification accuracy of 98.14%, average sensitivity of 98.51%, and average specificity of 97.78%, thereby enabling the epileptic patients to have a better quality of life.

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“…Currently, speech recording for AD-related research typically takes place in a quiet room with a guiding clinician. Given that smartphone technology is rapidly advancing, speech assessments using ML models trained on recordings obtained by smartphones offer a potentially simple-to-administer and inexpensive solution, scalable to the entire population, that can be performed anywhere, including the patient's home [8,9,10]. However, the problem of model robustness to acoustic noise becomes increasingly important when deploying ML models in real world [11].…”

Section: Introductionmentioning

confidence: 99%

Impact of Acoustic Noise on Alzheimer's Disease Detection from Speech: Should You Let Baby Cry?

Novikova¹

2022

Preprint

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Research related to automatically detecting Alzheimer's disease (AD) is important, given the high prevalence of AD and the high cost of traditional methods. Since AD significantly affects the acoustics of spontaneous speech, speech processing and machine learning (ML) provide promising techniques for reliably detecting AD. However, speech audio may be affected by different types of background noise and it is important to understand how the noise influences the accuracy of ML models detecting AD from speech. In this paper, we study the effect of fifteen types of noise from five different categories on the performance of four ML models trained with three types of acoustic representations. We perform a thorough analysis showing how ML models and acoustic features are affected by different types of acoustic noise. We show that acoustic noise is not necessarily harmful -certain types of noise are beneficial for AD detection models and help increasing accuracy by up to 4.8%. We provide recommendations on how to utilize acoustic noise in order to achieve the best performance results with the ML models deployed in real world.

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Can Digital Technology Advance the Development of Treatments for Alzheimer’s Disease?

Cited by 14 publications

References 16 publications

Building clinically relevant outcomes across the Alzheimer's disease spectrum

Building clinically relevant outcomes across the Alzheimer's disease spectrum

Two-layer LSTM network-based prediction of epileptic seizures using EEG spectral features

Impact of Acoustic Noise on Alzheimer's Disease Detection from Speech: Should You Let Baby Cry?

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