Longitudinal Prediction Modeling of Alzheimer Disease using Recurrent Neural Networks

Tabarestani, Solale; Aghili, Maryamossadat; Shojaie, Mehdi; Freytes, Christian Yaphet; Cabrerizo, Mercedes; Barreto, Armando; Rishe, Naphtali; Curiel, Rosie E.; Loewenstein, David; Duara, Ranjan; Adjouadi, Malek

doi:10.1109/bhi.2019.8834556

Cited by 20 publications

(7 citation statements)

References 18 publications

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“…GRU and LSTM are unique kinds of RNN that are capable of learning long-term dependencies. The primary difference between them arises in the fundamental architectural unit/cell [ 49 ]. The LSTM has three gates in their fundamental unit/cell, forget gate to decide what information to be retained and what to be discarded, input gate to decide and update the cells values, output gate to decide the next hidden state.…”

Section: Proposed Methodologymentioning

confidence: 99%

Dementia Detection from Speech Using Machine Learning and Deep Learning Architectures

Kumar

Vekkot

Lalitha

et al. 2022

Sensors

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Dementia affects the patient’s memory and leads to language impairment. Research has demonstrated that speech and language deterioration is often a clear indication of dementia and plays a crucial role in the recognition process. Even though earlier studies have used speech features to recognize subjects suffering from dementia, they are often used along with other linguistic features obtained from transcriptions. This study explores significant standalone speech features to recognize dementia. The primary contribution of this work is to identify a compact set of speech features that aid in the dementia recognition process. The secondary contribution is to leverage machine learning (ML) and deep learning (DL) models for the recognition task. Speech samples from the Pitt corpus in Dementia Bank are utilized for the present study. The critical speech feature set of prosodic, voice quality and cepstral features has been proposed for the task. The experimental results demonstrate the superiority of machine learning (87.6 percent) over deep learning (85 percent) models for recognizing Dementia using the compact speech feature combination, along with lower time and memory consumption. The results obtained using the proposed approach are promising compared with the existing works on dementia recognition using speech.

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Section: Proposed Methodologymentioning

confidence: 99%

Dementia Detection from Speech Using Machine Learning and Deep Learning Architectures

Kumar

Vekkot

Lalitha

et al. 2022

Sensors

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“…These techniques are based on recurrent neural networks (RNN) or CNN architectures, which are powerful and can extract deep longitudinal features from fused multivariate time series data [21,27,42]. Tabarestani et al [43] used two variations of an RNN, namely a long short-term memory (LSTM) and a gated recurrent units (GRU), to predict the patient's status for the next three time points using the previous three historical time points. Alternatively, several studies have formulated AD progression detection as a regression task based on CSs [17,18] considering the fact that CSs are highly predictive factors for AD progression [31,44].…”

Section: Deep Learning Based Ad Predictionmentioning

confidence: 99%

Robust hybrid deep learning models for Alzheimer’s progression detection

Abuhmed

El–Sappagh

Alonso

2021

Knowledge-Based Systems

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“…These recurrent modules have proven successful in many different tasks to temporal modelling of events, including the analysis of other neurodegenerative diseases. 26,27 Formally, the set of frame covariances F = {f 1 , f 2 . .…”

Section: A Continuous Multimodal Motion Pattern Quantificationmentioning

confidence: 99%

A recurrent approach for predicting Parkinson stage from multimodal videos

Archila

Manzanera

Martínez

2021

17th International Symposium on Medical Information Processing and Analysis

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Parkinson's disease is a neurodegenerative disease that affects more than 6.1 million people worldwide. In the clinical routine, the main tool to diagnose and monitor disease progression is based on motor impairments, such as postural instability, bradykinesia, tremor, among others. Besides, new biomarkers based on motion patterns have emerged to describe disease findings. Nonetheless, this motor characterization has low sensitivity, especially at early stages, and is largely expert-dependent, because protocols are mainly based on visual observations. However, most of these analyses require complex and some invasive systems that additionally only bring global information of complete recordings. This work introduces a multimodal approach that integrates gait and eye motion videos to quantify and predict patient stage on-the-fly. This method starts by computing dense apparent velocity maps that represent the local displacement of the person seen from the gait in a sagittal plane and as micro-movements during the fixation experiment. Then, each frame is described as a covariance descriptor of deep feature activation maps computed over the motion field at each video time. Then, the covariance video manifold is mapped to a recurrent LSTM network to learn higher non-local dependencies and quantify a motion descriptor. Also, an end-to-end scheme allows to lately fuse both modalities (gait and fixational eye) to obtain a more sensitive Parkinson disease descriptor. In a study with 25 subjects, the proposed approach reaches an average F1-score of 0.83 with an average recall of 0.78. In a temporal prediction analysis, the approach reports major correlations with the disease considering swing phase.

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Longitudinal Prediction Modeling of Alzheimer Disease using Recurrent Neural Networks

Cited by 20 publications

References 18 publications

Dementia Detection from Speech Using Machine Learning and Deep Learning Architectures

Dementia Detection from Speech Using Machine Learning and Deep Learning Architectures

Robust hybrid deep learning models for Alzheimer’s progression detection

A recurrent approach for predicting Parkinson stage from multimodal videos

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