A Multihead ConvLSTM for Time Series Classification in eHealth Industry 4.0

Wang, Yilin; Sun, Le; Peng, Dandan

doi:10.1155/2022/8773900

Cited by 6 publications

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Such classification involves predicting the class or category of a time series dataset based on historical observations. Within the realm of DL-based time series classification methods, instead of the conventional practice of utilizing numeric representations as input to the network for time series data, there exists a notable research interest in the utilization of image conversion techniques, wherein data is transformed into image format and subsequently used with convolutional neural networks (CNN) and long shortterm memory (LSTM) models [8], [9], [23], [24], [25]. CNNs are particularly effective in extracting relevant features from raw time series data by performing convolution operations on the input signal.…”

Section: Techniques In Time Series Classificationmentioning

confidence: 99%

Image Encoded Time Series Classification of Small Datasets: An Innovative Architecture Using Deep Learning Ensembles

Indrasiri,

Kashyap,

Pathirana

2024

Preprint

View full text Add to dashboard Cite

Convolutional neural networks (CNNs) are often favored for their strong learning abilities in tackling automatic intelligent models. The classification of time series data streams spans across many applications of intelligent systems. However, the scarcity of effective Machine Learning architectures to handle limited time-series data adversely affects the realization of some crucial applications. In particular, healthcare-related applications are inherently concerned with limited time series datasets. Indeed, building effective artificial intelligence (AI) models for rare diseases using conventional techniques can pose a significant challenge. Utilizing recent advances in deep learning and signal processing techniques, this study introduces a new ensemble deep learning (DL) approach for time series categorization in the presence of limited datasets. Physiological data, such as ECG and voice, are used to demonstrate the functionality of the proposed DL architecture with data obtained from IoT and non-IoT devices. The proposed framework comprises a self-designed deep CNN-LSTM along with ResNet50 and MobileNet transfer learning approaches. The CNN-LSTM architecture includes an enhanced squeeze and excitation block that improves overall performance.This architecture processes time series data transformed into a 3-Channel image structure via improved recurrence plot (RP), Gramian angular field (GAF), and fuzzy recurrence plot (FRP) methods. The proposed model demonstrated superior classification accuracy on the ECG5000 and TESS datasets compared to other state-of-the-art techniques, validating its efficacy for binary and multiclass classification.

show abstract

Section: Techniques In Time Series Classificationmentioning

confidence: 99%

Image Encoded Time Series Classification of Small Datasets: An Innovative Architecture Using Deep Learning Ensembles

Indrasiri,

Kashyap,

Pathirana

2024

Preprint

View full text Add to dashboard Cite

show abstract

Prediction and clustering of Alzheimer’s disease by race and sex: a multi-head deep-learning approach to analyze irregular and heterogeneous data

Chang,

Slowiejko,

Win

2024

Sci Rep

View full text Add to dashboard Cite

Early detection of Alzheimer’s disease (AD) is crucial to maximize clinical outcomes. Most disease progression analyses include people with diagnoses of cognitive impairment, limiting understanding of AD risk among those with normal cognition. The objective was to establish AD progression models through a deep learning approach to analyze heterogeneous, multi-modal datasets, including clustering analyses of population subsets. A multi-head deep-learning architecture was built to process and learn from biomedical and imaging data from the National Alzheimer’s Coordinating Center. Shapley additive explanation algorithms for feature importance ranking and pairwise correlation analysis were used to identify predictors of disease progression. Four primary disease progression clusters (slow, moderate and rapid converters or non-converters) were subdivided into groups by race and sex, yielding 16 sub-clusters of participants with distinct progression patterns. A multi-head and early-fusion convolutional neural network achieved the most competitive performance and demonstrated superiority over a single-head deep learning architecture and conventional tree-based machine-learning methods, with 97% test accuracy, 96% F1 score and 0.19 root mean square error. From 447 features, 2 sets of 100 predictors of disease progression were extracted. Feature importance ranking, correlation analysis and descriptive statistics further enriched cluster analysis and validation of the heterogeneity of risk factors. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-024-77829-1.

show abstract

STAD-FEBTE, a shallow and supervised framework for time series anomaly detection by automatic feature engineering, balancing, and tree-based ensembles: An industrial case study

Harandi

Schou

et al. 2023

2023 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)

View full text Add to dashboard Cite

A Multihead ConvLSTM for Time Series Classification in eHealth Industry 4.0

Cited by 6 publications

References 13 publications

Image Encoded Time Series Classification of Small Datasets: An Innovative Architecture Using Deep Learning Ensembles

Image Encoded Time Series Classification of Small Datasets: An Innovative Architecture Using Deep Learning Ensembles

Prediction and clustering of Alzheimer’s disease by race and sex: a multi-head deep-learning approach to analyze irregular and heterogeneous data

STAD-FEBTE, a shallow and supervised framework for time series anomaly detection by automatic feature engineering, balancing, and tree-based ensembles: An industrial case study

Contact Info

Product

Resources

About