Time–frequency time–space LSTM for robust classification of physiological signals

AbstractAutomated analysis of physiological time series is utilized for many clinical applications in medicine and life sciences. Long short-term memory (LSTM) is a deep recurrent neural network architecture used for classification of time-series data. Here time–frequency and time–space properties of time series are introduced as a robust tool for LSTM processing of long sequential data in physiology. Based on classification results obtained from two databases of sensor-induced physiological signals, the proposed approach has the potential for (1) achieving very high classification accuracy, (2) saving tremendous time for data learning, and (3) being cost-effective and user-comfortable for clinical trials by reducing multiple wearable sensors for data recording.

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Short and Medium-Term Prediction of Winter Wheat NDVI Based on the DTW–LSTM Combination Method and MODIS Time Series Data

Remote Sensing ◽

10.3390/rs13224660 ◽

2021 ◽

Vol 13 (22) ◽

pp. 4660

Author(s):

Fa Zhao ◽

Guijun Yang ◽

Hao Yang ◽

Yaohui Zhu ◽

Yang Meng ◽

...

Keyword(s):

Time Series ◽

Winter Wheat ◽

Vegetation Index ◽

Time Series Data ◽

Short Term Memory ◽

Combination Method ◽

Series Data ◽

Medium Term ◽

Short Term ◽

Deep Recurrent Neural Network

The normalized difference vegetation index (NDVI) is an important agricultural parameter that is closely correlated with crop growth. In this study, a novel method combining the dynamic time warping (DTW) model and the long short-term memory (LSTM) deep recurrent neural network model was developed to predict the short and medium-term winter wheat NDVI. LSTM is well-suited for modelling long-term dependencies, but this method may be susceptible to overfitting. In contrast, DTW possesses good predictive ability and is less susceptible to overfitting. Therefore, by utilizing the combination of these two models, the prediction error caused by overfitting is reduced, thus improving the final prediction accuracy. The combined method proposed here utilizes the historical MODIS time series data with an 8-day time resolution from 2015 to 2020. First, fast Fourier transform (FFT) is used to decompose the time series into two parts. The first part reflects the inter-annual and seasonal variation characteristics of winter wheat NDVI, and the DTW model is applied for prediction. The second part reflects the short-term change characteristics of winter wheat NDVI, and the LSTM model is applied for prediction. Next, the results from both models are combined to produce a final prediction. A case study in Hebei Province that predicts the NDVI of winter wheat at five prediction horizons in the future indicates that the DTW–LSTM model proposed here outperforms the LSTM model according to multiple evaluation indicators. The results of this study suggest that the DTW–LSTM model is highly promising for short and medium-term NDVI prediction.

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Multi-Channel Fusion Classification Method Based on Time-Series Data

Sensors ◽

10.3390/s21134391 ◽

2021 ◽

Vol 21 (13) ◽

pp. 4391

Author(s):

Xue-Bo Jin ◽

Aiqiang Yang ◽

Tingli Su ◽

Jian-Lei Kong ◽

Yuting Bai

Keyword(s):

Time Series ◽

Time Series Data ◽

Short Term Memory ◽

Evidence Theory ◽

Recurrence Plot ◽

Series Data ◽

Public Datasets ◽

Application Fields ◽

Original Time

Time-series data generally exists in many application fields, and the classification of time-series data is one of the important research directions in time-series data mining. In this paper, univariate time-series data are taken as the research object, deep learning and broad learning systems (BLSs) are the basic methods used to explore the classification of multi-modal time-series data features. Long short-term memory (LSTM), gated recurrent unit, and bidirectional LSTM networks are used to learn and test the original time-series data, and a Gramian angular field and recurrence plot are used to encode time-series data to images, and a BLS is employed for image learning and testing. Finally, to obtain the final classification results, Dempster–Shafer evidence theory (D–S evidence theory) is considered to fuse the probability outputs of the two categories. Through the testing of public datasets, the method proposed in this paper obtains competitive results, compensating for the deficiencies of using only time-series data or images for different types of datasets.

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Classification of Variable Stars Light Curves Using Long Short Term Memory Network

Frontiers in Astronomy and Space Sciences ◽

10.3389/fspas.2021.718139 ◽

2021 ◽

Vol 8 ◽

Author(s):

Saksham Bassi ◽

Kaushal Sharma ◽

Atharva Gomekar

Keyword(s):

Time Series ◽

Time Series Data ◽

Short Term Memory ◽

Traditional Approach ◽

Variable Stars ◽

Light Curves ◽

Series Data ◽

Automated Method ◽

Lstm Network

Owing to the current and upcoming extensive surveys studying the stellar variability, accurate and quicker methods are required for the astronomers to automate the classification of variable stars. The traditional approach of classification requires the calculation of the period of the observed light curve and assigning different variability patterns of phase folded light curves to different classes. However, applying these methods becomes difficult if the light curves are sparse or contain temporal gaps. Also, period finding algorithms start slowing down and become redundant in such scenarios. In this work, we present a new automated method, 1D CNN-LSTM, for classifying variable stars using a hybrid neural network of one-dimensional CNN and LSTM network which employs the raw time-series data from the variable stars. We apply the network to classify the time-series data obtained from the OGLE and the CRTS survey. We report the best average accuracy of 85% and F1 score of 0.71 for classifying five classes from the OGLE survey. We simultaneously apply other existing classification methods to our dataset and compare the results.

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High-pass filtering artifacts in multivariate classification of neural time series data

Journal of Neuroscience Methods ◽

10.1016/j.jneumeth.2021.109080 ◽

2021 ◽

Vol 352 ◽

pp. 109080

Author(s):

Joram van Driel ◽

Christian N.L. Olivers ◽

Johannes J. Fahrenfort

Keyword(s):

Time Series ◽

Time Series Data ◽

Series Data ◽

Multivariate Classification ◽

High Pass

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Landslide Prediction Using Long Short Term Memory(LSTM)Neural Network on time series data in Pakistan

2021 International Conference on Artificial Intelligence (ICAI) ◽

10.1109/icai52203.2021.9445236 ◽

2021 ◽

Author(s):

Mehreen Mubashar ◽

Gul Muhammad Khan ◽

Ramla Khan

Keyword(s):

Neural Network ◽

Time Series ◽

Time Series Data ◽

Short Term Memory ◽

Series Data ◽

Short Term ◽

Landslide Prediction ◽

Term Memory ◽

Long Short Term Memory

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Particularities of data mining in medicine: lessons learned from patient medical time series data analysis

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-019-1582-2 ◽

2019 ◽

Vol 2019 (1) ◽

Cited By ~ 2

Author(s):

Shadi Aljawarneh ◽

Aurea Anguera ◽

John William Atwood ◽

Juan A. Lara ◽

David Lizcano

Keyword(s):

Data Mining ◽

Time Series ◽

Knowledge Discovery ◽

Time Series Data ◽

Medical Patient ◽

Lessons Learned ◽

Physiological Signals ◽

Knowledge Discovery In Databases ◽

Series Data ◽

Data Mining Techniques

AbstractNowadays, large amounts of data are generated in the medical domain. Various physiological signals generated from different organs can be recorded to extract interesting information about patients’ health. The analysis of physiological signals is a hard task that requires the use of specific approaches such as the Knowledge Discovery in Databases process. The application of such process in the domain of medicine has a series of implications and difficulties, especially regarding the application of data mining techniques to data, mainly time series, gathered from medical examinations of patients. The goal of this paper is to describe the lessons learned and the experience gathered by the authors applying data mining techniques to real medical patient data including time series. In this research, we carried out an exhaustive case study working on data from two medical fields: stabilometry (15 professional basketball players, 18 elite ice skaters) and electroencephalography (100 healthy patients, 100 epileptic patients). We applied a previously proposed knowledge discovery framework for classification purpose obtaining good results in terms of classification accuracy (greater than 99% in both fields). The good results obtained in our research are the groundwork for the lessons learned and recommendations made in this position paper that intends to be a guide for experts who have to face similar medical data mining projects.

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Time-frequency time-space long short-term memory networks for image classification of histopathological tissue

Scientific Reports ◽

10.1038/s41598-021-93160-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Tuan D. Pham

Keyword(s):

Image Analysis ◽

Short Term Memory ◽

Disease Diagnosis ◽

Short Term ◽

Term Memory ◽

Time Frequency ◽

Time Space ◽

Histopathological Images ◽

Long Short Term Memory

AbstractImage analysis in histopathology provides insights into the microscopic examination of tissue for disease diagnosis, prognosis, and biomarker discovery. Particularly for cancer research, precise classification of histopathological images is the ultimate objective of the image analysis. Here, the time-frequency time-space long short-term memory network (TF-TS LSTM) developed for classification of time series is applied for classifying histopathological images. The deep learning is empowered by the use of sequential time-frequency and time-space features extracted from the images. Furthermore, unlike conventional classification practice, a strategy for class modeling is designed to leverage the learning power of the TF-TS LSTM. Tests on several datasets of histopathological images of haematoxylin-and-eosin and immunohistochemistry stains demonstrate the strong capability of the artificial intelligence (AI)-based approach for producing very accurate classification results. The proposed approach has the potential to be an AI tool for robust classification of histopathological images.

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