Ambulatory seizure forecasting with a wrist-worn device using long-short term memory deep learning

AbstractThe ability to forecast seizures minutes to hours in advance of an event has been verified using invasive EEG devices, but has not been previously demonstrated using noninvasive wearable devices over long durations in an ambulatory setting. In this study we developed a seizure forecasting system with a long short-term memory (LSTM) recurrent neural network (RNN) algorithm, using a noninvasive wrist-worn research-grade physiological sensor device, and tested the system in patients with epilepsy in the field, with concurrent invasive EEG confirmation of seizures via an implanted recording device. The system achieved forecasting performance significantly better than a random predictor for 5 of 6 patients studied, with mean AUC-ROC of 0.80 (range 0.72–0.92). These results provide the first clear evidence that direct seizure forecasts are possible using wearable devices in the ambulatory setting for many patients with epilepsy.

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The Impact of Data Imputation and Feature Extraction on PM2.5 Forecasting Performance in Bangkok Using Long Short-Term Memory Neural Networks

Proceedings of the 11th International Conference on Advances in Information Technology ◽

10.1145/3406601.3406625 ◽

2020 ◽

Author(s):

Panupong Rong-O ◽

Nuwee Wiwatwatana

Keyword(s):

Neural Networks ◽

Feature Extraction ◽

Short Term Memory ◽

Data Imputation ◽

Short Term ◽

Term Memory ◽

Forecasting Performance ◽

Long Short Term Memory ◽

The Impact

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Forecasting container throughput with long short-term memory networks

Industrial Management & Data Systems ◽

10.1108/imds-07-2019-0370 ◽

2019 ◽

Vol 120 (3) ◽

pp. 425-441 ◽

Cited By ~ 4

Author(s):

Sonali Shankar ◽

P. Vigneswara Ilavarasan ◽

Sushil Punia ◽

Surya Prakash Singh

Keyword(s):

Time Series ◽

Deep Learning ◽

Short Term Memory ◽

Moving Average ◽

Short Term ◽

Error Matrix ◽

Term Memory ◽

Content Type ◽

Forecasting Performance ◽

Long Short Term Memory

Purpose Better forecasting always leads to better management and planning of the operations. The container throughput data are complex and often have multiple seasonality. This makes it difficult to forecast accurately. The purpose of this paper is to forecast container throughput using deep learning methods and benchmark its performance over other traditional time-series methods. Design/methodology/approach In this study, long short-term memory (LSTM) networks are implemented to forecast container throughput. The container throughput data of the Port of Singapore are used for empirical analysis. The forecasting performance of the LSTM model is compared with seven different time-series forecasting methods, namely, autoregressive integrated moving average (ARIMA), simple exponential smoothing, Holt–Winter’s, error-trend-seasonality, trigonometric regressors (TBATS), neural network (NN) and ARIMA + NN. The relative error matrix is used to analyze the performance of the different models with respect to bias, accuracy and uncertainty. Findings The results showed that LSTM outperformed all other benchmark methods. From a statistical perspective, the Diebold–Mariano test is also conducted to further substantiate better forecasting performance of LSTM over other counterpart methods. Originality/value The proposed study is a contribution to the literature on the container throughput forecasting and adds value to the supply chain theory of forecasting. Second, this study explained the architecture of the deep-learning-based LSTM method and discussed in detail the steps to implement it.

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