Attention-based convolutional neural network and long short-term memory for short-term detection of mood disorders based on elicited speech responses

Huang, Kun-Yi; Wu, Chung Hsien; Su, Ming Hsiang

doi:10.1016/j.patcog.2018.12.016

Cited by 43 publications

(17 citation statements)

References 29 publications

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“…The proposed models include CNNs, RNNs, autoencoders, as well as hybrid models based on the above ones. In particular, CNNs were leveraged to encode the temporal and spectral features from the voice signals [76][77][78][79][80] and static facial or physical expression features from the video frames 79,[81][82][83][84] . Autoencoders were used to learn low-dimensional representations for people's vocal 85,86 and visual expression 87,88 , and RNNs were engaged to characterize the temporal evolution of emotion based on the CNN-learned features and/or other handcraft features 76,81,[84][85][86][87][88][89][90] .…”

Section: Vocal and Visual Expression Datamentioning

confidence: 99%

Deep learning in mental health outcome research: a scoping review

et al. 2020

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Mental illnesses, such as depression, are highly prevalent and have been shown to impact an individual's physical health. Recently, artificial intelligence (AI) methods have been introduced to assist mental health providers, including psychiatrists and psychologists, for decision-making based on patients' historical data (e.g., medical records, behavioral data, social media usage, etc.). Deep learning (DL), as one of the most recent generation of AI technologies, has demonstrated superior performance in many real-world applications ranging from computer vision to healthcare. The goal of this study is to review existing research on applications of DL algorithms in mental health outcome research. Specifically, we first briefly overview the state-of-the-art DL techniques. Then we review the literature relevant to DL applications in mental health outcomes. According to the application scenarios, we categorize these relevant articles into four groups: diagnosis and prognosis based on clinical data, analysis of genetics and genomics data for understanding mental health conditions, vocal and visual expression data analysis for disease detection, and estimation of risk of mental illness using social media data. Finally, we discuss challenges in using DL algorithms to improve our understanding of mental health conditions and suggest several promising directions for their applications in improving mental health diagnosis and treatment.

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Section: Vocal and Visual Expression Datamentioning

confidence: 99%

Deep learning in mental health outcome research: a scoping review

et al. 2020

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“…LSTM networks are a variant of RNNs that have been applied in fields such as biomedical science [25], speech recognition [26], sentiment analysis [27], and image classification [28]. However, LSTM recurrent neural networks have not yet been applied in tidal water level forecasting.…”

Section: B Lstmmentioning

confidence: 99%

Long Short-Term Memory Recurrent Neural Network for Tidal Level Forecasting

Yang

Hsieh

2020

IEEE Access

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Tide is a phenomenon of water level change caused by gravity. Tidal level forecasting is not only a key theoretical topic but also crucial in coastal and ocean engineering applications. The waiting time before a cargo ship enters a port affects the efficiency of cargo transportation, the tidal difference affects the establishment of turbine generators, and an excessive tidal water level reduces vessel safety. With the proliferation of information technology, the application of deep learning models in the analysis and study of hydrological problems has become increasingly common. This study proposed a deep learning model to predict the tidal water level. A forecasting model was developed on the basis of the long short-term memory (LSTM) recurrent neural network for predicting the water levels of 17 harbors in Taiwan. Tidal water level data for 21 years were collected from different observation stations. To objectively evaluate model performance, the developed model was compared with six other forecasting models in terms of the mean absolute percentage error (MAPE) and root mean square error (RMSE) of the forecasting results. The results indicated that the LSTM model had the lowest forecasting error for the tidal water level for up to 30 days. The average MAPE and RMSE values for the developed model were 6.97% and 0.049 m, respectively; thus, the model could effectively reduce the overlapping problems caused by machine learning methods in continuous forecasting. INDEX TERMS-Deep learning, long short-term memory, tidal level forecasting, time series.

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“…Huang et al [7] propose an attention-based convolutional neural network (CNN) and long short-term memory (LSTM) approach for distinguishing between MDD and BD. The proposed approach identifies mood disorders on the basis of responses given to 6 video sequences.…”

Section: Related Workmentioning

confidence: 99%

“…Several mental health monitoring approaches using mobile devices have been proposed. Most of them [1][2][3][5][6][7][8][9][10][11]13,14] are based on (1) collecting and analyzing smartphone features such as activity, localization, and phone calls, and (2) launching interactive questionnaires such as PHQ-9 3 and BDI 4 . "Active" monitoring approaches (i.e., requiring a patient's intervention) are less used and less effective than "passive" ones (i.e., not requiring a patient's intervention) in practice.…”

Section: Introductionmentioning

confidence: 99%

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DL4DED: Deep Learning for Depressive Episode Detection on Mobile Devices

Mdhaffar

Cherif²,

Kessentini

et al. 2019

How AI Impacts Urban Living and Public Health

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This paper presents a deep learning approach for depressive episode detection on mobile devices, called DL4DED. It is based on a convolutional neural network and a long short-term memory network to identify the status of a patient's voice extracted from spontaneous phone calls. To run DL4DED on mobile devices, two neural network model compression techniques are used: quantization and pruning. DL4DED protects data privacy, since it can be executed on a patient's smartphone. Our proposal is validated on the DAIC-WOZ database. The obtained results show that the accuracy of DL4DED with model compression is only slightly lower than the accuracy of DL4DED without model compression. Furthermore, our experiments indicate that the power consumption of DL4DED is reasonably low.

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Attention-based convolutional neural network and long short-term memory for short-term detection of mood disorders based on elicited speech responses

Cited by 43 publications

References 29 publications

Deep learning in mental health outcome research: a scoping review

Deep learning in mental health outcome research: a scoping review

Long Short-Term Memory Recurrent Neural Network for Tidal Level Forecasting

DL4DED: Deep Learning for Depressive Episode Detection on Mobile Devices

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