Embedding stacked bottleneck vocal features in a LSTM architecture for automatic pain level classification during emergency triage

Tsai, Fu-Sheng; Weng, Yi-Ming; Ng, Chip-Jin; Lee, Chi-Chun

doi:10.1109/acii.2017.8273618

Cited by 22 publications

(26 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another study [5] recognized the pain level based on speech analysis and the use of long short-term memory (LSTM) NNs. First, they employed an unsupervised learning NN to extract the vocal features using the Chinese corpus.…”

Section: Speech Analysismentioning

confidence: 99%

Deep-Learning-Based Models for Pain Recognition: A Systematic Review

2020

View full text Add to dashboard Cite

Traditional standards employed for pain assessment have many limitations. One such limitation is reliability linked to inter-observer variability. Therefore, there have been many approaches to automate the task of pain recognition. Recently, deep-learning methods have appeared to solve many challenges such as feature selection and cases with a small number of data sets. This study provides a systematic review of pain-recognition systems that are based on deep-learning models for the last two years. Furthermore, it presents the major deep-learning methods used in the review papers. Finally, it provides a discussion of the challenges and open issues.

show abstract

Section: Speech Analysismentioning

confidence: 99%

Deep-Learning-Based Models for Pain Recognition: A Systematic Review

2020

View full text Add to dashboard Cite

show abstract

“…LSTM provides a very elegant way to deal with TSD through time that embodies the correlations among the TSD in time order [15], [16], and lots of research have presented the superiority of LSTM on TSD. In literature [17], researchers embedded bottleneck vocal features in LSTM architecture to measure the pain level through voice characteristics, and the results showed that the classification accuracy are higher than other comparisons. Reference [18] utilized LSTM to predict the coding unit splitting, and the experimental results demonstrated its advantages in high efficiency.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced LSTM for Trend Following of Time Series

Sun

Nie

et al. 2019

IEEE Access

View full text Add to dashboard Cite

Mining and analysis of time series data (TSD) have drawn a great concern, especially in the TSD clustering, classification, and forecast. In the industrial field, e.g., the work condition monitoring and the environmental safety, it is crucial to follow the trend of the corresponding TSD for a safety forecast, and few studies have been devoted to such a trend following. Motivated by this, we propose a trend following the strategy of TSD by using a long short-term memory (LSTM) network for safety forecast, in which the training method aggregates the particle swarm optimization (PSO) algorithm with gradient descent (GD) to obtain more competitive model parameters. Three kinds of trend representations of TSD are first defined based on the corresponding research in stock option. Then, the LSTM optimized with the PSO-GD is developed to perform the trend following. From the viewpoint of safety forecast, the trends varied in different time length are further predicted and analyzed. The superiority of the proposed algorithm is experimentally demonstrated by applying it to the electromagnetic radiation intensity TSD sampled from an actual coal mine and PM2.5 in UCI repository.

show abstract

“…While many research has already indicated that the facial muscle movements, i.e., action units, provide an indication of different pain levels [8,9], several recent works have started to investigate the relationship between pain intensity and vocal cues. For example, Oshrat et al analyzed the prosodic variation as a bio-signaling indicator of pain [10], Ren et al recently proposed a database for evaluating pain from speech [11], Tsai et al proposed several automated machine learning methods for recognizing self-reported pain-levels using speech and face multimodally in a real triage database [12,13]. These studies tend to focus more on the prosodic and spectral properties of speech.…”

Section: Introductionmentioning

confidence: 99%

Investigating the Variability of Voice Quality and Pain Levels as a Function of Multiple Clinical Parameters

Hong

Weng³

et al. 2019

Interspeech 2019

Self Cite

View full text Add to dashboard Cite

Pain is an internal construct with vocal manifestation that varies as a function of personal and clinical attributes. Understanding the vocal indicators of pain-levels is important in providing an objective analytic in clinical assessment and intervention. In this work, we focus on investigating the variability of voice quality as a function of multiple clinical parameters at different pain-levels, specifically for emergency room patients during triage. Their pain-induced pathological voice quality characteristics are naturally affected by an individual attributes such as age, gender and pain-sites. We conduct a detailed multivariate statistical analysis on a 181 unique patient's vocal quality using recordings of real triage sessions. Our analysis show several important insights, 1) voice quality only varies statistically with pain-levels when interacting effect from other clinical parameters is considered, 2) senior group shows a higher value of voicing probability and shimmer when experiencing severe pain, 3) patients with abdomen pain have a lower jitter and shimmer during severe pain that is different from patients experiencing musculoskeletal pathology, and 4) there could be a relationship between the variation in the voice quality and the neural pathway of pain as evident by interacting with the pain-site factor.

show abstract

Embedding stacked bottleneck vocal features in a LSTM architecture for automatic pain level classification during emergency triage

Cited by 22 publications

References 23 publications

Deep-Learning-Based Models for Pain Recognition: A Systematic Review

Deep-Learning-Based Models for Pain Recognition: A Systematic Review

An Enhanced LSTM for Trend Following of Time Series

Investigating the Variability of Voice Quality and Pain Levels as a Function of Multiple Clinical Parameters

Contact Info

Product

Resources

About