Major Depression Detection from EEG Signals Using Kernel Eigen-Filter-Bank Common Spatial Patterns

Liao, Shih‐Cheng; Wu, C. T.; Huang, Hao-Chuan; Cheng, Wei; Liu, Yi-Hung

doi:10.3390/s17061385

Cited by 139 publications

(73 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nevertheless, high accuracy and interrater agreement were obtained from the models in both machine learning phases. For comparison, studies [ 24 , 25 , 28 , 29 ], and [ 23 ] have 87.2%, 81%, 81.23%, 89.71% and 73% as accuracy for predicting depression, respectively. [ 31 ] reports 73.6% accuracy for predicting dementia and [ 30 ] reports 99.9% TNR and 78.8% TPR.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Speech Quality Feature Analysis for Classification of Depression and Dementia Patients

Sumali

Mitsukura

Liang

et al. 2020

Sensors

View full text Add to dashboard Cite

Loss of cognitive ability is commonly associated with dementia, a broad category of progressive brain diseases. However, major depressive disorder may also cause temporary deterioration of one’s cognition known as pseudodementia. Differentiating a true dementia and pseudodementia is still difficult even for an experienced clinician and extensive and careful examinations must be performed. Although mental disorders such as depression and dementia have been studied, there is still no solution for shorter and undemanding pseudodementia screening. This study inspects the distribution and statistical characteristics from both dementia patient and depression patient, and compared them. It is found that some acoustic features were shared in both dementia and depression, albeit their correlation was reversed. Statistical significance was also found when comparing the features. Additionally, the possibility of utilizing machine learning for automatic pseudodementia screening was explored. The machine learning part includes feature selection using LASSO algorithm and support vector machine (SVM) with linear kernel as the predictive model with age-matched symptomatic depression patient and dementia patient as the database. High accuracy, sensitivity, and specificity was obtained in both training session and testing session. The resulting model was also tested against other datasets that were not included and still performs considerably well. These results imply that dementia and depression might be both detected and differentiated based on acoustic features alone. Automated screening is also possible based on the high accuracy of machine learning results.

show abstract

Section: Discussionmentioning

confidence: 99%

“…[ 24 , 25 , 26 ], biosignals (electroencephalogram, heart rate, respiration, etc.) [ 27 , 28 , 29 , 30 ], and auditory features (intensity, tone, speed of speech, etc.) [ 23 , 31 ].…”

Section: Introductionmentioning

confidence: 99%

Speech Quality Feature Analysis for Classification of Depression and Dementia Patients

Sumali

Mitsukura

Liang

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…When the CR from different components and stations are summed, the highest cumulative CR occurs when k = 3. Accordingly, we set k as 3 in the present study, as the same setting in some studies related to pattern recognition (Liao et al, ).…”

Section: K‐nn Classifiermentioning

confidence: 99%

Investigation of Single‐Station Classification for Short Tectonic Tremor in Taiwan

et al. 2019

View full text Add to dashboard Cite

Tectonic tremor in Taiwan is known for its short duration and weak amplitude. Recent observations from the mountain area (Central Range) allows us to document such type of tectonic tremor recorded at a close distance and to investigate a single station technique for a minute‐long tremor. To effectively distinguish tremor from earthquakes and noise, we applied the Fisher's class separability criterion to automatically select the optimal feature subset from a set of commonly adopted feature candidates. During the study period of 1 January to 16 September 2016 when a local seismic array was deployed, we successfully differentiated tremor from local earthquakes and noise with high accuracy of 86.6% to 98.9% from three stations using a k‐nearest neighbors classifier. Other than the maximum amplitude, number of peaks, and energy of the 2‐ to 8‐Hz passband, the spikiness of discrete Fourier transforms median in time is also found to be important to separate tremor from noise.

show abstract

“…Liao et al proposed a new method based on scalp EEG signals and a robust spectral‐spatial EEG feature extractor called kernel eigen‐filter‐bank common spatial pattern (KEFB‐CSP) to efficiently and effectively dissect the heterogeneity of MDD. The KEFB‐CSP was able to achieve an average EEG classification accuracy of 81.23%.…”

Section: Related Workmentioning

confidence: 99%

Cloud‐aided online EEG classification system for brain healthcare: A case study of depression evaluation with a lightweight CNN

Dan

Shah

et al. 2018

Softw Pract Exp

View full text Add to dashboard Cite

Summary Brain healthcare, when supported by Internet of Things, can perform online and accurate analysis of brain big data for the classification of multivariate Electroencephalogram (EEG), which is a prerequisite for the recent boom in neurofeedback applications and clinical practices. However, it remains a grand research challenge due to (1) the embedded intensive noises and the intrinsic nonstationarity determined by the evolution of brain states; and (2) the lack of a user‐friendly computing platform to sustain the complicated analytics. This study presents the design of an online EEG classification system aided by Cloud centering on a lightweight Convolutional Neural Network (CNN). The system incrementally trains the CNN on Cloud and enables hot deployment of the trained classifier without the need to restart the gateway to adapt to the users' needs. The classifier maintains a High Convolutional Layer to gain the ability of processing high‐dimensional EEG segments. The number of hidden layers is minimized to ensure the efficiency of training. The lightweight CNN adopts an “hourglass” block of fully connected layers to reduce the number of neurons quickly toward the output end. A case study of depression evaluation has been performed against raw EEG datasets to distinguish between (1) Healthy and Major Depression Disorder with an accuracy, sensitivity, and specificity of [98.59% ± 0.28%], [97.77% ± 0.63%], and [99.51% ± 0.19%], respectively; and (2) Effective and Noneffective treatment outcome with an accuracy, sensitivity, and specificity of [99.53% ± 0.002%], [99.50% ± 0.01%], and [99.58% ± 0.02%], respectively. The results show that the classification can be completed several magnitudes faster when EEG is collected on the gateway (several milliseconds vs. 4 seconds).

show abstract

Major Depression Detection from EEG Signals Using Kernel Eigen-Filter-Bank Common Spatial Patterns

Cited by 139 publications

References 39 publications

Speech Quality Feature Analysis for Classification of Depression and Dementia Patients

Speech Quality Feature Analysis for Classification of Depression and Dementia Patients

Investigation of Single‐Station Classification for Short Tectonic Tremor in Taiwan

Cloud‐aided online EEG classification system for brain healthcare: A case study of depression evaluation with a lightweight CNN

Contact Info

Product

Resources

About