A k-nearest-neighbor classifier with heart rate variability feature-based transformation algorithm for driving stress recognition

Wang, Jeen Shing; Lin, Che; Yang, Yujuan

doi:10.1016/j.neucom.2011.10.047

Cited by 107 publications

(60 citation statements)

References 29 publications

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“…In this data matrix, not all the elements are nonnegative. We used our real value matrix factorization method to represent this matrix and then use the factor vectors for Knearest neighbors classification [55], [56]. The factorization result is shown in Figure 4.…”

Section: Methodsmentioning

confidence: 99%

Nonnegative matrix factorization: When data is not nonnegative

Wang

2014

2014 7th International Conference on Biomedical Engineering and Informatics

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In this paper, we present a new variations of the popular nonnegative matrix factorization (NMF) approach to extend it to the data with negative values. When a NMF problem is formulated as ≈ , we try to develop a new method that only allows to contain nonnegative values, but allows both and to have both nonnegative and negative values. In this way, the original NMF is extended to be used for real value data matrix instead restricted to only negative value data matrix. To this end, we develops novel method to factorize the real value data matrix. The method is evaluated experimentally and the results showed its effectiveness.

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Section: Methodsmentioning

confidence: 99%

Nonnegative matrix factorization: When data is not nonnegative

Wang

2014

2014 7th International Conference on Biomedical Engineering and Informatics

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“…KNN categorizes the feature spaces into binary or multiclass clusters by employing a training dataset to further classify the data points according to the closest data points to in the training dataset. KNN has been used in medical informatics, such as the detection of epilepsy [89], stress [90], and depression [85,91].…”

Section: Knnmentioning

confidence: 99%

A Pervasive Approach to EEG‐Based Depression Detection

et al. 2018

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Nowadays, depression is the world's major health concern and economic burden worldwide. However, due to the limitations of current methods for depression diagnosis, a pervasive and objective approach is essential. In the present study, a psychophysiological database, containing 213 (92 depressed patients and 121 normal controls) subjects, was constructed. The electroencephalogram (EEG) signals of all participants under resting state and sound stimulation were collected using a pervasive prefrontal-lobe threeelectrode EEG system at Fp1, Fp2, and Fpz electrode sites. After denoising using the Finite Impulse Response filter combining the Kalman derivation formula, Discrete Wavelet Transformation, and an Adaptive Predictor Filter, a total of 270 linear and nonlinear features were extracted. Then, the minimal-redundancy-maximal-relevance feature selection technique reduced the dimensionality of the feature space. Four classification methods (Support Vector Machine, K-Nearest Neighbor, Classification Trees, and Artificial Neural Network) distinguished the depressed participants from normal controls. The classifiers' performances were evaluated using 10-fold cross-validation. The results showed that K-Nearest Neighbor (KNN) had the highest accuracy of 79.27%. The result also suggested that the absolute power of the theta wave might be a valid characteristic for discriminating depression. This study proves the feasibility of a pervasive three-electrode EEG acquisition system for depression diagnosis.

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“…To extract high-level HRV features from R-R intervals, we followed earlier studies on stress inference using HRV [19][20][21][22]46]. After the pre-processing method proposed above, we extracted the following hand-engineered HRV features: a) HRV F1: LF Power b) HRV F2: HF Power c) HRV F3: LF/HF ratio d) HRV F4: SDNN (standard deviation of R-R intervals) e) HRV F5: RMSSD (root mean square of the successive differences of R-R intervals) f) HRV F6: pNN50 (ratio of the number of the successive differences of R-R intervals greater than 50ms of the total number of R-R intervals)…”

Section: Self-report Of Perceived Mental Stressmentioning

confidence: 99%

“…The first algorithm we use is a knearest neighbor classifier (denoted as kNN, k=1), using the extracted high level features listed above as in [46]. The second algorithm is a feed-forward and backpropagation based single layer neural network which uses the original input sources (i.e., 1D R-R intervals and 1Hz sampled thermal sequences) as low-level features.…”

Section: Labeling Strategy and Machine Learning Classifiersmentioning

confidence: 99%

Instant Stress: Detection of Perceived Mental Stress Through Smartphone Photoplethysmography and Thermal Imaging (Preprint)

Cho¹,

Julier²,

Bianchi-Berthouze³

2018

Preprint

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Background: A smartphone is a promising tool for daily cardiovascular measurement and mental stress monitoring. Photoplethysmography (PPG) and lowcost thermography can be used to create cheap, convenient and mobile systems. However, to achieve robustness, a person has to remain still for several minutes while a measurement is being taken. This is very cumbersome, and limits the usage in applications such producing instant measurements of stress. Objective: We propose to use smartphone-based mobile PPG and thermal imaging to provide a fast binary measure of stress responses to an event using dynamical physiological changes which occur within 20 seconds of the event finishing. Methods: We propose a system that uses a smartphone and its physiological sensors to reliably and continuously measure over a short window of time a person's blood volume pulse, the time interval between heartbeats (R-R interval) and the 1D thermal signature of the nose tip. 17 healthy participants, involved in a series of stress-inducing mental activities, measured their physiological response to stress in the 20 second-window immediately following each activity. A 10-cm Visual Analogue Scale was used by them to self-report their level of mental stress. As a main labeling strategy, normalized K-means clustering is used to better treat interpersonal differences in ratings. By taking an array of the R-R intervals and thermal directionality as a low-level feature input, we mainly use an artificial neural network to enable the automatic feature learning and the machine learning inference process.

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A k-nearest-neighbor classifier with heart rate variability feature-based transformation algorithm for driving stress recognition

Cited by 107 publications

References 29 publications

Nonnegative matrix factorization: When data is not nonnegative

Nonnegative matrix factorization: When data is not nonnegative

A Pervasive Approach to EEG‐Based Depression Detection

Instant Stress: Detection of Perceived Mental Stress Through Smartphone Photoplethysmography and Thermal Imaging (Preprint)

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