Electrogastrogram-Derived Features for Automated Sickness Detection in Driving Simulator
Abstract
:1. Introduction
1.1. Rationale for Introduction of New EGG-Based Parameters
1.2. Noise Effect on EGG-Based Parameters
1.3. Aims of the Study
- We present an extended list of EGG-based features for nausea assessment following pertinent reasoning for their calculation.
- We report on the sensitivity/robustness of the proposed EGG-based parameters to different levels of SNRs and the noise effect on nausea detection.
2. Materials and Methods
2.1. Available EGG Data and Recording Procedure
- Baseline measurement before the driving simulation.
- Driving simulation in autonomous vehicle.
- EGG measurement while resting after driving simulation.
2.2. EGG Preprocessing and Creation of Semi-Synthetic EGG Dataset
2.3. Automated Procedure for EGG-Based Features Extraction
2.4. Statistical Analysis and Machine Learning Approach
3. Results
4. Discussion
4.1. Effect of Noise on EGG-Based Parameters
4.2. Effect of Noise on Random Forest Classifier for Nausea Detection
4.3. Effect of Noise on Detection of Nausea through Statistical Tests
4.4. Limitations of the Study
- We use a discrete set of predefined SNRs, and one should note that the actual SNRs were much higher, as our data were already contaminated with noises and artifacts. Despite the linear Butterworth filtering applied in the preprocessing stage, the noise with overlapping frequency content probably remains present in the semi-synthetic EGG dataset. Future efforts towards the generation of synthetic noises would provide a firm basis for exact SNR contamination and more reliable analysis.
- It should also be noted that sample entropy scaling parameter r is kept constant at the 0.15 of the noiseless data standard deviation. This value was determined empirically based on the recommendations [24]. Adjusting this value for different SNRs may have a further effect on the results and should be investigated in the future.
- We apply procedures for automatic feature calculation. However, a guided visual observation and manual corrections are still considered a gold standard for the evaluation of EGG-based parameters especially in cases of excessive noises [10,70,71]. We use visual inspection only for channel selection. Despite this drawback, we obtained promising results in nausea assessment by both statistical and ML approaches.
- We select the embedded dimension m for sample entropy calculation empirically. For future selection and discussion on embedding dimension selection, one may look at outstanding reasoning by Matilla-García et al. [72].
- We did not apply unimodal or multi-modal machine learning algorithms, and we do not provide comparison of existing machine learning techniques as in [67].
- Our method is applied only for nausea occurrence. Further customization of presented EGG-based parameters and complementary approach by RF and statistical analysis should yield at assessment of sickness levels similarly as in [67].
- The dataset used for the analysis contains more male than female participants. However, we do not consider this to be a major drawback of our study, as we were not interested in the differences between the genders but focused on the relationships between the occurrence of nausea, the EGG parameters, and noise. Moreover, a systematic review performed by Grassini and Laumann [73] showed conflicting results in published studies focused on determining sex differences in experiencing simulator sickness.
- We did not use multi biomarkers for the assessment of sickness occurrence as our focus was solely on the direct assessment of gastric activity. However, future studies should be focused on a promising heterogeneous approach as, for example, suggested by Dennison et al. [67].
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Feature | Explanation | Unit | References |
---|---|---|---|
RMS (Root Mean Square) | RMS of the amplitude of EGG selected segment | μV | [4,10] |
median | Median frequency of PSD of selected EGG segment | cpm (cycles per minute) | |
DF (Dominant Frequency) | Dominant frequency of PSD of selected EGG segment | cpm | [4,10,48] |
MagDF | Magnitude of DF in PSD of selected EGG segment | mV2/Hz | [4,10,48] |
CS (Crest Factor) | CS of PSD of selected EGG segments | / | [4,10] |
SDV (Spectral Variation Distribution) | PSD with magnitude higher than 25% of DF | % | [4] |
SampEntT (Sample Entropy of Time Series) | Embedding dimensions m = 2, 3, and 4 | / | Introduced here and inspired by [49] |
SampEntP (Sample Entropy of PSD) | Embedding dimensions m = 2, 3, and 4 | / | |
SpectEnt (Spectral Entropy) | / | / | |
Autocorr (Autocorrelation zero-crossing) | The first lag of autocorrelation function of EGG at which autocorrelation equals 0 | S | Introduced here and inspired by [46] |
SD1 | Transverse line of the Poincaré plot in the perpendicular direction. A Poincaré plot presents a scatter plot of the current EGG sample in relation to the prior EGG sample. | μV | Introduced here and inspired by [31,50] |
SD2 | Longitudinal line of the Poincaré plot in the perpendicular direction. | μV | |
SDEGG | Standard deviation of EGG samples obtained from the SD1 and SD2. | μV |
Features | SNR = 17 dB | SNR = 7 dB | SNR = −3 dB | SNR = −13 dB | SNR = −23 dB |
---|---|---|---|---|---|
RMS | V = 321, p < 0.001, Cdelta = 0.021 | V = 77, p < 0.001, Cdelta = 0.092 | V = 0, p < 0.001, Cdelta = 0.344 | V = 0, p < 0.001, Cdelta = 0.733 | V = 0, p < 0.001, Cdelta = 0.951 |
median | V = 188.5, p = 0.750, Cdelta = 0.002 | t = −1.761, df = 67, p = 0.083, Cd = −0.060 | t = −2.852, df = 67, p = 0.006, Cd = −0.265 | t = −4.5567, df = 67, p < 0.001, Cd = −0.652 | V = 460, p < 0.001, Cdelta = −0.457 |
MagDF | V = 739, p = 0.008, Cdelta = −0.007 | V = 812, p = 0.028, Cdelta = −0.031 | V = 206, p < 0.001, Cdelta = −0.254 | V = 0, p < 0.001, Cdelta = −0.653 | V = 0, p < 0.001, Cdelta = −0.919 |
DF | V = 64, p = 0.476, Cdelta = 0.007 | V = 240, p = 0.461, Cdelta = −0.061 | V = 508.5, p = 0.730, Cdelta = −0.034 | V = 647, p = 0.009, Cdelta = −0.253 | V = 845, p = 0.045, Cdelta = −0.176 |
CS | V = 1591, p = 0.011, Cdelta = 0.031 | V = 1688, p = 0.002, Cdelta = 0.098 | V = 1872, p < 0.001, Cdelta = 0.246 | V = 2108, p < 0.001, Cdelta = 0.533 | V = 2136, p < 0.001, Cdelta = 0.546 |
SDV | V = 495, p = 0.342, Cdelta = −0.012 | V = 335, p < 0.001, Cdelta = −0.135 | V = 344.5, p < 0.001, Cdelta = −0.344 | V = 173.5, p < 0.001, Cdelta = −0.674 | V = 97.5, p < 0.001, Cdelta = −0.764 |
SampEntT_m2 | V = 920, p = 0.46, Cdelta = 0.031 | V = 610, p = 0.256, Cdelta = −0.052 | V = 1213, p = 0.249, Cdelta = 0.085 | V = 1918, p < 0.001, Cdelta = 0.413 | V = 2330, p < 0.001, Cdelta = 0.754 |
SampEntT_m3 | V = 626.5, p = 0.350, Cdelta = 0.067 | V = 444, p = 0.556, Cdelta = −0.018 | V = 787, p = 0.083, Cdelta = 0.136 | V = 1666, p < 0.001, Cdelta = 0.410 | V = 2285, p < 0.001, Cdelta = 0.682 |
SampEntT_m4 | V = 254, p = 0.914, Cdelta = 0.039 | V = 221, p = 0.948, Cdelta = 0.018 | V = 437, p = 0.338, Cdelta = 0.101 | V = 1598, p < 0.001, Cdelta = 0.419 | V = 2209, p < 0.001, Cdelta = 0.671 |
SampEntP_m2 | V = 1289, p = 0.480, Cdelta = 0.030 | V = 1349, p = 0.284, Cdelta = 0.040 | V = 1278, p = 0.523, Cdelta = −0.006 | V = 1373, p = 0.223, Cdelta = 0.030 | V = 1221, p = 0.772, Cdelta = 0.008 |
SampEntP_m3 | V = 1306, p = 0.418, Cdelta = 0.015 | V = 1252, p = 0.631, Cdelta = 0.006 | V = 1275, p = 0.535, Cdelta = −0.013 | V = 1369, p = 0.232, Cdelta = 0.083 | V = 1086, p = 0.597, Cdelta = −0.042 |
SampEntP_m4 | V = 1293, p = 0.465, Cdelta = 0.008 | V = 1344, p = 0.297, Cdelta = −0.009 | V = 1417, p = 0.137, Cdelta = 0.020 | V = 1385, p = 0.196, Cdelta = 0.047 | V = 955, p = 0.184, Cdelta = −0.154 |
SpectEnt | V = 727, p = 0.006, Cdelta = −0.019 | V = 340, p < 0.001, Cdelta = −0.154 | V = 180, p < 0.001, Cdelta = −0.416 | V = 156, p < 0.001, Cdelta = −0.695 | V = 161, p < 0.001, Cdelta = −0.724 |
Autocorr | V = 30, p = 0.351, Cdelta = 0.024 | V = 27, p = 0.608, Cdelta = 0.027 | V = 540, p < 0.001, Cdelta = 0.245 | V = 1066, p < 0.001, Cdelta = 0.492 | V = 1215, p < 0.001, Cdelta = 0.557 |
SD1 | V = 205, p < 0.001, Cdelta = −0.021 | V = 19, p < 0.001, Cdelta = −0.096 | V = 0, p < 0.001, Cdelta = −0.384 | V = 0, p < 0.001, Cdelta = −0.766 | V = 0, p < 0.001, Cdelta = −0.958 |
SD2 | V = 327, p < 0.001, Cdelta = −0.022 | V = 80, p < 0.001, Cdelta = −0.093 | V = 0, p < 0.001, Cdelta = −0.345 | V = 0, p < 0.001, Cdelta = −0.733 | V = 0, p < 0.001, Cdelta = −0.951 |
SDEGG | V = 321, p < 0.001, Cdelta = −0.021 | V = 76, p < 0.001, Cdelta = −0.093 | V = 0, p < 0.001, Cdelta = −0.344 | V = 0, p < 0.001, Cdelta = −0.733 | V = 0, p < 0.001, Cdelta = −0.951 |
Evaluation Classifier Metrics | Original Dataset | Noisy Data | ||||
SNR = 17 dB | SNR = 7 dB | SNR = −3 dB | SNR = −13 dB | SNR = −23 dB | ||
Kappa | 0.452 | 0.452 | 0.301 | 0.452 | −0.214 | −0.097 |
95% CI | (0.636, 0.985) | (0.636, 0.985) | (0.566, 0.962) | (0.636, 0.985) | (0.383, 0.858) | (0.501, 0.932) |
Accuracy | 0.882 | 0.882 | 0.823 | 0.882 | 0.647 | 0.765 |
Sensitivity | 1.000 | 1.000 | 0.929 | 1.000 | 0.786 | 0.929 |
Specificity | 0.333 | 0.333 | 0.333 | 0.333 | 0 | 0 |
Precision | 0.875 | 0.875 | 0.867 | 0.875 | 0.786 | 0.812 |
Recall | 1.000 | 1.000 | 0.929 | 1.000 | 0.786 | 0.929 |
AUC (training) | 0.616 | 0.616 | 0.616 | 0.616 | 0.616 | 0.616 |
AUC (test) | 0.667 | 0.667 | 0.631 | 0.667 | 0.393 | 0.464 |
Evaluation Classifier Metrics | Noisy Test Data | ||||
---|---|---|---|---|---|
SNR = 17 dB | SNR = 7 dB | SNR = −3 dB | SNR = −13 dB | SNR = −23 dB | |
Kappa | 0.452 | 0.452 | 0.452 | 0 | 0 |
95% CI | (0.636, 0.985) | (0.636, 0.985) | (0.636, 0.985) | (0.566, 0.962) | (0.566, 0.962) |
Accuracy | 0.882 | 0.882 | 0.882 | 0.823 | 0.823 |
Sensitivity | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 |
Specificity | 0.333 | 0.333 | 0.333 | 0 | 0 |
Precision | 0.875 | 0.875 | 0.875 | 0.823 | 0.823 |
Recall | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 |
AUC (training) | 0.616 | 0.616 | 0.616 | 0.616 | 0.616 |
AUC (test) | 0.667 | 0.667 | 0.667 | 0.500 | 0.500 |
Features | Original | SNR = 17 dB | SNR = 7 dB | SNR = −3 dB | SNR = −13 dB | SNR = −23 dB |
---|---|---|---|---|---|---|
RMS | W = 245, p = 0.145, Cdelta = −0.270 | W = 247, p = 0.154, Cdelta = −0.265 | W = 243, p = 0.137, Cdelta = −0.277 | W = 256, p = 0.201, Cdelta = −0.238 | W = 297, p = 0.536, Cdelta = −0.116 | W = 299, p = 0.557, Cdelta = −0.110 |
median | t = −0.8408, df = 19.075, p = 0.411, Cd = −0.232 | W = 268, p = 0.277, Cdelta = −0.202 | t = −1.7105, df = 18.665, p = 0.104, Cd = −0.480 | t = −1.7556, df = 14.249, p = 0.101, Cd = −0.641 | t = −2.97, df = 18.279, p = 0.008, Cd = −0.846 | W = 246, p = 0.150, Cdelta = −0.268 |
MagDF | W = 261, p = 0.231, Cdelta = −0.223 | W = 263, p = 0.243, Cdelta = −0.217 | W = 263, p = 0.243, Cdelta = −0.217 | W = 263, p = 0.243, Cdelta = −0.217 | W = 301, p = 0.579, Cdelta = −0.104 | W = 317, p = 0.766, Cdelta = −0.056 |
DF | W = 319.5, p = 0.796, Cdelta = −0.049 | W = 293.5, p = 0.498, Cdelta = −0.126 | W = 245.5, p = 0.147, Cdelta = −0.269 | W = 191, p = 0.020, Cdelta = −0.431 | W = 296.5, p = 0.530, Cdelta = −0.117 | W = 315.5, p = 0.747, Cdelta = −0.061 |
CS | W = 469, p = 0.033, Cdelta = 0.396 | W = 468, p = 0.034, Cdelta = 0.393 | W = 408, p = 0.250, Cdelta = 0.214 | W = 377, p = 0.515, Cdelta = 0.122 | W = 343, p = 0.917, Cdelta = 0.021 | W = 385, p = 0.435, Cdelta = 0.146 |
SDV | t = −2.7527, df = 36.441, p = 0.009, Cd = −0.559 | W = 252, p = 0.179, Cdelta = −0.250 | W = 266, p = 0.263 2, Cdelta = −0.208 | W = 353, p = 0.791, Cdelta = 0.050 | W = 287.5, p = 0.439, Cdelta = −0.144 | W = 308.5, p = 0.663, Cdelta = −0.082 |
SampEntT_m2 | W = 421, p = 0.174, Cdelta = 0.253 | W = 486.5, p = 0.016, Cdelta = 0.448 | W = 443.5, p = 0.085, Cdelta = 0.320 | W = 411.5, p = 0.228, Cdelta = 0.225 | W = 393.5, p = 0.359, Cdelta = 0.171 | W = 372, p = 0.568, Cdelta = 0.107 |
SampEntT_m3 | W = 415.5, p = 0.198, Cdelta = 0.237 | W = 475, p = 0.025, Cdelta = 0.414 | W = 440, p = 0.091, Cdelta = 0.309 | W = 406.5, p = 0.259, Cdelta = 0.210 | W = 421, p = 0.174, Cdelta = 0.253 | W = 388, p = 0.407, Cdelta = 0.155 |
SampEntT_m4 | W = 420, p = 0.136, Cdelta = 0.250 | W = 459.5, p = 0.034, Cdelta = 0.367 | W = 446, p = 0.054, Cdelta = 0.327 | W = 399.5, p = 0.293, Cdelta = 0.189 | W = 382, p = 0.464, Cdelta = 0.137 | W = 370, p = 0.590, Cdelta = 0.101 |
SampEntP_m2 | W = 413, p = 0.218, Cdelta = 0.229 | W = 438, p = 0.102, Cdelta = 0.303 | W = 406, p = 0.263, Cdelta = 0.208 | W = 420, p = 0.179, Cdelta = 0.250 | W = 504, p = 0.007, Cdelta = 0.500 | W = 256, p = 0.201, Cdelta = −0.238 |
SampEntP_m3 | W = 408, p = 0.250, Cdelta = 0.214 | W = 404, p = 0.277, Cdelta = 0.202 | W = 401, p = 0.299, Cdelta = 0.193 | W = 404, p = 0.277, Cdelta = 0.202 | W = 525, p = 0.002, Cdelta = 0.562 | W = 273, p = 0.315, Cdelta = −0.187 |
SampEntP_m4 | W = 410, p = 0.237, Cdelta = 0.220 | W = 416, p = 0.201, Cdelta = 0.238 | W = 442, p = 0.090, Cdelta = 0.315 | W = 466, p = 0.037, Cdelta = 0.387 | W = 427, p = 0.145, Cdelta = 0.271 | W = 339, p = 0.968, Cdelta = 0.009 |
SpectEnt | W = 172, p = 0.008, Cdelta = −0.488 | W = 175, p = 0.010, Cdelta = −0.479 | W = 160, p = 0.005, Cdelta = −0.524 | W = 226, p = 0.078, Cdelta = −0.327 | t = −2.032, df = 17.237, p = 0.058, Cd = −0.606 | t = −2.055, df = 17.409, p = 0.055, Cd = −0.608 |
Autocorr | W = 439, p = 0.084, Cdelta = 0.306 | W = 438.5, p = 0.082, Cdelta = 0.305 | W = 447.5, p = 0.060, Cdelta = 0.332 | W = 446.5, p = 0.047, Cdelta = 0.329 | W = 399, p = 0.261, Cdelta = 0.187 | W = 369, p = 0.548, Cdelta = 0.098 |
SD1 | W = 232, p = 0.096, Cdelta = −0.309 | W = 232, p = 0.096, Cdelta = −0.309 | W = 231, p = 0.093, Cdelta = −0.312 | W = 249, p = 0.164, Cdelta = −0.259 | W = 285, p = 0.417, Cdelta = −0.152 | W = 294, p = 0.504, Cdelta = −0.125 |
SD2 | W = 245, p = 0.145, Cdelta = −0.271 | W = 246, p = 0.150, Cdelta = −0.268 | W = 243, p = 0.137, Cdelta = −0.277 | W = 257, p = 0.207, Cdelta = −0.235 | W = 298, p = 0.546, Cdelta = −0.113 | W = 299, p = 0.557, Cdelta = −0.110 |
SDEGG | W = 245, p = 0.145, Cdelta = −0.271 | W = 247, p = 0.154, Cdelta = −0.265 | W = 243, p = 0.137, Cdelta = −0.277 | W = 256, p = 0.201, Cdelta = −0.238 | W = 297, p = 0.536, Cdelta = −0.116 | W = 299, p = 0.557, Cdelta = −0.110 |
Feature | Proportions of Reported Nausea Corresponding to the Selected Feature (Low/High) | Proportions of Regular EGG Corresponding to the Selected Features (Low/High) | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Original | SNR = 17 dB | SNR = 7 dB | SNR = −3 dB | SNR = −13 dB | SNR = −23 dB | Original | SNR = 17 dB | SNR = 7 dB | SNR = −3 dB | SNR = −13 dB | SNR = −23 dB | |
SampEntT_m2 | 0.58/0.25 | 0.58/0.17 | 0.50/0.25 | 0.42/0.25 | 0.67/0.08 | 0.92/0.00 | 0.27/0.30 | 0.21/0.25 | 0.23/0.32 | 0.25/0.12 | 0.70/0.07 | 0.93/0.00 |
SampEntT_m3 | 0.50/0.42 | 0.75/0.25 | 0.67/0.33 | 0.67/0.33 | 0.75/0.17 | 0.92/0.00 | 0.37/0.61 | 0.39/0.55 | 0.41/0.59 | 0.48/0.43 | 0.84/0.09 | 0.98/0.02 |
SampEntT_m4 | 0.33/0.58 | 0.50/0.50 | 0.42/0.58 | 0.50/0.50 | 0.67/0.33 | 0.92/0.08 | 0.30/0.70 | 0.25/0.71 | 0.29/0.70 | 0.36/0.62 | 0.70/0.25 | 0.95/0.05 |
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Jakus, G.; Sodnik, J.; Miljković, N. Electrogastrogram-Derived Features for Automated Sickness Detection in Driving Simulator. Sensors 2022, 22, 8616. https://doi.org/10.3390/s22228616
Jakus G, Sodnik J, Miljković N. Electrogastrogram-Derived Features for Automated Sickness Detection in Driving Simulator. Sensors. 2022; 22(22):8616. https://doi.org/10.3390/s22228616
Chicago/Turabian StyleJakus, Grega, Jaka Sodnik, and Nadica Miljković. 2022. "Electrogastrogram-Derived Features for Automated Sickness Detection in Driving Simulator" Sensors 22, no. 22: 8616. https://doi.org/10.3390/s22228616
APA StyleJakus, G., Sodnik, J., & Miljković, N. (2022). Electrogastrogram-Derived Features for Automated Sickness Detection in Driving Simulator. Sensors, 22(22), 8616. https://doi.org/10.3390/s22228616