# Automatic Identification of Children with ADHD from EEG Brain Waves

^{*}

## Abstract

**:**

## 1. Introduction

- An EEG ML pipeline is presented for ADHD detection, explaining each stage of the pipeline (including signal preprocessing and data preparation) with thorough explanations and rationale.
- Unlike other studies, we employed only the first four sub-bands of EEG, eliminating signals more than 30 Hz and thus reducing the computational load for ML model training while keeping mean accuracy of 93.2%.
- Simple EEG linear features are emphasized in our proposed model development, whereas other works were only based on complex nonlinear features.
- The model was trained on a large dataset of 120 children (the highest of other models was 49) collected from two different sessions at two different places, eliminating the measurement bias in data collection. Also, the experimental setup was child-friendly, easy to reproduce in local settings, and could be employed for future ADHD detection.
- We also performed rigorous validation (unlike other works) to ensure that our model is not impacted by bias and overfitting, which commonly appear in the ML pipeline.

## 2. Related Works

## 3. Materials and Methods

## 4. Preprocessing

## 5. Feature Extraction and Feature Selection

## 6. Result

## 7. Discussion

- To improve the accuracy, we may need to evaluate more features through the use of different machine learning models for comparison of results.
- We will also try different window sizes (0.5 s or 5 s, for example) in future studies.
- We will next work with only two EEG frequency bands (theta and beta), as these two have significant changes in ADHD patients, and investigate more into the sub-band of the EEG signals.

## 8. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 1.**The 10–20 system for electrode position with A1 and A2 reference electrodes [29].

**Figure 4.**K-fold (10-fold) cross-validation process [55].

Sub-Band | Frequency Range | Associated Brain Function |
---|---|---|

Delta | 0.5–4 Hz | Deep sleep or unconsciousness |

Theta | 4–8 Hz | Sleep or drowsiness and recall |

Alpha | 8–13 Hz | Eye closed and visual stimuli are limited |

Beta | 13–30 Hz | Attentive to stimuli or problem-solving |

Gamma | 30 Hz and above | Movement, emotional processing, and high-level mental activity |

**Table 2.**Information about the participants [25].

Boys | Girls | Age | Mean Age | Dominant Hand | |
---|---|---|---|---|---|

Healthy Children | 50 | 10 | 7–12 | 9.85 ± 1.77 | Right-Handed |

ADHD | 48 | 12 | 7–12 | 9.62 ± 1.75 | Right-Handed |

Feature Name | Definition | Mathematical Description |
---|---|---|

Standard Deviation | It is a statistical feature that is a measure of how spread out the data is to the mean. | $\sigma =\sqrt{\frac{{{\displaystyle \sum}}_{n=1}^{N}{({x}_{n}-\mu )}^{2}}{N-1}}$ x _{n} = n-th data sample, N = TotalNo. of samples, µ = mean [33,34] |

RMS | RMS is the square root-mean-square value of a signal | $RMS=\sqrt{\frac{{{\displaystyle \sum}}_{n=1}^{N}{({x}_{n})}^{2}}{N}}$ x _{n} = n-th data sample, N = TotalNo. of samples [35] |

Skewness | Skewness is the measure of the lack of symmetry from the mean of the dataset. | $g=\frac{{{\displaystyle \sum}}_{n=1}^{N}{({x}_{n}-\mu )}^{3}/N}{{\sigma}^{3}}$ x _{n} = n-th data sample, N = TotalNo. of samples, µ = mean [33] |

Kurtosis | Kurtosis is a measure of whether the data are heavy-tailed or light-tailed relative to a normal distribution. | $k=\frac{{{\displaystyle \sum}}_{n=1}^{N}{({x}_{n}-\mu )}^{4}/N}{{\sigma}^{4}}-3$ x _{n} = n-th data sample, N = TotalNo. of samples, µ = mean [36] |

Hjorth Activity | It is the variance of the amplitude of the signal in a time function. Represent the signal power. | Ha = var(x(t)) x(t) = amplitude of time-varying signal [37,38] |

Hjorth Mobility | The mobility is the square root of the activity of the first derivative of the signal divided by the activity of the signal. Represents the mean frequency. | $Hm=\sqrt{\frac{var({x}^{\prime}\left(t\right))}{var\left(x\left(t\right)\right)}}$ x′(t) = 1st derivation of the amplitude of the signal [37,38] |

Hjorth Complexity | It represents the change in frequency. It is defined as the ratio between the mobility of the first derivative of the signal and the mobility of the signal. | $Hc=\frac{Hm({x}^{\prime}\left(t\right))}{Hm\left(x\left(t\right)\right)}$ [37,38] |

Shannon’s Entropy | Shannon’s entropy measures the uncertainty/randomness in a dataset | $H={{\displaystyle \sum}}_{n=1}^{N}-\left({P}_{n}\times log{P}_{n}\right)$ P _{n} = probability of occurrence x_{n} [39] |

Spectral Entropy (SEN) | SEN is the normalized Shannon’s entropy | $SEN=\frac{-{{\displaystyle \sum}}_{n=0}^{N-1}{P}_{k}lo{g}_{2}{P}_{k}}{logN}$ P = spectral of normalized frequency, N = number of frequencies in binary. [28] |

Power Spectral Density (PSD) | PSD of the signal describes the power present in the signal as a function of frequency [40] | |

Band Power | It measures both power and power spectral density in a specified channel bandwidth [41] |

% Variance | Accuracy (Validation) | Accuracy (Test) | Confusion Matrix (Test) | |||
---|---|---|---|---|---|---|

TP | TN | FP | FN | |||

80% | 88.49% | 88.06% | 967 | 1263 | 166 | 124 |

85% | 92.18% | 92.51% | 1011 | 1312 | 122 | 75 |

90% | 93.17% | 93.24% | 994 | 1354 | 139 | 33 |

95% | 88.29% | 88.36% | 867 | 1358 | 266 | 29 |

97% | 85.0% | 85.5% | 769 | 1344 | 342 | 16 |

Number | Accuracy (Validation) | Accuracy (Test) | Confusion Matrix (Test) | |||
---|---|---|---|---|---|---|

TP | TN | FP | FN | |||

1 | 93.4% | 93.9% | 990 | 1331 | 120 | 30 |

2 | 93.1% | 92.7% | 969 | 1322 | 141 | 39 |

3 | 93.0% | 93.8% | 992 | 1326 | 118 | 35 |

4 | 92.7% | 93.0% | 959 | 1339 | 152 | 21 |

5 | 92.3% | 93.6% | 982 | 1330 | 128 | 31 |

6 | 91.3% | 92.5% | 953 | 1332 | 158 | 28 |

7 | 93.3% | 93.6% | 987 | 1325 | 124 | 35 |

8 | 91.6% | 93.0% | 976 | 1321 | 135 | 39 |

9 | 93.1% | 93.2% | 981 | 1321 | 129 | 40 |

10 | 93.17% | 93.24% | 994 | 1354 | 139 | 33 |

Mean | 93.2% | |||||

STD | 0.44 |

% Variance | Accuracy (Validation) | Accuracy (Test) | Confusion Matrix (Test) | |||
---|---|---|---|---|---|---|

TP | TN | FP | FN | |||

80% | 89.3% | 89.7% | 650 | 828 | 90 | 79 |

85% | 92.7% | 93.9% | 681 | 866 | 59 | 41 |

90% | 93.4% | 94.2% | 665 | 886 | 75 | 21 |

95% | 88.5% | 88.3% | 560 | 895 | 180 | 12 |

97% | 84.8% | 84.4% | 493 | 897 | 247 | 10 |

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## Share and Cite

**MDPI and ACS Style**

Alim, A.; Imtiaz, M.H.
Automatic Identification of Children with ADHD from EEG Brain Waves. *Signals* **2023**, *4*, 193-205.
https://doi.org/10.3390/signals4010010

**AMA Style**

Alim A, Imtiaz MH.
Automatic Identification of Children with ADHD from EEG Brain Waves. *Signals*. 2023; 4(1):193-205.
https://doi.org/10.3390/signals4010010

**Chicago/Turabian Style**

Alim, Anika, and Masudul H. Imtiaz.
2023. "Automatic Identification of Children with ADHD from EEG Brain Waves" *Signals* 4, no. 1: 193-205.
https://doi.org/10.3390/signals4010010