Neural network Bayesian classifier, training, and validation statistics
Cluster (C)
Training set
Validation set
Members
Correct classification (%)
Members
Correct classification (%)
1
37
91.89
35
90.05
2
24
91.67
32
93.75
3
16
81.25
11
81.82
4
8
50.00
13
69.23
5
19
100.00
14
100.00
6
11
100.00
10
97.00
Total
115
89.56
115
87.94
A total correct classification of 89.6% was obtained during the training of the probabilistic neural network classifier (PNNC) with 115 subjects selected by random split of the total sample and 87.9% during validation with the remaining 115 subjects. Input factors: z-scored P300 latency (P300l), P300 amplitude (P300a), wideband power (zWBP), delta power (zDP), z-scored fast beta power (26–29 Hz sub-band, regional) (zFBP), maturational asymmetry (zMA), P50 ratio (zP50r). photic response at 20 Hz (zHR) coded bisynchronous spike waves (BSSW) and centrotemporal spikes (CTS), The spacing parameter was 0.198438 (optimized by jackknifing during training)
The authors declare that they have no conflicts of interest.
Ethical approval
The project “Neurophysiological findings in Attention Deficit Hyperactivity Disorder, a Pandora’s box with therapeutic implications” was reviewed and approved by the Ethics Committee of the Hospital Español de México (signed and sealed acceptance letter).
Consent to participate
Informed consent to participate in the study was obtained from all the participants/parents.
Consent to publication
Not applicable.
Availability of data and materials
The raw and analyzed EEG/ERP data and the neuropsychological scores are available on request to the corresponding author to all qualified investigators.
Open Exploration maintains a neutral stance on jurisdictional claims in published institutional affiliations and maps. All opinions expressed in this article are the personal views of the author(s) and do not represent the stance of the editorial team or the publisher.
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