Artificial Neural Network of Polygraph Signals.
Abstract
The purpose of this research was to investigate the use of artificial neural networks (ANN) in classifying psychophysiological detection of deception (PDD) examinations as deceptive or non-deceptive. ANNs are mathematical models of the computing architecture of the human brain. An ANN was designed to accept all four signals (galvanic skin resistance, cardiovascular activity, thoracic respiration and abdominal respiration) from the polygraph output in their entirety. The PDD data used in the study consisted of confirmed Zone Comparison Technique (ZCT) examinations of 56 subjects, of which only 15 were non-deceptive. The ANN application resulted in an 87% correct classification of non-deceptive subjects and a 95% correct classification of deceptive subjects. The misclassifications were evenly split: 2 misclassified deceptives (out of 41) and 2 misclassified non-deceptives (out of 15). The two non-deceptives were just slightly over the classification threshold, into the deceptive region of the classification space, and could potentially be called inconclusive. While these results are promising, they are based on a limited set of data, so generalization to a claim that they will successfully address the overall polygraph classification problem requires more extensive evaluation and demonstration on a much larger database of subjects. (AN)
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 01, 1993
- Accession Number
- ADA305751
Entities
People
- John E. Angus
- Patrick F. Castelaz
Organizations
- Claremont Graduate University