| Literature DB >> 30923326 |
Amélie Catala1,2, Marine Grandgeorge3, Jean-Luc Schaff4,5,6, Hugo Cousillas7, Martine Hausberger8, Jennifer Cattet9.
Abstract
Although different studies have shown that diseases such as breast or lung cancer are associated with specific bodily odours, no study has yet tested the possibility that epileptic seizures may be reflected in an olfactory profile, probably because there is a large variety of seizure types. The question is whether a "seizure-odour", that would be transversal to individuals and types of seizures, exists. This would be a pre requisite for potential anticipation, either by electronic systems (e.g., e-noses) or trained dogs. The aim of the present study therefore was to test whether trained dogs, as demonstrated for cancer or diabetes, may discriminate a general epileptic seizure odor (different from body odours of the same person in other contexts and common to different persons). The results were very clear: all dogs discriminated the seizure odour. The sensitivity and specificity obtained were amongst the highest shown up to now for discrimination of diseases. This constitutes a first proof that, despite the variety of seizures and individual odours, seizures are associated with olfactory characteristics. These results open a large field of research on the odour signature of seizures. Further studies will aim to look at potential applications in terms of anticipation of seizures.Entities:
Mesh:
Year: 2019 PMID: 30923326 PMCID: PMC6438971 DOI: 10.1038/s41598-019-40721-4
Source DB: PubMed Journal: Sci Rep ISSN: 2045-2322 Impact factor: 4.379
Latency, sensitivity and specificity for 5 dogs evaluated for representative results.
| Dogs | Sensitivity (%) | Specificity (%) | Binomial Test P Value |
|---|---|---|---|
| Casey | 100 | 100 | <1E−5 |
| Dodger | 100 | 100 | <1E−5 |
| Lana | 67 | 95 | 4E−4 |
| Zoey | 100 | 100 | <1E−5 |
| Roo | 67 | 95 | 4E−4 |
| Total | 86,8 | 98 | — |
Calculation of p value assumed binomial distribution probability of success = 0.14 and trials = 9 per dog. Total samples presented to each dog = 63.
Figure 1Mean time spent exploring (seconds) each odour type and 95% confidence intervals represented. (***p < 0.001). Odour types were from a seizure (seizure), two from a sports session (physical exercise 1 & 2), and four taken pseudo-randomly on different days during calm activity (calm activity 1, 2, 3 & 4).
Figure 2Exploration time (all dogs and five repeated subjects pooled) per type of odour, per repetition. Line = regression linear line for seizure sample, dots = means of exploration times per sample type, dotted lines = standard error for seizure sample. Prism’s linear regression analysis revealed no effect of learning (slope not different from 0) in any of the conditions [seizure: F(1,3) = 1.037, p = 0.38; Physical exercise 1: F(1,3) = 0.001, p = 0.97, Physical exercise 2: F(1,3) = 1.718, p = 0.28; d 1: F(1,3) = 0.32, p = 0.607; d 2: F(1,3) = 0.318, p = 0.612; d 3: F(1,3) = 0.329, p = 0.606; d 4: F(1,3) = 1.853, p = 0.267.