Abstract
One of the most interesting and everyday natural phenomenon is the formation of different patterns after the evaporation of liquid droplets on a solid surface. The analysis of dried patterns from blood droplets has recently gained a lot of attention, experimentally and theoretically, due to its potential application in diagnostic medicine and forensic science. This paper presents evidence that images of dried blood droplets have a signature revealing the exhaustion level of the person, and discloses an entirely novel approach to studying human dried blood droplet patterns. We took blood samples from 30 healthy young male volunteers before and after exhaustive exercise, which is well known to cause large changes to blood chemistry. We objectively and quantitatively analysed 1800 images of dried blood droplets, developing sophisticated image processing analysis routines and optimising a multivariate statistical machine learning algorithm. We looked for statistically relevant correlations between the patterns in the dried blood droplets and exercise-induced changes in blood chemistry. An analysis of the various measured physiological parameters was also investigated. We found that when our machine learning algorithm, which optimises a statistical model combining Principal Component Analysis (PCA) as an unsupervised learning method and Linear Discriminant Analysis (LDA) as a supervised learning method, is applied on the logarithmic power spectrum of the images, it can provide up to 95% prediction accuracy, in discriminating the physiological conditions, i.e., before or after physical exercise. This correlation is strongest when all ten images taken per volunteer per condition are averaged, rather than treated individually. Having demonstrated proof-of-principle, this method can be applied to identify diseases.
Highlights
The generation of complex and varied patterns as a result of the liquid drying process is a common yet intriguing phenomenon in nature[1]
Www.nature.com/scientificreports dynamics of evaporation is often described by five separate stages[21,22]
Linear discriminant analysis (LDA)[57,58] is a supervised learning method that is commonly applied in conjunction with principal component analysis (PCA) for discrimination tasks
Summary
The generation of complex and varied patterns as a result of the liquid drying process is a common yet intriguing phenomenon in nature[1]. In this paper we develop sophisticated image processing routines and a highly-accurate machine learning algorithm to quantitatively and objectively discriminate the patterns from a large number of dried blood droplets, removing much of the subjectivity inherent in previous studies. Our overall goal is to develop an image processing method to identify and extract the most distinguishing features in the images and apply an advanced multivariate statistical machine learning algorithm to quantitatively and objectively discriminate between the images. This will allow us to identify, from images of dried blood droplets, the physiological states of the cyclists; whether the person is at rest or has performed physical exercise.
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