Fractal analysis of stride interval time series is a useful tool in human gait research which could be used as a marker for gait adaptability, gait disorder, and fall risk among patients with movement disorders. This study is designed to systematically and comprehensively investigate two practical aspects of fractal analysis which significantly affect the outcome: the series length and the parameters used in the algorithm. The Hurst exponent, scaling exponent, and/or fractal dimension are computed from both simulated and experimental data using three fractal methods, namely detrended fluctuation analysis, box-counting dimension, and Higuchi's fractal dimension. The advantages and drawbacks of each method are discussed, in terms of biases and variability. The results demonstrate that a careful selection of fractal analysis methods and their parameters is required, which is dependent on the aim of study (either analyzing differences between experimental groups or estimating an accurate determination of fractal features). A set of guidelines for the selection of the fractal methods and the length of stride interval time series is provided, along with the optimal parameters for a robust implementation for each method.

Early diagnosis of lung cancer greatly improves the likelihood of survival and remission, but limitations in existing technologies like low-dose computed tomography have prevented the implementation of widespread screening programs. Breath-based solutions that seek disease biomarkers in exhaled volatile organic compound (VOC) profiles show promise as affordable, accessible and non-invasive alternatives to traditional imaging. In this pilot work, we present a lung cancer detection framework using cavity ring-down spectroscopy (CRDS), an effective and practical laser absorption spectroscopy technique that has the ability to advance breath screening into clinical reality. The main aims of this work were to 1) test the utility of infrared CRDS breath profiles for discriminating non-small cell lung cancer (NSCLC) patients from controls, 2) compare models with VOCs as predictors to those with patterns from the CRDS spectra (breathprints) as predictors, and 3) present a robust approach for identifying relevant disease biomarkers. First, based on a proposed learning curve technique that estimated the limits of a model's performance at multiple sample sizes (10-158), the CRDS-based models developed in this work were found to achieve classification performance comparable or superior to like mass spectroscopy and sensor-based systems. Second, using 158 collected samples (62 NSCLC subjects and 96 controls), the accuracy range for the VOC-based model was 65.19%-85.44% (51.61%-66.13% sensitivity and 73.96%-97.92% specificity), depending on the employed cross-validation technique. The model based on breathprint predictors generally performed better, with accuracy ranging from 71.52%-86.08% (58.06%-82.26% sensitivity and 80.21%-88.54% specificity). Lastly, using a protocol based on consensus feature selection, three VOCs (isopropanol, dimethyl sulfide, and butyric acid) and two breathprint features (from a local binary pattern transformation of the spectra) were identified as possible NSCLC biomarkers. This research demonstrates the potential of infrared CRDS breath profiles and the developed early-stage classification techniques for NSCLC biomarker detection and screening.

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e13579 Background: Population-level screening programs aimed at early detection and treatment of breast cancer saves lives. Analyzing breath using infrared spectroscopy offers a highly sensitive, non-invasive, and cost-effective mechanism for identifying exhaled volatile organic chemicals, and it is hypothesized that it may identify differences in the “breathprint” of women with breast cancer relative to those without a breast cancer diagnosis. Methods: Alveolar breath samples (10 L) were collected using a Breathe BioMedical alveolar breath sampler onto Tenax TA sorbent tubes. Corresponding room air samples (10 L) were collected in the same manner. Absorption spectra of the samples at a desorb temperature of 75 °C were measured by infrared cavity ring-down spectroscopy (IR-CRDS), a highly sensitive method of measuring absorption coefficients due to trace volatile organic compounds (VOCs) present in exhaled breath. After subtracting room air absorption and ordering each measured spectrum by increasing wavelength, missing values were imputed using spline interpolation. The absorption spectra were then normalized using one of four techniques: min-max, vector, peak or standard normal variate normalization. The first derivatives of the normalized absorption coefficients (187 values in total) were then used as features for discriminating samples from subjects with breast cancer and controls. The most useful features were selected based on minimum redundancy and maximum relevance (mRMR) and were used to train a linear support vector machine (SVM) classifier. Performance of classification models was estimated based on two data splitting configurations, non-nested leave-one-out cross-validation (LOOCV) and nested LOOCV. These approaches provide upper and lower bounds of expected model performance. Classification performance was used for tuning the number of features included in each model. Results: The analysis of this study is based on the spectra obtained from 70 participants (38 breast cancer and 32 controls), collected at the Saint John Regional Hospital in New Brunswick, Canada. Table below shows the non-nested and nested performance characteristics of classifiers with the best performing normalization technique. The number of features given for the nested model is not an integer as it indicates an average across the cross-validation folds. Conclusions: These results suggest that the classification of alveolar breath using IR-CRDS is a promising technique for the detection of breast cancer. Performance of classification models. AUC is the area under the receiver operator characteristics curve.[Table: see text]

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