ECGMiner: A flexible software for accurately digitizing ECG.

BACKGROUND AND OBJECTIVE: The electrocardiogram (ECG) is the most important non-invasive method for elucidating information about heart and cardiovascular disease diagnosis. Typically, the ECG system manufacturing companies provide ECG images, but store the numerical data in a proprietary format that is not interpretable and is not therefore useful for automatic diagnosis. There have been many efforts to digitize paper-based ECGs. The main limitations of previous works in ECG digitization are that they require manual selection of the regions of interest, only partly provide signal digitization, and offer limited accuracy. METHODS: We have developed the ECGMiner, an open-source software to digitize ECG images. It is precise, fast, and simple to use. This software digitizes ECGs in four steps: 1) recognizing the image composition; 2) removing the gridline; 3) extracting the signals; 4) post-processing and storing the data. RESULTS: We have evaluated the ECGMiner digitization capabilities using the Pearson Correlation Coefficient (PCC) and the Root Mean Square Error (RMSE) measures, and we consider ECG from two large, public, and widely used databases, LUDB and PTB-XL. The actual and digitized values of signals in both databases have been compared. The software's ability to correctly identify the location of characteristic waves has also been validated. Specifically, the PCC values are between 0.971 and 0.995, and the RMSE values are between 0.011 and 0.031 mV. CONCLUSIONS: The ECGMiner software presented in this paper is open access, easy to install, easy to use, and capable of precisely recovering the paper-based/digital ECG signal data, regardless of the input format and signal complexity. ECGMiner outperforms existing digitization algorithms in terms of PCC and RMSE values.

Functional clustering of neuronal signals with FMM mixture models.

The identification of unlabeled neuronal electric signals is one of the most challenging open problems in neuroscience, widely known as Spike Sorting. Motivated to solve this problem, we propose a model-based approach within the mixture modeling framework for clustering oscillatory functional data called MixFMM. The core of the approach is the FMM (Frequency Modulated Möbius) waves, which are non-linear parametric time functions, flexible enough to describe different oscillatory patterns and simple enough to be estimated efficiently. In particular, specific model parameters describe the phase, amplitude and shape of the waveforms. A mixture model is defined using FMM waves as basic functions and gaussian errors, and an EM algorithm is proposed for estimating the parameters. Spike Sorting (SS) has received considerable attention in the literature, and different functional clustering approaches have been considered. We have conducted a fair comparative analysis of the MixFMM with three competitors. Two of them are traditional methods in functional clustering and widely used in Spike Sorting. The third is an approach that has proven superior to many others solving Spike Sorting problems. The datasets used for validation include benchmarking simulated and real cases. The internal and external validation indexes confirm a better performance of the MixFMM on real data sets against the three competitors and an outstanding performance in simulated data against traditional approaches.

CIRCUST: A novel methodology for temporal order reconstruction of molecular rhythms; validation and application towards a daily rhythm gene expression atlas in humans.

The circadian system drives near-24-h oscillations in behaviors and biological processes. The underlying core molecular clock regulates the expression of other genes, and it has been shown that the expression of more than 50 percent of genes in mammals displays 24-h rhythmic patterns, with the specific genes that cycle varying from one tissue to another. Determining rhythmic gene expression patterns in human tissues sampled as single timepoints has several challenges, including the reconstruction of temporal order of highly noisy data. Previous methodologies have attempted to address these challenges in one or a small number of tissues for which rhythmic gene evolutionary conservation is assumed to be preserved. Here we introduce CIRCUST, a novel CIRCular-robUST methodology for analyzing molecular rhythms, that relies on circular statistics, is robust against noise, and requires fewer assumptions than existing methodologies. Next, we validated the method against four controlled experiments in which sampling times were known, and finally, CIRCUST was applied to 34 tissues from the Genotype-Tissue Expression (GTEx) dataset with the aim towards building a comprehensive daily rhythm gene expression atlas in humans. The validation and application shown here indicate that CIRCUST provides a flexible framework to formulate and solve the issues related to the analysis of molecular rhythms in human tissues. CIRCUST methodology is publicly available at https://github.com/yolandalago/CIRCUST/.

A unique cardiac electrocardiographic 3D model. Toward interpretable AI diagnosis.

Mathematical models of cardiac electrical activity are one of the most important tools for elucidating information about heart diagnostics. In this paper, we present an efficient mathematical formulation for this modeling simple enough to be easily parameterized and rich enough to provide realistic signals. It relies on a five dipole representation of the cardiac electric source, each one associated with the well-known waves of the electrocardiogram signal. Beyond the physical basis of the model, the parameters are physiologically interpretable as they characterize the wave shape, similar to what a physician would look for in signals, thus making them very useful in diagnosis. The model accurately reproduces the electrocardiogram signals of any diseased or healthy heart. This new discovery represents a significant advance in electrocardiography research. It is especially useful for diagnosis, patient follow-up or decision-making on new therapies; is also a promising tool for well-performing, transparent and interpretable AI approaches.

Cerium dioxide nanoparticles modulate antioxidant defences and change vascular response in the human saphenous vein.

Nanoparticles have a promising future in biomedical applications and knowing whether they affect ex vivo vascular reactivity is a necessary step before their use in patients. In this study, we have evaluated the vascular effect of cerium dioxide nanoparticles (CeO2NPs) on the human saphenous vein in response to relaxing and contractile agonists. In addition, we have measured the protein expression of key enzymes related to vascular homeostasis and oxidative stress. We found that CeO2NPs increased expression of both SOD isoforms, and the consequent reduction of superoxide anion would enhance the bioavailability of NO explaining the increased vascular sensitivity to sodium nitroprusside in the presence of CeO2NPs. The NOX4 reduction induced by CeO2NPs may lead to lower H2O2 synthesis associated with vasodilation through potassium channels explaining the lower vasodilation to bradykinin. In addition, we showed for the first time, that CeO2NPs increase the expression of ACE2 in human saphenous vein, and it may be the cause of the reduced contraction to angiotensin II. Moreover, we ruled out that CeO2NPs have effect on the protein expression of eNOS, sGC, BKca channels and angiotensin II receptors or modify the vascular response to noradrenaline, endothelin-1 and TXA2 analogue. In conclusion, CeO2NPs show antioxidant properties, and together with their vascular effect, they could be postulated as adjuvants for the treatment of cardiovascular diseases.

Compelling new electrocardiographic markers for automatic diagnosis.

BACKGROUND AND OBJECTIVE: The automatic diagnosis of heart diseases from the electrocardiogram (ECG) signal is crucial in clinical decision-making. However, the use of computer-based decision rules in clinical practice is still deficient, mainly due to their complexity and a lack of medical interpretation. The objetive of this research is to address these issues by providing valuable diagnostic rules that can be easily implemented in clinical practice. In this research, efficient diagnostic rules friendly in clinical practice are provided. METHODS: In this paper, interesting parameters obtained from the ECG signals analysis are presented and two simple rules for automatic diagnosis of Bundle Branch Blocks are defined using new markers derived from the so-called FMMecg delineator. The main advantages of these markers are the good statistical properties and their clear interpretation in clinically meaningful terms. RESULTS: High sensitivity and specificity values have been obtained using the proposed rules with data from more than 35,000 patients from well known benchmarking databases. In particular, to identify Complete Left Bundle Branch Blocks and differentiate this condition from subjects without heart diseases, sensitivity and specificity values ranging from 93% to 99% and from 96% to 99%, respectively. The new markers and the automatic diagnosis are easily available at https://fmmmodel.shinyapps.io/fmmEcg/, an app specifically developed for any given ECG signal. CONCLUSIONS: The proposal is different from others in the literature and it is compelling for three main reasons. On the one hand, the markers have a concise electrocardiographic interpretation. On the other hand, the diagnosis rules have a very high accuracy. Finally, the markers can be provided by any device that registers the ECG signal and the automatic diagnosis is made straightforwardly, in contrast to the black-box and deep learning algorithms.

Dynamics of gliotoxin and bis(methylthio)gliotoxin production during the course of Aspergillus fumigatus infection.

As recently described, fungal secondary metabolism activates during infection in response to a hostile host environment. Gliotoxin and bis(methylthio)gliotoxin are two recognized secondary metabolites produced by Aspergillus fumigatus with differential cytotoxicity and involved in virulence. We sought to describe the temporal dynamics of gliotoxin and bis(methylthio)gliotoxin during A. fumigatus progression to further explore their role in the infection. First, we optimized the production of the mycotoxins under different in vitro growth conditions and then specifically measured them using an UHPLC/PDA method. The analytical conditions were selected after testing different parameters such as extraction procedures, column type, and mobile phase composition. We found that gliotoxin and bis(methylthio)gliotoxin are differentially excreted to the extracellular media during the course of A. fumigatus infection regardless of the growth format tested. Dynamic profiles show an early production of gliotoxin, which, after reaching a maximum, decreases coinciding with the increase in the production of the inactive derivative bis(methylthio)gliotoxin. Presence of gliotoxin may indicate an early phase of fungal development, whereas detection of bis(methylthio)gliotoxin may correspond to a more advanced stage of infection. Our chromatographic method successfully characterizes these secondary metabolites. Thus, it may potentially be used to further understand Aspergillus infection. LAY SUMMARY: Aspergillus fumigatus secondary metabolites may contribute to fungal survival. A new chromatographic method was applied to simultaneously characterize two relevant metabolites. Presence of toxic gliotoxin may indicate an early phase of development, whereas the detection of the inactive derivate may represent an advanced infection stage.

Predicting COVID-19 progression from diagnosis to recovery or death linking primary care and hospital records in Castilla y León (Spain).

This paper analyses COVID-19 patients' dynamics during the first wave in the region of Castilla y León (Spain) with around 2.4 million inhabitants using multi-state competing risk survival models. From the date registered as the start of the clinical process, it is assumed that a patient can progress through three intermediate states until reaching an absorbing state of recovery or death. Demographic characteristics, epidemiological factors such as the time of infection and previous vaccinations, clinical history, complications during the course of the disease and drug therapy for hospitalised patients are considered as candidate predictors. Regarding risk factors associated with mortality and severity, consistent results with many other studies have been found, such as older age, being male, and chronic diseases. Specifically, the hospitalisation (death) rate for those over 69 is 27.2% (19.8%) versus 5.3% (0.7%) for those under 70, and for males is 14.5%(7%) versus 8.3%(4.6%)for females. Among patients with chronic diseases the highest rates of hospitalisation are 26.1% for diabetes and 26.3% for kidney disease, while the highest death rate is 21.9% for cerebrovascular disease. Moreover, specific predictors for different transitions are given, and estimates of the probability of recovery and death for each patient are provided by the model. Some interesting results obtained are that for patients infected at the end of the period the hazard of transition from hospitalisation to ICU is significatively lower (p < 0.001) and the hazard of transition from hospitalisation to recovery is higher (p < 0.001). For patients previously vaccinated against pneumococcus the hazard of transition to recovery is higher (p < 0.001). Finally, internal validation and calibration of the model are also performed.

Electrophysiological and Transcriptomic Features Reveal a Circular Taxonomy of Cortical Neurons.

The complete understanding of the mammalian brain requires exact knowledge of the function of each neuron subpopulation composing its parts. To achieve this goal, an exhaustive, precise, reproducible, and robust neuronal taxonomy should be defined. In this paper, a new circular taxonomy based on transcriptomic features and novel electrophysiological features is proposed. The approach is validated by analysing more than 1850 electrophysiological signals of different mouse visual cortex neurons proceeding from the Allen Cell Types database. The study is conducted on two different levels: neurons and their cell-type aggregation into Cre lines. At the neuronal level, electrophysiological features have been extracted with a promising model that has already proved its worth in neuronal dynamics. At the Cre line level, electrophysiological and transcriptomic features are joined on cell types with available genetic information. A taxonomy with a circular order is revealed by a simple transformation of the first two principal components that allow the characterization of the different Cre lines. Moreover, the proposed methodology locates other Cre lines in the taxonomy that do not have transcriptomic features available. Finally, the taxonomy is validated by Machine Learning methods which are able to discriminate the different neuron types with the proposed electrophysiological features.

A simple parametric representation of the Hodgkin-Huxley model.

The Hodgkin-Huxley model, decades after its first presentation, is still a reference model in neuroscience as it has successfully reproduced the electrophysiological activity of many organisms. The primary signal in the model represents the membrane potential of a neuron. A simple representation of this signal is presented in this paper. The new proposal is an adapted Frequency Modulated Möbius multicomponent model defined as a signal plus error model in which the signal is decomposed as a sum of waves. The main strengths of the method are the simple parametric formulation, the interpretability and flexibility of the parameters that describe and discriminate the waveforms, the estimators' identifiability and accuracy, and the robustness against noise. The approach is validated with a broad simulation experiment of Hodgkin-Huxley signals and real data from squid giant axons. Interesting differences between simulated and real data emerge from the comparison of the parameter configurations. Furthermore, the potential of the FMM parameters to predict Hodgkin-Huxley model parameters is shown using different Machine Learning methods. Finally, promising contributions of the approach in Spike Sorting and cell-type classification are detailed.

Adaptation of the emerging pathogenic yeast Candida auris to high caspofungin concentrations correlates with cell wall changes.

Candida auris has emerged as a fungal pathogen that causes nosocomial outbreaks worldwide. Diseases caused by this fungus are of concern, due to its reduced susceptibility to several antifungals. C. auris exhibits paradoxical growth (PG; defined as growth at high, but not intermediate antifungal concentrations) in the presence of caspofungin (CPF). We have characterized the cellular changes associated with adaptation to CPF. Using EUCAST AFST protocols, all C. auris isolates tested showed PG to CPF, although in some isolates it was more prominent. Most isolates also showed a trailing effect (TE) to micafungin and anidulafungin. We identified two FKS genes in C. auris that encode the echinocandins target, namely ß-1,3-glucan synthase. FKS1 contained the consensus hot-spot (HS) 1 and HS2 sequences. FKS2 only contained the HS1 region which had a change (F635Y), that has been shown to confer resistance to echinocandins in C. glabrata. PG has been characterized in other species, mainly C. albicans, where high CPF concentrations induced an increase in chitin, cell volume and aggregation. In C. auris CPF only induced a slight accumulation of chitin, and none of the other phenomena. RNAseq experiments demonstrated that CPF induced the expression of genes encoding several GPI-anchored cell wall proteins, membrane proteins required for the stability of the cell wall, chitin synthase and mitogen-activated protein kinases (MAPKs) involved in cell integrity, such as BCK2, HOG1 and MKC1 (SLT2). Our work highlights some of the processes induced in C. auris to adapt to echinocandins.

The hidden waves in the ECG uncovered revealing a sound automated interpretation method.

A novel approach for analysing cardiac rhythm data is presented in this paper. Heartbeats are decomposed into the five fundamental P, Q, R, S and T waves plus an error term to account for artifacts in the data which provides a meaningful, physical interpretation of the heart's electric system. The morphology of each wave is concisely described using four parameters that allow all the different patterns in heartbeats to be characterized and thus differentiated This multi-purpose approach solves such questions as the extraction of interpretable features, the detection of the fiducial marks of the fundamental waves, or the generation of synthetic data and the denoising of signals. Yet the greatest benefit from this new discovery will be the automatic diagnosis of heart anomalies as well as other clinical uses with great advantages compared to the rigid, vulnerable and black box machine learning procedures, widely used in medical devices. The paper shows the enormous potential of the method in practice; specifically, the capability to discriminate subjects, characterize morphologies and detect the fiducial marks (reference points) are validated numerically using simulated and real data, thus proving that it outperforms its competitors.

Order restricted inference in chronobiology.

This paper is motivated by applications in oscillatory systems where researchers are typically interested in discovering components of those systems that display rhythmic temporal patterns. The contributions of this paper are twofold. First, a methodology is developed based on a circular signal plus error model that is defined using order restrictions. This mathematical formulation of rhythmicity is simple, easily interpretable and very flexible, with the latter property derived from the nonparametric formulation of the signal. Second, we address various commonly encountered problems in the analysis of oscillatory systems data. Specifically, we propose a methodology for (a) detecting rhythmic signals in an oscillatory system and (b) estimating the unknown sampling time that occurs when tissues are obtained from subjects whose time of death is unknown. The proposed methodology is computationally efficient, outperforms the existing methods, and is broadly applicable to address a wide range of questions related to oscillatory systems.

Retrospective study on transmissible viral proventriculitis and chicken proventricular necrosis virus (CPNV) in the UK.

Chicken proventricular necrosis virus (CPNV) is a recently described birnavirus, which has been proposed to be the cause of transmissible viral proventriculitis (TVP). The understanding of the epidemiology of both the virus and the disease is very limited. A retrospective investigation on TVP and CPNV in broiler chicken submissions from the UK from between 1994 and 2015 was performed with the aims of assessing the longitudinal temporal evolution of TVP and CPNV, and to review the histological proventricular lesions in the studied chickens. Ninety-nine of the 135 included submissions (73.3%) fulfilled the TVP-diagnostic criteria, while the remaining 36 submissions (26.7%) displayed only lymphocytic proventriculitis (LP). The first detection of CPNV by PCR dated from 2009. Results showed a rise in the number of both TVP and positive CPNV RT-PCR submissions from 2009 with a peak in 2013, suggesting that they may be an emerging or re-emerging disease and pathogen, respectively. Twenty-two out of the 99 submissions displaying TVP lesions (22%) and four out of the 36 (11%) submissions with LP gave positive CPNV RT-PCR results, further supporting the association between CPNV and TVP and confirming that CPNV is present in a low proportion of proventriculi that do not fulfil the TVP-diagnostic criteria. In addition, intranuclear inclusion bodies were observed in 22 of the submissions with TVP. The vast majority of these cases (21 of 22, 96%) gave negative CPNV RT-PCR results, raising the question of whether a virus other than CPNV is responsible for some of these TVP-affected cases.RESEARCH HIGHLIGHTSTVP and CPNV have been present in British broilers since at least 1994 and 2009, respectively.TVP and CPNV seem to be an emerging and re-emerging disease and pathogen, respectively.CPNV was detected in proventriculi with both TVP and LP-lesions.Viruses other than CPNV may be responsible for some TVP-affected cases.

Frequency Modulated Möbius Model Accurately Predicts Rhythmic Signals in Biological and Physical Sciences.

Motivated by applications in physical and biological sciences, we developed a Frequency Modulated Möbius (FMM) model to describe rhythmic patterns in oscillatory systems. Unlike standard symmetric sinusoidal models, FMM is a flexible parametric model that allows deformations to sinusoidal shape to accommodate commonly seen asymmetries in applications. FMM model parameters are easy to estimate and the model is easy to interpret complex rhythmic data. We illustrate FMM model in three disparate applications, namely, circadian clock gene expression, corticoptropin levels in depressed patients and the temporal light intensity patterns of distant stars. In each case, FMM model is demonstrated to be flexible, scientifically plausible and easy to interpret. Analysis of synthetic data derived from patterns of real data, suggest that FMM model fits the data very well both visually as well as in terms of the goodness of fit measure total mean squared error. An R language based software for implementing FMM model is available.

Microarray Data Normalization and Robust Detection of Rhythmic Features.

Data derived from microarray technologies are generally subject to various sources of noise and accordingly the raw data are pre-processed before formally analysed. Data normalization is a key pre-processing step when dealing with microarray experiments, such as circadian gene-expressions, since it removes systematic variations across arrays. A wide variety of normalization methods are available in the literature. However, from our experience in the study of rhythmic expression patterns in oscillatory systems (e.g. cell-cycle, circadian clock), the choice of the normalization method may substantially impair the identification of rhythmic genes. Hence, the identification of a gene as rhythmic could be just as an artefact of how the data were normalized. Yet, gene rhythmicity detection is crucial in modern toxicological and pharmacological studies, thus a procedure to truly identify rhythmic genes that are robust to the choice of a normalization method is required.To perform the task of detecting rhythmic features, we propose a rhythmicity measure based on bootstrap methodology to robustly identify rhythmic genes in oscillatory systems. Although our methodology can be extended to any high-throughput experiment, in this chapter, we illustrate how to apply it to a publicly available circadian clock microarray gene-expression data and give full details (both statistical and computational) so that the methodology can be used in an easy way. We will show that the choice of normalization method has very little effect on the proposed methodology since the results derived from the bootstrap-based rhythmicity measure are highly rank correlated for any pair of normalization methods considered. This suggests, on the one hand, that the rhythmicity measure proposed is robust to the choice of the normalization method, and on the other hand, that gene rhythmicity detected using this measure is potentially not a mere artefact of the normalization method used. In this way the researcher using this methodology will be protected against the possible effect of different normalizations, as the conclusions obtained will not depend so strongly on them. Additionally, the described bootstrap methodology can also be employed as a tool to simulate gene-expression participating in an oscillatory system from a reference data set.

Dynamics of breast-cancer relapse reveal late-recurring ER-positive genomic subgroups.

The rates and routes of lethal systemic spread in breast cancer are poorly understood owing to a lack of molecularly characterized patient cohorts with long-term, detailed follow-up data. Long-term follow-up is especially important for those with oestrogen-receptor (ER)-positive breast cancers, which can recur up to two decades after initial diagnosis1-6. It is therefore essential to identify patients who have a high risk of late relapse7-9. Here we present a statistical framework that models distinct disease stages (locoregional recurrence, distant recurrence, breast-cancer-related death and death from other causes) and competing risks of mortality from breast cancer, while yielding individual risk-of-recurrence predictions. We apply this model to 3,240 patients with breast cancer, including 1,980 for whom molecular data are available, and delineate spatiotemporal patterns of relapse across different categories of molecular information (namely immunohistochemical subtypes; PAM50 subtypes, which are based on gene-expression patterns10,11; and integrative or IntClust subtypes, which are based on patterns of genomic copy-number alterations and gene expression12,13). We identify four late-recurring integrative subtypes, comprising about one quarter (26%) of tumours that are both positive for ER and negative for human epidermal growth factor receptor 2, each with characteristic tumour-driving alterations in genomic copy number and a high risk of recurrence (mean 47-62%) up to 20 years after diagnosis. We also define a subgroup of triple-negative breast cancers in which cancer rarely recurs after five years, and a separate subgroup in which patients remain at risk. Use of the integrative subtypes improves the prediction of late, distant relapse beyond what is possible with clinical covariates (nodal status, tumour size, tumour grade and immunohistochemical subtype). These findings highlight opportunities for improved patient stratification and biomarker-driven clinical trials.

Optical Coherence Tomography Thickness Measurements of the Extraocular Rectus Muscle Tendons in Graves' Ophthalmopathy.

PURPOSE: To examine extraocular rectus muscle tendons in patients with Graves' ophthalmopathy using optical coherence tomography (OCT). METHODS: This was a cross-sectional observational study conducted with 55 healthy controls, 45 patients with clinically inactive Graves' ophthalmopathy, and 12 patients with clinically active Graves' ophthalmopathy. Scanning was performed at the 3- and 9-o'clock positions. The medial rectus tendon thickness was measured at 7.2 and 9.2 mm from the limbus and the lateral rectus tendon thickness was measured at 8.5 and 10.5 mm from the limbus. RESULTS: The 9.2-mm medial rectus, 8.5-mm lateral rectus, and 10.5-mm lateral rectus tendons were thicker in the inactive Graves' ophthalmopathy group than the control group (240 ± 70, 231 ± 63, and 228 ± 54 µm vs 201 ± 71, 199 ± 53, and 200 ± 32 µm, respectively; P ≤ .011), whereas the 8.5-mm lateral rectus and 9.2-mm medial rectus tendons were thicker in patients with active Graves' ophthalmopathy than patients with inactive Graves' ophthalmopathy (274 ± 77 and 283 ± 68 µm vs 231 ± 63 and 240 ± 70 µm, respectively; P ≤ .048). A correlation was detected between lateral rectus and medial rectus tendon thicknesses and the Graves' ophthalmopathy clinical activity score (R = 0.252, P = .035; and R = 0.291, P = .013, respectively). CONCLUSIONS: OCT emerged as an accurate method for measuring medial rectus and lateral rectus tendon thicknesses in patients with Graves' ophthalmopathy. The imaging tool was able to detect thicker horizontal rectus tendons in patients with inactive Graves' ophthalmopathy than in controls, and in patients with active compared to inactive disease. [J Pediatr Ophthalmol Strabismus. 2018;55(6):356-362.].

A Bootstrap Based Measure Robust to the Choice of Normalization Methods for Detecting Rhythmic Features in High Dimensional Data.

Motivation: Gene-expression data obtained from high throughput technologies are subject to various sources of noise and accordingly the raw data are pre-processed before formally analyzed. Normalization of the data is a key pre-processing step, since it removes systematic variations across arrays. There are numerous normalization methods available in the literature. Based on our experience, in the context of oscillatory systems, such as cell-cycle, circadian clock, etc., the choice of the normalization method may substantially impact the determination of a gene to be rhythmic. Thus rhythmicity of a gene can purely be an artifact of how the data were normalized. Since the determination of rhythmic genes is an important component of modern toxicological and pharmacological studies, it is important to determine truly rhythmic genes that are robust to the choice of a normalization method. Results: In this paper we introduce a rhythmicity measure and a bootstrap methodology to detect rhythmic genes in an oscillatory system. Although the proposed methodology can be used for any high-throughput gene expression data, in this paper we illustrate the proposed methodology using several publicly available circadian clock microarray gene-expression datasets. We demonstrate that the choice of normalization method has very little effect on the proposed methodology. Specifically, for any pair of normalization methods considered in this paper, the resulting values of the rhythmicity measure are highly correlated. Thus it suggests that the proposed measure is robust to the choice of a normalization method. Consequently, the rhythmicity of a gene is potentially not a mere artifact of the normalization method used. Lastly, as demonstrated in the paper, the proposed bootstrap methodology can also be used for simulating data for genes participating in an oscillatory system using a reference dataset. Availability: A user friendly code implemented in R language can be downloaded from http://www.eio.uva.es/~miguel/robustdetectionprocedure.html.