Number Of False Positive Detections Research Articles

A survey of methane point source emissions from coal mines in Shanxi province of China using AHSI on board Gaofen-5B

Abstract. Satellite-based detection of methane (CH4) point sources is crucial in identifying and mitigating anthropogenic emissions of CH4, a potent greenhouse gas. Previous studies have indicated the presence of CH4 point source emissions from coal mines in Shanxi, China, which is an important source region with large CH4 emissions, but a comprehensive survey has remained elusive. This study aims to conduct a survey of CH4 point sources over Shanxi's coal mines based on observations of the Advanced Hyperspectral Imager (AHSI) on board the Gaofen-5B satellite (GF-5B/AHSI) between 2021 and 2023. The spectral shift in centre wavelength and change in full width at half-maximum (FWHM) from the nominal design values are estimated for all spectral channels, which are used as inputs for retrieving the enhancement of the column-averaged dry-air mole fraction of CH4 (ΔXCH4) using a matched-filter-based algorithm. Our results show that the spectral calibration on GF-5B/AHSI reduced estimation biases of the emission flux rate by up to 5.0 %. We applied the flood-fill algorithm to automatically extract emission plumes from ΔXCH4 maps. We adopted the integrated mass enhancement (IME) model to estimate the emission flux rate values from each CH4 point source. Consequently, we detected CH4 point sources in 32 coal mines with 93 plume events in Shanxi province. The estimated emission flux rate ranges from 761.78 ± 185.00 to 12 729.12 ± 4658.13 kg h−1. Our results show that wind speed is the dominant source of uncertainty contributing about 84.84 % to the total uncertainty in emission flux rate estimation. Interestingly, we found a number of false positive detections due to solar panels that are widely spread in Shanxi. This study also evaluates the accuracy of wind fields in ECMWF ERA5 reanalysis by comparing them with a ground-based meteorological station. We found a large discrepancy, especially in wind direction, suggesting that incorporating local meteorological measurements into the study CH4 point source are important to achieve high accuracy. The study demonstrates that GF-5B/AHSI possesses capabilities for monitoring large CH4 point sources over complex surface characteristics in Shanxi.

Comparative planetology – Comparing cirques on Mars and Earth using a CNN

New approaches for comparative planetology are vital to recognize and assess possible shared formative processes that shape landforms on different planetary bodies, and in the case of this study to identify possible cirques preserved from past martian climates that may have been more favorable to equatorial ice. We used “RetinaNet”, an augmented Convolutional Neural Network (CNN) initially trained with digital elevation data from over 1950 terrestrial cirques, to identify similar forms along a 50x50km area of the equatorial martian Dichotomy Boundary east of Gale Crater. A significant number of false positive detections associated with CTX stereo pair derived DEM artifacts and tile edges as well as craters were automatically removed. High confidence levels assigned by the machine learning algorithm suggest that the 16-remaining cirque-like forms may have formed and evolved under similar conditions to their terrestrial analogs. Morphometric data extracted for each of these cirque-like forms was statistically compared to terrestrial analog morphometric measures. While cirque length and width were significantly larger than their terrestrial counterparts and the area of these forms was ~150 % larger, on average headwall heights were ~25 % shorter. Allometric relationships suggest that the shape of these martian features changes proportionally as they grow in contrast to terrestrial forms where length increases faster than width and height. This could be due to the weaker gravitational force on Mars (3.721 m/s2) and other effects related to martian climate and environmental conditions, such as cold based glaciation. The identification of cirques in an equatorial region has implications for cryosphere distribution/redistribution and martian geologic and climatic history.

Genotyping by sequencing for estimating relative abundances of diatom taxa in mock communities

BackgroundDiatoms are present in all waters and are highly sensitive to pollution gradients. Therefore, they are ideal bioindicators for water quality assessment. Current indices used in these applications are based on identifying diatom species and counting their abundances using traditional light microscopy. Several molecular techniques have been developed to help automate different steps of this process, but obtaining reliable estimates of diatom community composition and species abundance remains challenging.ResultsHere, we evaluated a recently developed quantification method based on Genotyping by Sequencing (GBS) for the first time in diatoms to estimate the relative abundances within a species complex. For this purpose, a reference database comprised of thousands of genomic DNA clusters was generated from cultures of Nitzschia palea. The sequencing reads from calibration and mock samples were mapped against this database for parallel quantification. We sequenced 25 mock diatom communities containing up to five taxa per sample in different abundances. Taxon abundances in these communities were also quantified by a diatom expert using manual counting of cells on light microscopic slides. The relative abundances of strains across mock samples were over- or under-estimated by the manual counting method, and a majority of mock samples had stronger correlations using GBS. Moreover, one previously recognized putative hybrid had the largest number of false positive detections demonstrating the limitation of the manual counting method when morphologically similar and/or phylogenetically close taxa are analyzed.ConclusionsOur results suggest that GBS is a reliable method to estimate the relative abundances of the N. palea taxa analyzed in this study and outperformed traditional light microscopy in terms of accuracy. GBS provides increased taxonomic resolution compared to currently available quantitative molecular approaches, and it is more scalable in the number of species that can be analyzed in a single run. Hence, this is a significant step forward in developing automated, high-throughput molecular methods specifically designed for the quantification of [diatom] communities for freshwater quality assessments.

A two-staged classifier to reduce false positives: On device detection of atrial fibrillation using phase-based distribution of poincaré plots and deep learning

BackgroundMobile Cardiac Outpatient Telemetry (MCOT) can be used to screen high risk patients for atrial fibrillation (AF). These devices rely primarily on algorithmic detection of AF events, which are then stored and transmitted to a clinician for review. It is critical the positive predictive value (PPV) of MCOT detected AF is high, and this often leads to reduced sensitivity, as device manufacturers try to limit false positives. ObjectiveThe purpose of this study was to design a two stage classifier using artificial intelligence (AI) to improve the PPV of MCOT detected atrial fibrillation episodes whilst maintaining high levels of detection sensitivity. MethodsA low complexity, RR-interval based, AF classifier was paired with a deep convolutional neural network (DCNN) to create a two-stage classifier. The DCNN was limited in size to allow it to be embedded on MCOT devices. The DCNN was trained on 491,727 ECGs from a proprietary database and contained 128,612 parameters requiring only 158 KB of storage. The performance of the two-stage classifier was then assessed using publicly available datasets. ResultsThe sensitivity of AF detected by the low complexity classifier was high across all datasets (>93%) however the PPV was poor (<76%). Subsequent analysis by the DCNN increased episode PPV across all datasets substantially (>11%), with only a minor loss in sensitivity (<5%). This increase in PPV was due to a decrease in the number of false positive detections. Further analysis showed that DCNN processing was only required on around half of analysis windows, offering a significant computational saving against using the DCNN as a one-stage classifier. ConclusionDCNNs can be combined with existing MCOT classifiers to increase the PPV of detected AF episodes. This reduces the review burden for physicians and can be achieved with only a modest decrease in sensitivity.

Automated Detection of Sleep Apnea-Hypopnea Events Based on 60 GHz Frequency-Modulated Continuous-Wave Radar Using Convolutional Recurrent Neural Networks: A Preliminary Report of a Prospective Cohort Study.

Radar is a promising non-contact sensor for overnight polysomnography (PSG), the gold standard for diagnosing obstructive sleep apnea (OSA). This preliminary study aimed to demonstrate the feasibility of the automated detection of apnea-hypopnea events for OSA diagnosis based on 60 GHz frequency-modulated continuous-wave radar using convolutional recurrent neural networks. The dataset comprised 44 participants from an ongoing OSA cohort, recruited from July 2021 to April 2022, who underwent overnight PSG with a radar sensor. All PSG recordings, including sleep and wakefulness, were included in the dataset. Model development and evaluation were based on a five-fold cross-validation. The area under the receiver operating characteristic curve for the classification of 1-min segments ranged from 0.796 to 0.859. Depending on OSA severity, the sensitivities for apnea-hypopnea events were 49.0–67.6%, and the number of false-positive detections per participant was 23.4–52.8. The estimated apnea-hypopnea index showed strong correlations (Pearson correlation coefficient = 0.805–0.949) and good to excellent agreement (intraclass correlation coefficient = 0.776–0.929) with the ground truth. There was substantial agreement between the estimated and ground truth OSA severity (kappa statistics = 0.648–0.736). The results demonstrate the potential of radar as a standalone screening tool for OSA.

ARX-based EEG data balancing for error potential BCI

Objective. Deep learning algorithms employed in brain computer interfaces (BCIs) need large electroencephalographic (EEG) datasets to be trained. These datasets are usually unbalanced, particularly when error potential (ErrP) experiment are considered, being ErrP’s epochs much rarer than non-ErrP ones. To face the issue of unbalance of rare epochs, this paper presents a novel, data balancing methods based on ARX–modelling. Approach. AutoRegressive with eXogenous input (ARX)-models are identified on the EEG data of the ‘Monitoring error-related potentials’ dataset of the BNCI Horizon 2020 and then employed to generate new synthetic data of the minority class of ErrP epochs. The balanced dataset is used to train a classifier of non-ErrP vs. ErrP epochs based on EEGNet. Main results. Compared to classical techniques (e.g. class weights, CW) for data balancing, the new method outperforms the others in terms of resulting accuracy (i.e. ARX vs CW ), F1-score (i.e. ARX vs CW ) and balanced accuracy (i.e. ARX vs CW ) and also reduces the number of false positive detection (i.e. ARX 51 vs CW 104). Moreover, the ARX-based method shows a better generalization capability of the whole model to classify and predict new data. Significance. The results obtained suggest that the proposed method can be used in BCI application for tackling the issue of data unbalance and obtain more reliable and robust performances.

Improving the Model for Person Detection in Aerial Image Sequences Using the Displacement Vector: A Search and Rescue Scenario

Recent results in person detection using deep learning methods applied to aerial images gathered by Unmanned Aerial Vehicles (UAVs) have demonstrated the applicability of this approach in scenarios such as Search and Rescue (SAR) operations. In this paper, the continuation of our previous research is presented. The main goal is to further improve detection results, especially in terms of reducing the number of false positive detections and consequently increasing the precision value. We present a new approach that, as input to the multimodel neural network architecture, uses sequences of consecutive images instead of only one static image. Since successive images overlap, the same object of interest needs to be detected in more than one image. The correlation between successive images was calculated, and detected regions in one image were translated to other images based on the displacement vector. The assumption is that an object detected in more than one image has a higher probability of being a true positive detection because it is unlikely that the detection model will find the same false positive detections in multiple images. Based on this information, three different algorithms for rejecting detections and adding detections from one image to other images in the sequence are proposed. All of them achieved precision value about 80% which is increased by almost 20% compared to the current state-of-the-art methods.

Deep Learning for Detection of Pulmonary Metastasis on Chest Radiographs.

Background A computer-aided detection (CAD) system may help surveillance for pulmonary metastasis at chest radiography in situations where there is limited access to CT. Purpose To evaluate whether a deep learning (DL)-based CAD system can improve diagnostic yield for newly visible lung metastasis on chest radiographs in patients with cancer. Materials and Methods A regulatory-approved CAD system for lung nodules was implemented to interpret chest radiographs from patients referred by the medical oncology department in clinical practice. In this retrospective diagnostic cohort study, chest radiographs interpreted with assistance from a CAD system after the implementation (January to April 2019, CAD-assisted interpretation group) and those interpreted before the implementation (September to December 2018, conventional interpretation group) of the CAD system were consecutively included. The diagnostic yield (frequency of true-positive detections) and false-referral rate (frequency of false-positive detections) of formal reports of chest radiographs for newly visible lung metastasis were compared between the two groups using generalized estimating equations. Propensity score matching was performed between the two groups for age, sex, and primary cancer. Results A total of 2916 chest radiographs from 1521 patients (1546 men, 1370 women; mean age, 62 years) and 5681 chest radiographs from 3456 patients (2941 men, 2740 women; mean age, 62 years) were analyzed in the CAD-assisted interpretation and conventional interpretation groups, respectively. The diagnostic yield for newly visible metastasis was higher in the CAD-assisted interpretation group (0.86%, 25 of 2916 [95% CI: 0.58, 1.3] vs 0.32%, 18 of 568 [95% CI: 0.20, 0.50%]; P = .004). The false-referral rate in the CAD-assisted interpretation group (0.34%, 10 of 2916 [95% CI: 0.19, 0.64]) was not inferior to that in the conventional interpretation group (0.25%, 14 of 5681 [95% CI: 0.15, 0.42]) at the noninferiority margin of 0.5% (95% CI of difference: -0.15, 0.35). Conclusion A deep learning-based computer-aided detection system improved the diagnostic yield for newly visible metastasis on chest radiographs in patients with cancer with a similar false-referral rate. © RSNA, 2021 Online supplemental material is available for this article.

Performance of Deep Learning and Genitourinary Radiologists in Detection of Prostate Cancer Using 3-T Multiparametric Magnetic Resonance Imaging.

Several deep learning-based techniques have been developed for prostate cancer (PCa) detection using multiparametric magnetic resonance imaging (mpMRI), but few of them have been rigorously evaluated relative to radiologists' performance or whole-mount histopathology (WMHP). To compare the performance of a previously proposed deep learning algorithm, FocalNet, and expert radiologists in the detection of PCa on mpMRI with WMHP as the reference. Retrospective, single-center study. A total of 553 patients (development cohort: 427 patients; evaluation cohort: 126 patients) who underwent 3-T mpMRI prior to radical prostatectomy from October 2010 to February 2018. 3-T, T2-weighted imaging and diffusion-weighted imaging. FocalNet was trained on the development cohort to predict PCa locations by detection points, with a confidence value for each point, on the evaluation cohort. Four fellowship-trained genitourinary (GU) radiologists independently evaluated the evaluation cohort to detect suspicious PCa foci, annotate detection point locations, and assign a five-point suspicion score (1: least suspicious, 5: most suspicious) for each annotated detection point. The PCa detection performance of FocalNet and radiologists were evaluated by the lesion detection sensitivity vs. the number of false-positive detections at different thresholds on suspicion scores. Clinically significant lesions: Gleason Group (GG)≥ 2 or pathological size≥ 10 mm. Index lesions: the highest GG and the largest pathological size (secondary). Bootstrap hypothesis test for the detection sensitivity between radiologists and FocalNet. For the overall differential detection sensitivity, FocalNet was 5.1% and 4.7% below the radiologists for clinically significant and index lesions, respectively; however, the differences were not statistically significant (P= 0.413 and P=0.282, respectively). FocalNet achieved slightly lower but not statistically significant PCa detection performance compared with GU radiologists. Compared with radiologists, FocalNet demonstrated similar detection performance for a highly sensitive setting (suspicion score≥ 1) or a highly specific setting (suspicion score=5), while lower performance in between. 3 TECHNICAL EFFICACY STAGE: 2.

Machine learning for filtering out false positive grey matter atrophies in single subject voxel based morphometry: A simulation based study

Single subject VBM (SS-VBM), has been used as an alternative tool to standard VBM for single case studies. However, it has the disadvantage of producing an excessively large number of false positive detections. In this study we propose a machine learning technique widely used for automated data classification, namely Support Vector Machine (SVM), to refine the findings produced by SS-VBM. A controlled set of experiments was conducted to evaluate the proposed approach using three-dimensional T1 MRI scans from control subjects collected from the publicly available IXI dataset. The scans were artificially atrophied at different locations and with different sizes to mimic the behavior of neurological disorders. Results empirically demonstrated that the proposed method is able to significantly reduce the amount of false positive clusters (p < 0.05), with no statistical differences in the true positive findings (p > 0.05). This evidence was observed to be consistent for different atrophied areas and sizes of atrophies. This approach could be potentially be applied to alleviate the intensive manual analysis that radiologists and clinicians have to perform to filter out miss-detections of SS-VBM, increasing its usability for image reading.

Performance of an implantable loop recorder in the detection of heart rhythm disturbances in patients with syncope and embolic strokes

Abstract Aims The primary goal of this study was the evaluation of the arrhythmia detection performance of an implantable loop recorder (ILR) in patients with syncope of unknown etiology and patients with embolic stroke of undetermined source (ESUS). Secondary goals were the evaluation of diagnostic yield, time to diagnosis and established therapeutical consequences. Methods The Reveal LINQ ILR was implanted in n=143 patients (n=84 after ESUS, n=59 after syncope). Automatically detected episodes (n=3213) were transmitted via internet and validated by two experienced cardiologists and either classified as correct or incorrect. Positive-predictive value (ppv = true-positive episodes/true-positive episodes + false-positive episodes) was calculated for each available arrhythmia type. Incorrect episodes were classified as undersensing, oversensing, artifacts or supraventricular/ventricular ectopic beats. The diagnostic yield was defined by asystole ≥3 seconds or the detection of atrial fibrillation. Time to diagnosis was calculated as the time from implantation to detection of an asystole or atrial fibrillation. Results Every second asystole episode in syncope-patients was false-positive (ppv 52%), mostly due to undersensing. The atrial fibrillation detection performance in ESUS-patients was 70% (ppv-average). The majority of false-positive episodes was due to premature atrial and ventricular complexes (80%), followed by artifacts (14%) and oversensing (4%). Undersensing accounted for 2%. 36% of syncope-patients were diagnosed with an asystole after a mean time to event of 114 days. In 47% of syncope-patients diagnosed with asystole, a pacemaker was implanted. In 31% of ESUS-patients a diagnosis of atrial fibrillation was established, after a mean time to event of 92 days. 85% of ESUS-patients diagnosed with atrial fibrillation were ultimately treated with an oral anticoagulant. Conclusion and discussion The Reveal Linq ILR - advertised as an automatic diagnostic tool for syncope- and ESUS-patients - has its caveats. A high number of false-positive detections due to undersensing limited the asystole detection performance. The atrial fibrillation detection performance presented more reliable, despite a substantial number of false-positive detections. A time-consuming manual episode review still represents an indispensable step in the diagnostic process, and enough human resources have to be factored before starting an ILR program. Funding Acknowledgement Type of funding source: None

Weakly supervised object detection with 2D and 3D regression neural networks

Finding automatically multiple lesions in large images is a common problem in medical image analysis. Solving this problem can be challenging if, during optimization, the automated method cannot access information about the location of the lesions nor is given single examples of the lesions. We propose a new weakly supervised detection method using neural networks, that computes attention maps revealing the locations of brain lesions. These attention maps are computed using the last feature maps of a segmentation network optimized only with global image-level labels. The proposed method can generate attention maps at full input resolution without need for interpolation during preprocessing, which allows small lesions to appear in attention maps. For comparison, we modify state-of-the-art methods to compute attention maps for weakly supervised object detection, by using a global regression objective instead of the more conventional classification objective. This regression objective optimizes the number of occurrences of the target object in an image, e.g. the number of brain lesions in a scan, or the number of digits in an image. We study the behavior of the proposed method in MNIST-based detection datasets, and evaluate it for the challenging detection of enlarged perivascular spaces - a type of brain lesion - in a dataset of 2202 3D scans with point-wise annotations in the center of all lesions in four brain regions. In MNIST-based datasets, the proposed method outperforms the other methods. In the brain dataset, the weakly supervised detection methods come close to the human intrarater agreement in each region. The proposed method reaches the best area under the curve in two out of four regions, and has the lowest number of false positive detections in all regions, while its average sensitivity over all regions is similar to that of the other best methods. The proposed method can facilitate epidemiological and clinical studies of enlarged perivascular spaces and help advance research in the etiology of enlarged perivascular spaces and in their relationship with cerebrovascular diseases.

Non-Intrusive Detection of Adversarial Deep Learning Attacks via Observer Networks

Deep learning models are known to be vulnerable to specifically crafted adversarial inputs that are quasi-imperceptible to humans. We propose a novel method to detect adversarial inputs, by augmenting the main classification network with multiple binary detectors (observer networks) which take inputs from the hidden layers of the original network (convolutional kernel outputs) and classify the input as clean or adversarial. During inference, the detectors are treated as a part of an ensemble network and the input is deemed adversarial if at least half of the detectors classify it as so. The proposed method addresses the trade-off between accuracy of classification on clean and adversarial samples, as the original classification network is not modified during the detection process. The use of multiple observer networks makes attacking the detection mechanism non-trivial even when the attacker is aware of the victim classifier. We achieve a 99.5 percent detection accuracy on the MNIST dataset and 97.5 percent on the CIFAR-10 dataset using the Fast Gradient Sign Attack in a semi-white box setup. The number of false positive detections is a mere 0.12 percent in the worst case scenario.

Tooth detection and classification on panoramic radiographs for automatic dental chart filing: improved classification by multi-sized input data.

Dental state plays an important role in forensic radiology in case of large scale disasters. However, dental information stored in dental clinics are not standardized or electronically filed in general. The purpose of this study is to develop a computerized system to detect and classify teeth in dental panoramic radiographs for automatic structured filing of the dental charts. It can also be used as a preprocessing step for computerized image analysis of dental diseases. One hundred dental panoramic radiographs were employed for training and testing an object detection network using fourfold cross-validation method. The detected bounding boxes were then classified into four tooth types, including incisors, canines, premolars, and molars, and three tooth conditions, including nonmetal restored, partially restored, and completely restored, using classification network. Based on the visualization result, multisized image data were used for the double input layers of a convolutional neural network. The result was evaluated by the detection sensitivity, the number of false-positive detection, and classification accuracies. The tooth detection sensitivity was 96.4% with 0.5 false positives per case. The classification accuracies for tooth types and tooth conditions were 93.2% and 98.0%. Using the double input layer network, 6 point increase in classification accuracy was achieved for the tooth types. The proposed method can be useful in automatic filing of dental charts for forensic identification and preprocessing of dental disease prescreening purposes.

Analyzing multi-variate water quality signals for water quality monitoring station placement in water distribution systems

Abstract Placing fixed water quality monitoring stations in a water distribution system can greatly improve the security of the system via prompt detection of poor water quality. In the event that a harmful substance is injected into a water distribution system, large populations can be put at risk of exposure to the contaminant. Promptly detecting the presence of a contaminant will reduce the number of people put at risk of exposure. However, to protect against a wide variety of possible contaminants, a water quality monitoring station will need to identify contamination via recognition of anomalous changes in a suite of surrogate water quality indicators (chlorine, pH, etc.). This work attempts to place water quality monitoring stations within the water distribution at locations that best detect contamination events via surrogate water quality signals. Networks of water quality monitoring stations are designed to minimize the population affected prior to contamination event detection, and simultaneously minimize the expected number of false positive detections, under uncertain water quality conditions. Solutions generated in this study are compared to solutions designed via classical detection methods. Results show the sensor networks designed without consideration to detection via surrogate water quality parameters have higher false positive detection rates.

Detection of Spherical Gold Fiducials in kV X-Ray Images Using Intensity-Estimation-Based Method

Fiducial marker detection algorithms in kilovoltage x-ray images using physical characteristics of transmission x-ray have been proposed. It, however, has been suggested recently that factors besides transmission x-ray affect x-ray images. The purpose of this study was to develop a new fiducial detection algorithm using fiducial intensity estimation based on physical characteristics of x-ray images with gold fiducials. First, x-ray images of a fiducial on a water-equivalent phantom were acquired. It was observed that the ratio of background to fiducial intensity in the images decreased as phantom thickness increased. Based on the negative correlation, we identified a function for estimating fiducial intensity that consists of background intensity and the amount of scattered radiation by the other x-ray source of an orthogonal imaging system and a treatment beam. Then, we developed an algorithm that extracts fiducial candidates using the estimation function. Its performance was measured using x-ray images which had 3824 fiducials altogether. The average number of false-positive detection of the proposed algorithm in single image was one-tenth of an algorithm considering only transmission x-ray. The proposed algorithm detected 99.5% of all fiducials under an error of 1.0 mm, while the other algorithm detected 94.7% or less (Clinical trial number: UMIN000005324).

Non-target evaluation of contaminants in honey bees and pollen samples by gas chromatography time-of-flight mass spectrometry

This work presents a non-targeted screening approach for the detection and quantitation of contaminants in bees and pollen, collected from the same hive, by GC-EI-ToF-MS. It consists of a spectral library datasets search using a compound database followed by a manual investigation and analytical standard confirmation together with semi-quantitation purposes. Over 20% of the compounds found automatically by the library search could not be confirmed manually. This number of false positive detections was mainly a consequence of an inadequate ion ratio criterion (±30%), not considered in the automatic searching procedure. Eight compounds were detected in bees and pollen. They include insecticides/acaricides (chlorpyrifos, coumaphos, fluvalinate-tau, chlorfenvinphos, pyridaben, and propyl cresol) at a concentration range of 1–1207 μg kg−1, herbicides (oxyfluorfen) at a concentration range of 212–1773 μg kg−1 and a growth regulator hormone (methoprene). Some compounds were detected only in pollen; such as herbicides (clomazone), insecticides/acaricides and fungicides used to control Varroa mites as benzylbenzoate, bufencarb, allethrin, permethrin, eugenol and cyprodinil. Additional compounds were detected only in bees: flamprop-methyl, 2-methylphenol (2–49 μg kg−1) and naphthalene (1–23 μg kg−1). The proposed method presents important advantages as it can avoid the use of an unachievable number of analytical standards considered target compounds “a priori” but not present in the analyzed samples.

Taller Peaks: an improved spike detection algorithm that simultaneously reduces type I and type II errors for Wave{\_}clus

Wave_clus is an unsupervised spike detection and sorting algorithm that has been used in dozens of experimental studies as a spike sorting tool. It is often used as a benchmark for comparing the performance of new spike sorting algorithms. For these reasons, the spike detection performance of Wave_clus is important for both experimental and computational studies that involve spike sorting. Two measures of spike detection performance are the number of false positive detections (type I error) and the number of missed spikes (type II error). Here, a new spike detection algorithm is proposed that reduces the number of misses and false positives of Wave_clus in a widely used simulated data set across the entire range of commonly used detection thresholds. The algorithm accepts a spike if its amplitude is larger than the amplitude of its two immediate neighbors, where an immediate neighbor is the nearest peak of the same polarity within $\pm $1 refractory period. The simultaneous reduction that is achieved in the number of false positives and misses is important for experimental and computational studies that use Wave_clus as a spike sorting tool or as a benchmark. A software patch that incorporates the algorithm into Wave_clus as an optional spike detection algorithm is provided.

Detection of Intracranial Signatures of Interictal Epileptiform Discharges from Concurrent Scalp EEG.

Interictal epileptiform discharges (IEDs) are transient neural electrical activities that occur in the brain of patients with epilepsy. A problem with the inspection of IEDs from the scalp electroencephalogram (sEEG) is that for a subset of epileptic patients, there are no visually discernible IEDs on the scalp, rendering the above procedures ineffective, both for detection purposes and algorithm evaluation. On the other hand, intracranially placed electrodes yield a much higher incidence of visible IEDs as compared to concurrent scalp electrodes. In this work, we utilize concurrent scalp and intracranial EEG (iEEG) from a group of temporal lobe epilepsy (TLE) patients with low number of scalp-visible IEDs. The aim is to determine whether by considering the timing information of the IEDs from iEEG, the resulting concurrent sEEG contains enough information for the IEDs to be reliably distinguished from non-IED segments. We develop an automatic detection algorithm which is tested in a leave-subject-out fashion, where each test subject's detection algorithm is based on the other patients' data. The algorithm obtained a [Formula: see text] accuracy in recognizing scalp IED from non-IED segments with [Formula: see text] accuracy when trained and tested on the same subject. Also, it was able to identify nonscalp-visible IED events for most patients with a low number of false positive detections. Our results represent a proof of concept that IED information for TLE patients is contained in scalp EEG even if they are not visually identifiable and also that between subject differences in the IED topology and shape are small enough such that a generic algorithm can be used.

Identification of Rotors during Human Atrial Fibrillation Using Contact Mapping and Phase Singularity Detection: Technical Considerations.

To explore technical challenges of phase singularity (PS) mapping during atrial fibrillation (AF) using direct contact electrograms. AF mapping was performed in high-density epicardial recordings of human paroxysmal (PAF) or persistent (PersAF) (N = 20 pts) AF with an array of 16×16 electrodes placed on atrial epicardium. PS points were detected using subsets of electrodes forming rings of varying sizes. PS detection using a 2×2 electrode ring identified 0.88±1.00 PS/s in PAF group and 3.91 ± 2.51 per s in PersAF group (p < 0.001) in 2.4 × 2.4 cm mapping area. All detected PS had a short lifespan with the longest being 1100 ms (6.8 rotations). Exploration of the PS detection in a numerical model demonstrated that at least eight electrodes are required to avoid frequent false positive PS detection due to chance. Application of a detection grid consisting a double ring of electrodes (2 × 2 and 4 × 4 rings) decreased the number of false positive detections. The double ring was more resilient to electrode swapping (with just three instances of false positives versus 4380 false positives using 2 × 2 ring). The number of detected rotors critically depends upon the parameters of the detection algorithm, especially the number of electrodes used to detect PS. Based on our results, we recommend double ring comprised of 2 × 2 and 4 × 4 grid of electrodes for robust rotor detection. Great methodological care has to be taken before equating detected PS with rotating waves and using PS detection algorithms to guide catheter ablation of AF.