Filtering Artifacts Research Articles

Wearable ECG Device and Machine Learning for Heart Monitoring.

With cardiovascular diseases (CVD) remaining a leading cause of mortality, wearable devices for monitoring cardiac activity have gained significant, renewed interest among the medical community. This paper introduces an innovative ECG monitoring system based on a single-lead ECG machine, enhanced using machine learning methods. The system only processes and analyzes ECG data, but it can also be used to predict potential heart disease at an early stage. The wearable device was built on the ADS1298 and a microcontroller STM32L151xD. A server module based on the architecture style of the REST API was designed to facilitate interaction with the web-based segment of the system. The module is responsible for receiving data in real time from the microcontroller and delivering this data to the web-based segment of the module. Algorithms for analyzing ECG signals have been developed, including band filter artifact removal, K-means clustering for signal segmentation, and PQRST analysis. Machine learning methods, such as isolation forests, have been employed for ECG anomaly detection. Moreover, a comparative analysis with various machine learning methods, including logistic regression, random forest, SVM, XGBoost, decision forest, and CNNs, was conducted to predict the incidence of cardiovascular diseases. Convoluted neural networks (CNN) showed an accuracy of 0.926, proving their high effectiveness for ECG data processing.

An unsupervised H&E-based machine-learning approach for precise prediction of tumor microenvironment subtypes.

e13593 Background: Precision oncology increasingly relies on accurate classification of the tumor microenvironment (TME), which influences therapeutic responses and prognostication in cancer. While histological observation via H&E staining remains the gold standard due to the detailed spatial context and morphological information provided, its lack of reproducibility limits its use in clinics. Next-generation sequencing (NGS) reliably provides valuable insights into the molecular landscape of tumors. Here we debut an unsupervised machine-learning approach that leverages both the rich detail in H&E slides and the reproducibility of NGS to enhance TME classification. Methods: An ML-based image analysis algorithm was developed using a convolutional neural network (CNN) deep learning model. Using over 600 images from the TCGA COAD and READ datasets and from 139 colorectal cancer (CRC) samples in an internal cohort, we trained the CNN model to curate features from small patches of whole slide images (WSIs). Then, clusters were created from these H&E WSIs without prior labeling. The clusters were validated by experienced pathologists to confirm cluster homogeneity and filter artifacts. We trained a classifier to categorize molecular features from the WSIs into TME subtypes. Performance was measured using the area under the receiver operating characteristic curve (AUC-ROC), based on the model's ability to correctly classify the Fibrotic (F) and Immune Desert (D) TME subtypes (1), as well as MSI status identified by whole exome sequencing. The F subtype corresponds with high stromal content, low immune infiltration, and the worst prognosis. The D subtype corresponds with a cold immune TME and an intermediate prognosis. Progression-free survival (PFS) data from TCGA were used to examine prognostic significance. Results: We obtained 0.86, 0.79, 0.72, and 0.71 AUC scores for predicting the ID and F TMEs, MSI status, and KRAS mutation status, respectively. PFS data analysis showed our H&E-based approach to yield similar or better prognostic significance compared to NGS-based TME classification (ID vs others, p=0.05 (H&E) and 0.2 (NGS); F vs others, p=0.07 (H&E) and 0.09 (NGS)). Our findings showed a notable correlation with RNA- and DNA-based predictions of ID and F TMEs and MSI, with enhanced precision and more robust subtype identification while achieving high embedding consistency. Conclusions: By harnessing the inherently rich detail in histological images, our H&E-based approach complements NGS-based TME analysis. Our findings advocate for the integration of advanced image analysis into the standard TME characterization workflow to refine prognostic and therapeutic strategies for CRC. We will expand this algorithm for use with other cancer types to realize its potential as a promising adjunct to NGS for precise TME classification. 1. Bagaev et al., 2021.

Reactive organic carbon air emissions from mobile sources in the United States.

Mobile sources are responsible for a substantial controllable portion of the reactive organic carbon (ROC) emitted to the atmosphere, especially in urban environments of the United States. We update existing methods for calculating mobile source organic particle and vapor emissions in the United States with over a decade of laboratory data that parameterize the volatility and organic aerosol (OA) potential of emissions from on-road vehicles, nonroad engines, aircraft, marine vessels, and locomotives. We find that existing emission factor information from Teflon filters combined with quartz filters collapses into simple relationships and can be used to reconstruct the complete volatility distribution of ROC emissions. This new approach consists of source-specific filter artifact corrections and state-of-the-science speciation including explicit intermediate-volatility organic compounds (IVOCs), yielding the first bottom-up volatility-resolved inventory of US mobile source emissions. Using the Community Multiscale Air Quality model, we estimate mobile sources account for 20 %-25 % of the IVOC concentrations and 4.4 %-21.4 % of ambient OA. The updated emissions and air quality model reduce biases in predicting fine-particle organic carbon in winter, spring, and autumn throughout the United States (4.3 %-11.3 % reduction in normalized bias). We identify key uncertain parameters that align with current state-of-the-art research measurement challenges.

Convoluted filtering for process cycle modeling

AbstractPrinciples of materials science and engineering, physics, mathematics, and information science are used to extract knowledge and insights from the process‐structure–property‐performance relationships hidden in materials data. The process‐structure modeling can be accelerated without loss of interpretability, with artificial intelligence tools that mimic the salient features of the process and process‐structure relations. In this work, a novel convoluted model‐filtering technique was exploited to build and successfully train the Convoluted Filter (CoFi) artifacts for Fe‐based alloy heat treatment cycles. The artifacts were pre‐trained to filter out deep models that change the surrogate microstructure state after the heat treatment at ambient conditions. Direct representation of the thermal cycle features within knowledge Graph facilitated development of meaningful data models for microstructure evolution, which reduce overfitting to limited datasets.

A logistic binary Jaya optimization-based channel selection scheme for motor-imagery classification in brain-computer interface

BCI systems use motor imagery to allow users to control external devices through their brain activity. They extract neural signals from the brain using a large number of EEG channels. However, high-dimensional data from multichannel BCI systems increases the computational burden, leading to slower processing and higher costs. In this study, we proposed a Logistic S-shaped Binary Jaya Optimization Algorithm (LS-BJOA), which combines a logistic map with the Jaya optimization algorithm to alleviate the computational burden caused by many channels. The logistic map introduces stochasticity to better simulate the chaotic behavior of brain signals and improve predictive accuracy. Our method initializes a set of three electrodes as a candidate solution and subsequently determines the most relevant channels iteratively. We used a bi-objective fitness function to evaluate the significance of the selected channels, which involves maximizing classification accuracy and minimizing the length of the channel subset. Initially, a fifth-order bandpass filter with Independent Component Analysis (ICA) was applied to filter MI signals and artifacts reduction, respectively. The Regularized Common Spatial Pattern (RCSP) was used to extract spatiotemporal features from the selected channels. Finally, the three classifiers: (1) Support Vector Machine (SVM), (2) Naïve Bayes (NB), and (3) Linear Discriminant Analysis (LDA) were used to determine maximum classification accuracy. The experiment is validated on three public EEG datasets (BCI Competition IV- 2008 – IIA, BCI Competition IV- dataset 1, BCI competition III – dataset IVa). Our method achieved superior classification accuracy (83.59%, 82.09%, and 89.02% on datasets 1, 2, and 3 respectively) with fewer channels than baseline methods. Additionally, computation time was significantly reduced without compromising accuracy. Topographical mapping revealed frontal lobe involvement in MI tasks during physical activities. Topographical mapping of the selected channels showed that the frontal lobe executes various MI tasks during physical activities.

The ‘ramp filter’ artifact in filtered back projection is due to attenuation not ramp filtering

Artifactually decreased counts in regions of the left ventricle adjacent to intense splanchnic activity have been described in single photon emission computed tomography imaging and attributed to the side lobes of ramp filtering, a crucial step in filtered back projection. However, filtered back projection is based on a mathematical proof that makes no assumptions about the distribution of activity implying there is another cause for this artifact. Computer simulations of a left ventricle with varying intensity, proximity and size of adjacent splanchnic activity was created with and without attenuation. Both filtered back projection and an iterative expectation maximization algorithm were performed. Volume weighted bullseyes were generated then the ratio of average activity in regions in the anterior and inferior walls of the bullseye were calculated. In the absence of attenuation, there was no effect on uptake despite intense adjacent splanchnic activity. The effect of attenuation alone was mild but was greatly amplified by adjacent splanchnic uptake. This effect varied with the intensity, proximity and size of adjacent activity. Moving the attenuation to the apex of the left ventricle produced a paradoxical increase in counts to the inferior wall. The effect on iterative reconstruction was less pronounced than with filtered back projection and the paradoxical increase with attenuation at the apex did not occur. The ramp filter artifact in single photon emission computed tomography reconstruction is due to attenuation, not ramp filtering per se. Adjacent splanchnic activity can result in either an increase or decrease in counts. These effects are less pronounced and more consistent with iterative reconstruction.

Raman spectroscopy is a powerful tool in molecular paleobiology: An analytical response to Alleon etal. (https://doi.org/10.1002/bies.202000295).

A recent article argued that signals from conventional Raman spectroscopy of organic materials are overwhelmed by edge filter and fluorescence artefacts. The article targeted a subset of Raman spectroscopic investigations of fossil and modern organisms and has implications for the utility of conventional Raman spectroscopy in comparative tissue analytics. The inferences were based on circular reasoning centered around the unconventional analysis of spectra from just two samples, one modern, and one fossil. We validated the disputed signals with in situ Fourier-Transform Infrared (FT-IR) Spectroscopy and through replication with different lasers, filters, and operators in independent laboratories. Our Raman system employs a holographic notch filter which is not affected by edge filter or other artefacts. Multiple lines of evidence confirm that conventional Raman spectra of fossils contain biologically and geologically meaningful information. Statistical analyses of large Raman and FT-IR spectral data sets reveal patterns in fossil composition and yield valuable insights into the history of life.

Sleep Apnea Classification Algorithm Development Using a Machine-Learning Framework and Bag-of-Features Derived from Electrocardiogram Spectrograms.

Background: Heart rate variability (HRV) and electrocardiogram (ECG)-derived respiration (EDR) have been used to detect sleep apnea (SA) for decades. The present study proposes an SA-detection algorithm using a machine-learning framework and bag-of-features (BoF) derived from an ECG spectrogram. Methods: This study was verified using overnight ECG recordings from 83 subjects with an average apnea–hypopnea index (AHI) 29.63 (/h) derived from the Physionet Apnea-ECG and National Cheng Kung University Hospital Sleep Center database. The study used signal preprocessing to filter noise and artifacts, ECG time–frequency transformation using continuous wavelet transform (CWT), BoF feature generation, machine-learning classification using support vector machine (SVM), ensemble learning (EL), k-nearest neighbor (KNN) classification, and cross-validation. The time length of the spectrogram was set as 10 and 60 s to examine the required minimum spectrogram window time length to achieve satisfactory accuracy. Specific frequency bands of 0.1–50, 8–50, 0.8–10, and 0–0.8 Hz were also extracted to generate the BoF to determine the band frequency best suited for SA detection. Results: The five-fold cross-validation accuracy using the BoF derived from the ECG spectrogram with 10 and 60 s time windows were 90.5% and 91.4% for the 0.1–50 Hz and 8–50 Hz frequency bands, respectively. Conclusion: An SA-detection algorithm utilizing BoF and a machine-learning framework was successfully developed in this study with satisfactory classification accuracy and high temporal resolution.

Ramp filter artifact associated with filtered backprojection, resolved with iterative reconstruction

Ramp filter artifact associated with filtered backprojection, resolved with iterative reconstruction

A nomenclature for sequence-based forensic DNA analysis

Forensic DNA analysis of casework samples using massively parallel sequencing (MPS) technology requires a system of nomenclature for uniquely labeling sequence-based alleles and artifacts. The DNA Commission of the ISFG has published considerations concerning a nomenclature format that addresses the requirement for unique labeling of sequences. Nomenclatures based on this format can be used in databasing, or communicating sequence types, but the format is lengthy for software interfaces. The sequence identifier (SID) nomenclature addresses this gap by generating short labels able to uniquely identify all sequences (allelic and artifactual) in single-source or casework profiles. Sequences in casework profiles can be uniquely labeled with only two or three SID characters, making the format compact. SID labels can be used in algorithms for identifying and filtering artifacts, and for expressing associations between artifacts and their likely parent alleles. The nomenclature is suitable for use in downstream mixture analysis by any software able to accept character values rather than numeral values. The SID nomenclature is described, and its ability to discriminate sequence-based alleles and artifacts is demonstrated, and its applicability to forensic mixture analysis is demonstrated.

An Unsupervised Method for Artefact Removal in EEG Signals.

Objective: The activity of the brain can be recorded by means of an electroencephalogram (EEG). An EEG is a multichannel signal related to brain activity. However, EEG presents a wide variety of undesired artefacts. Removal of these artefacts is often done using blind source separation methods (BSS) and mainly those based on Independent Component Analysis (ICA). ICA-based methods are well-accepted in the literature for filtering artefacts and have proved to be satisfactory in most scenarios of interest. Our goal is to develop a generic and unsupervised ICA-based algorithm for EEG artefacts removal. Approach: The proposed algorithm makes use of a new unsupervised artefact detection, ICA and a statistical criterion to automatically select the artefact related independent components (ICs) requiring no human intervention. The algorithm is evaluated using both simulated and real EEG data with artefacts (SEEG and AEEG). A comparison between the proposed unsupervised selection of ICs related to the artefact and other supervised selection is also presented. Main results: A new unsupervised ICA-based algorithm to filter artefacts, where ICs related to each artefact are automatically selected. It can be used in online applications, it preserves most of the original information among the artefacts and removes different types of artefacts. Significance: ICA-based methods for filtering artefacts prevail in the literature. The work in this article is important insofar as it addresses the problem of automatic selection of ICs in ICA-based methods. The selection is unsupervised, avoiding the manual ICs selection or a learning process involved in other methods. Our method is a generic algorithm that allows removing EEG artefacts of various types and, unlike some ICA-based algorithms, it retains most of the original information among the artefacts. Within the algorithm, the artefact detection method implemented does not require human intervention either.

Comparing methods for analysing time scale dependent correlations in irregularly sampled time series data

Time series derived from paleoclimate archives are often irregularly sampled in time and thus not analysable using standard statistical methods such as correlation analyses. Although measures for the similarity between time series have been proposed for irregular time series, they do not account for the time scale dependency of the relationship. Stochastically distributed temporal sampling irregularities act qualitatively as a low-pass filter reducing the influence of fast variations from frequencies higher than about 0.5 (Δtmax)−1, where Δtmax is the maximum time interval between observations. This may lead to overestimated correlations if the true correlation increases with time scale. Typically, correlations are underestimated due to a non-simultaneous sampling of time series.Here, we investigated different techniques to estimate time scale dependent correlations of weakly irregularly sampled time series, with a particular focus on different resampling methods and filters of varying complexity. The methods were tested on ensembles of synthetic time series that mimic the characteristics of Holocene marine sediment temperature proxy records. We found that a linear interpolation of the irregular time series onto a regular grid, followed by a simple Gaussian filter was the best approach to deal with the irregularity and account for the time scale dependence. This approach had both, minimal filter artefacts, particularly on short time scales, and a minimal loss of information due to filter length.

A convolutional neural network to filter artifacts in spectroscopic MRI.

Proton MRSI is a noninvasive modality capable of generating volumetric maps of in vivo tissue metabolism without the need for ionizing radiation or injected contrast agent. Magnetic resonance spectroscopic imaging has been shown to be a viable imaging modality for studying several neuropathologies. However, a key hurdle in the routine clinical adoption of MRSI is the presence of spectral artifacts that can arise from a number of sources, possibly leading to false information. A deep learning model was developed that was capable of identifying and filtering out poor quality spectra. The core of the model used a tiled convolutional neural network that analyzed frequency-domain spectra to detect artifacts. When compared with a panel of MRS experts, our convolutional neural network achieved high sensitivity and specificity with an area under the curve of 0.95. A visualization scheme was implemented to better understand how the convolutional neural network made its judgement on single-voxel or multivoxel MRSI, and the convolutional neural network was embedded into a pipeline capable of producing whole-brain spectroscopic MRI volumes in real time. The fully automated method for assessment of spectral quality provides a valuable tool to support clinical MRSI or spectroscopic MRI studies for use in fields such as adaptive radiation therapy planning.

A novel image processing workflow for the in vivo quantification of skin microvasculature using dynamic optical coherence tomography.

Currently, imaging technologies that can accurately assess or provide surrogate markers of the human cutaneous microvessel network are limited. Dynamic optical coherence tomography (D-OCT) allows the detection of blood flow in vivo and visualization of the skin microvasculature. However, image processing is necessary to correct images, filter artifacts, and exclude irrelevant signals. The objective of this study was to develop a novel image processing workflow to enhance the technical capabilities of D-OCT. Single-center, vehicle-controlled study including healthy volunteers aged 18-50years. A capsaicin solution was applied topically on the subject's forearm to induce local inflammation. Measurements of capsaicin-induced increase in dermal blood flow, within the region of interest, were performed by laser Doppler imaging (LDI) (reference method) and D-OCT. Sixteen subjects were enrolled. A good correlation was shown between D-OCT and LDI, using the image processing workflow. Therefore, D-OCT offers an easy-to-use alternative to LDI, with good repeatability, new robust morphological features (dermal-epidermal junction localization), and quantification of the distribution of vessel size and changes in this distribution induced by capsaicin. The visualization of the vessel network was improved through bloc filtering and artifact removal. Moreover, the assessment of vessel size distribution allows a fine analysis of the vascular patterns. The newly developed image processing workflow enhances the technical capabilities of D-OCT for the accurate detection and characterization of microcirculation in the skin. A direct clinical application of this image processing workflow is the quantification of the effect of topical treatment on skin vascularization.

Unobtrusive Nocturnal Heartbeat Monitoring by a Ballistocardiographic Sensor in Patients with Sleep Disordered Breathing

Sleep disordered breathing (SDB) is known for fluctuating heart rates and an increased risk of developing arrhythmias. The current reference for heartbeat analysis is an electrocardiogram (ECG). As an unobtrusive alternative, we tested a sensor foil for mechanical vibrations to perform a ballistocardiography (BCG) and applied a novel algorithm for beat-to-beat cycle length detection. The aim of this study was to assess the correlation between beat-to-beat cycle length detection by the BCG algorithm and simultaneously recorded ECG. In 21 patients suspected for SDB undergoing polysomnography, we compared ECG to simultaneously recorded BCG data analysed by our algorithm. We analysed 362.040 heartbeats during a total of 93 hours of recording. The baseline beat-to-beat cycle length correlation between BCG and ECG was rs = 0.77 (n = 362040) with a mean absolute difference of 15 ± 162 ms (mean cycle length: ECG 923 ± 220 ms; BCG 908 ± 203 ms). After filtering artefacts and improving signal quality by our algorithm, the correlation increased to rs = 0.95 (n = 235367) with a mean absolute difference in cycle length of 4 ± 72 ms (ECG 920 ± 196 ms; BCG 916 ± 194 ms). We conclude that our algorithm, coupled with a BCG sensor foil provides good correlation of beat-to-beat cycle length detection with simultaneously recorded ECG.

EEG artifacts reduction by multivariate empirical mode decomposition and multiscale entropy for monitoring depth of anaesthesia during surgery.

Electroencephalography (EEG) has been widely utilized to measure the depth of anaesthesia (DOA) during operation. However, the EEG signals are usually contaminated by artifacts which have a consequence on the measured DOA accuracy. In this study, an effective and useful filtering algorithm based on multivariate empirical mode decomposition and multiscale entropy (MSE) is proposed to measure DOA. Mean entropy of MSE is used as an index to find artifacts-free intrinsic mode functions. The effect of different levels of artifacts on the performances of the proposed filtering is analysed using simulated data. Furthermore, 21 patients' EEG signals are collected and analysed using sample entropy to calculate the complexity for monitoring DOA. The correlation coefficients of entropy and bispectral index (BIS) results show 0.14±0.30 and 0.63±0.09 before and after filtering, respectively. Artificial neural network (ANN) model is used for range mapping in order to correlate the measurements with BIS. The ANN method results show strong correlation coefficient (0.75±0.08). The results in this paper verify that entropy values and BIS have a strong correlation for the purpose of DOA monitoring and the proposed filtering method can effectively filter artifacts from EEG signals. The proposed method performs better than the commonly used wavelet denoising method. This study provides a fully adaptive and automated filter for EEG to measure DOA more accuracy and thus reduce risk related to maintenance of anaesthetic agents.

SU‐F‐R‐43: Recursive K‐Means Filter for Preserving Signals of Interest

Purpose:To use recursive cluster analysis to filter noises and artifacts out of useful signals. The method was applied to CT or CBCT for preserving interested low Hounsfield Unit (HU) signals such as lung tissues while the HU of lung tissues is often overlapped with CT (or CBCT) artifacts.Methods:Cluster analysis is to search for the similarity among data points, which could employ multi‐dimensional techniques. For our purpose, we searched for a robust, fast, and automatic process for sorting interested low HU object in CT, Therefore, 1‐D cluster analysis is suitable for the purpose. However, the 1‐D cluster analysis might not be able to sensitively separate the groups with overlap in values, for example, lung tissue and low‐HU CT artifacts. We found the recursive k‐mean (initialized by k‐mean++ over Euclidean distance) with division of small size groups can efficiently strip the low noises out and reveal the interested group by a repetitive fashion.Results:The algorithm of repetitively regrouping and filtering with a size of small division can effectively remove the low‐HU values of artifacts. Because artifacts are usually scattered separate groups, the lung tissues buried in the low‐HU groups can be revealed with an appropriate recursive steps.Conclusion:The filtering technique is efficient, robust and can be applied to many different applications as long as the distribution of interested signals can be considered as a cluster in value.

Constructing texture maps using enhanced Beltrami method

Image quality enhancement is a crucial requirement in many applications of digital image and video processing. Removing artifacts which are suffered from image compression will lose simultaneously image texture components. This paper combines Beltrami method and the window derivative to construct the texture map in an attempt to preserve image details during filtering artifacts. Texture map enhancement is also proposed. Simulation results show that the texture map is robust to noise and matches to real texture components of image.

Optimal digital filters for analyzing the mid-latency auditory P50 event-related potential in patients with Alzheimer’s disease

BackgroundFiltering is an effective pre-processing technique for improving the signal-to-noise ratio of ERP waveforms. Filters can, however, introduce substantial distortions into the time-domain representations of ERP waveforms. Inappropriate filter parameters may lead to the presence of statistically significant but artificial effects, whereas true effects may appear as insignificant. New methodThe present study aimed to determine the optimal digital filters for analyzing the auditory P50 component in patients with Alzheimer’s disease. To provide evidence of the optimal filter settings, different high-pass and low-pass filters were applied to ERP waveforms obtained from a conditioning-testing paradigm. The results facilitate practical recommendations for selection of filters that maximize the signal-to-noise ratio of the P50 components without introducing significant distortions. ResultsThe present study confirms that filter parameters have a significant effect on the amplitude and gating measures of the P50 component. Setting the high-pass cut-off at 0.1Hz and the low-pass cut-off at 90Hz (or above) is recommended for P50 component analyses. Comparison with existing methodsThe majority of ERP studies on sensory gating report using high-pass filters with 10-Hz cut-offs to measure P50 suppression. Such a high cut-off appeared to induce significant distortions into the ERP waveforms; thus, the authors advise against using these excessive high-pass cut-offs. ConclusionsFiltering broadband signals, such as ERP signals, necessary results in time-domain distortions. However, by adjusting the filter parameters carefully according to the components of interest, it is possible to minimize filter artifacts and obtain more easily interpretable ERP waveforms.

Characterization and source apportionment of organic aerosol using offline aerosol mass spectrometry

Abstract. Field deployments of the Aerodyne Aerosol Mass Spectrometer (AMS) have significantly advanced real-time measurements and source apportionment of non-refractory particulate matter. However, the cost and complex maintenance requirements of the AMS make its deployment at sufficient sites to determine regional characteristics impractical. Furthermore, the negligible transmission efficiency of the AMS inlet for supermicron particles significantly limits the characterization of their chemical nature and contributing sources. In this study, we utilize the AMS to characterize the water-soluble organic fingerprint of ambient particles collected onto conventional quartz filters, which are routinely sampled at many air quality sites. The method was applied to 256 particulate matter (PM) filter samples (PM1, PM2.5, and PM10, i.e., PM with aerodynamic diameters smaller than 1, 2.5, and 10 µm, respectively), collected at 16 urban and rural sites during summer and winter. We show that the results obtained by the present technique compare well with those from co-located online measurements, e.g., AMS or Aerosol Chemical Speciation Monitor (ACSM). The bulk recoveries of organic aerosol (60–91 %) achieved using this technique, together with low detection limits (0.8 µg of organic aerosol on the analyzed filter fraction) allow its application to environmental samples. We will discuss the recovery variability of individual hydrocarbon ions, ions containing oxygen, and other ions. The performance of such data in source apportionment is assessed in comparison to ACSM data. Recoveries of organic components related to different sources as traffic, wood burning, and secondary organic aerosol are presented. This technique, while subjected to the limitations inherent to filter-based measurements (e.g., filter artifacts and limited time resolution) may be used to enhance the AMS capabilities in measuring size-fractionated, spatially resolved long-term data sets.