Baseline Filtering Research Articles

A compact and high-performance setup of capillary electrophoresis with capacitively coupled contactless conductivity detection (CE-C4D).

Capillary electrophoresis with capacitively coupled contactless conductivity detection (CE-C4D) has the advantages of high throughput (simultaneous detection of multiple ions), high separation efficiency (higher than 105 theoretical plates) and rapid analysis capability (less than 5 min for common inorganic ions). A compact CE-C4D system is ideal for water quality control and on-site analysis. It is suitable not only for common cations (e.g. Na+, K+, Li+, NH4+, Ca2+, etc.) and anions (e.g. Cl-, SO42-, BrO3-, etc.) but also for some ions (e.g. lanthanide ions, Pb2+, Cd2+, etc.) that require complex derivatization procedures to be detected by ion chromatography (IC). However, an obvious limitation of the CE-C4D method is that its sensitivity (e.g. 0.3-1 μM for common inorganic ions) is often insufficient for trace analysis (e.g. 1 ppb or 20 nM level for common inorganic ions) without preconcentration. For this technology to become a powerful and routine analytical technique, the system should be made compact while maintaining trace analysis sensitivity. In this study, we developed an all-in-one version of the CE-C4D instrument with custom-made modular components to make it a convenient, compact and high-performance system. The system was designed using direct digital synthesis (DDS) technology to generate programmable sinusoidal waveforms with any frequency for excitation, a kilovolt high-voltage power supply for capillary electrophoresis separation, and an "effective" differential C4D cell with a low-noise circuitry for high-sensitivity detection. We characterized the system with different concentrations of Cs+, and even a low concentration of 20 nM was detectable without preconcentration. Moreover, the optimized CE-C4D setup was applied to analyse mixed ions at a trace concentration of 200 nM with excellent signal-to-noise ratios. In typical applications, the limits of detection based on the 3σ criterion (without baseline filtering) were 9, 10, 24, 5, and 12 nM for K+, Cs+, Li+, Ca2+, and Mg2+, respectively, and about 7, 6, 6 and 6 nM for Br-, ClO4-, BrO3- and SO42-, respectively. Finally, the setup was also applied for the analysis of all 14 lanthanide ions and rare-earth minerals, and it showed an improvement in sensitivity by more than 25 times.

Combining human and automated scores for the improved assessment of non-native speech

In this study, we propose an efficient way to combine human and automated scoring to increase the reliability and validity of a system used to assess spoken responses in the context of an international English language assessment. A set of filtering systems are used to automatically identify various classes of spoken responses that are difficult to score with an automated scoring system, for example, due to a high level of noise or imperfections in components of the overall system. Finally, these flagged responses are then routed to and scored by human raters. The vast majority of responses are not flagged by the filtering system and receive scores by the automated scoring system, resulting in a hybrid scoring approach. The overall hybrid speech scoring system presented here is comprised of multiple subprocesses, including the recording of spoken responses, transcription generation based on an automated speech recognizer, linguistic feature generation, filtering of problematic responses, automated score generation, human rater scoring, and final score combination. We evaluate this scoring approach with pilot data from a novel international English proficiency assessment. It achieves a substantial improvement in scoring performance and score validity with a limited amount of human scoring and most responses scored automatically; the correlation between the baseline system (baseline filtering with imputation) with human raters’ scores is 0.72, and using an extended filtering model, the performance improves to 0.82. The improvement can be attributed in part to the extended filtering model itself that identified more classes of non-scorable responses, and in part to the combination of machine and human scores in our hybrid system.

A compact and high‐resolution version of a capacitively coupled contactless conductivity detector

An all-in-one version of a capacitively coupled contactless conductivity detector is introduced. The absence of moving parts (potentiometers and connectors) makes it compact (6.5 cm(3)) and robust. A local oscillator, working at 1.1 MHz, was optimized to use capillaries of id from 20 to 100 microm. Low noise circuitry and a high-resolution analog-to-digital converter (ADC) (21 bits effective) grant good sensitivities for capillaries and background electrolytes currently used in capillary electrophoresis. The fixed frequency and amplitude of the signal generator is a drawback that is compensated by the steady calibration curves for conductivity. Another advantage is the possibility of determining the inner diameter of a capillary by reading the ADC when air and subsequently water flow through the capillary. The difference of ADC reading may be converted into the inner diameter by a calibration curve. This feature is granted by the 21-bit ADC, which eliminates the necessity of baseline compensation by hardware. In a typical application, the limits of detection based on the 3sigma criterion (without baseline filtering) were 0.6, 0.4, 0.3, 0.5, 0.6, and 0.8 micromol/L for K(+), Ba(2+), Ca(2+), Na(+), Mg(2+), and Li(+), respectively, which is comparable to other high-quality implementations of a capacitively coupled contactless conductivity detector.

The importance of assessing the fidelity of electrocardiographs by using a simple calibration signal

ECG distortion, mimicking changes seen in acute myocardial infarction, occurred in traces transmitted by the South East Coast NHS Ambulance Trust. This was due to low frequency phase nonlinearity produced by baseline filtering. The very narrow calibration pulses made the detection of this distortion difficult. A 1 mV square wave signal revealed the cause. Adequate calibration pulses 0.3 s wide should be recorded with all ECGs, to show that the signal is being reproduced reliably.

Signal-averaged P wave analysis for delineation of interatrial conduction – Further validation of the method

BackgroundThe study was designed to investigate the effect of different measuring methodologies on the estimation of P wave duration. The recording length required to ensure reproducibility in unfiltered, signal-averaged P wave analysis was also investigated. An algorithm for automated classification was designed and its reproducibility of manual P wave morphology classification investigated.MethodsTwelve-lead ECG recordings (1 kHz sampling frequency, 0.625 μV resolution) from 131 healthy subjects were used. Orthogonal leads were derived using the inverse Dower transform. Magnification (100 times), baseline filtering (0.5 Hz high-pass and 50 Hz bandstop filters), signal averaging (10 seconds) and bandpass filtering (40–250 Hz) were used to investigate the effect of methodology on the estimated P wave duration. Unfiltered, signal averaged P wave analysis was performed to determine the required recording length (6 minutes to 10 s) and the reproducibility of the P wave morphology classification procedure. Manual classification was carried out by two experts on two separate occasions each. The performance of the automated classification algorithm was evaluated using the joint decision of the two experts (i.e., the consensus of the two experts).ResultsThe estimate of the P wave duration increased in each step as a result of magnification, baseline filtering and averaging (100 ± 18 vs. 131 ± 12 ms; P < 0.0001). The estimate of the duration of the bandpass-filtered P wave was dependent on the noise cut-off value: 119 ± 15 ms (0.2 μV), 138 ± 13 ms (0.1 μV) and 143 ± 18 ms (0.05 μV). (P = 0.01 for all comparisons).The mean errors associated with the P wave morphology parameters were comparable in all segments analysed regardless of recording length (95% limits of agreement within 0 ± 20% (mean ± SD)). The results of the 6-min analyses were comparable to those obtained at the other recording lengths (6 min to 10 s).The intra-rater classification reproducibility was 96%, while the interrater reproducibility was 94%. The automated classification algorithm agreed with the manual classification in 90% of the cases.ConclusionThe methodology used has profound effects on the estimation of P wave duration, and the method used must therefore be validated before any inferences can be made about P wave duration. This has implications in the interpretation of multiple studies where P wave duration is assessed, and conclusions with respect to normal values are drawn.P wave morphology and duration assessed using unfiltered, signal-averaged P wave analysis have high reproducibility, which is unaffected by the length of the recording. In the present study, the performance of the proposed automated classification algorithm, providing total reproducibility, showed excellent agreement with manually defined P wave morphologies.