Use Of Detectors Research Articles

QRS detection in single-lead, telehealth electrocardiogram signals: Benchmarking open-source algorithms.

A key step in electrocardiogram (ECG) analysis is the detection of QRS complexes, particularly for arrhythmia detection. Telehealth ECGs present a new challenge for automated analysis as they are noisier than traditional clinical ECGs. The aim of this study was to identify the best-performing open-source QRS detector for use with telehealth ECGs. The performance of 18 open-source QRS detectors was assessed on six datasets. These included four datasets of ECGs collected under supervision, and two datasets of telehealth ECGs collected without clinical supervision. The telehealth ECGs, consisting of single-lead ECGs recorded between the hands, included a novel dataset of 479 ECGs collected in the SAFER study of screening for atrial fibrillation (AF). Performance was assessed against manual annotations. A total of 12 QRS detectors performed well on ECGs collected under clinical supervision (F1 score ≥0.96). However, fewer performed well on telehealth ECGs: five performed well on the TELE ECG Database; six performed well on high-quality SAFER data; and performance was poorer on low-quality SAFER data (three QRS detectors achieved F1 of 0.78-0.84). The presence of AF had little impact on performance. The Neurokit and University of New South Wales QRS detectors performed best in this study. These performed sufficiently well on high-quality telehealth ECGs, but not on low-quality ECGs. This demonstrates the need to handle low-quality ECGs appropriately to ensure only ECGs which can be accurately analysed are used for clinical decision making.

Characterization of Transition Edge Sensors for Decay Energy Spectrometry

By using a superconducting transition edge sensor (TES) to measure the thermal energy of individual decay events with high energy resolution, decay energy spectrometry provides a unique fingerprint to identify each radionuclide in a sample. The proposed measurement requires optimizing the thermal parameters of the detector for use with 5 MeV scale energy deposited by alpha decay of the sample radionuclides. The thermal performance of deep-etched silicon TES chips is examined with the use of an onboard resistive heater. With known heater power and bath temperature, the thermal conductance, heat capacity, and frame temperature are calculated and compared to theory.

A systematic investigation of detectors for low signal-to-noise ratio EMG signals

Background Active participation of stroke survivors during robot-assisted movement therapy is essential for sensorimotor recovery. Robot-assisted therapy contingent on movement intention is an effective way to encourage patients’ active engagement. For severely impaired stroke patients with no residual movements, a surface electromyogram (EMG) has been shown to be a viable option for detecting movement intention. Although numerous algorithms for EMG detection exist, the detector with the highest accuracy and lowest latency for low signal-to-noise ratio (SNR) remains unknown. Methods This study, therefore, investigates the performance of 13 existing EMG detection algorithms on simulated low SNR (0dB and -3dB) EMG signals generated using three different EMG signal models: Gaussian, Laplacian, and biophysical model. The detector performance was quantified using the false positive rate (FPR), false negative rate (FNR), and detection latency. Any detector that consistently showed FPR and FNR of no more than 20%, and latency of no more than 50ms, was considered an appropriate detector for use in robot-assisted therapy. Results The results indicate that the Modified Hodges detector – a simplified version of the threshold-based Hodges detector introduced in the current study – was the most consistent detector across the different signal models and SNRs. It consistently performed for ~90% and ~40% of the tested trials for 0dB and -3dB SNR, respectively. The two statistical detectors (Gaussian and Laplacian Approximate Generalized Likelihood Ratio) and the Fuzzy Entropy detectors have a slightly lower performance than Modified Hodges. Conclusions Overall, the Modified Hodges, Gaussian and Laplacian Approximate Generalized Likelihood Ratio, and the Fuzzy Entropy detectors were identified as the potential candidates that warrant further investigation with real surface EMG data since they had consistent detection performance on low SNR EMG data.

A systematic investigation of detectors for low signal-to-noise ratio EMG signals.

Active participation of stroke survivors during robot-assisted movement therapy is essential for sensorimotor recovery. Robot-assisted therapy contingent on movement intention is an effective way to encourage patients' active engagement. For severely impaired stroke patients with no residual movements, a surface electromyogram (EMG) has been shown to be a viable option for detecting movement intention. Although numerous algorithms for EMG detection exist, the detector with the highest accuracy and lowest latency for low signal-to-noise ratio (SNR) remains unknown. This study, therefore, investigates the performance of 13 existing EMG detection algorithms on simulated low SNR (0dB and -3dB) EMG signals generated using three different EMG signal models: Gaussian, Laplacian, and biophysical model. The detector performance was quantified using the false positive rate (FPR), false negative rate (FNR), and detection latency. Any detector that consistently showed FPR and FNR of no more than 20%, and latency of no more than 50ms, was considered an appropriate detector for use in robot-assisted therapy. The results indicate that the Modified Hodges detector - a simplified version of the threshold-based Hodges detector introduced in the current study - was the most consistent detector across the different signal models and SNRs. It consistently performed for ~90% and ~40% of the tested trials for 0dB and -3dB SNR, respectively. The two statistical detectors (Gaussian and Laplacian Approximate Generalized Likelihood Ratio) and the Fuzzy Entropy detectors have a slightly lower performance than Modified Hodges. Overall, the Modified Hodges, Gaussian and Laplacian Approximate Generalized Likelihood Ratio, and the Fuzzy Entropy detectors were identified as the potential candidates that warrant further investigation with real surface EMG data since they had consistent detection performance on low SNR EMG data.

A systematic investigation of detectors for low signal-to-noise ratio EMG signals

Background Active participation of stroke survivors during robot-assisted movement therapy is essential for sensorimotor recovery. Robot-assisted therapy contingent on movement intention is an effective way to encourage patients’ active engagement. For severely impaired stroke patients with no residual movements, a surface electromyogram (EMG) has been shown to be a viable option for detecting movement intention. Although numerous algorithms for EMG detection exist, the detector with the highest accuracy and lowest latency for low signal-to-noise ratio (SNR) remains unknown. Methods This study, therefore, investigates the performance of 13 existing EMG detection algorithms on simulated low SNR (0dB and -3dB) EMG signals generated using three different EMG signal models: Gaussian, Laplacian, and biophysical model. The detector performance was quantified using the false positive rate (FPR), false negative rate (FNR), and detection latency. Any detector that consistently showed FPR and FNR of no more than 20%, and latency of no more than 50ms, was considered an appropriate detector for use in robot-assisted therapy. Results The results indicate that the Modified Hodges detector – a simplified version of the threshold-based Hodges detector introduced in the current study – was the most consistent detector across the different signal models and SNRs. It consistently performed for ~90% and ~40% of the tested trials for 0dB and -3dB SNR, respectively. The two statistical detectors (Gaussian and Laplacian Approximate Generalized Likelihood Ratio) and the Fuzzy Entropy detectors have a slightly lower performance than Modified Hodges. Conclusions Overall, the Modified Hodges, Gaussian and Laplacian Approximate Generalized Likelihood Ratio, and the Fuzzy Entropy detectors were identified as the potential candidates that warrant further investigation with real surface EMG data since they had consistent detection performance on low SNR EMG data.

High‐Performance Broadband Flexible Photodetectors Based on Ti3C2Tx MXene/Pyramidal Thin Si Heterostructures with Light Trapping Effect for Heart Rate Detection

AbstractHigh‐performance flexible photodetectors are urgently demanded for application in many emerging domains including flexible, wearable, portable, and stretchable optoelectronics. In this work, a highly efficient flexible photodetector made of a Ti3C2Tx MXene/pyramidal thin Si heterostructure is successfully fabricated. Thanks to the remarkable light trapping effect as confirmed by numerical modeling based on finite‐element method, the as‐constructed detector demonstrates excellent photoresponse performance in a broadband wavelength spectrum. The optimal responsivity, specific detectivity, and response speed reach ≈530 mA W−1, ≈1.21 × 1012 Jones and 30/14 µs, respectively, at zero bias under near‐infrared light illumination. Specifically, the responsivity is greatly enhanced by ≈2.65 times, as compared with a planar counterpart. In addition, the light detector can maintain its outstanding photoresponse properties upon 1000 cycles of bending test or at diverse bending radii of curvature, owing to its prominent mechanical flexibility and robust bending endurance. Finally, the capability of monitoring the heart rate of a person in a wideband wavelength spectrum and at various bending radii of curvature is presented. The above results imply the huge potential of the flexible light detector for use in some applications such as wearable health monitoring.

Fast neutron radiography detector with polyethylene converter and MCP: development and performance evaluation

We present a new position-sensitive fast neutron detector for 14 MeV fast neutron radiography, comprising a high-density polyethylene converter, a micro-channel plate (MCP), a P43 phosphor screen, a reflection mirror, and a CCD camera. Experimental tests are conducted using a DT neutron generator at CIAE, with different samples and a knife-edge measurement used to determine overall image resolution. Results show that the detector is feasible for fast neutron radiography, producing a fast neutron image with a neutron flux of ∼ 3.22 × 104 n · s-1 · cm-2 and 120 s exposure time. Our study demonstrates the potential of this detector for use in fast neutron radiography applications.

Characterization of a Timepix detector for use in SEM acceleration voltage range

Hybrid pixel direct electron detectors are gaining popularity in electron microscopy due to their excellent properties. Some commercial cameras based on this technology are relatively affordable which makes them attractive tools for experimentation especially in combination with an SEM setup. To support this, a detector characterization (Modulation Transfer Function, Detective Quantum Efficiency) of an Advacam Minipix and Advacam Advapix detector in the 15–30 keV range was made. In the current work we present images of Point Spread Function, plots of MTF/DQE curves and values of DQE(0) for these detectors. At low beam currents, the silicon detector layer behaviour should be dominant, which could make these findings transferable to any other available detector based on either Medipix2, Timepix or Timepix3 provided the same detector layer is used.

Investigation of problematic dye combinations to inform optimal panel design in spectral cytometry

Abstract The adoption of high complexity flow cytometry is increasing as spectral analyzers and sorters can support detection of 40+ parameters. While experimental design and panel building can be straightforward for small and mid-sized conventional and spectral panels, high parameter panel building requires special considerations. Reaching the full capability of any cytometer requires the user to build a complex panel with all the available detectors in use. While smaller panels can rely on techniques such as separation of fluorophores based on laser excitation and emission peaks and selection of more spectrally clean dyes, increasingly complex higher parameter panels often require the use of problematic fluorophore combinations. Fluorophore combinations can be categorized as problematic for several reasons, including but not limited to; high spectral similarity, overlapping peak channel detection, high cross-laser excitation, and relative brightness. We present data intended to help inform the selection of fluorophores when building a complex panel. To address this, we designed mini panels composed of problematic fluorophore combinations to investigate which properties of such combinations contributed most significantly to overall panel usability, as well as to make recommendations for which fluorophores can be used together successfully. Data collected from both conventional and spectral cytometers showed that some combinations fail to resolve target populations or cause insurmountable compensation or unmixing errors, while others can be used together effectively. Thus, strategic dye selection can improve panel resolution as well as increase the number of detectable parameters, serving to maximize cytometer potential.

A systematic investigation of detectors for low signal-to-noise ratio EMG signals

Background: Active participation of stroke survivors during robot-assisted movement therapy is essential for sensorimotor recovery. Robot-assisted therapy contingent on movement intention is an effective way to encourage patients' active engagement. For severely impaired stroke patients with no residual movements, a surface electromyogram (EMG) has been shown to be a viable option for detecting movement intention. Although numerous algorithms for EMG detection exist, the detector with the highest accuracy and lowest latency for low signal-to-noise ratio (SNR) remains unknown. Methods: This study, therefore, investigates the performance of 13 existing EMG detection algorithms on simulated low SNR (0dB and -3dB) EMG signals generated using three different EMG signal models: gaussian, Laplacian, and biophysical model. The detector performance was quantified using the false positive rate (FPR), false negative rate (FNR), and detection latency. Any detector that consistently showed FPR and FNR of no more than 20%, and latency of no more than 50ms, was considered an appropriate detector for use in robot-assisted therapy. Results: The results indicate that the Modified Hodges detector – a simplified version of the threshold-based Hodges detector introduced in the current study – was the most consistent detector across the different signal models and SNRs. It consistently performed for ~90% and ~40% of the tested trials for 0dB and -3dB SNR, respectively. The two statistical detectors (Gaussian and Laplacian Approximate Generalized Likelihood Ratio) and the Fuzzy Entropy detectors have a slightly lower performance than Modified Hodges. Conclusions: Overall, the Modified Hodges, Gaussian and Laplacian Approximate Generalized Likelihood ratio, and the Fuzzy Entropy detectors were identified as the potential candidates that warrant further investigation with real surface EMG data since they had consistent detection performance on low SNR EMG data.

Low-cost electron detector for scanning electron microscope

Electron microscopy is an indispensable tool for the characterization of (nano) materials. Electron microscopes are typically very expensive and their internal operation is often shielded from the user. This situation can provide fast and high quality results for researchers focusing on e.g. materials science if they have access to the relevant instruments. For researchers focusing on technique development, wishing to test novel setups, however, the high entry price can lead to risk aversion and deter researchers from innovating electron microscopy technology further. The closed attitude of commercial entities about how exactly the different parts of electron microscopes work, makes it even harder for newcomers in this field. Here we propose an affordable, easy-to-build electron detector for use in a scanning electron microscope (SEM). The aim of this project is to shed light on the functioning of such detectors as well as show that even a very modest design can lead to acceptable performance while providing high flexibility for experimentation and customization.

Development of a β-TOF detector: An enhancement of the α-TOF detector for use with β-decaying nuclides

AbstractPreviously, we developed an α-TOF detector for correlated measurements of atomic masses and decay properties of radioactive isotopes using a multi-reflection time-of-flight mass spectrograph, and successfully demonstrated α-decay-correlated mass measurements of heavy and superheavy nuclides. In this work, we develop a β-TOF detector, an improvement upon the α-TOF detector, to extend this technology to β-decaying nuclides. Online commissioning with 81Ga yielded a measured half-life of 1.44(31) s, in agreement with the previous literature, and confirmed the efficiency of detecting a decay-correlated event to be consistent with the solid angle limitation.

Evaluation of CZT Drift Strip Detectors for Use in 3-D Molecular Breast Imaging

X-ray mammography is a widely used technique for breast cancer screening. However, the technique is imprecise when it comes to radiographically dense breast tissue, and therefore a supplemental screening technique, such as molecular breast imaging (MBI), could increase the cancer detection rate. Emerging technologies within MBI require excellent detector performance, preferably with a submillimeter intrinsic spatial resolution. A collaboration between Kromek and DTU Space aims to advance the DTU Space developed 3-D CdZnTe (CZT) drift strip technology, for application in new emerging MBI systems. This collaboration has resulted in ten compact 3-D MBI test modules with the goal of producing high-performance and high yield detectors. In this article, we present overall excellent detector performance, submillimeter position resolution at both 122 and 661.6 keV, and good energy resolution for the applied electrode deposition and contact optimization. Although the current experimental setup and results suffer from high electronic noise (using discrete NIM standard charge sensitive preamplifiers), we conclude that the measured spatial and spectral performance fulfills the expected requirements for the modules, with room for improvement, especially within limiting electronic noise. The detector test modules indicate a promising future for the 3-D CZT drift strip technology within future emerging MBI systems.

Experimental evaluation of the performance of a novel planar segmented HPGe detector for use in gamma emission tomography

The use of segmented HPGe detectors for gamma-ray tracking applications is well established. The spectroscopic capabilities of these detectors make them most suitable for such applications. For similar reasons, the use of such detectors in the tomographic measurement of irradiated nuclear fuel has also been envisioned. Especially, these detectors can facilitate faster fuel examination with excellent energy resolution. We have proposed and designed a novel planar segmented HPGe detector for use in gamma emission tomography. The design of the detector segmentation and the mode of operation is unique and offers six simultaneous detection channels for tomographic measurements. This work reports the first experimental evaluation of the performance of the detector. Important characteristics of the detector have been obtained, such as energy resolution of the segments in 1-fold (one segment) and 2-fold (two segments) modes, throughput curves, crosstalk energy corrections, and mislocalisation rate. Collimated source tests have been performed and the results have been compared with the MCNP simulations results. The obtained results are as expected and in good agreement with the simulation results, and it is estimated that using this detector can speed up the data collection by 3.2 times in comparison to an unsegmented detector of the same overall dimensions, in a tomographic application. Further improvements are foreseeable if scaling up to a larger detector with greater segmentation.

Soft-output signal detection for cetacean vocalizations using spectral entropy, k-means clustering and the continuous wavelet transform

Underwater passive acoustic monitoring systems record many hours of audio data for marine research, making fast and reliable non-causal signal detection paramount. Such detectors assist in reducing the amount of labor required for signal annotations, which often contain large portions devoid of signals.Cetacean vocalization detection based on spectral entropy is investigated as a means of vocalization discovery. Previous techniques using spectral entropy mostly consider time–frequency enhancement of the entropy measure, and utilize the short time Fourier transform (STFT) as its time–frequency (TF) decomposition. Spectral entropy methods also requires the user to set a detection threshold manually, which call for knowledge of the produced entropy measures.This paper considers median filtering as a simple, effective way to provide temporal stabilization to the entropy measure, and considers the continuous wavelet transform (CWT) as an alternative TF decomposition. K-means clustering is used to determine the threshold required to accurately separate the signal/no-signal entropy measures, resulting in a one-dimensional, two-class classification problem. The class means are used to perform pseudo-probabilistic soft class assignment, which is a useful metric in algorithmic development. The effect of median filtering, signal-to-noise ratio and the chosen TF decomposition are investigated.The accuracy and specificity measures of the proposed detection technique are simulated using a pulsed frequency modulated sweep, corrupted by a sample of ocean noise. The results show that median filtering is particularly effective for low signal-to-noise ratios. Both the STFT and CWT prove to be effective TF analyses for signal detection purposes, each presenting with different advantages and drawbacks. The simulated results provide insight into configuring the proposed detector, which is compared to a conventional STFT-based spectral entropy detector using manually annotated humpback whale (Megaptera novaeangliae) songs recorded in False Bay, South Africa, July2021.The proposed method shows a significant improvement in detection accuracy and specificity, while also providing a more interpretable detection threshold setting via soft class assignment, providing a detector for use in development of adaptive algorithms.

Energy-dependence investigation for a range of clinically used detectors from 70kV to 6MV.

Accurate measurement of out-of-field dose in radiotherapy directly impacts beam data modeling in treatment planning systems, verification of implanted electronic devices/lens/fetus dose, secondary cancer risk estimation, and organ-at-risk dose reporting. When performing out-of-field dosimetry, it is therefore imperative that the response of the detector has been well characterized. Due to the softening of the radiation beam out-of-field, many detectors will exhibit energy dependence. This study investigated the energy dependence of a range of clinical available detectors over typical energies experienced out-of-field. The response of detectors to photon beams from 70kV to 6MV was measured. The relative change in response from 6MV down to 70kV highlighted the expected deviation in the response of detectors that would typically be calibrated in-field for use out-of-field. The Pinpoint detector displayed the most energy-independent response over the energy range investigated. The Micro-Lion detector was the only detector to show an under-response to all low-energy beams relative to 6MV. The diode-type detectors showed the largest energy dependence. When considering detectors for use in out-of-field dose measurements, it is important that the energy dependence is investigated over a low-energy range as out-of-field the energy spectra comprise a larger component of photons in the 50-100-keV range. This study highlights the variation in response of a range of clinically available detectors to low-energy radiation beams relative to 6MV for out-of-field dosimetry. The Pinpoint detector was the most energy-independent detector with a response close to unity over the entire energy range investigated.

Compact Camera Fluorescence Detector for Parallel-Light Lens-Based Real-Time PCR System.

The polymerase chain reaction is an important technique in biological research. However, it is time consuming and has a number of disadvantages. Therefore, real-time PCR technology that can be used in real-time monitoring has emerged, and many studies are being conducted regarding its use. Real-time PCR requires many optical components and imaging devices such as expensive, high-performance cameras. Therefore, its cost and assembly process are limitations to its use. Currently, due to the development of smart camera devices, small, inexpensive cameras and various lenses are being developed. In this paper, we present a Compact Camera Fluorescence Detector for use in parallel-light lens-based real-time PCR devices. The proposed system has a simple optical structure, the system cost can be reduced, and the size can be miniaturized. This system only incorporates Fresnel lenses without additional optics in order for the same field of view to be achieved for 25 tubes. In the center of the Fresnel lens, one LED and a complementary metal-oxide semiconductor camera were placed in directions that were as similar as possible. In addition, to achieve the accurate analysis of the results, image processing was used to correct them. As a result of an experiment using a reference fluorescent substance and double-distilled water, it was confirmed that stable fluorescence detection was possible.

A closer look at the efficiency calibration of LaBr3(Ce) and NaI(Tl) scintillation detectors using MCNPX for various types of nuclear investigations

The nuclear spectroscopy method has long been used for advanced studies on nuclear physics. In order to decrease costs and increase the efficiency of nuclear radiation investigations, quick and efficient solutions are required. The purpose of this research was to calculate the whole energy peak efficiency values for a range of gamma-ray energies, from 30.973 keV to 1408 keV, at various source-detector distances using the MCNPX Monte Carlo code, which is extensively used in nuclear medicine, industry, and scientific research. As a result, the modeled detectors' full-energy peak efficiencies were calculated and compared to both experimental data and Monte Carlo simulations. Experiment results and prior studies using Monte Carlo simulations were found to be very consistent with these results. The counting efficiency against source-detector distance is then calculated using the modeled detectors. The data we have show that LaBr3(Ce) has outstanding detection properties. This study’s findings might be used to improve the design of detectors for use in wide range of high-tech gamma spectroscopy and nuclear research applications.

Sub-pixel energy-weighting techniques for metallic contaminant highlighting in a pharmaceutical hard capsule using a Timepix3 CdZnTe hybrid pixel detector

The presented work describes the capabilities of a state-of-the-art spectroscopic photon-counting hybrid pixel detector for use in non-destructive testing, as a contaminant detector with potential uses in the food and pharmaceutical industries. A pharmaceutical hard capsule containing vitamin powder, contaminated with Steel (ST) and Tungsten (W) was prepared under controlled conditions. Both contaminants are distinguished from the powder by combining sub-pixel imaging with spectroscopic imaging analysis, made possible by the use of a 2 mm CdZnTe sensor coupled to a Timepix3 ASIC, operated in Time-over-Threshold and Time-of-Arrival mode. The sub-pixelization technique achieves a spatial resolution of 18.3 μm, with a maximum photon flux of 2.5× 106 photons/s· cm2. For the spectral analysis technique implemented, each energy bin image is energy-weighted, with weights optimally defined by considering the bin contrast and noise behavior, and combined to form an integrated-spectrum enhanced image with increased contaminant visibility. Contrast-to-Noise Ratio (CNR) for each contaminant type was used for performance assessment of the approach. A CNR increase of 106% above the reference unweighted-spectrum image was achieved for the ST contaminant, while the denser W contaminant showed a CNR increase of 80%. This means that the presented spectroscopic imaging technique is feasible for implementation in material visibility enhancement.

A Frequency-Locked Oscillator Using Complex RC Impedance IQ-Balancing

This article presents a frequency-locked loop (FLL) oscillator using an impedance-sensing frequency detector for use in fully-integrated, on-chip frequency references. By detecting the real and imaginary components of a complex RC impedance, the FLL can be locked into the pole frequency where both components are balanced. A phase-mixing technique is also introduced to reconfigure the output frequency without altering the amplifier operating voltage. A prototype chip demonstrating this IQ-balanced approach was designed and fabricated in a general-purpose 0.18- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS process. From measured results across multiple dice, the prototype integrated circuit (IC) chips achieve a temperature stability of 22 ppm/°C from −40°C to 80 °C and 10-ppm Allan deviation at an average output frequency of 647 kHz with 1.27 pJ/cycle energy efficiency, and 34.4 ppm/°C from −40°C to 80 °C and 15-ppm Allan deviation at an average output frequency of 1.39 MHz with 0.89 pJ/cycle energy efficiency.