Signal Conditioning Algorithms Research Articles

Use of Advanced Materials and Artificial Intelligence in Electromyography Signal Detection and Interpretation

Electromyography (EMG) is an integral part of many biomedical and healthcare applications. It has been used as a metric for tracking rehabilitation progress and identifying diseases that affect muscle activation patterns. Although it is widely used in many disciplines, conventional EMG recording and interpretation techniques lack in providing precise signal detection and robust classification accuracy. In recent years, thanks to advances in both material science and artificial intelligence, EMG detection techniques are improving at a rapid pace. Materials that allow for enhanced biocompatibility have improved the quality of data recorded by electrodes. The use of machine learning algorithms has paved new ways to understand complex EMG signals, triggering diverse, novel, and improved application scenarios within the healthcare framework. To help readers establish a clear picture of the two most important components in EMG technology, i.e., electrodes and algorithms, while catching up with the latest research outcomes, this review article is composed. The article starts by introducing conventional EMG electrode materials and architectures, then explains how state‐of‐the‐art works have improved electrode utilization. Subsequently, EMG signal conditioning and interpretation algorithms are investigated. Finally, current challenges in the research domain and authors’ perspectives are discussed.

A universal, high-performance ECG signal processing engine to reduce clinical burden.

BackgroundElectrocardiogram (ECG) signal conditioning is a vital step in the ECG signal processing chain that ensures effective noise removal and accurate feature extraction.ObjectiveThis study evaluates the performance of the FDA 510 (k) cleared HeartKey Signal Conditioning and QRS peak detection algorithms on a range of annotated public and proprietary ECG databases (HeartKey is a UK Registered Trademark of B‐Secur Ltd).MethodsSeven hundred fifty‐one raw ECG files from a broad range of use cases were individually passed through the HeartKey signal processing engine. The algorithms include several advanced filtering steps to enable significant noise removal and accurate identification of the QRS complex. QRS detection statistics were generated against the annotated ECG files.ResultsHeartKey displayed robust performance across 14 ECG databases (seven public, seven proprietary), covering a range of healthy and unhealthy patient data, wet and dry electrode types, various lead configurations, hardware sources, and stationary/ambulatory recordings from clinical and non‐clinical settings. Over the NSR, MIT‐BIH, AHA, and MIT‐AF public databases, average QRS Se and PPV values of 98.90% and 99.08% were achieved. Adaptable performance (Se 93.26%, PPV 90.53%) was similarly observed on the challenging NST database. Crucially, HeartKey's performance effectively translated to the dry electrode space, with an average QRS Se of 99.22% and PPV of 99.00% observed over eight dry electrode databases representing various use cases, including two challenging motion‐based collection protocols.ConclusionHeartKey demonstrated robust signal conditioning and QRS detection performance across the broad range of tested ECG signals. It should be emphasized that in no way have the algorithms been altered or trained to optimize performance on a given database, meaning that HeartKey is potentially a universal solution capable of maintaining a high level of performance across a broad range of clinical and everyday use cases.

Signal Processing for Single Biomolecule Identification Using Nanopores: A Review

Nanopore sensors provide an innovative platform for rapid and label-free biomolecule detection. The analytical information extracted from resistive pulse sensing (RPS) measurement, though powerful has several limitations. To address these challenges, various signal conditioning algorithms have been researched which include efficient clustering and classification of data, applying probabilistic models to reduce information overlap and also leveraging transient analysis of ionic current instead of the steady state RPS measurement. Additionally, as the current is typically in few picoampere regime with high transition rates, its faithful measurement requires precision instrumentation like a patch clamp amplifier with low noise and high bandwidth. However, for practical deployment of such systems, it is necessary to develop portable electronic interface. Significant efforts have been directed globally to develop on-chip customized integrated circuits with low noise, high bandwidth amplifiers. Thus, it is absolutely timely and pertinent to conduct a review on the various signal processing schemes and electronic interfaces for single biomolecule identification using nanopores which will enable to comprehend the current status for its deployment as a commercial platform for genomics and ultrasensitive detection. To the best of our knowledge, the data extraction methodologies and electronic interface schemes for nanopores have not yet been summarized. In this paper, a comprehensive review emphasizing the signal processing algorithms for DNA sequencing and protein identification in complex analyte using functionalized and non-functionalized nanopores has been presented along with the on-chip low noise electronic interfaces required for field deployable measurement.

Artifact Elimination from EEG Signals using Error Normalized algorithms for Brain Computer Interface Systems

In this paper some efficient and low computation complex signal conditioning algorithms are proposed in distant health tracking applications, for improvement of the electroencephalogram (EEG) signal. Few artifacts are contaminated also mask small characteristics underlying EEG signal activity in medical environments during extraction of EEG signal. Low computational difficulty filters are appealing especially within distant healthcare surveillance. Therefore, we provided several effective and less computing adaptive noise cancellers (ANCs) in this work to improve EEG signal. Most of these techniques use easy addition as well as shift calculations also attain significant convergence performance compared to other standard techniques. Real EEG signals collected using emotional EEG systems are verified for proposed implementations. Using several performances measures our studies demonstrate that techniques suggested provides best performance than prevailing methods. This methodology is suitable in the analysis of brain computer interface (BCI) applications.

Non-invasive recordings of fetal electrocardiogram during pregnancy using electric potential sensors

In this letter, we report the early detection of fetal cardiac electrical activity recorded from the maternal abdomen non-invasively. We developed a portable and non-invasive, prototype based on electric potential sensing technology to monitor both: the mother and fetal heart activity during pregnancy. In this proof of principle demonstration, we show the suitability of our technology to monitor the fetal heart development starting at week twenty, when the fetus heart is approximately one-tenth the size of an adult’s heart. The study was conducted for ten weeks to demonstrate how the maturation of the fetus leads to a change on the heart rate dynamics as it approaches birth. Importantly, electrocardiogram information is presented without any post processing given that our device eliminates the requirement of signal conditioning algorithms such as having to un-mix both, the maternal and fetal cardiac waveforms. The provided ECG trace allows extracting the heart rate and other heart activity parameters useful for further diagnostics. Finally, our device does not require any gels to be applied so movement induced potential is eliminated. This technology has the potential to be used for determining possible heart related congenital disorders during pregnancy.

Signal conditioning for compensating nonlinearity and nonrepeatability of an optical frequency scanning laser implemented in a C-OFDR system.

Reported systems using coherent optical frequency-domain reflectometry, C-OFDR, rely on the linearity of the scanning rate of the tunable laser and sometimes on its repeatability. In this work, we present the implementation of signal-conditioning algorithms for a fiber temperature sensor system based on coherent optical frequency-domain reflectometry. Postacquisition signal conditioning removes any nonlinearity and nonrepeatability effects and allows synchronization of the whole system. A low reflectivity, 0.1%, fiber Bragg grating, placed in a reference interferometer, is implemented for removing the nonrepeatability of the optical source. The spatial resolution achieved by the temperature fiber sensor is 0.36 mm, with repeatability of 0.032°C, while using telecommunication-grade components.

Equilateral Measurement of Rotational Positions With Magnetic Encoders

A magnetic encoder with a novel arrangement of Hall-effect sensors is proposed for measurement of rotational angles. Three or six Hall-effect sensors are installed around a ring magnet to form an equilateral triangle. In contrast to a quadrature measurement, the equilateral arrangement is capable of eliminating harmonics of the third order and its multiples. Like quadrature measurements, even-order harmonics can also be canceled with a complementary set of Hall-effect sensors. As a result, measurement accuracy is greatly improved without resorting to lookup tables or sophisticated signal conditioning algorithms. The method is applied to a ring magnet revolving about an eccentric axis. Both numerical simulations and experimental results are conducted to verify its performance. It is shown that when a complementary set of Hall-effect sensors are used, the equilateral method yields a higher accuracy over the quadrature one by about 30 times in the simulations with ideal Hall-effect sensors. In the experiments with actual Hall-effect sensors, the proposed method still improves accuracy by about four to five times.

Abstract 17684: A Novel System for the Rapid and Automated Detection of Atrial Fibrillation

Introduction: Home telemetry monitoring with accurate automated rhythm classification can have important clinical benefits in the timely diagnosis and appropriate management of patients with atrial fibrillation (AF). We clinically validated a novel personal e-Health device and algorithm developed to distinguish AF from sinus rhythm (SR). Methods: A handheld electrocardiogram (ECG) recording system (Maestro) and signal processing platform were developed. The Maestro provides an LCD interface that continuously shows an ECG, heart rate, and heart rhythm status. Twenty second ECG signals analogous to Lead I were acquired from 66 patients presenting to the arrhythmia clinic at the University of Michigan Hospital either in SR or AF. Electrograms were segmented into non-overlapping 6-second samples and one random segment per patient was selected for analysis by the Maestro system. Simultaneous 5 or 12 lead ECGs were obtained from these patients and 3 expert physicians blinded to the Maestro analysis identified the rhythm as SR or AF. The Maestro system applied several signal conditioning algorithms to each ECG sample. The dimensionless temporal R-R interval variability (VRR) index and spectral frequency dispersion metric (FDM) were computed. Results: The 2-dimensional scatter-gram of the samples demonstrated 2 distinct clusters of VRR and FDM for patients with SR and AF. The VRR index clusters for SR and AF patients were 0.018 ± 0.013 and 0.187 ± 0.073 (mean±std), respectively (p < 0.001). The FDM clusters for SR and AF patients occurred at 10.5 ± 5.916 and 15.892 ± 3.337, respectively (p < 0.001). We developed a Gaussian Mixed Model (GMM) classifier to distinguish between the AF and SR clusters. Only after the GMM classifier was obtained were the Maestro classifications compared to the physicians’ readings. The algorithm correctly categorized AF (N = 46) and SR (N = 20) for all Maestro segments analyzed with 100% specificity and sensitivity. Conclusion: The Maestro handheld telemetry unit utilizes a novel classification algorithm and was demonstrated to acquire and automatically analyze 6-second electrograms for rapid and accurate classification of patients in SR or AF in this initial clinical validation trial.

Two-step Calibration of Sensor Signal Conditioners

Introduction Mixed-signal integrated circuits (ICs) for sensor signal conditioning are widely used today in sensor applications such as pressure, temperature and position monitoring. In these signal conditioners, the conditioning of the output signal from the sense element is performed with mixed-signal circuits, which are a combination of analog and digital circuits. Moreover, the actual conditioning of the sense-element signals is implemented in the digital domain. The conditioned signal is the output of the sensor signal conditioner. The sensor output is transmitted to a control or monitoring system either in analog or digital form. If an analog form of transmission is used, the processed digital signal must be converted back to analog form. This article examines the calibration of sensor signalconditioning algorithms implemented in signal conditioners that transmit data in analog form. Note that sensor calibration includes the sense-element non-idealities as well as signal-conditioner non-idealities, such as offset and gain errors. The calibration scheme will take care of analog signal-chain errors of the analog circuit that are in front and back of the digital circuits.

Efficient Signal Conditioning Techniques for Brain Activity in Remote Health Monitoring Network

This paper proposes several efficient and less complex signal conditioning algorithms for brain signal enhancement in remote healthcare monitoring applications. In clinical environment during electroencephalogram (EEG) recording, several artifacts encounter and mask tiny features underlying brain wave activity. Especially in remote clinical monitoring, low computational complexity filters are desirable. Hence, in our paper, we propose various efficient and computationally simple adaptive noise cancelers for EEG enhancement. These schemes mostly employ simple addition and shift operations, and achieve considerable speed over the other conventional realizations. We have tested the proposed implementations on real brain waves recorded using emotive EEG system. Our experiments show that the proposed realization gives better performance compared with existing realizations in terms of signal to noise ratio, computational complexity, convergence rate, excess mean square error, misadjustment, and coherence.

A new high-integrated weak field sensor for automotive applications

Abstract. Especially in the field of automotive applications smart sensor systems for magnetic field sensing face increasing requirements concerning low cost, low power consumption and high magnetic performance. Over the past years AMR sensors play a decisive role in this application area because of their robustness and stability. In the following a high-integrated smart sensor system for magnetic field sensing is presented. A novel approach for the detection of weak magnetic fields is shown, which is based on an AC-excitation of AMR elements. In contrast to common used sensors this concept is based on a nonlinear AMR element without Barber pole construction. By means of this methodology sensitivity as well as temperature and life time stability is significantly improved, while the production costs compared to common-used sensors are reduced. Within this novel approach new signal conditioning algorithms and analog circuit topologies are presented, which are able to meet low offset and low noise requirements.

Temporal analysis of moving dc electric fields in aquatic media

Many aquatic vertebrates can sense the weak electric fields generated by other animals and may also sense geoelectric or electromagnetic phenomena for use in orientation. All these sources generate stationary (dc) fields. In addition, fields from animals are modulated by respiration and other body movements. Since electroreceptors are insensitive to a pure dc field, it has been suggested that the ac modulation carries most of the relevant information for electrosensory animals. However, in a natural situation pure dc fields are rare since any relative movement between source and receiver will transform a dc field into a time varying signal. In this paper, we will describe the properties of such signals and how they are filtered at the first stage of electrosensory information processing in the brain. We will show that the signal perceived by an animal traversing a dc electric field contains all the information necessary to reconstruct the distance to the source and that the signal conditioning algorithms are perfectly adapted to preserve such information.

A sensor for the sugar cane harvester topper

A robust low cost refractometer has been developed together with signal conditioning algorithms to enable sucrose content to be measured during the mechanical harvesting of the sugar cane plant. This technology will be applied to the harvester process that removes the tops of the cane to assist the harvester operator to cut the cane at the optimum cutting height.

Digital signal conditioning and conversion

The process of conversion of an analogue signal into a digital version is examined; in particular the errors introduced by the conversion are analysed. The salient features of the main methods of conversion are then described. Since sampling of signals is inherent in the process of conversion, the mathematics of sampled signals is then examined and the discrete forms of the Laplace and Fourier transforms are developed. These are then used to show how signals may be processed in the digital domain. Finally, the principles of the design of circuits for the realisation of digital signal conditioning algorithms are described.