Diego S. Benitez

The use of the Hilbert transform in ECG signal analysis

This paper presents a new robust algorithm for QRS detection using the first differential of the ECG signal and its Hilbert transformed data to locate the R wave peaks in the ECG waveform. Using this method, the differentiation of R waves from large, peaked T and P waves is achieved with a high degree of accuracy. In addition, problems with baseline drift, motion artifacts and muscular noise are minimised. The performance of the algorithm was tested using standard ECG waveform records from the MIT-BITH Arrhythmia database. An average detection rate of 99.87%, a sensitivity (Se) of 99.94% and a positive prediction (+P) of 99.93% have been achieved against study records from the MIT-BITH Arrhythmia database. A detection error rate of less than 0.8% was achieved in every study case. The reliability of the proposed detector compares very favorably with published results for other QRS detectors.

A new QRS detection algorithm based on the Hilbert transform

A robust new algorithm for QRS defection using the properties of the Hilbert transform is proposed. The method allows R waves to be differentiated from large, peaked T and P waves with a high degree of accuracy and minimizes the problems associated with baseline drift, motion artifacts and muscular noise. The performance of the algorithm was tested using the records of the MIT-BIH Arrhythmia Database. Beat by beat comparison was performed according to the recommendation of the American National Standard for ambulatory ECG analyzers (ANSI/AAMI EC38-1998). A QRS detection rate of 99.64%, a sensitivity of 99.81% and a positive prediction of 99.83% was achieved against the MIT-BIH Arrhythmia database. The noise tolerance of the new proposed QRS detector was also tested using standard records from the MIT-BIH Noise Stress Test Database. The sensitivity of the detector remains about 94% even for signal-to-noise ratios (SNR) as low as 6 dB.

Within-day and between-days reliability of quadriceps isometric muscle fatigue using mechanomyography on healthy subjects.

This study aimed to examine within-day and between-days intratester reliability of mechanomyography (MMG) in assessing muscle fatigue. An accelerometer was used to detect the MMG signal from rectus femoris. Thirty one healthy subjects (15 males) with no prior knee problems initially performed three maximum voluntary contractions (MVCs) using an ISOCOM dynamometer. After 10 min rest, subjects performed a fatiguing protocol in which they performed three isometric knee extensions at 75% MVC for 40 s. The fatiguing protocol was repeated on two other days, two to four days apart for between-days reliability. MMG activity was determined by overall root mean squared amplitude (RMS), mean power frequency (MPF) and median frequency (MF) during a 40s contraction. RMS, MPF and MF linear regression slopes were also analysed. Intraclass Correlation Coefficients (ICC); ICC1,1 and ICC1,2 were used to assess within-day reliability and between-days reliability respectively. Standard error of measurement (SEM) and smallest detectable difference (SDD) described the within-subjects variability. MMG fatigue measures using linear regression slopes showed low reliability and large between-days error (ICC1,2=0.43-0.46; SDD=306.0-324.8% for MPF and MF slopes respectively). Overall MPF and MF, on the other hand, were reliable with high ICCs and lower SDDs compared to linear slopes (ICC1,2=0.79-0.83; SDD=21.9-22.8% for MPF and MF respectively). ICC1,2 for overall MMG RMS and linear RMS slopes were 0.81 and 0.66 respectively; however, the SDDs were high (56.4% and 268.8% respectively). The poor between-days reliability found in this study suggests caution in using MMG RMS, MPF and MF and their corresponding slopes in assessing muscle fatigue.

A Preliminary Magnetoinductive Sensor System for Real-Time Imaging of Steel Reinforcing Bars Embedded Within Concrete

This paper studies the feasibility of using solid-state magnetoinductive probes for detecting and imaging steel reinforcing bars embedded within prestressed and reinforced concrete. Changes in the inductance of the sensor material are directly proportional to the strength of the measured magnetic field parallel to the sensor. Using a square coil of 300 mm times 300 mm times 2.5 mm, 10-mm rebars can be imaged down up to a depth of 100 mm. Experimental results obtained by scanning steel bar specimens are presented. General performance characteristics and sensor limitations are also investigated.

Inductive and magnetic field inspection systems for rebar visualization and corrosion estimation in reinforced and pre-stressed concrete

Inductive and magnetic field inspection systems are becoming increasing popular for the nondestructive imaging and condition assessment of reinforcing components, such as steel reinforcing bars (rebars) and tendons in reinforced and prestressed concrete structures. In this article, we review the principles of this nascent technology, the research and commercial instruments that are now available, and the directions of future research. Magnetic field imaging technology has in general many potential benefits; it is truly nondestructive and non-invasive, it is non-hazardous, cost-effective and, most important, ignores the concrete matrix in which the reinforcing components are embedded. Most significantly, by analysing the impedance change in an excitation coil, it is also possible to obtain quantitative information (and image data) in relation to regions of corrosion. However, the testing industry has traditionally been reluctant to apply this methodology, for the important reason that the detection range is limited by the rapid attenuation of the magnetic field with increasing distance from the source. This limitation is now being addressed with research into novel coil arrangements, new, more stable and sensitive solid state sensors, and reconstruction algorithms that allow virtual three dimensional reconstruction of embedded components.

Development of a solid-state multi-sensor array camera for real time imaging of magnetic fields

The development of a real-time magnetic field imaging camera based on solid-state sensors is described. The final laboratory comprises a 2D array of 33 x 33 solid state, tri-axial magneto-inductive sensors, and is located within a large current-carrying coil. This may be excited to produce either a steady or time-varying magnetic field. Outputs from several rows of sensors are routed to a sub-master controller and all sub-masters route to a master-controller responsible for data coordination and signal pre-processing. The data are finally streamed to a host computer via a USB interface and the image generated and displayed at a rate of several frames per second. Accurate image generation is predicated on a knowledge of the sensor response, magnetic field perturbations and the nature of the target respecting permeability and conductivity. To this end, the development of the instrumentation has been complemented by extensive numerical modelling of field distribution patterns using boundary element methods. Although it was originally intended for deployment in the nondestructive evaluation (NDE) of reinforced concrete, it was soon realised during the course of the work that the magnetic field imaging system had many potential applications, for example, in medicine, security screening, quality assurance (such as the food industry), other areas of nondestructive evaluation (NDE), designs associated with magnetic fields, teaching and research.

A 1-D Solid-State-Sensor-Based Array System for Magnetic Field Imaging of Steel Reinforcing Bars Embedded Within Reinforced Concrete

This paper describes a linear 1-D solid-state-based magneto-inductive sensor array for detecting and imaging steel-reinforcing bars embedded within prestressed and reinforced concrete. Using a square coil of 300 mm times 300 mm times 2.5 mm and by only measures of the vertical component of the magnetic flux density, pictorial representation of embedded rebars of different diameters and configurations can be imaged down up to a depth of 100 mm. The system is also capable of imaging metallic objects of different shapes. Experimental results obtained by scanning different steel bar specimens are presented.

Modeling Studies on the Development of a System for Real-Time Magnetic-Field Imaging of Steel Reinforcing Bars Embedded Within Reinforced Concrete

This paper addresses fundamental issues associated with the development of a real-time magnetic-field imaging system for nondestructive testing of prestressed and reinforced concrete. Modeling results have shown that with a square coil of 300 times300 times2.5 mm3, 10-mm rebars can be imaged down to a depth of 100 mm. Studies also indicate that the vertical component of the induced magnetic field is most favorable as it can readily be reconstructed to yield geometry and dimensional information pertaining to the rebar structure.

THE APPLICATION OF MAGNETO INDUCTIVE SENSORS FOR NON-DESTRUCTIVE TESTING OF STEEL REINFORCING BARS EMBEDDED WITHIN PRE-STRESSED AND REINFORCED CONCRETE

This paper demonstrates the feasibility of using solid‐state magneto‐inductive probes for detecting and imaging of steel reinforcing bars embedded within pre‐stressed and reinforced concrete. Changes in the inductance of the sensor material are directly proportional to the strength of the measured magnetic field parallel to the sensor. Experimental results obtained by scanning steel bars specimens are presented. General performance characteristics and sensor output limitations are investigated by using different orientations, sensing distance, excitation intensity, bar sizes and geometries.

Determining the Main CSMA Parameters for Adequate Performance of WSN for Real-Time Volcano Monitoring System Applications

This paper presents a study in order to identify the value range of the main parameters within carrier sense multiple access (CSMA) defined in IEEE 802.15.4 that guarantees a satisfactory wireless sensor network (WSN) performance for a volcano monitoring application. Moreover, this study performs the comparison among several test-beds in outdoor scenarios with the purpose of distinguishing the optimal number of nodes for each gateway according the main constraints given by an existing sensor network for real-time (RT) volcano monitoring system such as sampling time, packet loss, and delay. We used a mathematical model that works with Markovian techniques and involves some parameters of CSMA mechanism within the model, such as the minimum value of the backoff exponent (BEmin), the contention window length (W), and the number of slots (L). We obtained the approximate values of these parameters by the interpolation of the normalized throughput curves from the deployment, and thus, we could obtain a mathematical model with the specifications required for the RT volcano monitoring. After validating the model with test-bed outdoor deployments we found that BEmin, W and L are key factors for determining the performance of a WSN, these parameters guarantee the range in which the WSN works according to the constraints imposed for this particular volcano monitoring application.