CCi-MOBILE: Design and Evaluation of a Cochlear Implant and Hearing Aid Research Platform for Speech Scientists and Engineers

Hearing loss is an increasingly prevalent condition resulting from damage to the inner ear which causes a reduction in speech intelligibility. The societal need for assistive hearing devices has increased exponentially over the past two decades; however, actual human performance with such devices has only seen modest gains relative to advancements in digital signal processing (DSP) technology. A major challenge with clinical hearing technologies is the limited ability to run complex signal processing algorithms requiring high computation power. The CCi-MOBILE platform, developed at UT-Dallas, provides the research community with an open-source, flexible, easy-to-use, software-mediated, powerful computing research interface to conduct a wide variety of listening experiments. The platform supports cochlear implants (CIs) and hearing aids (HAs) independently, as well as bimodal hearing (i.e., a CI in one ear and HA in the contralateral ear). The platform is ideally suited to address hearing research for: both quiet and naturalistic noisy conditions, sound localization, and lateralization. The platform uses commercially available smartphone/tablet devices as portable sound processors and can provide bilateral electric and acoustic stimulation. The hardware components, firmware, and software suite are presented to demonstrate safety to the speech scientist and CI/HA user, highlight user-specificity, and outline various applications of the platform for research.

Easily-Deployable Acoustic Local Positioning System Based on Auto-Calibrated Wireless Beacons

Self-calibrated Acoustic Local Positioning Systems (ALPS) generally require a high consumption of hardware and software resources to obtain the user’s position at an acceptable update rate. To address this limitation, this work proposes a self-calibrated ALPS based on a software/hardware co-design approach. This working architecture allows for efficient communications, signal processing tasks, and the running of the positioning algorithm on low-cost devices. This fact also enables the real-time system operation. The proposed system is composed of a minimum of four RF-synchronized active acoustic beacons, which emit spread-spectrum modulated signals to position an unlimited number of receiver nodes. Each receiver node estimates the beacons’ position by means of an auto-calibration process and then computes its own position by means of a 3D multilateration algorithm. A set of experimental tests has been carried out where the feasibility of the proposed system is demonstrated. In these experiments, accuracies below 0.1 m are obtained in the determination of the receptor node position with respect to the set of previously-calibrated beacons.

Simple computation of the approximated modulation transfer function (MTF) using spreadsheet-software: method and evaluation in five maxillofacial CBCT-devices

OBJECTIVES

To develop a simple way to compute the approximated modulation transfer function (MTF) manually using conventional spreadsheet software.

METHODS

Basing on an edge-image a method was developed, facilitating computation of the edge spread and line spread function in open-source spreadsheet software (Gnumeric; http://projects.gnome.org/gnumeric/downloads.shtml ). By means of the integrated fast Fourier transformation Fourier coefficients are obtained from the line spread function which can then be plotted vs spatial frequency to obtain MTF-plots. For the experimental evaluation an edge test object was exposed in five commercial CBCT devices for maxillofacial applications.

RESULTS

The MTF as obtained from the above-described manual method at 10% modulation ranged between 1.6 and 2.3 cycles/mm. Reproducibility as expressed as relative differences between single assessments ranging between 3% and 11%. The computed MTF-values compare well with results from the literature.

CONCLUSIONS

The method introduced can be used to manually and reliably estimate a CBCTs' MTF as one central parameter for the imaging systems' performance.

Non-Adaptive Methods for Fetal ECG Signal Processing: A Review and Appraisal

Fetal electrocardiography is among the most promising methods of modern electronic fetal monitoring. However, before they can be fully deployed in the clinical practice as a gold standard, the challenges associated with the signal quality must be solved. During the last two decades, a great amount of articles dealing with improving the quality of the fetal electrocardiogram signal acquired from the abdominal recordings have been introduced. This article aims to present an extensive literature survey of different non-adaptive signal processing methods applied for fetal electrocardiogram extraction and enhancement. It is limiting that a different non-adaptive method works well for each type of signal, but independent component analysis, principal component analysis and wavelet transforms are the most commonly published methods of signal processing and have good accuracy and speed of algorithms.

A 2D Magneto-Acousto-Electrical Tomography Method to Detect Conductivity Variation Using Multifocus Image Method

As magneto-acoustic-electrical tomography (MAET) combines the merits of high contrast and high imaging resolution, and is extremely useful for electrical conductivity measurement, so it is expected to be a promising medical imaging modalities for diagnosis of early-stage cancer. Based on the Verasonics system and the MC600 displacement platform, we designed and implemented a MAET system with a chirp pulse stimulation (MAET-CPS) method and a focal probe was utilized for stepscan focus excitation to enhance the imaging resolution. The relevant experiments were conducted to explore the influence of excitation positions of the single-focus point, and the effect of the excitation position on the amplitudes of the conductivity variation was clearly demonstrated. In order to take advantage of the merits of multifocus imaging, we firstly proposed a single focus MAET system with a chirp pulse stimulation (sfMAET-CPS) method and a multifocus MAET system with a chirp pulse stimulation (mfMAET-CPS) method for high-resolution conductivity imaging, and a homogenous gelatin phantom with a cuboid-shaped hole was used to investigate the accuracy of mfMAET-CPS. Comparative experiments were carried out on the same uniform phantom by the sfMAET-CPS and the mfMAET-CPS, respectively. The results showed that: (1) the electrical conductivity distributions of the homogenous phantom with a cuboid-shaped hole were detected by the sfMAET-CPS but were easily affected by the focal point, which demonstrated that the sfMAET-CPS had a low imaging resolution. (2) Compared with the sfMAET-CPS, the imaging effect of the mfMAET-CPS was much better than that of the sfMAET-CPS. (3) A linear interpolation algorithm was used to process the 2D conductivity distribution; it increased the smoothness of the conductivity distribution and improved the imaging effect. The stepscan focus excitation and the linearly frequency-modulated theory provide an alternative scheme for the clinical application of MAET.

Design and Validation of an FPGA-Based Configurable Transcranial Doppler Neurofeedback System for Chronic Pain Patients

Neurofeedback is a self-regulation technique that can be applied to learn to voluntarily control cerebral activity in specific brain regions. In this work, a Transcranial Doppler-based configurable neurofeedback system is proposed and described. The hardware configuration is based on the Red Pitaya board, which gives great flexibility and processing power to the system. The parameter to be trained can be selected between several temporal, spectral, or complexity features from the cerebral blood flow velocity signal in different vessels. As previous studies have found alterations in these parameters in chronic pain patients, the system could be applied to help them to voluntarily control these parameters. Two protocols based on different temporal lengths of the training periods have been proposed and tested with six healthy subjects that were randomly assigned to one of the protocols at the beginning of the procedure. For the purposes of the testing, the trained parameter was the mean cerebral blood flow velocity in the aggregated data from the two anterior cerebral arteries. Results show that, using the proposed neurofeedback system, the two groups of healthy volunteers can learn to self-regulate a parameter from their brain activity in a reduced number of training sessions.

Real-Time Processing Library for Open-Source Hardware Biomedical Sensors

Applications involving data acquisition from sensors need samples at a preset frequency rate, the filtering out of noise and/or analysis of certain frequency components. We propose a novel software architecture based on open-software hardware platforms which allows programmers to create data streams from input channels and easily implement filters and frequency analysis objects. The performances of the different classes given in the size of memory allocated and execution time (number of clock cycles) were analyzed in the low-cost platform Arduino Genuino. In addition, 11 people took part in an experiment in which they had to implement several exercises and complete a usability test. Sampling rates under 250 Hz (typical for many biomedical applications) makes it feasible to implement filters, sliding windows and Fourier analysis, operating in real time. Participants rated software usability at 70.2 out of 100 and the ease of use when implementing several signal processing applications was rated at just over 4.4 out of 5. Participants showed their intention of using this software because it was percieved as useful and very easy to use. The performances of the library showed that it may be appropriate for implementing small biomedical real-time applications or for human movement monitoring, even in a simple open-source hardware device like Arduino Genuino. The general perception about this library is that it is easy to use and intuitive.

[Design of magneto-acoustic-electrical detection system and verification of its linear sweep theory]

Clinical studies had demonstrated that early diagnosis of lesion could significantly reduce the risk of cancer. Magneto-acoustic-electrical tomography (MAET) is expected to become a new detection method due to its advantages of high resolution and high contrast. Based on thinking of modular design, a low-cost, digital magneto-acoustic conductivity detection system was designed and implemented in this study. The theory of MAET using chirp continuous wave excitation was introduced. The results of homogeneous phantom experiment with 0.5% NaCl clearly showed that the conductivity curve of homogeneous phantom was highly consistent with the actual physical size, which indicated that the chirp excitation theory in our proposed system was correct and feasible. Besides, the resolution obtained by 1 000 μs sweep time was better than that obtained by 500 μs and 1 500 μs, which means that sweep time is an important factor affecting the detection resolution of the conductivity. The same result was obtained in the experiments carried out on homogeneous phantoms with different concentrations of NaCl, which demonstrated the repeatability of our proposed MAET system.

Development of pulse shape algorithms for discrimination between gamma and X-ray radiation pulses

Influence of x-ray pulse generated from gamma spectrometers should be eliminated in applications, which typically uses pulse shape techniques between gamma and x-ray pulses. In this study, we proposed and tested several algorithms aiming to eliminate this influence. The algorithms are based on curve fitting (CF), artificial neural network (ANN), system identification, peak shape, amplitude search with curve fitting and pulse tracking methods. Gamma pulses and X-ray pulses are detected by NaI(TI) scintillator detector and Silicon lithium Si(Li) detector, respectively. The developed algorithms are tested using 32,000 total instantaneous detector events of acquired gamma pulses and 65,536 total instantaneous detector events of x-ray source. An algorithm using the least square curve fitting method is applied for differentiation between gamma and x-ray pulses. ANN is employed as a classifier for identification of extracted spectrum and Bispectrum features of gamma and x-ray pulses. A comparison between identification results due to extracted spectrum and Bispectrum features is established. System identification algorithm is then built to determine the detection system response of each radiation pulse, which includes various models to attain best fitting. These models are Auto-regressive model with external input (ARX), the linear parametric model (IV) and process models (P1D). The peak shape algorithm is also tried, which depends on the individual classification of pulse width. The amplitude search with curve fitting algorithm is implemented. Moreover, the pulse tracking algorithm is investigated for PSD between gamma and x-ray pulses. The maximum peak of contaminated pulse is tracked using a suggested peak search method. Then, pulse position is estimated using matrix method. Comparison between these algorithms is conducted based on the evaluation of light of residuals, fitting error and processing time. The results confirm that peak shape algorithm is the best one from computational speed point of view, while ANN algorithm using Bispectrum feature extraction method is the most appropriate one that yields 100% accuracy over noisy environment with longer processing time. In addition, the system identification algorithm is the optimal algorithm that achieves zero fitting error under clean environment. These proposed algorithms for PSD between gamma and x-ray pulses lead to design efficient spectrometers with optimal applicability in various environments.

Lyapunov estimation for high-speed demodulation in multifrequency atomic force microscopy

An important issue in the emerging field of multifrequency atomic force microscopy (MF-AFM) is the accurate and fast demodulation of the cantilever-tip deflection signal. As this signal consists of multiple frequency components and noise processes, a lock-in amplifier is typically employed for its narrowband response. However, this demodulator suffers inherent bandwidth limitations as high-frequency mixing products must be filtered out and several must be operated in parallel. Many MF-AFM methods require amplitude and phase demodulation at multiple frequencies of interest, enabling both z-axis feedback and phase contrast imaging to be achieved. This article proposes a model-based multifrequency Lyapunov filter implemented on a field-programmable gate array (FPGA) for high-speed MF-AFM demodulation. System descriptions and simulations are verified by experimental results demonstrating high tracking bandwidths, strong off-mode rejection and minor sensitivity to cross-coupling effects. Additionally, a five-frequency system operating at 3.5 MHz is implemented for higher harmonic amplitude and phase imaging up to 1 MHz.

Detecting Smoking Events Using Accelerometer Data Collected Via Smartwatch Technology: Validation Study

BACKGROUND

Smoking is the leading cause of preventable death in the world today. Ecological research on smoking in context currently relies on self-reported smoking behavior. Emerging smartwatch technology may more objectively measure smoking behavior by automatically detecting smoking sessions using robust machine learning models.

OBJECTIVE

This study aimed to examine the feasibility of detecting smoking behavior using smartwatches. The second aim of this study was to compare the success of observing smoking behavior with smartwatches to that of conventional self-reporting.

METHODS

A convenience sample of smokers was recruited for this study. Participants (N=10) recorded 12 hours of accelerometer data using a mobile phone and smartwatch. During these 12 hours, they engaged in various daily activities, including smoking, for which they logged the beginning and end of each smoking session. Raw data were classified as either smoking or nonsmoking using a machine learning model for pattern recognition. The accuracy of the model was evaluated by comparing the output with a detailed description of a modeled smoking session.

RESULTS

In total, 120 hours of data were collected from participants and analyzed. The accuracy of self-reported smoking was approximately 78% (96/123). Our model was successful in detecting 100 of 123 (81%) smoking sessions recorded by participants. After eliminating sessions from the participants that did not adhere to study protocols, the true positive detection rate of the smartwatch based-detection increased to more than 90%. During the 120 hours of combined observation time, only 22 false positive smoking sessions were detected resulting in a 2.8% false positive rate.

CONCLUSIONS

Smartwatch technology can provide an accurate, nonintrusive means of monitoring smoking behavior in natural contexts. The use of machine learning algorithms for passively detecting smoking sessions may enrich ecological momentary assessment protocols and cessation intervention studies that often rely on self-reported behaviors and may not allow for targeted data collection and communications around smoking events.

A Review of Hybrid Fiber-Optic Distributed Simultaneous Vibration and Temperature Sensing Technology and Its Geophysical Applications

Distributed sensing systems can transform an optical fiber cable into an array of sensors, allowing users to detect and monitor multiple physical parameters such as temperature, vibration and strain with fine spatial and temporal resolution over a long distance. Fiber-optic distributed acoustic sensing (DAS) and distributed temperature sensing (DTS) systems have been developed for various applications with varied spatial resolution, and spectral and sensing range. Rayleigh scattering-based phase optical time domain reflectometry (OTDR) for vibration and Raman/Brillouin scattering-based OTDR for temperature and strain measurements have been developed over the past two decades. The key challenge has been to find a methodology that would enable the physical parameters to be determined at any point along the sensing fiber with high sensitivity and spatial resolution, yet within acceptable frequency range for dynamic vibration, and temperature detection. There are many applications, especially in geophysical and mining engineering where simultaneous measurements of vibration and temperature are essential. In this article, recent developments of different hybrid systems for simultaneous vibration, temperature and strain measurements are analyzed based on their operation principles and performance. Then, challenges and limitations of the systems are highlighted for geophysical applications.

The performance of an automatic acoustic-based program classifier compared to hearing aid users' manual selection of listening programs

OBJECTIVE

To compare preference for and performance of manually selected programmes to an automatic sound classifier, the Phonak AutoSense OS.

DESIGN

A single blind repeated measures study. Participants were fit with Phonak Virto V90 ITE aids; preferences for different listening programmes were compared across four different sound scenarios (speech in: quiet, noise, loud noise and a car). Following a 4-week trial preferences were reassessed and the users preferred programme was compared to the automatic classifier for sound quality and hearing in noise (HINT test) using a 12 loudspeaker array.

STUDY SAMPLE

Twenty-five participants with symmetrical moderate-severe sensorineural hearing loss.

RESULTS

Participant preferences of manual programme for scenarios varied considerably between and within sessions. A HINT Speech Reception Threshold (SRT) advantage was observed for the automatic classifier over participant's manual selection for speech in quiet, loud noise and car noise. Sound quality ratings were similar for both manual and automatic selections.

CONCLUSIONS

The use of a sound classifier is a viable alternative to manual programme selection.

Physiological Signal Processing for Individualized Anti-nociception Management During General Anesthesia: a Review

OBJECTIVE

The aim of this paper is to review existing technologies for the nociception / anti-nociception balance evaluation during surgery under general anesthesia.

METHODS

General anesthesia combines the use of analgesic, hypnotic and muscle-relaxant drugs in order to obtain a correct level of patient non-responsiveness during surgery. During the last decade, great efforts have been deployed in order to find adequate ways to measure how anesthetic drugs affect a patient's response to surgical nociception. Nowadays, though some monitoring devices allow obtaining information about hypnosis and muscle relaxation, no gold standard exists for the nociception / anti-nociception balance evaluation. Articles from the PubMed literature search engine were reviewed. As this paper focused on surgery under general anesthesia, articles about nociception monitoring on conscious patients, in post-anesthesia care unit or in intensive care unit were not considered.

RESULTS

In this article, we present a review of existing technologies for the nociception / anti-nociception balance evaluation, which is based in all cases on the analysis of the autonomous nervous system activity. Presented systems, based on sensors and physiological signals processing algorithms, allow studying the patients' reaction regarding anesthesia and surgery.

CONCLUSION

Some technological solutions for nociception / antinociception balance monitoring were described. Though presented devices could constitute efficient solutions for individualized anti-nociception management during general anesthesia, this review of current literature emphasizes the fact that the choice to use one or the other mainly relies on the clinical context and the general purpose of the monitoring.

Linear and non-linear analysis of uterine contraction signals obtained with tocodynamometry in prediction of operative vaginal delivery

OBJECTIVE

The aim of this study was to explore whether linear and non-linear analysis of uterine contraction (UC) signals obtained with external tocodynamometry can predict operative vaginal delivery (OVD).

MATERIALS AND METHODS

The last 2 h before delivery (H1 and H2) of 55 UC recordings acquired with external tocodynamometry in the labour ward of a tertiary care hospital were analysed. Signal processing involved the quantification of UCs/segment (UCN), and the linear and non-linear indices: Sample Entropy (SampEn) measuring signal irregularity; interval index (II) measuring signal variability, both of which may be associated with uterine muscle fatigue, and high frequency (HF), associated with maternal breathing movements. Thirty-two women had normal deliveries and 23 OVDs. Statistical inference was performed using 95% confidence intervals (95% CIs) for the median, and areas under the receiver operating curves (auROCs), with univariate and bivariate analyses.

RESULTS

A significant association was found between maternal body mass index (BMI) and UC signal quality in H1, with moderate/poor signal quality being more frequent with higher maternal BMI. There was an overall increase in contraction frequency (UCN), signal regularity (SampEn), signal variability (II), and maternal breathing (HF) from H1 to H2. The OVD group exhibited significantly higher values of signal irregularity and variability (SampEn and II) in H1, and higher contraction frequency (UCN) and maternal breathing (HF) in H2. Modest auROCs were obtained with these indices in the discrimination between normal and OVDs.

CONCLUSIONS

The results of this exploratory study suggest that analysis of UC signals obtained with tocodynamometry, using linear and non-linear indices associated with muscle fatigue and maternal breathing, identifies significant changes occurring during labour, and differences between normal and OVDs, but their discriminative capacity between the two types of delivery is modest. Further refinement of this analysis is needed before it may be clinically useful.

Real-Time Digital Signal Processing Based on FPGAs for Electronic Skin Implementation

Enabling touch-sensing capability would help appliances understand interaction behaviors with their surroundings. Many recent studies are focusing on the development of electronic skin because of its necessity in various application domains, namely autonomous artificial intelligence (e.g., robots), biomedical instrumentation, and replacement prosthetic devices. An essential task of the electronic skin system is to locally process the tactile data and send structured information either to mimic human skin or to respond to the application demands. The electronic skin must be fabricated together with an embedded electronic system which has the role of acquiring the tactile data, processing, and extracting structured information. On the other hand, processing tactile data requires efficient methods to extract meaningful information from raw sensor data. Machine learning represents an effective method for data analysis in many domains: it has recently demonstrated its effectiveness in processing tactile sensor data. In this framework, this paper presents the implementation of digital signal processing based on FPGAs for tactile data processing. It provides the implementation of a tensorial kernel function for a machine learning approach. Implementation results are assessed by highlighting the FPGA resource utilization and power consumption. Results demonstrate the feasibility of the proposed implementation when real-time classification of input touch modalities are targeted.

A review of demodulation techniques for amplitude-modulation atomic force microscopy

In this review paper, traditional and novel demodulation methods applicable to amplitude-modulation atomic force microscopy are implemented on a widely used digital processing system. As a crucial bandwidth-limiting component in the z-axis feedback loop of an atomic force microscope, the purpose of the demodulator is to obtain estimates of amplitude and phase of the cantilever deflection signal in the presence of sensor noise or additional distinct frequency components. Specifically for modern multifrequency techniques, where higher harmonic and/or higher eigenmode contributions are present in the oscillation signal, the fidelity of the estimates obtained from some demodulation techniques is not guaranteed. To enable a rigorous comparison, the performance metrics tracking bandwidth, implementation complexity and sensitivity to other frequency components are experimentally evaluated for each method. Finally, the significance of an adequate demodulator bandwidth is highlighted during high-speed tapping-mode atomic force microscopy experiments in constant-height mode.

Representation Method for Spectrally Overlapping Signals in Flow Cytometry Based on Fluorescence Pulse Time-Delay Estimation

Flow cytometry is being applied more extensively because of the outstanding advantages of multicolor fluorescence analysis. However, the intensity measurement is susceptible to the nonlinearity of the detection method. Moreover, in multicolor analysis, it is impossible to discriminate between fluorophores that spectrally overlap; this influences the accuracy of the fluorescence pulse signal representation. Here, we focus on spectral overlap in two-color analysis, and assume that the fluorescence follows the single exponential decay model. We overcome these problems by analyzing the influence of the spectral overlap quantitatively, which enables us to propose a method of fluorescence pulse signal representation based on time-delay estimation (between fluorescence and scattered pulse signals). First, the time delays are estimated using a modified chirp Z-transform (MCZT) algorithm and a fine interpolation of the correlation peak (FICP) algorithm. Second, the influence of hardware is removed via calibration, in order to acquire the original fluorescence lifetimes. Finally, modulated signals containing phase shifts associated with these lifetimes are created artificially, using a digital signal processing method, and reference signals are introduced in order to eliminate the influence of spectral overlap. Time-delay estimation simulation and fluorescence signal representation experiments are conducted on fluorescently labeled cells. With taking the potentially overlap of autofluorescence as part of the observed fluorescence spectrum, rather than distinguishing the individual influence, the results show that the calculated lifetimes with spectral overlap can be rectified from 8.28 and 4.86 ns to 8.51 and 4.63 ns, respectively, using the comprehensive approach presented in this work. These values agree well with the lifetimes (8.48 and 4.67 ns) acquired for cells stained with single-color fluorochrome. Further, these results indicate that the influence of spectral overlap can be eliminated effectively. Moreover, modulation, mixing with reference signals, and low-pass filtering are performed with a digital signal processing method, thereby obviating the need for a high-speed analog device and complex circuit system. Finally, the flexibility of the comprehensive method presented in this work is significantly higher than that of existing methods.

Novel Approach to Analyzing MFE of Noncoding RNA Sequences

Genomic studies have become noncoding RNA (ncRNA) centric after the study of different genomes provided enormous information on ncRNA over the past decades. The function of ncRNA is decided by its secondary structure, and across organisms, the secondary structure is more conserved than the sequence itself. In this study, the optimal secondary structure or the minimum free energy (MFE) structure of ncRNA was found based on the thermodynamic nearest neighbor model. MFE of over 2600 ncRNA sequences was analyzed in view of its signal properties. Mathematical models linking MFE to the signal properties were found for each of the four classes of ncRNA analyzed. MFE values computed with the proposed models were in concordance with those obtained with the standard web servers. A total of 95% of the sequences analyzed had deviation of MFE values within ±15% relative to those obtained from standard web servers.

Assessing the reproducibility of high definition urethral pressure profilometry and its correlation with an air-charged system

INTRODUCTION

Recently, a new urodynamic method for the assessment of stress urinary incontinence called high definition urethral pressure profilometry (HD-UPP) has been introduced. This method combines a novel microtip catheter with advanced signal processing to enable spatial data location and the reconstruction of a pressure image inside the urethra. In order to assess the reproducibility of HD-UPP data, we statistically evaluate HD-UPP datasets and compare them to data from a double balloon air-charged system.

MATERIALS AND METHODS

Both catheters are used on sedated female minipigs. Data from the microtip catheter are processed through a signal reconstruction algorithm, urodynamic features are extracted, and compared to the air-charged system. Reproducibility of HD-UPP data is assessed by statistically evaluating consecutive, intra-individual datasets.

RESULTS

HD-UPP delivers results in agreement with previous comparisons of microtip and air-charged systems. The average deviation of two consecutive, intra-individual pressure images is very low at 7 cm H₂ O.

CONCLUSIONS

HD-UPP provides physicians with detailed information on the pressure distribution inside the urethra. Through comparison with an air-charged catheter, it is shown that HD-UPP delivers results in agreement with previous studies on the comparison of microtip and air-charged catheters. It provides excellent reproducibility, as the difference between sequentially measured profiles from the same minipig is significantly lower than the one between profiles from different minipigs.

Prediction of potential barcoding sites on ITS1 by wavelet transform

For sustainable development, biodiversity conservation and life-quality improvement must be simultaneously considered. Molecular techniques have greatly impacted biotechnology. These methods have, in particular, improved the capability to investigate the fine differences among organisms and, as a consequence, to better investigate the effects on environmental factors on them. We propose an approach to support the optimal selection of molecular probes for barcoding application in many biotechnological fields. The aim of our work is specificity maximization. To this purpose, we have integrated a filter system based on wavelet transforms with biological knowledge about the sequence proneness to mutation and post-translational modification. Specifically, we have tested the proposed method on ITS1 sequences that are a region of the rRNA locus. Our analysis has shown the presence of other local relative stable conformations in addition to known cleavage site. Their characteristics differ within the group of mammals selected for our analysis. These variations could be used to design new species-specific barcoding probes or other quick molecular screening tools.

A Practical Tablet-Based Hearing Aid Configuration as an Exemplar Project for Students of Instrumentation

This paper presents the configuration and digital signal processing details of a tablet-based hearing aid transmitting wirelessly to standard earphones, whereby the tablet performs full sound processing rather than solely providing a means of setting adjustment by streaming to conventional digital hearing aids. The presented device confirms the recognized advantages of this tablet-based approach (e.g., in relation to cost, frequency domain processing, amplification range, versatility of functionality, component battery rechargeability), and flags the future wider-spread availability of such hearing solutions within mainstream healthcare. The use of a relatively high sampling frequency was found to be beneficial for device performance, while the use of optional off-the-shelf add-on components (e.g., data acquisition device, high fidelity microphone, compact wireless transmitter/receiver, wired headphones) are also discussed in relation to performance optimization. The easy-to-follow configuration utilized is well suited to student learning/research instrumentation projects within the health and biomedical sciences. In this latter regard, the presented device was pedagogically integrated into a flipped classroom approach for the teaching of bioinstrumentation within an Allied Health Sciences School, with the subsequent establishment of positive student engagement outcomes.

Expanding the potential of standard flow cytometry by extracting fluorescence lifetimes from cytometric pulse shifts

Fluorescence lifetime measurements provide information about the fluorescence relaxation, or intensity decay, of organic fluorophores, fluorescent proteins, and other inorganic molecules that fluoresce. The fluorescence lifetime is emerging in flow cytometry and is helpful in a variety of multiparametric, single cell measurements because it is not impacted by nonlinearity that can occur with fluorescence intensity measurements. Yet time-resolved cytometry systems rely on major hardware modifications making the methodology difficult to reproduce. The motivation of this work is, by taking advantage of the dynamic nature of flow cytometry sample detection and applying digital signal processing methods, to measure fluorescence lifetimes using an unmodified flow cytometer. We collect a new lifetime-dependent parameter, referred to herein as the fluorescence-pulse-delay (FPD), and prove it is a valid representation of the average fluorescence lifetime. To verify we generated cytometric pulses in simulation, with light emitting diode (LED) pulsation, and with true fluorescence measurements of cells and microspheres. Each pulse is digitized and used in algorithms to extract an average fluorescence lifetime inherent in the signal. A range of fluorescence lifetimes is measurable with this approach including standard organic fluorophore lifetimes (∼1 to 22 ns) as well as small, simulated shifts (0.1 ns) under standard conditions (reported herein). This contribution demonstrates how digital data acquisition and signal processing can reveal time-dependent information foreshadowing the exploitation of full waveform analysis for quantification of similar photo-physical events within single cells.

Evolution of accelerometer methods for physical activity research

The technology and application of current accelerometer-based devices in physical activity (PA) research allow the capture and storage or transmission of large volumes of raw acceleration signal data. These rich data not only provide opportunities to improve PA characterisation, but also bring logistical and analytic challenges. We discuss how researchers and developers from multiple disciplines are responding to the analytic challenges and how advances in data storage, transmission and big data computing will minimise logistical challenges. These new approaches also bring the need for several paradigm shifts for PA researchers, including a shift from count-based approaches and regression calibrations for PA energy expenditure (PAEE) estimation to activity characterisation and EE estimation based on features extracted from raw acceleration signals. Furthermore, a collaborative approach towards analytic methods is proposed to facilitate PA research, which requires a shift away from multiple independent calibration studies. Finally, we make the case for a distinction between PA represented by accelerometer-based devices and PA assessed by self-report.

Spatial repolarization heterogeneity and survival in Chagas disease

OBJECTIVES

We investigated if cardiac spatial repolarization heterogeneity might be associated with an increased risk of death in patients with chronic Chagas disease.

METHODS

Repolarization heterogeneity was assessed using the V-index, a recently introduced metric founded on a biophysical model of the ECG. This metric provides an estimate of the standard deviation of the repolarization times across the heart. We analyzed 113 patients (aged 21- 67 years) enrolled between 1998 and 1999 who had a known serological status showing positive reactions to Trypanosoma cruzi. Fourteen subjects died during a 10-year follow-up period.

RESULTS

The V-index was significantly lower in survivor (S) than in non-survivor (NS) subjects (S: 31.2 ± 13.3 ms vs NS: 41.2 ± 18.6 ms, single-tail t-test: p = 0.009, single-tail Wilcoxon rank sum test: p = 0.029). A V-index larger than 36.3 ms was related to a significantly higher risk of death in a univariate Cox proportional-hazards analysis (hazard ratio, HR = 5.34, p = 0.0046). In addition, V-index > 36.3 ms retained its prognostic value in a multivariate Cox proportional-hazards analysis after adjustment for other three clinical variables (left ventricular ejection factor < 0.50, QRS duration > 133 ms, ventricular tachycardia during stress testing or 24 hours Holter) and for T-wave amplitude variability > 30 μV, even using shrinkage, a statistical procedure that protects against over-fitting due to small sample size.

CONCLUSIONS

The study showed that an increased dispersion of repolarization times in patients with Chagas disease, as measured by the V-index, is significantly correlated with the risk of death in a univariate survival analysis. The V-index captures prognostic information not immediately available from the analysis of other established risk factors.

Evaluation of dental morphometrics during the orthodontic treatment

BACKGROUND

Diagnostic orthodontic and prosthetic procedures commence with an initial examination, during which a number of individual findings on occlusion or malocclusion are clarified. Nowadays we try to replace standard plaster casts by scanned objects and digital models.

METHOD

Geometrically calibrated images aid in the comparison of several different steps of the treatment and show the variation of selected features belonging to individual biomedical objects. The methods used are based on geometric morphometrics, making a new approach to the evaluation of the variability of features. The study presents two different methods of measurement and shows their accuracy and reliability.

RESULTS

The experimental part of the present paper is devoted to the analysis of the dental arch objects of 24 patients before and after the treatment using the distances between the canines and premolars as the features important for diagnostic purposes. Our work proved the advantage of measuring digitalized orthodontic models over manual measuring of plaster casts, with statistically significant results and accuracy sufficient for dental practice.

CONCLUSION

A new method of computer imaging and measurements of a dental stone cast provides information with the precision required for orthodontic treatment. The results obtained point to the reduction in the variance of the distances between the premolars and canines during the treatment, with a regression coefficient RC=0.7 and confidence intervals close enough for dental practice. The ratio of these distances pointed to the nearly constant value of this measure close to 0.84 for the given set of 24 individuals.

Signal processing for molecular and cellular biological physics: an emerging field

Recent advances in our ability to watch the molecular and cellular processes of life in action--such as atomic force microscopy, optical tweezers and Forster fluorescence resonance energy transfer--raise challenges for digital signal processing (DSP) of the resulting experimental data. This article explores the unique properties of such biophysical time series that set them apart from other signals, such as the prevalence of abrupt jumps and steps, multi-modal distributions and autocorrelated noise. It exposes the problems with classical linear DSP algorithms applied to this kind of data, and describes new nonlinear and non-Gaussian algorithms that are able to extract information that is of direct relevance to biological physicists. It is argued that these new methods applied in this context typify the nascent field of biophysical DSP. Practical experimental examples are supplied.

Real-time FPGA processing for high-speed optical frequency domain imaging

We present a novel algorithm for reconstructing interferograms acquired in optical frequency domain imaging (OFDI). The algorithm was developed specifically for processing in field programmable gate arrays (FPGAs) and featured the use of a finite-impulse-response (FIR) filter implementation of B-spline interpolation for efficiently re-sampling k-space. When implemented in FPGAs, the algorithm allowed for real-time processing of interferograms acquired with a high-speed OFDI system at 54 kHz and a sampling rate of 100 MS/s.

Signal propagation in cortical networks: a digital signal processing approach

This work reports a digital signal processing approach to representing and modeling transmission and combination of signals in cortical networks. The signal dynamics is modeled in terms of diffusion, which allows the information processing undergone between any pair of nodes to be fully characterized in terms of a finite impulse response (FIR) filter. Diffusion without and with time decay are investigated. All filters underlying the cat and macaque cortical organization are found to be of low-pass nature, allowing the cortical signal processing to be summarized in terms of the respective cutoff frequencies (a high cutoff frequency meaning little alteration of signals through their intermixing). Several findings are reported and discussed, including the fact that the incorporation of temporal activity decay tends to provide more diversified cutoff frequencies. Different filtering intensity is observed for each community in those networks. In addition, the brain regions involved in object recognition tend to present the highest cutoff frequencies for both the cat and macaque networks.

A portable and autonomous magnetic detection platform for biosensing

This paper presents a prototype of a platform for biomolecular recognition detection. The system is based on a magnetoresistive biochip that performs biorecognition assays by detecting magnetically tagged targets. All the electronic circuitry for addressing, driving and reading out signals from spin-valve or magnetic tunnel junctions sensors is implemented using off-the-shelf components. Taking advantage of digital signal processing techniques, the acquired signals are processed in real time and transmitted to a digital analyzer that enables the user to control and follow the experiment through a graphical user interface. The developed platform is portable and capable of operating autonomously for nearly eight hours. Experimental results show that the noise level of the described platform is one order of magnitude lower than the one presented by the previously used measurement set-up. Experimental results also show that this device is able to detect magnetic nanoparticles with a diameter of 250 nm at a concentration of about 40 fM. Finally, the biomolecular recognition detection capabilities of the platform are demonstrated by performing a hybridization assay using complementary and non-complementary probes and a magnetically tagged 20mer single stranded DNA target.

FPGA-Based Front-End Electronics for Positron Emission Tomography

Modern Field Programmable Gate Arrays (FPGAs) are capable of performing complex discrete signal processing algorithms with clock rates above 100MHz. This combined with FPGA's low expense, ease of use, and selected dedicated hardware make them an ideal technology for a data acquisition system for positron emission tomography (PET) scanners. Our laboratory is producing a high-resolution, small-animal PET scanner that utilizes FPGAs as the core of the front-end electronics. For this next generation scanner, functions that are typically performed in dedicated circuits, or offline, are being migrated to the FPGA. This will not only simplify the electronics, but the features of modern FPGAs can be utilizes to add significant signal processing power to produce higher resolution images. In this paper two such processes, sub-clock rate pulse timing and event localization, will be discussed in detail. We show that timing performed in the FPGA can achieve a resolution that is suitable for small-animal scanners, and will outperform the analog version given a low enough sampling period for the ADC. We will also show that the position of events in the scanner can be determined in real time using a statistical positioning based algorithm.

A new strategy for fast radiofrequency CW EPR imaging: direct detection with rapid scan and rotating gradients

Rapid field scan on the order of T/s using high frequency sinusoidal or triangular sweep fields superimposed on the main Zeeman field, was used for direct detection of signals without low-frequency field modulation. Simultaneous application of space-encoding rotating field gradients have been employed to perform fast CW EPR imaging using direct detection that could, in principle, approach the speed of pulsed FT EPR imaging. The method takes advantage of the well-known rapid-scan strategy in CW NMR and EPR that allows arbitrarily fast field sweep and the simultaneous application of spinning gradients that allows fast spatial encoding. This leads to fast functional EPR imaging and, depending on the spin concentration, spectrometer sensitivity and detection band width, can provide improved temporal resolution that is important to interrogate dynamics of spin perfusion, pharmacokinetics, spectral spatial imaging, dynamic oxymetry, etc.

Digital hearing AIDS from the perspective of one consumer/audiologist

Recent developments in hearing aids are reviewed in the context of the author's personal experience as an audiologist and as a hearing aid wearer. The need for evidence of benefit specific to digital signal processing in hearing aids is stressed, as well as addressing cost-benefit ratios in view of the high cost of digital hearing aids.

A historical perspective on digital hearing AIDS: how digital technology has changed modern hearing AIDS

This article provides the author's perspective on the development of digital hearing aids and how digital signal processing approaches have led to changes in hearing aid design. Major landmarks in the evolution of digital technology are identified, and their impact on the development of digital hearing aids is discussed. Differences between analog and digital approaches to signal processing in hearing aids are identified.

Complex Permittivity of Planar Building Materials Measured With an Ultra-Wideband Free-Field Antenna Measurement System

Building materials are often incorporated into complex, multilayer macrostructures that are simply not amenable to measurements using coax or waveguide sample holders. In response to this, we developed an ultra-wideband (UWB) free-field measurement system. This measurement system uses a ground-plane-based system and two TEM half-horn antennas to transmit and receive the RF signal. The material samples are placed between the antennas, and reflection and transmission measurements made. Digital signal processing techniques are then applied to minimize environmental and systematic effects. The processed data are compared to a plane-wave model to extract the material properties with optimization software based on genetic algorithms.

Integration of digital signal processing technologies with pulsed electron paramagnetic resonance imaging

The integration of modern data acquisition and digital signal processing (DSP) technologies with Fourier transform electron paramagnetic resonance (FT-EPR) imaging at radiofrequencies (RF) is described. The FT-EPR system operates at a Larmor frequency (L(f)) of 300MHz to facilitate in vivo studies. This relatively low frequency L(f), in conjunction with our approximately 10MHz signal bandwidth, enables the use of direct free induction decay time-locked subsampling (TLSS). This particular technique provides advantages by eliminating the traditional analog intermediate frequency downconversion stage along with the corresponding noise sources. TLSS also results in manageable sample rates that facilitate the design of DSP-based data acquisition and image processing platforms. More specifically, we utilize a high-speed field programmable gate array (FPGA) and a DSP processor to perform advanced real-time signal and image processing. The migration to a DSP-based configuration offers the benefits of improved EPR system performance, as well as increased adaptability to various EPR system configurations (i.e., software configurable systems instead of hardware reconfigurations). The required modifications to the FT-EPR system design are described, with focus on the addition of DSP technologies including the application-specific hardware, software, and firmware developed for the FPGA and DSP processor. The first results of using real-time DSP technologies in conjunction with direct detection bandpass sampling to implement EPR imaging at RF frequencies are presented.