Simple Peak Detection Research Articles

Automated Heart Rate Detection in Seismocardiograms Using Electrocardiogram-Based Algorithms-A Feasibility Study.

In recent decades, much work has been implemented in heart rate (HR) analysis using electrocardiographic (ECG) signals. We propose that algorithms developed to calculate HR based on detected R-peaks using ECG can be applied to seismocardiographic (SCG) signals, as they utilize common knowledge regarding heart rhythm and its underlying physiology. We implemented the experimental framework with methods developed for ECG signal processing and peak detection to be applied and evaluated on SCGs. Furthermore, we assessed and chose the best from all combinations of 15 peak detection and 6 preprocessing methods from the literature on the CEBS dataset available on Physionet. We then collected experimental data in the lab experiment to measure the applicability of the best-selected technique to the real-world data; the abovementioned method showed high precision for signals recorded during sitting rest (HR difference between SCG and ECG: 0.12 ± 0.35 bpm) and a moderate precision for signals recorded with interfering physical activity-reading out a book loud (HR difference between SCG and ECG: 6.45 ± 3.01 bpm) when compared to the results derived from the state-of-the-art photoplethysmographic (PPG) methods described in the literature. The study shows that computationally simple preprocessing and peak detection techniques initially developed for ECG could be utilized as the basis for HR detection on SCG, although they can be further improved.

A Comparative Study of Narrow/Ultra-Wideband Microwave Sensors for the Continuous Monitoring of Vital Signs and Lung Water Level.

This article presents an in-depth investigation of wearable microwave antenna sensors (MASs) used for vital sign detection (VSD) and lung water level (LWL) monitoring. The study looked at two different types of MASs, narrowband (NB) and ultra-wideband (UWB), to decide which one was better. Unlike recent wearable respiratory sensors, these antennas are simple in design, low-profile, and affordable. The narrowband sensor employs an offset-feed microstrip transmission line, which has a bandwidth of 240 MHz at -10 dB reflection coefficient for the textile substrate. The UWB microwave sensor uses a CPW-fed line to excite an unbalanced U-shaped radiator, offering an extended simulated operating bandwidth from 1.5 to 10 GHz with impedance matching ≤-10 dB. Both types of microwave sensors are designed on a flexible RO 3003 substrate and textile conductive fabric attached to a cotton substrate. The specific absorption rate (SAR) of the sensors is measured at different resonant frequencies on 1 g and 10 g of tissue, according to the IEEE C95.3 standard, and both sensors meet the standard limit of 1.6 W/kg and 2 W/kg, respectively. A simple peak-detection algorithm is used to demonstrate high accuracy in the detection of respiration, heartbeat, and lung water content. Based on the experimental results on a child and an adult volunteer, it can be concluded that UWB MASs offer superior performance when compared to NB sensors.

P01.12.B Analysis of semi-spontaneous speech before, during and after awake craniotomy: a case study

Abstract Background The standard treatment for patients with eloquent gliomas is awake craniotomy (AC) with direct electrical stimulation, which results in a larger extent of resection while maintaining language function. Intraoperative language monitoring includes different standardized tasks and spontaneous speech elicitation. Despite careful monitoring, it was found that spontaneous speech deteriorated long-term after surgery. However, intraoperative spontaneous speech has not been investigated before. Detailed analyses may reveal possible predictors for language outcome. For the first time, we present a case study in which we investigate and quantify semi-spontaneous speech before, during and after AC. Material and Methods A left-handed 50-year old male with a glioblastoma (WHO grade 4) in the left parietal lobe underwent AC. Semi-spontaneous speech was collected (audio/video recording) using the Sabadel story retelling task at: 1 day before (T1), during (T2), 2 days (T3) and 2 months (T4) after surgery. Recordings were manually transcribed and language errors were quantified. A preliminary acoustic analysis with simple automatic peak detection was performed. Comparisons between test moments were made. Results Manual transcription showed 1) notable changes in the number of paraphasias (T1: 1% - T2: 1% - T3: 9% - T4: 5%), interjections (T1: 9% - T2: 9% - T3: 15% - T4: 10%), minimal utterances (T1: 4% - T2: 4% - T3: 6% - T4: 1%), false starts (T1: 3% - T2: 5% - T3: 6% - T4: 8%), self-corrections (T1: 3% - T2: 2% - T3: 4% - T4: 7%) and 2) small changes in repetitions (T1: 1% - T2: 0% - T3: 2% - T4: 3%), hesitations (T1: 1% - T2: 0% - T3: 2% - T4: 0%), incomplete sentences (T1: 1% - T2: 0% - T3: 0% - T4: 1%), grammatical errors (T1: 2% - T2: 1% - T3: 2% - T4: 2%). The total number of words (T1: 273 - T2: 283 - T3: 249 - T4: 287) and utterances (T1: 39 - T2: 41 - T3: 36 - T4: 34) varied slightly. Acoustic analysis showed a slight increase in word length during and directly after surgery but it recovered to preoperative level on the long-term (T1: .66 s - T2: .75 s - T3: .87 s - T4: .69 s). Conclusion The semi-spontaneous speech task in this case study was sensitive to capture changes in perioperative language status. Paraphasias, interjections, minimal utterances, hesitations and word length increased directly after surgery followed by a decrease 2 months later. This is a frequently reported pattern of transient language deficits. However, other errors (false starts, repetitions, self-corrections, incomplete sentences) increased at 2 months after surgery, indicating longer term language deficits. Future exploration of both the acoustic analysis, which shows potential for real-time feedback, and the manual transcriptions, could possibly lead to a better understanding of intra- and postoperative language performance as a prognostic factor for long-term language outcome, with possible implications for language therapy.

Estimation of the respiratory rate from ballistocardiograms using the Hilbert transform

BackgroundMeasuring the respiratory rate is usually associated with discomfort for the patient due to contact sensors or a high time demand for healthcare personnel manually counting it.MethodsIn this paper, two methods for the continuous extraction of the respiratory rate from unobtrusive ballistocardiography signals are introduced. The Hilbert transform is used to generate an amplitude-invariant phase signal in-line with the respiratory rate. The respiratory rate can then be estimated, first, by using a simple peak detection, and second, by differentiation.ResultsBy analysis of a sleep laboratory data set consisting of nine records of healthy individuals lasting more than 63 h and including more than 59,000 breaths, a mean absolute error of as low as 0.7 BPM for both methods was achieved.ConclusionThe results encourage further assessment for hospitalised patients and for home-care applications especially with patients suffering from diseases of the respiratory system like COPD or sleep apnoea.

Simple rule to automatically recognize the orientation of the sagittal plane foot angular velocity for gait analysis using IMUs on the feet of individuals with heterogeneous motor disabilities

Automatic sensor-to-foot alignment is required in clinical gait analysis using inertial sensors to avoid assumptions about sensors initial positions and orientations. Numerous studies have proposed alignment methods. The current study aimed at describing and accessing the performance of a simple rule to automatically recognize the orientation of the sagittal plane foot angular velocity that can be used with any alignment method and any populations including individuals with severe motor disorders such as patients with cerebral palsy (CP). Fifty-five participants (15 healthy, 15 with CP and 25 with various other motor disorders) wore IMUs on both feet during one or several visits of clinical gait analysis (CGA) with optical motion capture system as reference. The foot coordinate system was determined using acceleration during motionless periods and angular velocity during walking, as previously described in the literature. Based on the foot sagittal plane angular velocity, a novel rule is introduced to determine the latest uncertainty related to mediolateral axis direction which often causes errors. It consisted of massively filtering the signal and applying a simple peak detection, omitting the double peaks with the same sign. The time between the negative and positive peaks can inform on the axis direction. This verification showed excellent results with 99,94% sensibility against the reference. This simple rule could be used to further improve existing sensor-to-segment algorithms with inertial sensors located on the feet, and thus improve pathological gait analysis.

Objective measurement of adherence to wearing foot orthoses using an embedded temperature sensor

The objective of this study was to evaluate the validity of a temperature sensor for the measurement of adherence to wearing foot orthoses. Ten participants were provided with foot orthoses containing an embedded temperature sensor and wore the orthoses for a randomly-determined duration over a five-day period. Sensor-detected wear time was compared to a reference standard (objectively measured wear time using a smart-phone application). Ambient temperature and physical activity were recorded with a temperature gauge and wearable activity tracker, respectively. A simple peak detection algorithm which identified the largest one-minute changes in sensor temperature provided highly accurate wear time values (r = 0.999, coefficient of variation=0.2%). Ambient temperature and physical activity did not significantly influence temperature sensor scores. These findings demonstrate that the temperature sensor provides accurate foot orthosis wear time data and may therefore be a useful tool for documenting adherence in clinical practice and intervention studies.

Processing APT Spectral Backgrounds for Improved Quantification.

We describe a method to estimate background noise in atom probe tomography (APT) mass spectra and to use this information to enhance both background correction and quantification. Our approach is mathematically general in form for any detector exhibiting Poisson noise with a fixed data acquisition time window, at voltages varying through the experiment. We show that this accurately estimates the background observed in real experiments. The method requires, as a minimum, the z-coordinate and mass-to-charge-state data as input and can be applied retrospectively. Further improvements are obtained with additional information such as acquisition voltage. Using this method allows for improved estimation of variance in the background, and more robust quantification, with quantified count limits at parts-per-million concentrations. To demonstrate applications, we show a simple peak detection implementation, which quantitatively suppresses false positives arising from random noise sources. We additionally quantify the detectability of 121-Sb in a standardized-doped Si microtip as (1.5 × 10−5, 3.8 × 10−5) atomic fraction, α = 0.95. This technique is applicable to all modes of APT data acquisition and is highly general in nature, ultimately allowing for improvements in analyzing low ionic count species in datasets.

Node-based versus Server-based Data Validations in Internet of Things

Internet of Things (IoT) collects and sends data over TCP/IP network for the intended application. Some applications designed sensors to be lost after battery exhausted. Therefore, battery lifetime is important. On the other hand, data quality can be disrupted by the harsh environment and interfering noises, so that the transmitted data may be invalid. Transmitting invalid data may exhaust battery sooner, while implementing validation algorithm may absorb more power. This paper proposed a simple peak detection method in IoT sensor to avoid transmitting invalid data. The experimental evaluation was set up and the method was compared to server-based support vector machine classifier. The results show that the proposed method is able to cancel 86% error data transmission with 1.25% power savings.

The mountaineer's method for peak detection in photoplethysmographic signals

Several efforts have been made to develop algorithms for accurate peak detection in photoplethysmographic (PPG) signals. Most of those algorithms have been specifically conceived to perform under high motion artifact and baseline drift conditions. However, little has been done regarding peak detection in low-amplitude PPG signals. In an attempt to address this issue, a simple and real-time peak detection algorithm for PPG signals was proposed. In comparison with two other well-established peak detection algorithms, the proposed method was able to achieve over than 98% sensitivity and less than 3% failed detection rate, even when the amplitude of the PPG signal dropped to 0.2 V. Still, further work is needed to improve its robustness to motion artifacts.

A novel and low-complexity peak detection algorithm for heart rate estimation from low-amplitude photoplethysmographic (PPG) signals

Low-amplitude PPG signals are more affected by noise contamination and other undesirable effects because the signal strength is comparable to noise power. Although several authors claim that decreases in the amplitude of the PPG wave should be addressed from signal acquisition and conditioning stages such decreases can also be associated with changes in the patient condition. In that instance, it is important to ensure continuous and reliable HR monitoring which, in turn, depends on how robust is the peak detection method. Numerous efforts have been made to develop algorithms for accurate PPG peak detection under high motion artefact conditions. However, little has been done regarding peak detection in low-amplitude PPG signals. In an attempt to address this issue, a novel and simple peak detection algorithm for PPG signals was proposed. Results show that our method could be a good contribution for robust strategies that can dynamically adapt their peak detection method to circumstances in which a decrease in the amplitude of the PPG signal is expected. Still, more extensive testing under a wide range of conditions (e.g. intensive physical exercise) is needed to perform a rigorous validation.

POINT CLOUD GENERATION FROM GAUSSIAN DECOMPOSITION OF THE WAVEFORM LASER SIGNAL WITH GENETIC ALGORITHMS

Abstract: Recent developments in LIDAR technology lead to the availability of the waveform systems, which capture and digitize the whole return of the emitted LASER pulse. As many objects may cause multiple returns in the same echo, one task is to detect and separate different echoes within the same digitized measurement. In this paper the results of a study aimed at LASER signal waveform decomposition using genetic algorithms are introduced. The proposed method is based on the Gaussian decomposition approach and analyzes each digitized return to compute one or more points. Initially, the number of peaks contained in the waveform is determined by a simple peak detection method, with a local maximum point algorithm. When more than one peak is detected, genetic algorithms are applied to estimate the amplitude, time and standard deviation of each peak within the digitized signal. With this methodology it was possible to increase the number of points by approximately 17 % compared to the point cloud obtained using commercial software. The best results were obtained in areas with high vegetation, and thus the methodology can be applied to the generation of denser points cloud in forest areas.

Accuracy enhancement in low frequency gain and phase detector (AD8302) based bioimpedance spectroscopy system

Bioimpedance spectroscopy (BIS) is the measurement of the impedance of biological tissues. In the development of a low cost, portable BIS system the gain and phase difference detection (GPD) method is usually used. However this method suffers from low accuracy at low frequencies due to the presence of spikes or peaks. Detection and removal of these peaks will improve the performance of the GPD based BIS system at low frequencies. Peak detection in physiological signals is a well developed area. A lot of algorithms have been developed in the past for the determination of peaks in signals and their time of occurrence. For real-time processing, microcontroller based peak detection algorithms have also been developed in the past. In this paper, the problem of the GPD method at low frequency is examined closely and then three algorithms were used to mitigate it. The first is based on a moving average filter, the second is a simple peak detection and elimination (SPD) and the last is a peak detection based on the frequency of the input signal to the GPD (SFPD). These algorithms were evaluated with both simulated and measured data. Four parameters were used to indicate the performance of the algorithms; run-time, RMSE, sensitivity and positive predictability. The RMSEs were less than 0.17 for moving average, 0.07 for SPD and 0.08 for SFPD. The run-times was less than 10 ms for SFPD while that for the SPD and moving average were around 30 ms and 80 ms respectively. In all it was found that the algorithms based on peak detection have better results. The computational simplicity of the algorithms makes them suitable for microcontroller based implementation.

Accurate real-time depth control for CP-SSOCT distal sensor based handheld microsurgery tools.

This paper presents a novel intuitive targeting and tracking scheme that utilizes a common-path swept source optical coherence tomography (CP-SSOCT) distal sensor integrated handheld microsurgical tool. To achieve micron-order precision control, a reliable and accurate OCT distal sensing method is required; simultaneously, a prediction algorithm is necessary to compensate for the system delay associated with the computational, mechanical and electronic latencies. Due to the multi-layered structure of retina, it is necessary to develop effective surface detection methods rather than simple peak detection. To achieve this, a shifted cross-correlation method is applied for surface detection in order to increase robustness and accuracy in distal sensing. A predictor based on Kalman filter was implemented for more precise motion compensation. The performance was first evaluated using an established dry phantom consisting of stacked cellophane tape. This was followed by evaluation in an ex-vivo bovine retina model to assess system accuracy and precision. The results demonstrate highly accurate depth targeting with less than 5 μm RMSE depth locking.

A simple peak detection and label-free quantitation algorithm for chromatography-mass spectrometry.

BackgroundLabel-free quantitation of mass spectrometric data is one of the simplest and least expensive methods for differential expression profiling of proteins and metabolites. The need for high accuracy and performance computational label-free quantitation methods is still high in the biomarker and drug discovery research field. However, recent most advanced types of LC-MS generate huge amounts of analytical data with high scan speed, high accuracy and resolution, which is often impossible to interpret manually. Moreover, there are still issues to be improved for recent label-free methods, such as how to reduce false positive/negatives of the candidate peaks, how to expand scalability and how to enhance and automate data processing. AB3D (A simple label-free quantitation algorithm for Biomarker Discovery in Diagnostics and Drug discovery using LC-MS) has addressed these issues and has the capability to perform label-free quantitation using MS1 for proteomics study.ResultsWe developed an algorithm called AB3D, a label free peak detection and quantitative algorithm using MS1 spectral data. To test our algorithm, practical applications of AB3D for LC-MS data sets were evaluated using 3 datasets. Comparisons were then carried out between widely used software tools such as MZmine 2, MSight, SuperHirn, OpenMS and our algorithm AB3D, using the same LC-MS datasets. All quantitative results were confirmed manually, and we found that AB3D could properly identify and quantify known peptides with fewer false positives and false negatives compared to four other existing software tools using either the standard peptide mixture or the real complex biological samples of Bartonella quintana (strain JK31). Moreover, AB3D showed the best reliability by comparing the variability between two technical replicates using a complex peptide mixture of HeLa and BSA samples. For performance, the AB3D algorithm is about 1.2 - 15 times faster than the four other existing software tools.ConclusionsAB3D is a simple and fast algorithm for label-free quantitation using MS1 mass spectrometry data for large scale LC-MS data analysis with higher true positive and reasonable false positive rates. Furthermore, AB3D demonstrated the best reproducibility and is about 1.2- 15 times faster than those of existing 4 software tools.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-014-0376-0) contains supplementary material, which is available to authorized users.

Measurement of 3D Printed Structure Using a Peak Detection Method in Dispersive Interferometry

We describe an optical three dimensional (3D) measurement method using white-light dispersive interferometry in the range of 5 μm–150 μm. A spectrometer was employed for real-time wavelength scanning and the distance information was obtained using a simple peak detection method in the spectral domain. Fast measurement time enabled us real-time distance measurement of the specimen. The piezoelectric actuator and capacitance distance sensor was employed for nanometer positioning and measuring distance reference value. This technique can be implemented as a distance measurement unit in white-light scanning interferometry by simply attaching a spectrometer to the conventional system. The results showed an accuracy of better than 60 nm for any position within 5 μm–150 μm. The proposed technique can be used as shape-measuring tool for 3D printed products. Measurement results using the proposed method are also presented. These experimental results show that pthe roposed method can be used as a measurement tool for 3D printed products.

DFB laser diode interferometer with image capture timing control for surface profile reconstruction

A DFB laser diode interferometer with sinusoidal phase modulation (SPM) and image capture timing control was proposed for the reconstruction of surface profile. Sinusoidal phase modulation was achieved by controlling the injection current of light diode. The surface profile was reconstructed on four consecutive fringe images. Random phase error and low-frequency phase shift would be superposed on the interference fringes due to external disturbances such as mechanical vibration and temperature fluctuation. A simple peak detection circuit that would take less time consumption than phase generated carrier (PGC) was built for eliminating external disturbances, and the phase of fringe image could be stabilized in about one millisecond, which is conducive to real-time surface profile reconstruction. A novel method to capture four consecutive fringe images in adjacent modulation period was presented by controlling pulse sequence to trigger camera exposure and image readout operation. The whole system was tested on an optical table without vibration isolation and being kept in thermostatic chamber. The repeatability was about 0.018 wave (approximately 14nm for 760nm wavelength). The feasibility for high-resolution surface profile reconstruction had been verified.

Wavelength interrogation of fiber Bragg grating sensors using tapered hollow Bragg waveguides.

We describe an integrated system for wavelength interrogation, which uses tapered hollow Bragg waveguides coupled to an image sensor. Spectral shifts are extracted from the wavelength dependence of the light radiated at mode cutoff. Wavelength shifts as small as ~10 pm were resolved by employing a simple peak detection algorithm. Si/SiO₂-based cladding mirrors enable a potential operational range of several hundred nanometers in the 1550 nm wavelength region for a taper length of ~1 mm. Interrogation of a strain-tuned grating was accomplished using a broadband amplified spontaneous emission (ASE) source, and potential for single-chip interrogation of multiplexed sensor arrays is demonstrated.

Simple peak detection control algorithm of distribution static compensator for power quality improvement

This study deals with the control of a three-phase, four-wire distribution static compensator (DSTATCOM) based on the peak detection of load currents for power quality improvement under linear/non-linear loads. This control algorithm is simple and easy in real time implementation. In this control algorithm, an extraction of active and reactive power components of load currents is based on a low-pass filter and the voltage unit vector in time domain. It is based on the mathematical formulations for fast and accurate estimation of reference supply currents. A four-wire DSTATCOM is modelled and implemented for the compensation of linear and non-linear loads using a digital signal processor. Performance of DSTATCOM is found quite satisfactory under balanced and unbalanced loads in three-phase, four-wire distribution system.

Assessment of Quadrilateral Fitting of the Water Column Contribution in Lidar Waveforms on Bathymetry Estimates

A new approach based on a mixture of Gaussian and quadrilateral functions was developed to process bathymetric lidar waveforms. The approach was tested on two simulated data sets obtained from the existing Water-LIDAR (Wa-LID) waveform simulator. The first simulated data set corresponds to a sensor configuration modeled after a possible future satellite bathymetric lidar sensor that was previously studied. The second simulated data set corresponds to a lidar airborne configuration modeled using the HawkEye airborne lidar parameters. In the proposed approach, the lidar waveform is fitted into a combination of three functions, two Gaussians for both the water surface and water bottom contributions and a quadrilateral function to fit the water column contribution. The results show more accurate bathymetry estimates compared with the use of a triangular function to fit the column contribution or a simple peak detection method. For the satellite configuration, the bias is improved by 16.8 and 0.8 cm compared with the peak detection method and the use of a triangular function, respectively. For the airborne configuration, the bias is improved by 10.0 and 2.4 cm compared with the peak detection method and the use of a triangular function, respectively. The proposed waveform fitting using the quadrilateral function underestimates the bathymetry by -5.0 and - 6.1 cm for the simulated satellite and airborne data sets, respectively. The standard deviations of the bathymetry estimates are 6.0 and 8.2 cm, respectively. The obtained biases are inherent to overlaps between functions fitting the water surface, column, and bottom contributions.

A Bayesian approach to the alignment of mass spectra

The need to align spectra to correct for mass-to-charge experimental variation is a problem that arises in mass spectrometry (MS). Most of the MS-based proteomic data analysis methods involve a two-step approach, identify peaks first and then do the alignment and statistical inference on these identified peaks only. However, the peak identification step relies on prior information on the proteins of interest or a peak detection model, which are subject to error. Also numerous additional features such as peak shape and peak width are lost in simple peak detection, and these are informative for correcting mass variation in the alignment step. Here, we present a novel Bayesian approach to align the complete spectra. The approach is based on a parametric model which assumes that the spectrum and alignment function are Gaussian processes, but the alignment function is monotone. We show how to use the expectation-maximization algorithm to find the posterior mode of the set of alignment functions and the mean spectrum for a patient population. After alignment, we conduct tests while controlling for error attributable to multiple comparisons on the level of the peaks identified from the absolute mean spectra difference of two patient populations. cavanr@biostat.umn.edu.