peak detection low sampling rate
229704168
Real time ECG R peak detection by extremum sampling
In this paper, we propose and validate a novel self adaptive real time event driven ECG R peak detection algorithm based on extremum sampling. The criteria for algorithm development were low complexity and energy efficiency facilitating wearable and mobile applications. The proposed algorithm consists of three major steps: event driven identification of the QRS window, detection of ECG signal extremum, and awaiting the end of the current QRS complex. The threshold adaptation mechanism reduces the algorithm sensitivity to varying ECG amplitude range. The algorithm performance has been examined on MIT BIH Arrhythmia Database recordings achieving the following results: sensitivity and positive prediction rate equal respectively to 99.55% and 99.88% which effected in total false detection rate equal to 0.58% These results significantly outweigh the others reported in the area of low power R peak detection algorithms.
2020 6th International Conference on Event Based Control, Communication, and Signal Processing (EBCCSP)
Wojciech Reklewski Katarzyna Heryan Marek Miskowicz Piotr Augustyniak
62303423
An Enhanced Step Detection Algorithm with Threshold Function under Low Sampling Rate
ABSTRACT At the case of peak threshold algorithm, 3 axes data should sample step data over 20 Hz to get sufficient accuracy. But most of the digital sensors like 3 axes accelerometer have very low sampling rate caused by low data communication speed on limited SPI or I 2 C bandwidth of the low cost MPU for ubiquitous devices. If the data transfer rate of the 3 axes accelerometer is getting slow, the sampling rate also slows down and it finally degrades the data accuracy. In this study, we proved there is a distinct functional relation between the sampling rate and threshold on the peak threshold step detection algorithm under the 20Hz frequency, and made a threshold function through the experiments. As a result of experiments, when we apply threshold value from the threshold function instead of fixed threshold value, the step detection error rate can be lessen about 1.2% or under. Therefore, we can suggest a peak threshold based new step detection algorithm with threshold function and it can enhance the accuracy of step detection and step count. This algorithm not only can be applied on a digital step counter design, but also can be adopted any other low cost ubiquitous sensor devices subjected on low sampling rate.Keywords:Ubiquitous, Step Detection, Peak Threshold, Low Sampling Rate, Threshold Function
Boyeon Kim Yunseok Chang
115200775
Performance Investigation of a Cost and Power Effective High Nonlinearity Tolerance OFDMA PON Scheme Based on Sub Nyquist Sampling Rate and DFT Spread
We investigate the performance of a cost and power effective high nonlinearity tolerance orthogonal frequency division multiple access passive optical network scheme based on sub Nyquist sampling and discrete Fourier transform spread. The analog to digital converter with a sampling rate below the Nyquist sampling rate is used for signal detection for each optical network unit. The complicated pre process procedure is located in the optical line terminal. Only Low point FFT and IDFT procedures are required in the ONU, reducing the computational complexity and power consumption in the ONU, and the sampling rate of ADC, and improve the cost and power efficiency effectively. Moreover, the peak to average power ratio is reduced effectively with the scheme. The simulation results show that the measured bit error ratio of each ONU signal well less than the forward error correction limit of $3.8\times 10^ 3} is obtained after 40 km standard single mode fiber transmission.
IEEE Access
Yupeng Li Jiawei Han Xiaonan Zhao
4671641
TOA estimation using UWB with low sampling rate and clock drift calibration
In this paper, a time of arrival (TOA) estimation scheme with clock drift calibration is proposed using an impulse radio (IR) ultra wideband (UWB) signal. We adopt low rate stroboscopic sampling, which can achieve an equivalent sampling rate as high as the Nyquist sampling rate. The clock drift is one of the main error sources in TOA estimation for the stroboscopic sampling IR UWB system, since a long preamble is required to collect sufficient data samples. Therefore, the drift is taken into account in our system model. First, the maximum likelihood estimate of the drift is computed. Then, we employ the peak selection or the leading edge detection to estimate the TOA using the averaged data samples calibrated for the drift. Simulation results corroborate that the drift calibration significantly reduces the TOA estimation errors due to the drift, and that stroboscopic sampling can achieve the same estimation resolution as Nyquist sampling.
2009 IEEE International Conference on Ultra Wideband
Yiyin Wang Geert Leus Hakan Delic
46924641
Implementation of heart rate detection algorithm based on a low power chip
This paper presents a low power electrocardiogram (ECG) signal processing application specific integrated circuit (ASIC) chip for real time and reliable detection of heart rate (HR) At a sampling frequency of 250 Hz, the analog front end (AFE) accurately senses and digitizes the raw ECG signal, which is then sent to the digital signal processing (DSP) In DSP, after preprocessing, the R peak detection and RR interval cumulative deviation detection, data will be transmitted to terminal equipment via the universal asynchronous receiver/transmitter (UART) transfer protocol. The terminal will receive instantaneous heart rate (IHR) and mean heart rate (MHR) Under 0.18um standard CMOS process, the area and power are 0.3 mm2 and 10.9uW, respectively.
2018 China Semiconductor Technology International Conference (CSTIC)
Yingying Wu Hao Chen Zhongmin Lin Zirong Chen Xin-an Wang
3697955
Effect of sampling rate and filter settings on High Frequency Oscillation detections
OBJECTIVE High Frequency Oscillations (HFOs) are being studied as a biomarker of epilepsy, yet it is unknown how various acquisition parameters at different centers affect detection and analysis of HFOs. This paper specifically quantifies effects of sampling rate (FS) and anti aliasing filter (AAF) positions on automated HFO detection. METHODS HFOs were detected on intracranial EEG recordings (17 patients) with 5kHz FS. HFO detection was repeated on downsampled and/or filtered copies of the EEG data, mimicking sampling rates and low pass filter settings of various acquisition equipment. For each setting, we compared the HFO detection sensitivity, HFO features, and ability to identify the ictal onset zone. RESULTS The relative sensitivity remained above 80% for either FS 2kHz or AAF 500Hz. HFO feature distributions were consistent (AUROC<0.7) down to 1kHz FS or 200Hz AAF. HFO rate successfully identified ictal onset zone over most settings. HFO peak frequency was highly variable under most parameters (Spearman correlation<0.5) CONCLUSIONS We recommend at least FS 2kHz and AAF 500Hz to detect HFOs. Additionally, HFO peak frequency is not robust at any setting: the same HFO event can be variably classified either as a ripple <200Hz) or fast ripple >250Hz) under different acquisition settings. SIGNIFICANCE These results inform clinical centers on requirements to analyze HFO rates and features.
Clinical Neurophysiology
Stephen V Gliske Zachary T Irwin Cynthia A Chestek William C Stacey
22704390
The use of compressive sensing and peak detection in the reconstruction of microtubules length time series in the process of dynamic instability
Microtubules (MTs) are intra cellular cylindrical protein filaments. They exhibit a unique phenomenon of stochastic growth and shrinkage, called dynamic instability. In this paper, we introduce a theoretical framework for applying Compressive Sensing (CS) to the sampled data of the microtubule length in the process of dynamic instability. To reduce data density and reconstruct the original signal with relatively low sampling rates, we have applied CS to experimental MT lament length time series modeled as a Dichotomous Markov Noise (DMN) The results show that using CS along with the wavelet transform significantly reduces the recovery errors comparing in the absence of wavelet transform, especially in the low and the medium sampling rates. In a sampling rate ranging from 0.2 to 0.5, the Root Mean Squared Error (RMSE) decreases by approximately 3 times and between 0.5 and 1, RMSE is small. We also apply a peak detection technique to the wavelet coefficients to detect and closely approximate the growth and shrinkage of MTs for computing the essential dynamic instability parameters, i.e. transition frequencies and specially growth and shrinkage rates. The results show that using compressed sensing along with the peak detection technique and wavelet transform in sampling rates reduces the recovery errors for the parameters.
Comput. Biol. Medicine
Majid Mahrooghy Shantia Yarahmadian Vineetha Menon Vahid Rezania Jack Adam Tuszynski
7129280
An R peak detection method based on peaks of Shannon energy envelope
In a real time electrocardiogram (ECG) monitoring system, to detect the R peak in each beat with low delay, high accuracy and high speed is an important and fundamental task. Aiming at satisfying these requirements, a novel R peak detection algorithm based on Shannon energy envelope (PSEE) is introduced. It extracts Shannon energy envelope (SEE) from ECG firstly, then finds out the R peaks after SEE by three new introduced sub processes: peak detection, false R detection and false noise detection. Owing to these sub processes, the whole schema can deal with long pause or asystole case, and avoid segmenting ECG. Moreover, by splitting the calculation into every sampling time slot, the delay is guaranteed. The new method has been evaluated with all records in MIT BIH with 99.83% detection rate of R peaks and about 1.77 mu s computation time for each sample and with 251 records in Chinese Cardiovascular Disease Database with total accuracy 99.78% Therefore, it not only satisfies real time requirements but also improves the detection accuracy rate. (C) 2013 Elsevier Ltd. All rights reserved.
Biomed. Signal Process. Control.
Hong-hai Zhu Jun Dong
11709103
Scattering type scanning near field optical microscopy with low repetition rate pulsed light source through phase domain sampling
Scattering type scanning near field optical microscopy (s SNOM) allows spectroscopic imaging with spatial resolution below the diffraction limit. With suitable light sources, s SNOM is instrumental in numerous discoveries at the nanoscale. So far, the light sources have been limited to continuous wave or high repetition rate pulsed lasers. Low repetition rate pulsed sources cannot be used, due to the limitation of the lock in detection mechanism that is required for current s SNOM techniques. Here, we report a near field signal extraction method that enables low repetition rate pulsed light sources. The method correlates scattering signals from pulses with the mechanical phases of the oscillating s SNOM probe to obtain near field signal, by passing the apparent restriction imposed by the Nyquist Shannon sampling theorem on the repetition rate. The method shall enable s SNOM with low repetition rate pulses with high peak powers, such as femtosecond laser amplifiers, to facilitate investigations of strong light matter interactions and nonlinear processes at the nanoscale.
Nature communications
Haomin Wang Le Wang Xiaoji G Xu
17388544
A differentiation based adaptive double threshold method for real time electrocardiogram R peak detection
Electrocardiogram (ECG) is a fundamental and important noninvasive cardiac signal. In current decade, it has been popular to acquire and analyze ECG by wearable devices with main purposes of long term monitoring and potential cardiac event prediction. In this study, we aimed at developing a real time and accurate R peak detection method which may be useful for ECG analysis in the wearable devices. The method was proposed to detect R peaks using an adaptive double threshold judging strategy integrated with a speeding research operation both applied on the first derivative of filtered ECG with a low pass filter. The performance of the proposed method was then verified against R peak manual annotations on ECG data of 70 subjects (including 32 heart failure patients) collected in a previous bicycle exercise experiment. The results showed a high R peak detection accuracy (sensitivity: 99.80% positive predictivity: 99.89% and a short computation time (mean standard deviation is 0.0472 0.0039 s per 40 sec ECG data at a sampling rate of 200 Hz) The results in this study indicated that the proposed method is promising to be used for real time ECG R peak detection in wearable devices in the future.
2014 7th International Conference on Biomedical Engineering and Informatics
Jian'ai Ma Chao Shi Zhimin Zhang Jun Zhu P Zhan Yubo Fan Dongqing Li Ling Wang