scholarly journals Improving R Peak Detection in ECG Signal Using Dynamic Mode Selected Energy and Adaptive Window Sizing Algorithm with Decision Tree Algorithm

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6682
Author(s):  
Zubaer Md. Abdullah Al ◽  
Keshav Thapa ◽  
Sung-Hyun Yang
Keyword(s):  
Decision Tree ◽  
High Efficiency ◽  
Region Of Interest ◽  
Extraction Process ◽  
Peak Detection ◽  
Peak Position ◽  
Dynamic Mode ◽  
Ecg Signal ◽  
Forward Selection ◽  
Adaptive Window

R peak detection is crucial in electrocardiogram (ECG) signal analysis to detect and diagnose cardiovascular diseases (CVDs). Herein, the dynamic mode selected energy (DMSE) and adaptive window sizing (AWS) algorithm are proposed for detecting R peaks with better efficiency. The DMSE algorithm adaptively separates the QRS components and all non-objective components from the ECG signal. Based on local peaks in QRS components, the AWS algorithm adaptively determines the Region of Interest (ROI). The Feature Extraction process computes the statistical properties of energy, frequency, and noise from each ROI. The Sequential Forward Selection (SFS) procedure is used to find the best subsets of features. Based on these characteristics, an ensemble of decision tree algorithms detects the R peaks. Finally, the R peak position on the initial ECG signal is adjusted using the R location correction (RLC) algorithm. The proposed method has an experimental accuracy of 99.94%, a sensitivity of 99.98%, positive predictability of 99.96%, and a detection error rate of 0.06%. Given the high efficiency in detection and fast processing speed, the proposed approach is ideal for intelligent medical and wearable devices in the diagnosis of CVDs.

scholarly journals High-efficiency method for recycling lithium from spent LiFePO4 cathode

2020 ◽  
Vol 9 (1) ◽  
pp. 1586-1593
Author(s):  
Tingting Yan ◽  
Shengwen Zhong ◽  
Miaomiao Zhou ◽  
Xiaoming Guo ◽  
Jingwei Hu ◽  
...  
Keyword(s):  
Inductively Coupled Plasma ◽  
High Efficiency ◽  
Optical Emission ◽  
Extraction Process ◽  
Emission Spectrometry ◽  
X Ray Diffraction ◽  
Inductively Coupled ◽  
Moisture Analysis ◽  
Lifepo4 Cathode ◽  
Solid Liquid

Abstract The extraction of Li from the spent LiFePO4 cathode is enhanced by the selective removal using interactions between HCl and NaClO to dissolve the Li+ ion while Fe and P are retained in the structure. Several parameters, including the effects of dosage and drop acceleration of HCl and NaClO, reaction time, reaction temperature, and solid–liquid ratio on lithium leaching, were tested. The Total yields of lithium can achieve 97% after extraction process that lithium is extracted from the precipitated mother liquor, using an appropriate extraction agent that is a mixture of P507 and TBP and NF. The method also significantly reduced the use of acid and alkali, and the economic benefit of recycling is improved. Changes in composition, morphology, and structure of the material in the dissolution process are characterized by inductively coupled plasma optical emission spectrometry, scanning electron microscope, X-ray diffraction, particle size distribution instrument, and moisture analysis.

scholarly journals Tinjauan Algoritma RoI (Region of Interest) Dengan Metode Pengambangan Otsu Dan Klasterisasi K-Mean; Hasil Dan Tantangannya

2020 ◽  
Vol 16 (2) ◽  
pp. 75
Author(s):  
Didit Widiyanto
Keyword(s):  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Region Of Interest ◽  
Naïve Bayes ◽  
Support Vector ◽  
Gray Level

Akurasi sebuah klasifikasi citra ditentukan oleh pengklasifikasi.  Meskipun RoI (Region of Interest) tidak menentukan secara langsung akurasi, namun RoI menentukan lingkup klasifikasi citra.   Terdapat tiga algoritma yang dapat digunakan sebagai algoritma RoI yaitu; Balanced Histogram Thresholding (BHT), algoritma Otsu, dan algoritma klasterisasi K-Means.  Paper ini meninjau algoritma Otsu dan algoritma klasterisasi K-Means yang digunakan oleh lima peneliti.  Dari ke lima peneliti; tiga peneliti menerapkan algoritma Otsu dan dua peneliti menerapkan algoritma K-Means sebagai algoritma RoI. Setelah operasi RoI, ke lima peneliti menerapkan algoritma GLCM (Gray Level Co-occurance Matrix) sebagai pengekstraksi ciri tekstur.  Hasil ekstraksi ciri diklasifikasi dengan menggunakan berbagai pengklasifikasi antara lain SVM (Support Vector Machine), Naive Bayes, dan Decision Tree. Akhirnya dengan membandingkan hasil dari ke lima peneliti, akurasi tertinggi diperoleh sebesar 100% dengan pengklasifikasi SVM menggunakan algoritma Otsu sebagai algoritma RoI, dan akurasi terendah adalah sebesar52% yang menggunakan algoritma Otsu pada kanal S dari citra HSV (Hue, Saturation Value).

scholarly journals Region-of-interest-based rate control scheme for high-efficiency video coding

2014 ◽  
Vol 3 ◽  
Author(s):  
Marwa Meddeb ◽  
Marco Cagnazzo ◽  
Béatrice Pesquet-Popescu
Keyword(s):  
Video Coding ◽  
Rate Control ◽  
Control Algorithm ◽  
High Efficiency ◽  
Region Of Interest ◽  
Bit Allocation ◽  
Bit Rate ◽  
High Efficiency Video Coding ◽  
Control Scheme ◽  
Rate Control Algorithm

This paper presents a novel rate control scheme designed for the newest high efficiency video coding (HEVC) standard, and aimed at enhancing the quality of regions of interest (ROI) for a videoconferencing system. It is designed to consider the different regions at both frame level and coding tree unit (CTU) level. The proposed approach allocates a higher bit rate to the region of interest while keeping the global bit rate close to the assigned target value. The ROIs, typically faces in this application, are automatically detected and each CTU is classified in a region of interest map. This binary map is given as input to the rate control algorithm and the bit allocation is made accordingly. The algorithm is tested, first, using the initial version of the controller introduced in HEVC test model (HM.10), then, extended in HM.13. In this work, we first investigate the impact of differentiated bit allocation between the two regions using a fixed bit rate ratio in intra-coded frames (I-frames) and Bidirectionally predicted frames (B-frames). Then, unit quantization parameters (QPs) are computed independently for CTUs of different regions. The proposed approach has been compared to the reference controller implemented in HM and to a ROI-based rate control algorithm initially proposed for H.264 that we adopted to HEVC and implemented in HM.9. Experimental results show that our scheme has comparable performances with the ROI-based controller proposed for H.264. It achieves accurate target bit rates and provides an improvement in region of interest quality, both in objective metrics (up to 2 dB in PSNR) and based on subjective quality evaluation.

ECG Signal De-Noising with Asynchronous Averaging and Filtering Algorithm

2012 ◽  
pp. 199-205
Author(s):  
Alka Gautam ◽  
Hoon-Jae Lee ◽  
Wan-Young Chung
Keyword(s):  
Signal To Noise Ratio ◽  
Random Noise ◽  
Noise Cancellation ◽  
Peak Detection ◽  
Lms Algorithm ◽  
Ecg Signal ◽  
Maximum Point ◽  
Signal To Noise ◽  
Filtering Algorithm ◽  
Overall Performance

In this study, a new algorithm is proposed—Asynchronous Averaging and Filtering (AAF) for ECG signal de-noising. R-peaks are detected with another proposed algorithm—Minimum Slot and Maximum Point selecting method (MSMP). AAF algorithm reduces random noise (major component of EMG noise) from ECG signal and provides comparatively good results for baseline wander noise cancellation. Signal to noise ratio (SNR) improves in filtered ECG signal, while signal shape remains undistorted. The authors conclude that R-peak detection with MSMP method gives comparable results from existing algorithm like Pan-Tomkins algorithm. AAF algorithm is advantageous over adaptation algorithms like Wiener and LMS algorithm. Overall performance of proposed algorithms is comparatively good.

scholarly journals The System for Driver Fatigue Monitoring Using Decision Tree via Wireless Sensor Network for Intelligent Transport System

2018 ◽  
Vol 14 (10) ◽  
pp. 21
Author(s):  
Worawut Yimyam ◽  
Mahasak Ketcham
Keyword(s):  
Wireless Sensor Network ◽  
Decision Tree ◽  
Mobile Phone ◽  
Region Of Interest ◽  
Wireless Sensor ◽  
Intelligent Transport System ◽  
Intelligent Transport ◽  
Decision Tree Classification ◽  
Fatigue Monitoring ◽  
Opening And Closing

Researchers propose new algorithm to monitor fatigue of driver by tracking the movements of the eyes. This can classify the opening and closing eyes by using Haar-Like Features and Region of Interest techniques. The fatique can be classified by decision tree classification. The system works in real time and sends message to the mobile phone to alert the driver via Line Application. The results showed that the program can detect face and classify opening and closing eyes accurately at 99.93 percent in which it yielded higher accuracy than other algorithms.

scholarly journals An Adaptive and Time-Efficient ECG R-Peak Detection Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Qin Qin ◽  
Jianqing Li ◽  
Yinggao Yue ◽  
Chengyu Liu
Keyword(s):  
Detection Algorithm ◽  
Peak Detection ◽  
Time Interval ◽  
Detection Accuracy ◽  
Ecg Signal ◽  
Peak Detection Algorithm ◽  
Wavelet Multiresolution Analysis ◽  
The Mean ◽  
Electrocardiogram Ecg ◽  
Mean Time

R-peak detection is crucial in electrocardiogram (ECG) signal analysis. This study proposed an adaptive and time-efficient R-peak detection algorithm for ECG processing. First, wavelet multiresolution analysis was applied to enhance the ECG signal representation. Then, ECG was mirrored to convert large negative R-peaks to positive ones. After that, local maximums were calculated by the first-order forward differential approach and were truncated by the amplitude and time interval thresholds to locate the R-peaks. The algorithm performances, including detection accuracy and time consumption, were tested on the MIT-BIH arrhythmia database and the QT database. Experimental results showed that the proposed algorithm achieved mean sensitivity of 99.39%, positive predictivity of 99.49%, and accuracy of 98.89% on the MIT-BIH arrhythmia database and 99.83%, 99.90%, and 99.73%, respectively, on the QT database. By processing one ECG record, the mean time consumptions were 0.872 s and 0.763 s for the MIT-BIH arrhythmia database and QT database, respectively, yielding 30.6% and 32.9% of time reduction compared to the traditional Pan-Tompkins method.

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