ECG Peak Research Articles

Detector-trigger-based cardiac multiphase micro-CT imaging for small animals.

Micro-computed tomography is important in cardiac imaging for preclinical small animal models, but motion artifacts may appear due to the rapid heart rates. To avoid influence of motion artifacts, the prospective ECG gating schemes based on an X-ray source trigger have been investigated. However, due to the lack of pulsed X-ray exposure modes, high-resolution micro-focus X-ray sources do not support source triggering in most cases. To develop a fast-cardiac multiphase acquisition strategy using prospective ECG gating for micro-focus X-ray tubes with a continuous emission mode. The proposed detector-trigger-based prospective ECG gating acquisition scheme (DTB-PG) triggers the X-ray detector at the R peak of ECG, and then collects multiple phase projections of the heart in one ECG cycle by sequence acquisition. Cardiac multiphase images are reconstructed after performing the same acquisition in all views. The feasibility of this strategy was verified in multiphase imaging experiments of a phantom with 150 ms motion period and a mouse heart on a micro-focus micro-CT system with continuous emission mode. Using a high frame-rate CMOS detector, DTB-PG discriminates the positions of the motion phantom well in 10 different phases and enables to distinguish the changes in the cardiac volume of the mouse in different phases. The acquisition rate of DTB-PG is much faster than other prospective gating schemes as demonstrated by theoretical analysis. DTB-PG combines the advantages of prospective ECG gating strategies and X-ray detector-trigger mode to suppress motion artifacts, achieve ultra-fast acquisition rates, and relax hardware limitations.

ASSESSING THE QUALITY OF PHOTOPLETHYSMOGRAPHY SIGNALS FOR ARTERIAL PULSE WAVE SENSING

Objective: Cardiovascular health has an impact on the arterial blood pulse wave features, such as shape and speed. Photoplethysmography (PPG) allows measuring the light absorption variation in the skin due to the blood pulse wave. This study aims at collecting good quality PPG signals for pulse wave sensing. First, the signal quality must be accurately defined and quantified. Then, it must be assessed for several sensor locations during both rest and exercise. Design and method: Based on ECG peaks, the PPG signal is split into beats, which are then fitted to a 4-long piece-wise linear function. Beats are valid only if they follow a specific slope pattern (the second slope has the opposite sign to the other three), and if they have a dichrotic notch. The signal quality is then quantified by the percentage of valid beats. The initial cohort of the clinical trial (NCT05393401, approved by Human Research Review Board (CPP SM I) and Health French Ministry) includes five healthy subjects (1F/4M, age [37-60] years). Their brachial and radial arteries are located by ultrasound. Each subject is monitored with a multi-sensing homemade platform with PPG sensors and ECG; the distance between the LED and the photodiode is 9mm. Measurements are realized with sensors directly above the artery on the elbow, the wrist and the carotid, first at rest and then during a physical exercise. Results: Based on these preliminary results, the quality of PPG signals is best at the wrist whether at rest (48%) or exercise (44%). The second best option is the forearm and the worst is the carotid. The quality during exercise is similar, with a decrease of 4% at the wrist. Conclusions: The signal quality of PPG sensors is strongly impacted by the position along the arteries. The pressure applied on sensors and the extended arm posture also improve the signal quality. Further work should be undertaken to study the feasibility of pulse wave analysis for ambulatory blood pressure monitoring.

Rationale and study design of the MINERVA study: Multicentre Investigation of Novel Electrocardiogram Risk markers in Ventricular Arrhythmia prediction—UK multicentre collaboration

IntroductionThe purpose of this study is to assess the ability of two new ECG markers (Regional Repolarisation Instability Index (R2I2) and Peak Electrical Restitution Slope) to predict sudden cardiac death...

Classification of ischemic and dilated cardiomyopathy patients based on the analysis of the pulse transit time.

Cardiomyopathies diseases affects a great number of the elderly population. An adequate identification of the etiology of a cardiomyopathy patient is still a challenge. The aim of this study was to classify patients by their etiology in function of indexes extracted from the characterization of the pulse transit time (PTT). This time series represents the time taken by the pulse pressure to propagate through the length of the arterial tree and corresponding to the time between R peak of ECG and the mid-point of the diastolic to systolic slope in the blood pressure signal. For each patient, the PTT time series was extracted. Thirty cardiomyopathy patients (CMP) classified as ischemic (ICM - 15 patients) and dilated (DCM - 15 patients) were analyzed. Forty-three healthy subjects (CON) were used as a reference. The PTT time series was characterized through statistical descriptive indices and the joint symbolic dynamics method. The best indices were used to build support vector machine models. The optimal model to classify ICM versus DCM patients achieved 89.6% accuracy, 78.5% sensitivity, and 100% specificity. When comparing CMP patients and CON subjects, the best model achieved 91.3% accuracy, 91.3% sensitivity, and 88.3% specificity. Our results suggests a significantly lower pulse transit time in ischemic patients.Clinical relevance- This study analyzed the suitability of the pulse transit time for the classification of ICM and DCM patients.

Superiority of cardiac magnetic resonance derived left-ventricular ejection fraction in the identification of true acute myocardial infarction among MINOCA patients

Abstract Background/Introduction MINOCA constitutes a clinical entity characterized by heterogeneous and poorly understood pathophysiological substrate, whereas current knowledge leaves significant gaps regarding the identification, risk stratification and therapeutical approach of these patients. Purpose The aim of our study is to investigate the potential role of clinical, hemodynamic, laboratory and imaging parameters in the early identification of true acute myocardial infarction (AMI) among patients with a working diagnosis of MINOCA. Methods Our study population included 62 patients admitted with acute coronary syndrome (ACS) fulfilling the diagnostic criteria of MINOCA. A subsequent cardiac magnetic resonance (CMR) performed at 54 patients demonstrated an ischemic pattern of late gadolinium enhancement (LGE) confirming the diagnosis of true AMI in 15 cases (27.8%). Other findings included Takotsubo syndrome (n=19; 35.2%) and myocarditis (n=4; 7.4%), whereas CMR failed to reveal abnormal findings at 16 cases (29.8%). Results Focusing on the combined population of true AMI and clear CMR groups (n=31; 51.6% male; mean age: 58±12 years old; 42% hypertensives (HTN), 16% with history of diabetes mellitus (DM), 28.6% smokers) no significant difference was observed regarding classic cardiovascular risk factors (HTN, DM, smoking, age, dyslipidemia) except for a tendency of overrepresentation of female sex (r=0.354; p=0.051) in the true AMI group. Coronary angiographic (CA) findings did not differ between the two groups: clear vessels: 16/31 (51.6%); lesions causing ≤50% stenosis: 7/31 (22.6%); bridges: 4/31 (12.9%); spontaneous coronary artery dissection (SCAD): 2/31 (6.5%); slow flow phenomenon or spontaneous epicardial spasm: 2/31 (6.5%). No difference was observed in treatment approach with beta-blockers, renin-angiotensin system blockers, statins or the selection of no, single or dual antiplatelet strategy. Univariate regression analysis demonstrated that CMR derived left-ventricular ejection fraction (CMR-LVEF) (OR, 0.846; CI 95%: 0.742–0.965; p=0.012), as well as admission ECG abnormalities (OR, 0.154; CI 95%: 0.026–0.914; p=0.04), admission (OR, 5.689; CI 95%: 1.374–23.553; p=0.016) and peak troponin levels (OR, 15.874; CI 95%: 2.486–101.367; p=0.003) were the only parameters significantly related to a true AMI. Statistical significance was retained in multivariate models adjusted for age, gender, history of HTN and DM. On the contrary echocardiography derived LVEF failed to predict true AMI. Conclusions These preliminary results further highlight the need of an early CMR evaluation of MINOCA patients. A timely identification of true AMI is expected to improve patient outcomes by guiding the treatment approach. Funding Acknowledgement Type of funding sources: None.

HEMODIALYSIS REDUCES CEREBRAL BLOOD FLOW PULSATILITY AND SMALL VESSEL STIFFNESS

Objective: Cognitive decline is highly prevalent amongst end-stage renal disease (ESRD) patients and is accelerated upon initiation of hemodialysis (HD). ESRD increases aortic stiffness and blood flow pulsatility, which may damage small vessels of target organs such as the brain. In this pilot study, we aimed to evaluate the acute effect of HD on cerebral blood flow and regional arterial stiffness. Design and method: Before, every hour during, and after HD (T0-T4), we measured cerebral flow velocity (FV) using transcranial Doppler, blood pressure (BP) via digital finger cuff (Nexfin), cardiac activity using ECG, and aortic pulse wave velocity (PWV) with Mobile-O-Graph. FV pulsatility index (PI) and transit times between ECG peak and the foot of both FV and BP waveforms (cerebral dT; digital dT) were computed using in house MATLAB-based analysis. Changes during HD were evaluated with Generalized Estimating Equation models adjusting for multiple comparisons in SPSS 26.0. Results: In eight participants aged 63 ± 17 y. old (4 diabetics, 3 women), peak FV decreased from baseline at T1 and T2 (-11.2 cm/s, p = 0.007; -12.2 cm/s, p < 0.001), PI decreased at T1 (0.81 to 0.77, p = 0.005), whilst minimum FV, mean BP and partial pressure of CO2 remained unchanged. Digital dT increased at T3 (0.19 to 0.22, p < 0.001) and cerebral dT increased early in HD (T1: p = 0.036; T2: p = 0.048), whereas aortic PWV did not change. Conclusions: During hemodialysis, cerebral and digital transit times increased, suggesting decreased stiffness of small peripheral vessels, without significant changes in aortic stiffness. Reduced stiffness of cerebral arteries may partially explain decreased cerebral flow pulsatility during HD.

POS-603 HEMODIALYSIS INDUCES TRANSIENT REDUCTIONS IN CEREBRAL BLOOD FLOW PULSATILITY AND REGIONAL ARTERIAL STIFFNESS

Cognitive decline is highly prevalent amongst end-stage renal disease (ESRD) patients and is accelerated upon initiation of hemodialysis (HD). ESRD increases aortic stiffness and blood flow pulsatility, which may damage small vessels of target organs such as the brain. In this pilot study, we aimed to evaluate the acute effect of HD on cerebral blood flow and its relation to arterial stiffness. Before, every hour during, and after HD (T0-T4), we measured cerebral flow velocity (FV) using transcranial Doppler, blood pressure (BP) via digital finger cuff (Nexfin), cardiac activity using ECG, and aortic pulse wave velocity (PWV) with Mobile-O-Graph. FV pulsatility index (PI) and transit times between ECG peak and the foot of both FV and BP waveforms (cerebral dT; digital dT) were computed using in house MATLAB-based analysis. Changes during HD were evaluated with Generalized Estimating Equation models adjusting for multiple comparisons in SPSS 26.0. In eight participants aged 63 ± 17 y. old (4 diabetics, 3 women), mean FV was significantly decreased from baseline at T1 and T2 (mean FV: -9.75 cm/s, p=0,012; -10.1 cm/s, p=0.012), peak FV was significantly decreased from baseline at T2 (-12.2 cm/s, p=0.031), PI decreased at T1 (0.81 to 0.77, p=0.005), whilst minimum FV, mean BP and partial pressure of CO2 remained unchanged. Digital dT increased at T3 (0.19 ± 0.01 to 0.22 ± 0.02, p<0.001) and cerebral dT increased throughout HD (T1-T4, p<0.005), whereas aortic PWV did not change. During hemodialysis, cerebral and digital transit times increased, suggesting decreased stiffness of small peripheral vessels, without significant changes in aortic stiffness. Reduced stiffness of cerebral arteries may partially explain decreased cerebral flow pulsatility.

Pitfall of heart rate variability analyses for autonomic nervous system activity with photoplethysmography.

The purpose of this study is to measure the temporary intervals between R peak with ECG and pulse peak with photoplethysmography (PPG) and then to compare their precisions of two types of heart rate variability (HRV) analyses, power spectral analysis and Tone-Entropy analysis. A lot of papers used to measure cardiac autonomic nervous system (CANS) activity with HRV from PPI without awareness of the issue on another variability except CANS activity. Fifteen young male subjects were participated in this study. Simultaneous PPG and ECG were recorded with a portable device in three different postures; supine, sitting and upright positions. Our results show that, although there are no significant differences on heart rate between R-R interval (RRI) and pulse peak interval (PPI), analytic outputs on HRV analyses between RRI and PPI are different; especially, in upright position, there are significant differences on LF and HF (p<; 0.05) and also Tone-Entropy plots are clearly distant between RRI and PPI. In conclusion, our results indicate that PPG should not be used for measurement of CANS activity from HRV because of analytic error depending on physiological states like posture change though PPG probably has little problem to be used as a heart rate monitor.

VERB: VFCDM-Based Electrocardiogram Reconstruction and Beat Detection Algorithm.

We have developed a novel method to accurately detect QRS complex peaks using the variable frequency complex demodulation (VFCDM) method. The approach's novelty stems from reconstructing an ECG signal using only the frequency components associated with the QRS waveforms by VFCDM decomposition. After signal reconstruction, both top and bottom sides of the signal are used for peak detection, after which we compare locations of the peaks detected from both sides to ensure false peaks are minimized. Finally, we impose position-dependent adaptive thresholds to remove any remaining false peaks from the prior step. We applied the proposed method to the widely benchmarked MIT-BIH arrhythmia dataset, and obtained among the best results compared to many of the recently published methods. Our approach resulted in 99.94% sensitivity, 99.95% positive predictive value and a 0.11% detection error rate. Three other datasets-the MIMIC III database, University of Massachusetts atrial fibrillation data, and SCUBA diving in salt water ECG data-were used to further test the robustness of our proposed algorithm. For all these three datasets, our method retained consistently higher accuracy when compared to the BioSig Matlab toolbox, which is publicly available and known to be reliable for ECG peak detection.

Heart rate monitoring and therapeutic devices: A wavelet transform based approach for the modeling and classification of congestive heart failure

Heart rate monitoring and therapeutic devices include real-time sensing capabilities reflecting the state of the heart. Current circuitry can be interpreted as a cardiac electrical signal compression algorithm representing the time signal information into a single event description of the cardiac activity. It is observed that some detection techniques developed for ECG signal detection like artificial neural network, genetic algorithm, Hilbert transform, hidden Markov model are some sophisticated algorithms which provide suitable results but their implementation on a silicon chip is very complicated. Due to less complexity and high performance, wavelet transform based approaches are widely used. In this paper, after a thorough analysis of various wavelet transforms, it is found that Biorthogonal wavelet transform is best suited to detect ECG signal's QRS complex. The main steps involved in ECG detection process consist of de-noising and locating different ECG peaks using adaptive slope prediction thresholding. Furthermore, the significant challenges involved in the wireless transmission of ECG data are data conversion and power consumption. As medical regulatory boards demand a lossless compression technique, lossless compression technique with a high bit compression ratio is highly required. Furthermore, in this work, LZMA based ECG data compression technique is proposed. The proposed methodology achieves the highest signal to noise ratio, and lowest root mean square error. Also, the proposed ECG detection technique is capable of distinguishing accurately between healthy, myocardial infarction, congestive heart failure and coronary artery disease patients with a detection accuracy, sensitivity, specificity, and error of 99.92%, 99.94%, 99.92% and 0.0013, respectively. The use of LZMA data compression of ECG data achieves a high compression ratio of 18.84. The advantages and effectiveness of the proposed algorithm are verified by comparing with the existing methods.

ECG arrhythmia classification using time frequency distribution techniques.

In this paper, we focus on classifying cardiac arrhythmias. The MIT-BIH database is used with 14 original classes of labeling which is then mapped into 5 more general classes, using the Association for the Advancement of Medical Instrumentation standard. Three types of features were selected with a focus on the time-frequency aspects of ECG signal. After using the Wigner-Ville distribution the time-frequency plane is split into 9 windows considering the frequency bandwidth and time duration of ECG segments and peaks. The summation over these windows are employed as pseudo-energy features in classification. The "subject-oriented" scheme is used in classification, meaning the train and test sets include samples from different subjects. The subject-oriented method avoids the possible overfitting issues and guaranties the authenticity of the classification. The overall sensitivity and positive predictivity of classification is 99.67 and 98.92%, respectively, which shows a significant improvement over previous studies.

Optimization of ECG Peaks (Amplitude and Duration) in Predicting ECG Abnormality using Artificial Neural Network

Artificial Neural Networks (ANN) adapted from neuron concept's, generally applied in various applications especially the fields of biomedical engineering. ANN techniques have been applied in order to provide educated solutions to assist in decision making for the medical purpose. The study was conducted for the purpose of determining the suitability and implementation of ANN to detect ECG abnormalities by using six features from ECG signal, both amplitude and duration of P, QRS and T peaks and used as input vector for ANN. In this study, Multilayer Perceptron (MLP) network is trained by using three different training/learning algorithms. The network is trained by using Bayesian Regularization (BR) algorithm has provided the highest accuracy performance (93.19%), followed by Levenberg Marquardt (LM) (92.88%) and Backpropagation (BP) (88.63%).

R peak Detection for Wireless ECG using DWT and Entropy of coefficients

Investigation of patient's electrocardiogram helps to diagnose various heart related diseases. With correct R peak detection in ECG wave, classification of arrhythmia can be carried out accurately. However, accurate R peak detection is a big challenge especially in wireless patient monitoring system. In wireless ECG system, in order to reduce the power consumption; it is desirable to capture ECG at lower sampling rate. This paper proposes an algorithm for R peak detection using discrete wavelet transform in which detailed coefficients are selected based on entropy. The proposed algorithm is validated with MIT-BIH database and its performance is compared with similar work. For MIT-BIH case, positive predictivity and sensitivity for proposed algorithm are 99.85 and 99.73, respectively. Application of proposed algorithm on wireless ECG, acquired at adjustable sampling rate from different subjects using prototype Bluetooth ECG module, shows efficacy of algorithm to detect R peak of ECG with high accuracy.

Integrated Wearable System for Monitoring Heart Rate and Step during Physical Activity

This paper integrates a heart rate (HR) monitoring system with step counter for use during physical activities. Novel step counter algorithm has been developed to enable the highly accurate detection of step. The proposed system comprises a wireless wearable device, a smartphone, and a remote server. Data transmission between a wearable device and a smartphone is conducted via Bluetooth low energy (BLE). An indirect contact measurement method has also been devised to eliminate the need for direct contact electrodes and likelihood of skin irritation. The proposed system is compact, lightweight, and comfortable to wear. A smartphone application provides the interface for the display of data related to HR, step count (SC), exercise intensity, speed, distance, and calories burned, as well as waveforms related to ECG and step cycle. ECG peak detection algorithm achieved accuracy of 99.7% using the MIT-BIH ST Change Database. Accuracy of 98.89% was achieved for HR and 98.96% for SC at treadmill speeds of 1.8 to 9.0 km/h.

Real-time ECG peak detection for heart rate measurement using wavelet packet transform

Continuous monitoring of patient's signals has the potential to improve the quality life of patients. This paper proposes a method to detect peaks in ECG signal for finding heart rate. It is one of the most important physiological parameter, which is being extensively used for knowing the state of the cardiac patients. ECG feature extraction is necessary for detection of cardiovascular abnormalities. The wavelet transform is considered to be the most appropriate tool to analyse the non-stationary signals. In this paper discrete wavelet packet transform algorithm is used to find heart rate from ECG signal. Proposed methodology has decomposition tree to eliminate the wavelet coefficients which contains the repeated information thereby it optimises the execution time and results in higher temporal resolution. Proposed work uses the improved algorithm to increase the accuracy in measuring the heart rate from the ECG signal.

A remote wireless sensor network/electrocardiographic approach to discriminating cocaine use

s / Drug and Alcohol Dependence 146 (2015) e202–e284 e209 Conclusions: These data indicate that there is an interactive relationship between mPFC 5-HT2AR and 5-HT2CR that may drive impulsive phenotypes. Methylation patterns of these genes maymediate a gene× environment interaction to control inherent impulsivity, such that high impulsivity associates with higher 5HT2CR methylation in a critical brain region linked to impulsivity. Thus, we have uncovered potential neurobiological mechanisms of importance in driving inherent impulsivity. Financial support: DA033374, UL1T71, DA024157, DA020087. http://dx.doi.org/10.1016/j.drugalcdep.2014.09.035 Workforce attitudes regarding the implementation of extended release naltrexone to treat opioid dependence Laura Andrich1, Katherine T. Garvey1, Elias M. Klemperer2,1, Traci R. Rieckmann1 1 Oregon Health & Science University, Portland, OR, United States 2 University of Vermont, Burlington, VT, United States Aims: Prior research indicates that organizational and workforce characteristics influence the implementation of evidencebased treatments. This study examined the workforce’s response to the utilization of extended release naltrexone to treat opioid addiction at time one (n=36) and time two (n=30). It explores staff opinions toward the implementation of a new medicated assisted treatment (MAT) for opioid addiction and documents the integration of an FDA approved treatment in an in-patient addictions facility. Methods: A mixed-methods approach was employed using semi-structured interviews and a quantitative survey with the staff at the addictions facility. Participants were support staff who interacted with patients. The quantitative survey was administered early in the new MAT program implementation (n=36) and again six months later (n=30). Data were analyzed using STATA and included descriptive statistics, correlations t-tests, and analyses of variance. Atlas-ti software was used to analyze codes in the qualitative data regarding attitudes and opinions about MAT. Results: Data from the survey suggest that over time,workforce knowledge about the new MAT increased (33.33–77.78%). There was also a slight increase inworkforce opinion that their coworkers supported the use of the newMAT (11.11–51.85%) as well as opinion that their supervisors supported the newMAT (48.57–70.37%), indicating an increase in perceived social support. The qualitative data indicate the importance of housing and peer support to successful recovery. Conclusions: Our findings indicate that counselors and support staff play a critical role in the adoption of efficacious MATs, with perceived social support at time two ranging from 51.85% for supervisors and 77.78% for coworkers. Greater attention to staff attitudes and perspectives when working to accelerate utilization of medications are recommended for improved patient outcomes. Financial support: This work was supported by the Hooper Detoxification Stabilization Center Vivitrol® Pilot Program State Funded Evaluation Project. http://dx.doi.org/10.1016/j.drugalcdep.2014.09.036 A remote wireless sensor network/electrocardiographic approach to discriminating cocaine use Gustavo A. Angarita1, Annamalai Natarajan2, Edward C. Gaiser1, Abhinav Parate2, Benjamin Marlin2, Ralitza Gueorguieva3, Deepak Ganesan2, Robert T. Malison1 1 Psychiatry, Yale University School of Medicine, New Haven, CT, United States 2 School of Computer Science, University of Massachusetts, Amherst, MA, United States 3 Biostatistics, Yale University School of Medicine, New Haven, CT, United States Aims: To establish the sensitivity/specificity of a RWSN/ECG approach for discriminating cocaine use from other cardiovascular stimulants. Hypothesis: Wearable “on body” sensors will reliably distinguish cocaine-induced ECG changes from those induced by methylphenidate (MPH) and exercise. Methods: Subjects: Five experienced cocaine users (4 male/1 female; 41±8 years; lifetime cocaine use =17±9 years). Procedures: Subjects wore the Zephyr BioHarness 3 chest band (left mid-axillary line) during 180min of self-regulated IV cocaine administration (8, 16, and 32mg/70kg), for 90min after a single 45mg dose of oral MPH, and during 15–30min of aerobic exercise (ping pong or stationary bike) on an inpatient research unit. Statistical Analyses: 1. A computational pipeline was used to preprocess ECG data and identify ECG (P, Q, R, S and T) peaks. We extracted two types of features: one, PQRSTwaves (waveform features); two, all morphological (AM) features (PR, QRS, QT and QTc intervals), following which a binary classification model (logistic regression) was assessed for its ability to distinguish between conditions (cocaine vs. MPH; cocaine vs. exercise). Classifier performance was expressed as Area Under Receiver Operating Characteristics curve (AUROC). Results: Classifiers consistently distinguished cocaine use from exercise (N=5) andMPH(N=4)withmeanAUROCof >0.9 and>0.95 for AM and waveforms, respectively. Conclusions: If sensitivity/specificity of RWSN/ECG approach (>90%) is confirmed in larger outpatient cohorts, suchmethodsmay be of value in medication development efforts (i.e., clinical trials) for cocaine. Financial support: Supported by R01 DA03373301 and CTSA Grant Number UL1 RR024139. http://dx.doi.org/10.1016/j.drugalcdep.2014.09.037 Comparison of synthetic substances: Diffusion of innovation framework Cynthia L. Arfken1, Cheryl Madeja1, Darlene Owens2 1 Wayne State University, Detroit, MI, United States 2 Southeast Michigan Community Alliance, Taylor,

STUDY OF SPECTRAL CHARACTERISTICS OF MOTION ARTIFACTS IN AMBULATORY ECG BY ANALYZING PCA RESIDUAL ERRORS

In this paper, the spectral characteristics of motion artifacts occurring in an ambulatory ECG signal have been studied using principal component analysis (PCA). The PCA residual errors characterize the spectral behavior of the motion artifacts occurring in ambulatory ECG signals. The ECG signals have been acquired from Biopac MP-36 system and a self-developed wearable ECG recorder. The performance is evaluated by power spectral density (PSD) plots of PCA residual errors as well as statistical parameters like mean, median and variance of PCA errors. The PSD plots clearly indicate that the peak frequency of the motion artifacts occurring due to various body movements (like left and right arms up–down, left and right legs up–down, waist twist, walking and sitting up–down) is located around 20–25 Hz against the ECG peak frequency around 5–10 Hz.

Improved cardiac arterial spin labelling in the mouse heart by optimisation of acquisition and analysis

Methods Perfusion maps are generated by pixel-wise comparison of T1 maps from slice-selective and global inversion recoveries. T1 was quantified using ECG-gated LookLocker approach [3], with 4 lines of k-space acquired every heartbeat to speed up acquisition by approximately four times compared with previous approaches (eg. [2],[3]). A data logger (CED) was used to record ECG, RF and respiration events and was programmed to gate on each ECG peak detected as well as to interpolate peaks missed by physiological monitoring software. Using the information recorded, lines of k-space were binned as ‘acquired during respiration’ or not. Images with many lines or centre lines of k-space acquired during respiration were rejected. ASL data were acquired on a 9.4T Varian system with 35mm-long volume RF coil (Rapid) (TE=1.18ms, flip=5°, resolution=200μm, slice thickness=1.5mm) and image analysis was performed in Matlab.

스마트 폰 어플리케이션 적용을 위한 UPC-A Bar code 인식 알고리즘 개발

This paper is about a bar code decoding algorithm developed for smart phone applications. The algorithm consists of bar code data extraction procedure, bar code signal estimation procedure, and bar code decoding procedure. To detect the peak bar code module, a DSTW had been applied because of its outstanding performance in ECG peak detection. In order to minimize errors due to non-uniform light effect, the proposed algorithm was acted as a baseline wandering filter based on module peaks detection. The algorithm had been tested to evaluate the performance under the conditions of blurring, non-uniformed light and white noises. The algorithm had shown excellent performance in reconstruction of bar code decoding, compared to other conventional methods. In order to show the possibility of applying the algorithm to a smart phone, a real UPC-A type 150 bar code pictures obtained from a smart phone camera was applied to the algorithm, achieving the correct recognition rate of 97.33%.

전신 마취 중 심박동변이도와 맥파전달시간 변화의 비교

Autonomic nervous system of the anesthetized patients can be influenced by the many kinds of stimulations such as intubation, surgical incision and so on. The changes of the heart rates and blood pressures are surrogates of responses of the autonomic system to the external stimulations. Recently, the power spectral analysis of the heart rate variability (HRV) made it easy to know the fractions and changes of sympathetic and parasympathetic autonomic systems. In this study, the changes of pulse transit time, one of the response of vessels to stimulations, was investigated in relation to the HRV. Ten patients were examined and average age is 22.5 11.04, average weight is 63 14.4 kg. The patients were anesthetized only by sevoflurane inhalation. Pulse transit time is determined by calculating the difference of the time between the R peak of ECG and the characteristic point of the plethysmography. Power spectral density (PSD) of the HRV was achieved in the frequency of 0.04-0.15 (LF) and 0.15-0.4 (HF). Compared to preanesthetic period the values of LF and LF/HF ratio of HRV were decreased (p