• Journal of Biomedical Engineering Research
• /
• v.34 no.2
• /
• pp.97-103
• /
• 2013

This paper proposes a new algorithm for sleep apnea detection based on stroke volume. It is very important to detect sleep apnea since it is a common and serious sleep-disordered breathing (SDB). In the previous studies, methods for sleep apnea detection using heart rate variability, airflow and blood oxygen saturation, tracheal sound have been proposed, but a method using stroke volume has not been studied. The proposed algorithm consists of detection of characteristic points in continuous blood pressure signal, estimation of stroke volume and detection of sleep apnea. To evaluate the performance of algorithm, the MIT-BIH Polysomnographic Database provided by Phsio- Net was used. As a result, the sensitivity of 85.99%, the specificity of 72.69%, and the accuracy of 84.34%, on the average were obtained. The proposed method showed comparable or higher performance compared with previous methods.

• Journal of Internet Computing and Services
• /
• v.10 no.5
• /
• pp.107-116
• /
• 2009

This paper presents an approach to detect arrhythmia using heart rate variability and a fuzzy neural network. The proposed algorithm diagnoses arrhythmia using 32 RR-intervals that are 25 seconds on average. We extract six statistical values from the 32 RR-intervals, which are used to input data of the fuzzy neural network. This paper uses the neural network with weighted fuzzy membership functions(NEWFM) to diagnose arrhythmia. The NEWFM used in this algorithm classifies normal and arrhythmia. The performances by Tsipouras using the 48 records of the MIT-BIH arrhythmia database was below 80% of SE(sensitivity) and SP(specificity) in both. The detection algorithm of arrhythmia shows 88.75% of SE, 82.28% of SP, and 86.31% of accuracy.

• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.35S no.5
• /
• pp.48-58
• /
• 1998

In this paper, we proposed a new algorithm using characteristics of th ereconstructed phase trajectory by topological mapping developed for a real-tiem detection of the QRS complexes of ECG signals. Using fill-factor algorithm and mutual information algorithm which are in genral used to find out the chaotic characteristics of sampled signals, we inferred the proper mapping parameter, time delay, in ECG signals and investigated QRS detection rates with varying time delay in QRS complex detection. And we compared experimental time dealy with the theoretical one. As a result, it shows that the experimental time dealy which is proper in topological mapping from ECG signals is 20ms and theoretical time delays of fill-factor algorithm and mutual information algorithm are 20.+-.0.76ms and 28.+-.3.51ms, respectively. From these results, we could easily infer that the fill-factor algorithm in topological mapping from one-dimensional sampled ECG signals to two-dimensional vectors, is a useful algorithm for the detemination of the proper ECG signals to two-dimensional vectors, is a useful algorithm for the detemination of the proper time delay. Also with the proposed algorithm which is very simple and robust to low-frequency noise as like baseline wandering, we could detect QRS complex in real-time by simplifying preprocessing stages. For the evaluation, we implemented the proposed algorithm in C-language and applied the MIT/BIH arrhythmia database of 48 patients. The proposed algorithm provides a good performance, a 99.58% detection rate.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.66 no.7
• /
• pp.1072-1077
• /
• 2017

Mean-reverting analysis refers to a way of estimating the underlining tendency after new data has evoked the variation in the equilibrium state. In this paper, we propose a new method to interpret the specular portraits of Premature Ventricular Contraction(PVC) arrhythmia by applying K-means unsupervised learning algorithm on electrocardiogram(ECG) data. Aiming at this purpose, we applied a mean-reverting model to analyse Heart Rate Variability(HRV) in terms of the modified poincare plot by considering PVC rhythm as the component of disrupting the homeostasis state. Based on our experimental tests on MIT-BIH ECG database, we can find the fact that the specular patterns portraited by K-means clustering on mean-reverting HRV data can be more clearly visible and the Euclidean metric can be used to identify the discrepancy between the normal sinus rhythm and PVC beats by the relative distance among cluster-centroids.

• Journal of Information Processing Systems
• /
• v.8 no.4
• /
• pp.669-684
• /
• 2012

Discrete wavelet transforms are extensively preferred in biomedical signal processing for denoising, feature extraction, and compression. This paper presents a new denoising method based on the modeling of discrete wavelet coefficients of ECG in selected sub-bands with Kernel density estimation. The modeling provides a statistical distribution of information and noise. A Gaussian kernel with bounded support is used for modeling sub-band coefficients and thresholds and is estimated by placing a sliding window on a normalized cumulative density function. We evaluated this approach on offline noisy ECG records from the Cardiovascular Research Centre of the University of Glasgow and on records from the MIT-BIH Arrythmia database. Results show that our proposed technique has a more reliable physical basis and provides improvement in the Signal-to-Noise Ratio (SNR) and Percentage RMS Difference (PRD). The morphological information of ECG signals is found to be unaffected after employing denoising. This is quantified by calculating the mean square error between the feature vectors of original and denoised signal. MSE values are less than 0.05 for most of the cases.

• Journal of Sensor Science and Technology
• /
• v.22 no.5
• /
• pp.338-345
• /
• 2013

Premature ventricular contractions are the most common of all arrhythmias and may cause more serious situation like ventricular fibrillation and ventricular tachycardia in some patients. Therefore, the detection of this arrhythmia becomes crucial in the early diagnosis and the prevention of possible life threatening cardiac diseases. Most methods for detecting arrhythmia require pp interval, or the diversity of P wave morphology, but they are difficult to detect the p wave signal because of various noise types. Thus, it is necessary to use noise-free R wave. So, the new approach for the detection of PVC is presented based on the rhythm analysis and the beat matching in this paper. For this purpose, we removed baseline wandering of low frequency band and made summed signals that are composed of two high frequency bands including the frequency component of QRS complex using the wavelet filter. And then we designed R wave detection algorithm using the adaptive threshold and window through RR interval. Also, we developed algorithm to classify PVC using RR interval. The performance of R wave and PVC detection is evaluated by using MIT-BIH arrhythmia database. The achieved scores indicate average detection rate of 99.76%, sensitivity of 99.30% and specificity of 98.66%; accuracy respectively for R wave and PVC detection.

• Proceedings of the KOSOMBE Conference
• /
• v.1996 no.05
• /
• pp.31-35
• /
• 1996

This paper presents a real time algorithm for monitoring of the arrythmia of ECG signal. A real time monitoring, following by detecting a QRS complex, is the most important. Using 2-dimensional time-delay coordinates which are reconstructed by the phase portrait plotting special trajectory, we detect QRS complexes. In this study, arrythmias are detected by matching the past standard template with tile present pattern when changing abruptly In order to matching with each other, we propose modified chain coding algorithm which applies vetor table consisting of eight orthonormal code(=binary code) to the phase portraits. This algorithm using logical function increases the weight if exceeding to the threshold determinded by correlation value and the distance from a straight line(y=x). Evaluating the performance of the proposed algorithm, we use standard MIT/BIH database. The results are fellowing, 1) Improve the speed of matching template than that of cross-correlation ever has been used. 2) Because the proposed algorithm is robust to varing fiducial point, it is possible to monitor the ECG signal with irregular RR interval. 3) In spite of baseline wandering owing to the low frequency noise, monitoring performance is not reduced.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.2 no.2
• /
• pp.15-21
• /
• 2009

This paper proposes a novel algorithm to assess the abnormal heart beats such as PVC (Premature Ventricular Contraction) and its subsequent RUNs. Our Arrhythmic detection scheme is based on only the R-R Interval features extracted from ECG waveforms and MIT-BIH arrhythmia database is evaluated to validate the efficiency of our algorithm in terms of sensitivity, specificity, FPR(%) and FNR(%).

• Journal of IKEEE
• /
• v.25 no.1
• /
• pp.212-217
• /
• 2021

Premature contraction arrhythmia is the most common disease among arrhythmia and it may cause serious situations such as ventricular fibrillation and ventricular tachycardia. Most of arrhythmia clasification methods have been developed with the primary objective of the high detection performance without taking into account the computational complexity. Also, personalized difference of ECG signal exist, performance degradation occurs because of carrying out diagnosis by general classification rule. Therefore it is necessary to design efficient method that classifies arrhythmia by analyzing the persons's physical condition and decreases computational cost by accurately detecting minimal feature point based on only QRS features. We propose method for personalized specific classification of premature contraction arrhythmia based on QRS features in smart healthcare environments. For this purpose, we detected R wave through the preprocessing method and SOM and selected abnormal signal sets.. Also, we developed algorithm to classify premature contraction arrhythmia using QRS pattern, RR interval, threshold for amplitude of R wave. The performance of R wave detection, Premature ventricular contraction classification is evaluated by using of MIT-BIH arrhythmia database that included over 30 PVC(Premature Ventricular Contraction) and PAC(Premature Atrial Contraction). The achieved scores indicate the average of 98.24% in R wave detection and the rate of 97.31% in Premature ventricular contraction classification.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.14 no.9
• /
• pp.2105-2112
• /
• 2010

The noise component of electrocardiogram is not distributed in a certain frequency band. It is expressed in various types of signals by rater's physical and environmental conditions. Particularly, since the baseline wander is occurred by the mixture of the original signal and 0 ~ 2 [Hz] range of the frequency components according to muscle constraction of part attaching to an electrode and respiration rythm, it makes the ECG signal analysis difficult. Several methods have been proposed to eliminate the wandering effectually. However, they have some problems. In some methods, the high processing time is required due to the computational complexity, while in other cases ECG signal morphology can be distorted. This paper suggests a simple and effective algorithm that eliminates baseline wander with low computational complexity and without distorting signal morphology. First, the algorithm detects and segments a baseline wandering interval using slope analysis and curve point detection, second, approximates the wandering in the interval as a sinusoid, and then subtracts the sinusoid from signal. Finally, ecg signals without baseline wander are obtained. In order to evaluate the performance of the algorithm, several ECG signals with baseline wandering in MIT/BIH ECG database 101, 111, 113, 234 record were chosen and applied to the algorithm. It is found that the algorithm removes baseline wanders effectively without significant morphological distortion.