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In the process of ventricular premature beat (PVC) and normal sinus rhythm (NSR) identification base on electrocardiogram (ECG), there exists problems like negative effect from ECG rhythm and low recognition rate. This paper proposes the electrocardiogram PVC classification algorithm based on support vector machine (SVM) and wavelet algorithm. The algorithm uses the wavelet transform to analyze ECG beating model, which is not influenced by the change of ECG waveform. The two feature sets respectively compose of statistical parameters of the wavelet coefficients and the selected wavelet coefficients. PVC and NSR are analyzed by using SVM. The experimental results show that this method improves the recognition rate of ECG.

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.

조기심실수축 분류를 위한 기존 연구들은 분류의 정확성을 높이기 위해 신경망, 퍼지 이론, SVM 등과 같은 비선형 방법이 주로 사용되어 왔다. 이러한 대부분의 방법들은 P-QRS-T 지점의 정확한 측정을 필요로 하며, 데이터의 가공 및 연산이 복잡하다. 연산의 복잡도를 줄이기 위한 여러 가지 방법들이 제안되어 왔지만, 분류의 정확도가 떨어지는 문제점이 있었다. 또한 PVC는 개인의 특징에 따라 다양한 QRS 패턴이 존재하기 때문에 정확도에 한계가 있다. 따라서 이러한 문제점을 극복하기 위해서는 최소한의 특징점을 추출함으로써 연산의 복잡도를 줄이고, 개인마다 다른 QRS 패턴에 따라 PVC를 정확하게 분류할 수 있는 알고리즘이 필요하다. 따라서 본 연구에서는 QRS 패턴에 따른 QS 간격과 R파 진폭 변화율을 이용한 PVC 분류 방법을 제안한다. 이를 위해 전처리를 통해 잡음이 제거된 심전도 신호에서 R파, RR 간격, QRS 패턴을 추출한다. 이후 그 패턴에 따른 QS 간격과 R파의 진폭 변화율에 따라 PVC를 분류하였다. 제안한 방법의 우수성을 입증하기 위해 PVC가 30개 이상 포함된 MIT-BIH 9개의 레코드를 대상으로 한 R파의 평균 검출율은 99.02%의 성능을 나타내었으며, PVC 부정맥은 각각 93.72%의 평균 분류율을 나타내었다.

Previous works for detecting arrhythmia have mostly used nonlinear method such as artificial neural network, fuzzy theory, support vector machine to increase classification accuracy. Most methods require accurate detection of P-QRS-T point, higher computational cost and larger processing time. Even if some methods have the advantage in low complexity, but they generally suffer form low sensitivity. Also, it is difficult to detect PVC accurately because of the various QRS pattern by person's individual difference. Therefore it is necessary to design an efficient algorithm that classifies PVC based on QRS pattern in realtime and decreases computational cost by extracting minimal feature. In this paper, we propose PVC classification based on QRS pattern using QS interval and R wave amplitude. For this purpose, we detected R wave, RR interval, QRS pattern from noise-free ECG signal through the preprocessing method. Also, we classified PVC in realtime through QS interval and R wave amplitude. The performance of R wave detection, PVC classification is evaluated by using 9 record of MIT-BIH arrhythmia database that included over 30 PVC. The achieved scores indicate the average of 99.02% in R wave detection and the rate of 93.72% in PVC classification.

조기심실수축(PVC)은 가장 보편적인 부정맥으로 심실세동, 심실빈맥 등과 같은 위험한 상황을 유발할 수 있는 가능성을 가지고 있기 때문에 이의 조기 검출은 매우 중요하다. 하지만 ECG 신호의 개인 차이가 있음에도 불구하고, 일반적인 신호의 판단 규칙에 따라 진단을 수행함으로써 성능하락이 나타날 수 밖에 없다. 이러한 문제점을 극복하기 위해서는 개인에 따른 이상 신호를 검출한 후 다양한 QRS 패턴을 고려하여 PVC를 분류할 수 있는 알고리즘이 필요하다. 본 연구에서는 개인별 이상신호 검출과 QRS 패턴 변화에 따른 PVC 분류 기법을 제안한다. 이를 위해 전 처리 과정과 차감기법을 통해 R파를 검출하였으며, 개인별 이상신호를 검출하였다. 이후 QRS 패턴에 따른 QS 간격과 R파의 진폭 변화율에 따라 PVC를 분류하였다. 제안한 알고리즘의 이상 신호 검출 및 PVC 분류 성능을 평가하기 위해서 MIT-BIH 부정맥 데이터베이스를 사용하였다. 성능평가 결과, 이상 신호 검출률은 98.33%, PVC는 각각 94.46%의 평균 분류율을 나타내었다.

Premature ventricular contraction(PVC) is the most common disease among arrhythmia and it may cause serious situations such as ventricular fibrillation and ventricular tachycardia. Nevertheless personalized difference of ECG signal exist, performance degradation occurs because of carrying out diagnosis by general classification rule. In other words, the design of algorithm that exactly detects abnormal signal and classifies PVC by analyzing the persons's physical condition and/or environment and variable QRS pattern is needed. Thus, PVC classification by personalized abnormal signal detection and QRS pattern variability is presented in this paper. For this purpose, we detected R wave through the preprocessing method and subtractive operation method and selected abnormal signal sets. Also, we classified PVC in realtime through QS interval and R wave amplitude. The performance of abnormal beat detection and PVC classification is evaluated by using MIT-BIH arrhythmia database. The achieved scores indicate the average of 98.33% in abnormal beat classification error and 94.46% in PVC classification.

Previous works for detecting arrhythmia have mostly used nonlinear method such as artificial neural network, fuzzy theory, support vector machine to increase classification accuracy. Most methods require higher computational cost and larger processing time. Therefore it is necessary to design efficient algorithm that classifies PVC(premature ventricular contraction) and decreases computational cost by accurately detecting minimal feature point based on only R peak through optimal R wave. We propose an optimal R wave detection and PVC classification method through extracting minimal feature point in IoT environment. For this purpose, we detected R wave through optimal threshold value and extracted RR interval and R peak pattern from noise-free ECG signal through the preprocessing method. Also, we classified PVC in realtime through RR interval and R peak pattern. The performance of R wave detection and PVC classification is evaluated by using record of MIT-BIH arrhythmia database. The achieved scores indicate the average of 99.758% in R wave detection and the rate of 93.94% in PVC classification.

In our research, we will extract diagnostic parameters by LPC method and wavelet transform. Then, we will design artificial neural network which is based on RBF that can express input features in terms of fuzzy. Because PVC(Premature Ventricular Contraction) has possibility to cause heart attack, the detection of PVC is a very significant problem. To deal with this problem, LPC method which gives different coefficients or different morphologies and wavelet transform which has superior localization nature of time-frequency, are used to extract effective parameters or classification of normal and PVC. Because RBF network can allocate an input feature to the membership degree of each category, total system will be more flexible.

가정에서 분리 배출된 폐플라스틱은 일반적으로 수선별 등 분리선별 공정을 거친 후 종말품으로 회수되어, 현재 대부분이 매립이나 소각되고 있다. 혼합폐플라스틱 종말품의 경우 2006년 약 175만톤 발생된 것으로 예측되고 있으나, EPR실시 이후로 발생량은 2000년에 비해 2배 이상 크게 증가한 실정이다. 특히, 혼합폐플라스틱 종말품의 경우 PVC 함량(4 wt.% 이하)이 매우 높아 이들의 재활용에 커다란 제약이 되고 있다. 본 연구에서는 혼합폐플라스틱 종말품으로부터 풍력 및 습식 비중선별장치를 이용하여 폴리올리핀계 (PE, PP, PS) 플라스틱을 경량물로 회수하는 선별시스템을 개발하였다. 본 선별시스템은 풍력선별, 자력선별, 1단계 파쇄, 정량공급 및 습식 비중선별 공정으로 구성되어 있으며, 습식 비중선별 공정은 원심분리를 기본으로 혼합-세척-선별 및 탈수가 단일장치내에 집약된 특징이 있다. 또한, 본 연구에서는 연질 플라스틱의 분쇄 효율을 크게 개선한 파쇄기를 개발하였다. 개발된 습식 비중선별장치의 용량은 시간당 0.5 톤으로, 이를 이용하여 회수된 경량물의 PVC 함량은 0.3wt.% 이하이며, 경량 및 중량 회수물의 수분 함량도 각각 10% 이하를 달성하였다.

The mixed plastic waste generated from households after hand-picking and/or mechanical sorting processes amounts to 1,750,000 ton in 2006, and most of these waste are finally end up with landfill and/or incineration due to the lacks of separation technologies and economical reasons. The mixed plastic wastes can not be used as raw materials for chemical and/or thermal recycling processes because of their high content of PVC(upto 4.0 wt.%). In the present research, gravity separation system has been developed to remove PVC from the mixed plastic waste and to recover the PO-type plastics. This system mainly consists of air classification, magnetic separation, one-step crushing, feeding system at fixed rate and wet-type gravity separation system. The gravity system based on centrifugal separation has been developed at capacity of 0.5 ton/h and it consists of mixing, precleaning, separation, dewatering, recovery system and wastewater treatment system, etc. The main objective of this process is to achieve high separation efficiency of polyolefins with less than 0.3 wt.% PVC content and less than 10% moisture content in the final products. In addition, a crushing unit of with 8 rotor system is also developed to improve the crushing efficiency of soft-type plastics. The system with a capacity of 1.0 ton/h is developed and operational results are presented.