Ischemic classification techniques using an advanced neural network algorithm

Stamkopoulos, T.; Maglaveras, Nicos; Diamantaras, Konstantinos I.; Strintzis, Michael G.

doi:10.1109/cic.1997.647905

Cited by 4 publications

(6 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Long-term ECG monitoring is very useful for detecting the transient change of ST. Generally, physicians detect the transient ST changes manually, therefore a number of algorithms have been developed to detect transient ST change automatically [1][2][3]. Neural network and fuzzy theory are widely used in the detection of ischemic episodes [4][5][6]. Most of the algorithms mainly perform the detection of ST segment deviation to analyze transient ST change.…”

Section: Introductionmentioning

confidence: 99%

Morphological Classification of ST segment using Reference STs Set

Jeong

2007

2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

View full text Add to dashboard Cite

Morphological change of ECG is the important diagnostic parameter to finding the malfunction of a heart. An abnormal ST segment change especially is a very important for finding myocardial ischemia. Long-term ECG recording is needed because an ST change is transient. Accordingly, physicians try to find the transient change of the ST segment. The aim of this study is to classify ST according to its shape type using a polynomial approximation method and the reference STs set. The developed algorithm consists of feature point detection, ST level detection and ST shape classification. The first step of feature point detection is the detection of QRS complex, and this is accomplished using the morphological characteristics of QRS complex such as the steep slope and high amplitude. The other feature points are also detected using their morphological characteristics. The developed algorithm detects the ST level change, and then classifies the ST shape type using the polynomial approximation. The algorithm finds the least squares curve for the data between S wave and T wave in ECG. This curve is used for the classification of the ST shapes. ST type is classified by comparing the slopes between the reference ST type and the least square curve. We applied the developed algorithm to the ECG data in European ST database. Through the result from the developed algorithm, we can know when the ST level change occurs and what the ST shape type is.

show abstract

Section: Introductionmentioning

confidence: 99%

Morphological Classification of ST segment using Reference STs Set

Jeong

2007

2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

View full text Add to dashboard Cite

show abstract

“…Their evaluation process is based upon parametric modeling [3], wavelet theory [4], set of rules [5,6], artificial neural networks [7], genetic algorithms [8] and rule mining-based method [9]. Although these methods have achieved satisfactory results, improvements can be made.…”

Section: Introductionmentioning

confidence: 99%

“…Although these methods have achieved satisfactory results, improvements can be made. Neural networks have been widely used over the past few years as pattern and statistical classifiers in many application areas including medicine [7]. In this wok, classification using adaptive backpropagation neural network is proposed for the ischemic beat detection.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Ischemic Beat Classification Using Backpropagation Neural Network

Mohebbi

Moghadam

2007

2007 IEEE 15th Signal Processing and Communications Applications

View full text Add to dashboard Cite

This paper explains an adaptive backpropagation neural network (NN) for the detection of ischemic beats in electrocardiogram (ECG) recordings. The proposed method consists of a preprocessing stage for QRS detection, baseline wandering removal, and noise suppression. In this stage ST segments are extracted. In the next stage, the pattern length is reduced and subtracted from the normal template. In the third stage the extracted patterns are used for training a neural network and ischemic beats are detected. The algorithm used to train the NN is an adaptive backpropagation algorithm. An adaptive algorithm attempts to keep the learning step size as large as possible while keeping learning stable and then reduces the learning time. To evaluate the methodology, a cardiac beat dataset is constructed using several recordings of the European Society of Cardiology ST-T database. Our results were high both in sensitivity and positive predictivity. Specially, the obtained sensitivity and positive predictivity were 97.22% and 97.44%, respectively. These results are better than other any previously reported ones.

show abstract

“…The ECG beats are further classified into normal and abnormal ECG beats as stated in Section 6.1. In the literature review, automated algorithms for abnormal ECG classification use approaches based on the Karhunen-Loeve transfor-mation [66,73], linear prediction [69], filter banks [70], polynomial approximation [75], nonlinear-principal-component-analysis [74], hidden Markov models [71], wavelet transform [72], and neural network [74,72].…”

Section: Normal and Abnormal Ecg Classificationmentioning

confidence: 99%

“…Therefore, algorithms, which need a high computation effort, such as linear prediction, polynomial approximation, nonlinear-principal-component analysis, and wavelet transformation, are avoided. Note that the neural network approaches in[74,72] use nonlinear-principal-component analysis and wavelet transformation for feature extraction. The Karhunen-Loeve-transformation approach in[66] detects only abnormal QRS complexes, and the algorithm in[73] has many steps for feature extraction.…”

mentioning

confidence: 99%

Abnormal ECG search in long-term electrocardiographic recordings from an animal model of heart failure

Raphisak¹

View full text Add to dashboard Cite

Heart failure is one of the leading causes of death in the United States. Five million Americans suffer from heart failure. Advances in portable electrocardiogram (ECG) monitoring systems and large data storage space allow the ECG to be recorded continuously for long periods. Long-term monitoring could potentially lead to better diagnosis and treatment if the progression of heart failure could be followed. The challenge is to analyze the sheer mass of data. Manual analysis using the classical methods is impossible. In this dissertation, a framework for analysis of long-term ECG recording and methods for searching an abnormal ECG are presented. The data used in this research were collected from an animal model of heart failure. Chronic heart failure was gradually induced in rats by aldosterone infusion and a high Na and low Mg diet. The ECG was continuously recorded during the experimental period of 11-12 weeks through radiotelemetry. The ECG leads were placed subcutaneously in lead-II configuration. In the end, there were 80 GB of data from five animals. Besides the massive amount of data, noise and artifacts also caused problems in the analysis. The framework includes data preparation, ECG beat detection, EMG noise detection, baseline fluctuation removal, ECG template generation, feature extraction, and abnormal ECG search. The raw data was converted from its original format and stored in a database for data retrieval. The beat detection technique was improved from the original algorithm so that it was less sensitive to signal baseline jump and more sensitive to beat size variation. A method for estimating a parameter required for baseline fluctuation removal is proposed. It provides a good result on test signals. A new algorithm for EMG noise detection was developed using morphological filters and moving variance. The resulting sensitivity and specificity are 94% and 100%, respectively. A procedure for ECG template generation was proposed to capture gradual change in ECG morphology and manage the matching process if numerous ECG templates are created. RR intervals and heart rate variability parameters are extracted and plotted to display progressive changes as heart failure develops. In the abnormal ECG search, premature ventricular complexes, elevated ST segment, and split-R-wave ECG are considered. New features are extracted from ECG morphology. The Fisher linear discriminant analysis is used to classify the normal and abnormal ECG. The results provide classification rate, sensitivity, and specificity of 97.35%, 96.02%, and 98.91%, respectively.

show abstract

Ischemic classification techniques using an advanced neural network algorithm

Cited by 4 publications

References 12 publications

Morphological Classification of ST segment using Reference STs Set

Morphological Classification of ST segment using Reference STs Set

Real-Time Ischemic Beat Classification Using Backpropagation Neural Network

Abnormal ECG search in long-term electrocardiographic recordings from an animal model of heart failure

Contact Info

Product

Resources

About