Recognition of Mental Workload Levels using Diagnosis of ECG Signals

IJSTE - International Journal of Science Technology & Engineering | Volume 2 | Issue 10 | April 2016 ISSN (online): 2349-784X

Recognition of Mental Workload Levels using Diagnosis of ECG Signals Dharani R UG Scholar CCET

Muhil vinoliya M UG Scholar CCET

Thulasi devi S UG Scholar CCET

Vinothini S UG Scholar CCET SathisKumar R Assistant Professor CCET

Abstract ECG signal shows the electrical activity of the hearts. These signals are non-stationary; they display a fractal like self-similarity. It is one of the most important physiological parameter, which is being extensively used for knowing the state of cardiac patients. They may contain indicators of heart disease, or even warnings about impending diseases. The indicators may be present at all times or may occur at random. Soft-Computing approach is an important tool in which two or more successive ECG recordings are compared in order to find disorders in cardiac. This project presents the method to analyze ECG signal extract features and classification according to different arrhythmias.

Keywords: signal recognition, directional Filtering, ANFIS classifier ________________________________________________________________________________________________________ I.

INTRODUCTION

Electrocardiogram (ECG) is used for diagnosing and monitoring the patient body. Detection of ECG arrhythmias is necessary for the treatment of patients for diagnosing the heart disease at the early stage. Therefore the importance in developing the system that make this interpretation easier for non-trained people and the system could detect the disease with high levels of accuracy because many people who are died due to heart problems . This research is used by the other health care professionals to bring together knowledge from many technical sources to develop new procedures. This new procedure is used to solve medical problems. This compression algorithm is used to transmit the ECG signal to hospital server effectively. The data rate is reduced due to this algorithm. This paper proposes to perform real-time classification of Cardiac Vascular Disease (CVD).Here the ECG is done using feature extraction technique. This methods are divided into three work modules. Firstly, procedures to identify and annotate of ECG signal for Normal, Bradycardia, Tachycardia Arrhythmia and Ischemia characteristic. Secondly, using an algorithm extracting the features for each sample of heart disease. From Discrete Wavelet Transform the coefficients are derived. Lastly, this part presented the procedures of Adaptive Neural Fuzzy Inference System modeling which is used for classification process.

II. LITERATURE SURVEY In day-to-day-life many techniques are introduced for heart problems by researchers. When the recorded period of ECG signal is completed the System transmits the recorded data to the hospital. This recording is done by holter monitor system. The classification of the heart disease like Normal, Bradycardia, Tachycardia Arrhythmia and Ischemia characteristics are done by feature extraction. From the health care services to hospital server the compressed signals are transmitted by compression technique. The feature extraction and classifier stage are required for ECG analysis. The noise is removed from the ECG signal before feature extraction. For noise reduction many reduction techniques are proposed by different researchers. An approach to Cardiac Arrhythmia Analysis using Hidden Markov Models. By detecting and analyzing QRS complex and determining the R-R intervals to determine the ventricular arrhythmias this technique are classified. The Hidden Markov modeling addresses the problem of detecting low amplitude P waves in typical ambulatory ECG recordings. DWT-based extraction technique which gives higher performance. ECG analysis using wavelet transform method can distinguish the between the P,T wave and QRS wave. This technique also can distinguish noise, baseline drift and artifacts. So it can characterize the signal information analysis very well and suitable to process time-varying biomedical signals.

The higher-order statistics and hermite basis functions are two morphological feature extraction methods. To evaluate the generalization performance support vector machine are used to compare feature extraction methods and classification methods.

But more computation cost are needed by higher order models and caused over fitting problem in generalization performance. In

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Recognition of Mental Workload Levels using Diagnosis of ECG Signals (IJSTE/ Volume 2 / Issue 10 / 054)

term of accuracy, they found that their hierarchical classification method showed better classification performance than the conventional multiclass classification method with despite the loss in accuracy and sensitivities certain classes. The hierarchical classification improved the mean values of sensitivity mean. It agreed that their classification method can distinguish the multiclass heartbeats with the unbalanced data distribution.A new approach to feature extraction which is Karhunen Lo’eve Transform (KLT) which is an attractive and powerful approach to the feature-extraction and shape representation process. It has the solution if the probability densities of population of pattern vectors of a problem domain are unknown. The problem about this method is it is too sensitive to noisy pattern of ECG signal.

III. PROPOSED SYSTEM

Fig. 1: Block diagram of proposed stress system

IV. DIRECTIONAL FILTERING ALGORITHM\

Fig. 2: Flow diagram of Directional filtering algorithm

In Directional filtering algorithm the noise present in the pixel is removed by using median filter Algorithm: 1) 2) 3) 4) 5) 6) 7)

Step 1 : For each current pixel, take k*k neighboring window W. Step 2 : Divide W into four directional vectors.

Step 3 : Remove the current pixel under process from the selected directional vectors.

Step 4 : Sort each vectors and remove the lowest and highest elements.

Step 5 : Find the standard deviation for each vectors.

Step 6 : Find the minimum of 4 standard deviations and the corresponding direction is considered as optimum direction.

Step 7 : Compute the similarity parameter S between the current pixel under the process and the pixels in the optimum direction.

8) Step 8 : Set the threshold T and classify the current pixel under process is either original or noisy pixel.

9) Step 9 : Restore the corrupted noisy pixels only by using median filtering.

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Recognition of Mental Workload Levels using Diagnosis of ECG Signals (IJSTE/ Volume 2 / Issue 10 / 054)

10) Step 10 : Repeat the steps 1:9 to get new image with better visual quality.

Anfis Classifier: The extracted features are trained using ANFIS classifier in training mode. In the same way, the features are extracted from test ECG image and these features are classified with trained features to classify the ECG image in to normal and abnormal.

Fig. 3: Architecture of ANFIS Classifier

Here Adaptive Neuro Fuzzy Inference System Classifier is used to classify the normal and abnormal Signals

Fig. 4: Abnormal ECG Signal

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Recognition of Mental Workload Levels using Diagnosis of ECG Signals (IJSTE/ Volume 2 / Issue 10 / 054)

Table – 1 Comparison of various performance metrics

Fig. 5: Performance Analysis Graph

V. CONCLUSION This paper indicate that by ANFIS, the classification of Normal, Bardycardia Arrhythmia, Tachycardia Arrhythmia and Ischemia signals can be classified; therefore the primary objective of this study is achieved. The simulation results show the class of heart disease is well predicted using DWT and ANFIS system and the system working well since it achieve the 97.68% of classification accuracy rate. This result indicates that it has some potential and had been found to be successful in heart disease detection.

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