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Hand Gesture Recognition using Machine Learning | Opencv and Python

5,200.00

Description

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Objective:

The main objective of this project is to identify and to classify the heart abnormalities in an ECG signal. Based on the peak amplitude values can perform the classification operation.

ABSTRACT

Cardiac arrhythmia indicates abnormal electrical activity of heart that can be a great threat to human. So it needs to be identified for clinical diagnosis and treatment. Analysis of ECG signal plays an important role in diagnosing cardiac diseases. An efficient method of analyzing ECG signal and predicting heart abnormalities have been proposed in this paper. In the proposed scheme, the collected ECG Signal Database will Convert 1D ECG signals to Image using CWT scalogram and classify with Transfer Learning via pre-trained Alex Net deep CNN. The goal is to train a CNN to distinguish between Arrhythmias(ARR), Congestive Heart Failure (CHF), Normal Sinus Rhythm (NSR). The ECG Signals are obtained from 162 ECG recordings from three Physio Net databases (https://github.com/mathworks/physionet_ECG_data).

EXISTING SYSTEM:

  • SVM classifier
  • K means clustering

DRAWBACKS:

  • High Computational load
  • Poor discriminatory power
  • Less accuracy in classification

PROPOSED SYSTEM:

  • A CNN passes an image through the network layers and outputs a final class. The network can have tens or hundreds of layers, with each layer learning to detect different features. Filters are applied to each training image at different resolutions, and the output of each convolved image is used as the input to the next layer. The filters can start as very simple features, such as brightness and edges, and increase in complexity to features that uniquely define the object as the layers progress.

APPLICATIONS:

  • Medical applications

SOFTWARE REQUIREMENTS:

  • MATLAB 2018b

Reference:

[1] K. Thygesen et al., ?Third universal definition of myocardial infarction,? Circulation, vol. 126, no. 16, pp. 2020?2035, 2012.

[2] L. Schamroth, An Introduction to Electrocardiography, 7th ed. New York, USA: Wiley, 2009.

[3] S. Mitra, M. Mitra and B. B. Chaudhuri, “A Rough-Set-Based Inference Engine for ECG Classification,” IEEE Trans. Instrum. Meas., vol. 55, no. 6, pp. 2198-2206, Dec. 2006. doi: 10.1109/TIM.2006.884279

[4] Fayn, Jocelyne. “A classification tree approach for cardiac ischemia detection using spatiotemporal information from three standard ECG leads.” IEEE Trans. Biomed. Eng., vol. 58, no. 1, pp. 95-102, 2011.

[5] L. Sun et al., ?ECG analysis using multiple instance learning for myocardial infarction detection,? IEEE Trans. Biomed. Eng., vol. 59, no. 12, pp. 3348?3356, Dec. 2012.

[6] P.-C. Chang, J.-J. Lin, J.-C. Hsieh, and J. Weng, ?Myocardial infarction classification using multi-lead ECG using hidden Markov models and Gaussian mixture models,? Appl. SoftComput., vol. 12, no. 10, pp. 3165?3175, 2012.

[7] L. Sharma, R. Tripathy, and S. Dandapat, ?Multiscale energy and eigen space approach to detection and localization of myocardial infarction,? IEEE Trans.Biomed. Eng., vol. 62, no. 7, pp. 1827-1837, 2015.

[8] S. Padhy, and S. Dandapat, ?Third-order tensor based analysis of multilead ECG for classification of myocardial infarction,? Biomedical Signal Processing and Control, vol. 31, pp. 71-78, January 2017.

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