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Cardiac Arrhythmias Classification and Detection for Medical Industry Using Wavelet Transformation and Probabilistic Neural Network Architecture

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Abstract

One of the most important tasks in medical technology industry is to achieve a maximum correctness in the detection of cardiac arrhythmias disorders. In this paper, author tries to address the problem of extracting the features of Electrocardiogram (ECG) signals and then classifying them in order to automate the cardiac disorders fast and provide timely treatment to patients. Author proposes the combined approach for automatic detection of cardiac arrhythmias: discrete wavelet transformation (DWT) method in order to extract the features of ECG signals (selected from MIT-BIH database) and probabilistic neural network (PNN) model for signal classification. The feature extraction part during ECG signal analysis plays a vital role while analyzing cardiac arrhythmias. ECG signals were decomposed till 5th resolution level using wavelet transformation technique in order to calculate the four important arithmetic parameters: (mean median, mode, and standard deviation) of normal and abnormal ECG signals of persons. Then these parameters were provided as an input to PNN classifier with two distinct outputs, i.e., normal and arrhythmic patients which clearly differentiate between the normal signals from abnormal signals. Accuracy of the proposed work was assessed on the basis of feature extraction and the results show that the recommended classifier has some great possibilities in detecting arrhythmic signals. This approach gives sensitivity, specificity, and selectivity are 99.84, 99.20, 99.40, respectively, and overall accuracy is 99.82% as compared to the approaches discussed in existing literature. For experimental results, the MIT-BIH databases were used.

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Authors and Affiliations

  1. Tata Consultancy Services, Noida, India

    Rajan Tandon

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  1. Rajan Tandon
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Correspondence to Rajan Tandon .

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  1. Tata Consultancy Services Ltd, Pune, Maharashtra, India

    Neha Sharma

  2. Tata Consultancy Services Ltd, Pune, Maharashtra, India

    Mandar Bhatavdekar

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Tandon, R. (2022). Cardiac Arrhythmias Classification and Detection for Medical Industry Using Wavelet Transformation and Probabilistic Neural Network Architecture. In: Sharma, N., Bhatavdekar, M. (eds) World of Business with Data and Analytics. Studies in Autonomic, Data-driven and Industrial Computing. Springer, Singapore. https://doi.org/10.1007/978-981-19-5689-8_6

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  • DOI: https://doi.org/10.1007/978-981-19-5689-8_6

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  • Print ISBN: 978-981-19-5688-1

  • Online ISBN: 978-981-19-5689-8

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