Arrhythmia Classification Based on Combination of Heart Rate, Auto Regressive Coefficient and Spectral Entropy Using Probabilistic Neural Network
| Autor: | Nitin Sahai, Basant Kumar, Ravi Prakash Tewari, Rohini Srivastava, Dinesh Bhatia, Vikas Patidar |
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| Rok vydání: | 2018 |
| Předmět: |
020205 medical informatics
Computer science business.industry Spectral entropy 0206 medical engineering Cardiac arrhythmia Pattern recognition 02 engineering and technology 020601 biomedical engineering Probabilistic neural network Critical parameter Autoregressive model Heart rate cardiovascular system 0202 electrical engineering electronic engineering information engineering Classification methods cardiovascular diseases Artificial intelligence business |
| Zdroj: | 2018 15th IEEE India Council International Conference (INDICON). |
| DOI: | 10.1109/indicon45594.2018.8987120 |
| Popis: | This paper presents a classification method for arrhythmia using probabilistic neural network, based on unique combination of three Electrocardiogram (ECG) features; heart rate, Auto Regressive (AR) coefficients & spectral entropy (SE). Heart rate has been very critical parameter for the detection of life-threatening arrhythmia. The purpose of this paper is to develop a Probabilistic Neural Network (PNN) based algorithm for improved detection and broader classification of cardiac arrhythmia. The results show that the unique combination of ECG features considered in this work provides more accurate and robust classification of arrhythmias. |
| Databáze: | OpenAIRE |
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