Abstract
ECG signals are widely used for detecting any abnormality related to the heart. ECG signal has a number of cardiac cycles, and each cardiac cycle has P–QRS–T waves. The aim behind implementing this project is to detect cardiac arrhythmia using KNN and SVM classifiers. In this work, a total data of 48 subjects ECG signals are used. Zero-phase filter is used to eliminate the baseline noise. Daubechies wavelet 4 is used for feature extraction. KNN and SVM classifiers are used to classify the signals into normal and abnormal groups. The performance evaluations (accuracy, sensitivity, specificity) are calculated for both the classifiers. Accuracy for KNN classifier is 76.92%, whereas accuracy for SVM classifier is 79.48%. Sensitivity of KNN is 82.35%, and for SVM, it is 71.42%. Specificity for KNN classifier is 72.72%, and for SVM classifier, it is 100%. The performance of both the classifiers is compared with the help of confusion matrix.
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Edake, R.D. (2021). Human Heart Arrhythmia Identification Using ECG Signals: An Approach Towards Biomedical Signal Processing. In: Chakraborty, C., Banerjee, A., Kolekar, M., Garg, L., Chakraborty, B. (eds) Internet of Things for Healthcare Technologies. Studies in Big Data, vol 73. Springer, Singapore. https://doi.org/10.1007/978-981-15-4112-4_6
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DOI: https://doi.org/10.1007/978-981-15-4112-4_6
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