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ECG Signal Classification Using Various Machine Learning Techniques.

Electrocardiogram (ECG) signal is a process that records the heart rate by using electrodes and detects small electrical changes for each heat rate. It is used to investigate some types of abnormal heart function including arrhythmias and conduction disturbance. In this paper the proposed method is...

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Published in:Journal of Medical Systems Vol. 42; no. 12; pp. 1 - 2
Main Authors: Celin, S, Vasanth, K.
Format: equations & formulas pictorial research tables/charts tracings Journal Article
Published: Springer Nature Dec2018
Online Access:View this record in EBSCOhost
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      dt: Dec2018
      vid: 42
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      pub: Springer Nature
      place: New York, New York
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        10.1007/s10916-018-1083-6
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        atl: ECG Signal Classification Using Various Machine Learning Techniques.
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        au:
          Celin, S
          Vasanth, K.
        affil: Satyabama Institute of Science and Technology, Chennai, India
      sug:
        subj:
          Machine Learning Utilization
          Electrocardiography
          Signal Processing, Computer Assisted
          Waveforms
          Human
          Algorithms
          QRS Complex
          Descriptive Statistics
          Neural Networks (Computer)
      ab: Electrocardiogram (ECG) signal is a process that records the heart rate by using electrodes and detects small electrical changes for each heat rate. It is used to investigate some types of abnormal heart function including arrhythmias and conduction disturbance. In this paper the proposed method is used to classify the ECG signal by using classification technique. First the Input signal is preprocessed by using filtering method such as low pass, high pass and butter worth filter to remove the high frequency noise. Butter worth filter is to remove the excess noise in the signal. After preprocessing peak points are detected by using peak detection algorithm and extract the features for the signal are extracted using statistical parameters. Finally, extracted features are classified by using SVM, Adaboost, ANN and Naïve Bayes classifier to classify the ECG signal database into normal or abnormal ECG signal. Experimental result shows that the accuracy of the SVM, Adaboost, ANN and Naïve Bayes classifier is 87.5%, 93%, 94 and 99.7%. Compared to other classifier naïve bayes classifier accuracy is high.
      pubtype: Academic Journal
      doctype:
        equations & formulas
        pictorial
        research
        tables/charts
        tracings
        Journal Article
      ougenre: Article
    language: English
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