Key result
This survey discusses techniques proposed in the literature for noise removal, feature extraction, and classification of ECG signals for the recognition of cardiovascular diseases.
This survey reviews computer-based ECG signal processing techniques for automated detection and diagnosis of cardiac disorders.
May guide ECG pipeline selection for automated CVD detection; leaves open standardization and prospective clinical validation of techniques.
Electrocardiogram (ECG) is nearly a periodic signal widely used for the detection and diagnosis of cardiac abnormalities. Recently with the inception of computer based techniques, automated analysis of shape and pattern of ECG waveform has facilitated physician to obtain fast and accurate diagnosis of cardiac disorders. Abnormalities related to sinus rhythms can be detected by using ECG signal beat classification, whereas Ischemic Heart Disease and Myocardial Infarction can be detected by deviation in ST segment or inversion of T wave in ECG signal. This paper discusses techniques proposed earlier in the literature for noise removal, feature extraction and classification of ECG signal.
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Ahmed et al. (2016) conducted a review in Cardiovascular diseases. ECG signal processing techniques was evaluated. This survey discusses techniques proposed in the literature for noise removal, feature extraction, and classification of ECG signals for the recognition of cardiovascular diseases.
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