Cardiovascular Disease (CVD) is one of the most catastrophic and life threatening health issue nowadays. Early detection of CVD is an important solution to reduce its devastating effects on health. In this paper, an efficient CVD detection algorithm is identified. The algorithm uses patient demographic data as inputs, along with several ECG signal features extracted automatically through signal processing techniques. Cross-validation results show a 98.29 % accuracy for the decision tree classification algorithm. The algorithm has been integrated into a web based system that can be used at anytime by patients to check their heart health status. At one end of the system is the ECG sensor attached to the patient's body, while at the other end is the detection algorithm. Communication between the two ends is done through an Android application.
This paper highlights a new detection method based on higher spectral analysis techniques to distinguish the Electrocardiogram (ECG) of normal healthy subjects from that with a cardiac ischaemia (CI) patient. Higher spectral analysis techniques provide in-depth information other than available conventional spectral analysis techniques usually used with ECG analysis. They provide information within frequency parts and information regarding phase associations. Bispectral analysis- Bispectrum and Quadratic Phase Coupling techniques are utilized to detect as well as to characterize phase combined harmonics in ECG. The work is developed, tested and validated using Normal Sinus Rhythm Data from the MIT-BIH Database and CI data from the ST Petersburg European ST-T Database. The results validate the efficacy of the introduced method by maintaining 100% sensitivity and achieving 93.33% positive predictive accuracy. The simplicity and robustness of the proposed method makes it feasible to be used within available ECG systems.
Abstract. Nowadays, Cardiovascular Disease (CVD) is one of the most catastrophic and life threatening common health issues. Early detection of CVD is one of the most important solutions to reduce its devastating effects on health. In this paper, an efficient detection algorithm is identified. The algorithm uses patient demographic data as inputs, along with several ECG signal features extracted automatically through signal processing techniques. Cross-validation results show a 98.29% accuracy for the algorithm. The algorithm is also integrated into a web based system that can be used at anytime by patients to check their heart health status. At one end of the system is the ECG sensor attached to the patient's body, while at the other end is the detection algorithm. Communication between the two ends is done through an Android application.
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